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thesis entitled

INFLUENCE OF A COARSE-GRAINED INCISED-
VALLEY FILL ON GROUNDWATER FLOW IN FLUVIAL
FAN DEPOSITS, STANISLAUS COUNTY, MODESTO,
CALIFORNIA, USA

presented by

Amy LeVan Lansdale

has been accepted towards fulfillment
of the requirements for the

 

 

 

 

 

 

MS. degree in Geological Sciences
C/ Major Professor’s Signature
I Z‘ [3— 05'
Date

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INFLUENCE OF A COARSE-GRAINED INCISED-VALLEY FILL ON
GROUNDWATER FLOW IN FLUVIAL FAN DEPOSITS, STANISLAUS COUNTY,
MODESTO, CALIFORNIA, USA

By

Amy LeVan Lansdale

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF SCIENCE
Department of Geological Sciences

2005

ABSTRACT
INFLUENCE OF A COARSE-GRAINED INCISED-VALLEY FILL ON
GROUNDWATER FLOW IN F LUVIAL FAN DEPOSITS, STANISLAUS COUNTY,
MODESTO, CALIFORNIA, USA
By
Amy LeVan Lansdale
A relatively coarse grained incised-valley ﬁll (IVF) was identiﬁed beneath the

city of Modesto, California using geophysical logs. The Tuolumne River ﬂuvial fan
IVFs are the result of periods of degradation followed by rapid aggradation due to cycles
of Quaternary climate change. These IVFs were located through the use of driller’s well
logs. Results indicate that the location of the relatively coarse grained IVF can be
reasonably approximated by identifying the basal gravel unit of the IVF in the driller’s
well logs. From the well log analysis and the use of topographic maps, the wedge shaped
IVFs are approximately 0.7 to 1.6 km wide and approximately 3 to 30 meters thick with a
5 to 9 meter gravel base that thins down fan. The inﬂuence of these IVFs on the regional
groundwater ﬂow was tested using a steady-state saturated groundwater model adapted
from a USGS model (S. Phillips, unpublished model, 2005). Groundwater ﬂow and
contaminant transport models with and without the IVF deposits of the Tuolumne River
show IVFs signiﬁcantly inﬂuence groundwater ﬂow and contaminant transport.
Speciﬁcally, results indicate that the Tuolumne River IVFs provide potential for (1)
increased groundwater production rates (2) rapid contaminant transport within the IVF
sediments and (3) rapid contaminant movement from the IVF into the contiguous aquifer
sediments. The presence of an IVF beneath the city of Modesto, in particular, may have

implications for artiﬁcial aquifer recharge and regional water quality.

ACKNOWLEDGEMENTS

This thesis could not have been completed with the dedication and countless
hours of guidance from my adviser Gary Weissmann. I also want to thank my other
committee members, David Hyndman and Phanikumar Mantha for their
contributions. I especially would like to thank Dave for his role as my stand in
adviser, and for always making time to answer my “quick questions”. This research
was also made possible by the US. Geological Survey (U SGS) in Sacramento,
California, which afforded access to ofﬁce space for a summer as well as a substantial
amount of data (driller’s well logs, core samples, and digital spatial data). USGS
employees Karen Burow, Steve Phillips, Barb Dalgash, George Bennett, and Jen
Shelton especially provided guidance during my summer of research in California.

Funding for this research was provided by Michigan State University.
Acknowledgment is made to the Donors of the American Chemical Society
Petroleum Research Fund, for support of this research (PRF#3 77 31-G8).

Signiﬁcant contributions were also made by the students and faculty of the
Michigan State University Hydrostratigraphy and Hydrogeology research groups. A
special thanks to Nick Welty, Anthony Kendall, Dush J aywickreme, and Cheryl
Kendall for all of your suggestions. Finally, I want to thank my family and friends
who supported me through the long thesis writing process. Thank you Mom and Dad,

Garrett, Beth, and Karen for your endless patience.

iii

Table of Contents

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

List of Tables -- -- _ vi
List of Figures _ vii
Chapter 1: Introduction and Scope of the Study -- 1
INTRODUCTION .......................................................................................................... 1
PURPOSE OF STUDY .................................................................................................. 2
THESIS OUTLINE ......................................................................................................... 4
Chapter 2: Inﬂuence of a Coarse-Grained Incised-Valley Fill on Groundwater Flow
in Fluvial Fan Deposits, Stanislaus County, Modesto, California, USA 5
INTRODUCTION .......................................................................................................... 5
STUDY AREA ............................................................................................................... 6
Regional and Local Geology ...................................................................................... 6
GENERATING GEOLOGIC REALIZATIONS TO BE MODELED ........................ 10
Open Fan Deposit Character .................................................................................... 11
Incised- Valley Fill Geometry .................................................................................... l 1
GROUNDWATER MODEL DEVELOPMENT ......................................................... 14
USGS Model .............................................................................................................. 15
Boundary Conditions ............................................................................................ l7
Recharge ............................................................................................................... 19
Base-soil Evaporation ........................................................................................... 20
Wells ..................................................................................................................... 2O
Reservoirs ............................................................................................................. 21
Modifications to the USGS Model ............................................................................ 22
Addition of the IVF ............................................................................................... 22
Slope of Upper Model Layers and Vertical Discretization ................................... 23
Layer Property Flow (LPF) versus Block Centered Flow (BCF) ......................... 24
Addition of GHB Conditions along the Upper Reaches of the River ................... 25
Removal of GHB Cells from the Northeastern Portion of the Model .................. 26
Alteration of Vertical Head Gradient in GHB ...................................................... 26
Reduced Vertical Conductance of the Reservoirs ................................................ 28
Flow Model Simulations, Particle Tracking, Solute Transport Simulation .............. 28
RESULTS AND DISCUSSION ................................................................................... 28
Groundwater Modeling — Comparison of models without the I VF deposits ............ 29
Inﬂuence of the I VF Deposits on Groundwater Model Results ................................ 31
Head Difference .................................................................................................... 31
Advective Pathline mysis ................................................................................. 35
Solute Transport Simulation ................................................................................. 37
Comparison among Geologic Realizations .............................................................. 39
CONCLUSION ............................................................................................................. 40
Future Recommendations ......................................................................................... 41
Chapter 3: Conclusions and Future Work - - 66
CONCLUSIONS .......................................................................................................... 66 .

iv

Assessment of Stratigraphic Character .................................................................... 67

 

 

 

 

 

 

 

 

 

 

Simulation of Groundwater Flow and Solute Tracers .............................................. 68
RECCOMMENDATIONS FOR FUTURE INVESTIGATIONS ................................ 70
SUMMARY .................................................................................................................. 71

References -- - -_ - _ -- ...... - 74
Appendix A: Geologic Setting and Stratigraphic Character - -- 77
Appendix B: Open Fan Deposit Character Core Description 97
Appendix C: Drillers’ Well Log Analysis -- - - 157
Appendix D: Sequence Boundary Surface Generation - - -- - -_ —- - 166
Appendix E: USGS Model - - - _ -- - - - - 180
Appendix F: Model Generation _ -- - - 186
Appendix G: Incised-Valley Fill FORTRAN Code -- 200
Appendix H: Code to Calculate Kv and Kh/Kv 228
Appendix I: General Head Boundary Code - _ - 234
Appendix J: Well Code - - - -- -- 240

 

List of Tables

Table 1: Summary of the geologic scenarios developed from the locations of thick
gravels in the drillers’ well logs delineating potential IVF locations. These
scenarios will be incorporated into multiple, steady-state, saturated
groundwater flow models. -- - 44

 

Table 2: Summary of the hydraulic head maximum, minimum and mean elevations
for the USGS model and the modiﬁed model without the IVF. Percent
differences between the USGS model and the adapted model and between the
adapted model and the model with IVFs are also included. -- - 44

 

Table 3: Summary of the hydraulic head maximum, minimum and mean elevations
for the four geologic scenario models. Percent differences between the USGS
model and the adapted model and between the adapted model and the model
with IVFs are also included- ....... - -- - 44

 

Table B1: Lithologic descriptions of the proximally located soft sediment core
MREA. The facies column uses a number to denote the hydrofacies and areas
where core was not recovered. The numbers include: (0) no core recovered,
(1) gravel ﬂood deposits, (2) sandy channel deposits, (3) silty sands, silty clays,
and clay overbank deposits, (4) pedogenically altered deposits. - 98

 

Table BZ: Lithologic descriptions of the proximally located soft sediment core
MRWA. The facies column uses a number to denote the hydrofacies and areas
where core was not recovered. The numbers include: (0) no core recovered,
(1) gravel flood deposits, (2) sandy channel deposits, (3) silty sands, silty clays,
and clay overbank deposits, (4) pedogenically altered deposits. - -- -- 132

 

Table F1: Cells where the conductance in the upper reaches of the Tuolumne River
were reduced by 10 and then 2. It is assumed that the lithologic units here are
more consolidated, therefore their conductance is lower. 199

 

Table F2: Table listing the cells added to the head of the Tuolumne River to attempt
to reduce the amount of water within the river valley. 199

 

vi

List of Figures

Figure l: Generalized stratigraphy of the Great Valley of California, with the white and black
dashed boxes showing the approximate study area of the Tuolumne River area near Modesto,
California in the Eastern San Joaquin Valley. The geologic map, based on soil surveys, shows
the fluvial fan deposits in the eastern portion of the San Joaquin Valley (adapted from
Weissmann et al., 2006). Thick, slightly westward dipping fluvial fan deposits along the
Tuolumne River decrease in age to the west, toward the San Joaquin Basin center. Uplift and
erosion have exposed older fan units in the eastern portion of the basin. This study focuses on
the Turlock Lake, Riverbank, and the Modesto deposits. Geologic map shown in color. ......... 46

Figure 2: Schematic proﬁle of the Tuolumne River fan stratigraphy showing the westward stepping,
thin, stacked, ﬂuvial fan units (Weissmann et a1. 2006). 47

 

Figure 3: The Tuolumne River area near Modesto, California in the Eastern San Joaquin Valley.
The black solid box denotes the entire study area. The dashed line outlines the area shown in
Figure 13. 48

 

Figure 4: Each cyclic Quaternary deposit contains a relatively coarse-grained IVF nestled within
ﬁner open fan deposits of silt, clay and some sand. The IVF has a thick gravel base that fines
upward to sand then lines. 49

 

Figure 5: Conceptual model of the Riverbank IVF. The valley ﬁll is 0.7 to 1.6 km wide and ranges
from approximately 30 meters thick at the apex to 0.3 meters at the toe. It has a 5 to 9 m thick
gravel base that lines down fan- 50

 

Figure 6: Wells assigned a rank of l and 2, plotted spatially, roughly show a few east-west elongate
trends. The areas of particular interest are (l) the northwest trending line of wells that runs
beneath Modesto, (2) the line of wells that trends along just north of the modern Tuolumne
River, (3) a short string of wells that create a small loop to the south in the eastern portion of
the river, and (4) the line of wells that begins on the southeastern part of the river and trends to
the southwest. 51

 

Figure ‘7: Aerial photograph showing the potential location of the Modesto small IVF loop (outlined
by the light gray dashed line). The solid white line represents the current river valley. ............ 52

Figure 8: The specified ﬂux cells that represent the Stanislaus, Tuolumne, and Merced Rivers in
layer 1 of the USGS model were changed to GHB cells in the adapted model. The locations of
the reservoirs are outlined with a thin black line. The GHB cells are represented by the dark
gray dots around the perimeter of the model and over the locations of the rivers. The light gray
squares represent wells. 53

 

Figure 9: Four geologic scenarios were created to test the influence of the IV F: (from top left to
bottom right) RB/ST/ML, RB/ML, RB/ST/MS, and RB/MS. Vertical exaggeration of 100. ..... 54

Figure 10: Row 60 (160) in A: the USGS model and B: the newly discretized model. Vertical
exaggeration is 50. Each cell is 400 meters wide 55

 

vii

Figure 11: Comparison of (A) the USGS model and (B) the adapted model without the IVF
MODFLOW hydraulic head solutions. A: USGS head solution. The regional groundwater
flow is to the west-southwest. The large depression in the water table in the southeast corner of
the model is due to a heavy dependency on local groundwater with little recharge from
application of irrigation water. B: Adapted model without the IVF head solution. The general
flow direction is the same. The heads in the area of the reservoir are slightly lower and the
head gradient is slightly more gradual. The large depression in the southeast corner is
maintained in this adapted model. (Elevations are in meters and the vertical exaggeration is
50). Shown in color 56

 

Figure 12: Head differences between the model without IVFs and the IVF model (RB/ST/ML).
Positive residuals (yellow to red) indicate areas of the model where the head elevations are
lower in the IVF model than in the model without the IVF. Negative residuals (green to blue)
indicate areas were the head elevation is higher in the IVF model than in the model without the
IVF. (Elevations are in meters and the vertical exaggeration is 50). A: 63 isosurfaces. B: 20
isosurfaces (rotated for improved view). Shown in color. 57

 

Figure 13: Pathline analysis was done using MODPATH. The light gray triangles indicate where the
particle started and the black circles indicate where the particle ultimately ends up. A: Map
view of the model without the IVF. Pathlines have the same trend along the hydraulic gradient
across the model. B: Map view of the model with the IVF (RB/ST/ML). Pathline analysis in the
model with the IVFs (RB/ST/ML) shows that pathlines extend farther to the west and spread
laterally to the north and south 58

 

Figure 14: A cross-section of the particle pathlines displayed in the previous figure. The yellow
triangles indicate where the particle path started and the black circles indicate where the
particle pathline ultimately ends. Vertical exaggeration is 50. A: No IVF cross-section
showing a view of the pathlines looking north. Particles released in the northern portion of the
model have pathlines that trend downward near the bottom extent of the model. The other
pathlines remain relatively shallow, but do move from the unconfined to the confined portion of
the aquifer. B: NF (RB/ST/ML) cross-section showing a view of the pathlines looking north.
The pathlines from particles released in the north are redirected into the IV F and do not flow
as deep into the aquifer as they do in the model without the IVF. The other particle pathlines
also follow the path of the IVF then disperse into the aquifer. Shown in color. 59

 

Figure 15: Cross-section of the particle pathlines shown in the previous two ﬁgures viewed obliquely
from above the south edge. The yellow triangles indicate where the particle path started and
the black circles indicate where the particle path ultimately ends. Vertical exaggeration is 50.
A: In the model without the IVF, the particle pathlines follow a similar trend through the
aquifer and move deep into the confined portion of the aquifer. .B: In the model with the IVF
(RB/ST/ML) the particle pathlines follow the IVF then spread out displaying the various flow
paths into the deeper portions of the aquifer. Shown in color. 60

 

Figure 16: Map view of particles released within the general head boundary cells that delineate the
Tuolumne River: A: In the model without the IVF the advective pathlines are short and follow
the hydraulic gradient and B: In the model with the IVF, the advective pathlines are longer and
follow the IVF. Vertical exaggeration is 50. Shown in color. 61

 

viii

Figure 17: Cross-section of particles released within the general head boundary cells that delineate
the Tuolumne Rivers. Vertical exaggeration is 50. A: In the model without the IVF the
advective pathlines are short and follow the hydraulic gradient and B: The model with the IVF
shows, in losing portions of the stream, the advective pathlines move from the river into the
IVF. Advective pathlines from particles released within the river also migrate beneath the city
of Modesto. Shown in color. 62

Figure 18A: Solute transport simulations were run for 150 years. A: Shows the uniform plume
morphology in the modiﬁed model without the IVF. NOTE: The MS are NOT in this model.
The IVF outline is shown only to facilitate reference between figures. B: Shows the plume
morphology in the model with the IVFs. The plume preferentially follows the IVF and allows
higher concentrations of the contaminant to move farther distances and more deeply into the
aquifer in the same amount of time as the modified model without the IVFs. Shown in color.

..63

 

Figure 18B: Solute transport simulations were run for 150 years. B: Shows the plume morphology in
the model with the IVFs. The plume preferentially follows the IVF and allows higher
concentrations of the contaminant to move farther distances and more deeply into the aquifer
in the same amount of time as the modified model without the IVFs. Shown in color. ............. 64

Figure 19: Head differences between the model without IVFs and the IVF model (RB/ MS). Positive
residuals (yellow to red) indicate areas of the model where the head elevations are lower in the
IVF model than in the model without the IVF. Negative residuals (green to blue) indicate areas
were the head elevation is higher in the IVF model than in the model without the IVF.
(Elevations are in meters and the vertical exaggeration is 50). A: 63 isosurfaces. B: 20
isosurfaces. Shown in color. 65

 

Figure A1: Map and cross-sectional view of the balance between aggrading and degrading reaches in
deposits on the Kings River. Changes in accumulation space are inferred by a shifting
intersection point that marked the aggrading (lower fan) and degrading reaches (incised
channel) of the river. (adapted from Weissmann et at, 2002b). 90

 

Figure A2: Four diagrams of various depositional regimes though a full glacial to interglacial cycle.
The intersection point of the fan, or the transition between aggrading and degrading reaches,
will shift up and down the fan as sediment supply:discharge ratios increase and decrease,
respectively, with changes from glacial to interglacial episodes. (adapted from Weissmann et aL,
2002b)= 91

 

Figure A3: An example of the infrequent matrix-supported gravel facies. This facies represents only
a small portion of the core samples and is presumed to be rare. Shown in color. ...................... 92

Figure A4: Typical distal and proximal fan examples of the channel sand facies. The distal sediments
are typically more fine grained and display less iron staining. Shown in color 93

 

Figure A5: Typical distal and proximal fan examples of the overbank silty clay deposits. These
deposits are typically composed of silty fine to very fine sand, silty clay, clayey silt. They are
usually gray to light brown with some sandy lenses, slight to no pedogenic alteration, and can
be thinly laminated or massive. Some deposits displayed root traces or burrows and iron
staining or mottling. Shown in color. 94

 

Figure A6: Typical distal and proximal fan examples of the pedogenically altered facies. This facies
is typically reddish in color with significant pedogenic alteration (sand with medium to thick
clay coats). Some slickensides and root traces were also noted in descriptions of this facies.
Shown in color. 95

ix

Figure A7: All driller’s log wells entered in the ArcMap database ranked 1 through 4........ .............. 96

Figure Cl: Resistivity log of selected test holes near Modesto, San Joaquin Valley, California.
(Adapted from Burow, et al., 2004) 161

 

Figure C2: Driller's Well Log: Rank 1 example log. This log was drilled with a cable tool and
includes detail as well as the expected IVF stratigraphy. NOTE: The well logs record depth

 

 

 

from the surface in feet. 162
Figure C3: Driller's Well Log: Rank 2 example log. 163
Figure C4: Driller's Well Log: Rank 3 example log. 164
Figure C5: Driller's Well Log: Rank 4 example log. 165

 

Figure D]: This ﬁgure shows a map view of the use of ghost wells used to delineate the IVF. In this
example, the black dots show the ghost wells used to delineate the RB IVF. The surrounding
gray dots represent the elevation of the paleolsol surface. This point data was used to
interpolate the Turlock Lake sequence boundary surface. 174

 

Figure D2: This map shows the location of the Corcoran Clay wells found in the USGS database.
The dense clusters of points in the eastern portion of the map mark areas where the Turlock
Lake formation outcrops. These points were used to interpolate the UTL sequence boundary
surface. 175

 

Figure D3: The Riverbank Formation surface with the Tuolumne River large Modesto IVF above the
upper Turlock Lake Formation surface with the Tuolumne River Riverbank IVF (RBML).
NOTE: Surfaces are exploded 100 meters. 176

 

Figure D4: The Riverbank Formation surface with the Tuolumne River small Modesto IVF above
the upper Turlock Lake Formation surface with the Tuolumne River Riverbank IVF (RBMS).
NOTE: Surfaces are exploded 100 meters. 177

 

Figure D5: The Riverbank Formation surface with the Tuolumne River large Modesto IVF above the
upper Turlock Lake Formation surface with the Tuolumne River Riverbank IVF and the
Stanislaus River Riverbank IVF (RBSTML). NOTE: Surfaces are exploded 100 meters. .....178

Figure D6: The Riverbank Formation surface with the Tuolumne River small Modesto IVF above
the upper Turlock Lake Formation surface with the Tuolumne River Riverbank IVF and the
Stanislaus River Riverbank IVF (RBSTMS). NOTE: Surfaces are exploded 100 meters ....... 179

Figure G1: A ﬂow chart of the conditional statements used to deﬁne the Kh of the IVF based on its
stratigraphic position. This example uses the Modesto large IVF and the Tuolumne River
Riverbank IVF (RBML). 206

 

Figure G2: Cells located within the IVF were assigned a Kh based on the elevation of the center of
the cell relative to the sequence boundary surfaces (in red), gravel (in orange), sand (in yellow),
and fines (in blue) surfaces within the IVF. Shown in color 207

 

Chapter 1: Introduction and Scope of the Study

INTRODUCTION

Modeling in heterogeneous ﬂuvial aquifers can be a very difﬁcult venture.
Heterogeneities often exist at various scales within aquifer sediments and have the
potential to largely impact or entirely control the direction and velocity of groundwater
ﬂow and contaminant movement.

Without detailed ﬁeld investigations of the spatial distribution of hydraulic
conductivity (K), aquifer heterogeneity is signiﬁcantly more difﬁcult to replicate in
groundwater models. For this reason, many studies have begun to employ more
qualitative information into approaches that generate K distributions in ﬂuvial aquifer
deposits, utilizing known geologic relationships to develop aquifer conductivity ﬁelds
(e. g. Fogg, 1986; Schiebe and F reyberg, 1995; Webb and Anderson, 1996; Weissmann et
al, 2004). These studies highlight the importance of preserving the inﬂuence of geologic
structure in modeling groundwater ﬂow and contaminant transport.

Maintaining the geologic structure within a ﬂuvially deposited aquifer becomes
particularly important in scenarios where the contrast between the hydraulic
conductivities of the aquifer deposits is high (Webb and Anderson, 1996). Results from
Webb and Anderson (1996) showed that in these types of settings, the geologic structure
that coincides with the hydraulic conductivity (in their study the braided channel forms)
can control regional groundwater ﬂow and particle movement.

In this study, I will examine a similar scenario to determine the inﬂuence of a
relatively coarse grained incised valley ﬁll (IVF) on groundwater ﬂow in the Modesto

area in California. Within the study area, there is a lack of detailed ﬁeld data on aquifer

heterogeneity, but the geological framework is reasonably well understood (J anda, 1966;
Marchand, 1977; Huntington, 1980; Marchand and Allwardt, 1981, Lettis, 1988; Burow
et al., 2004; Weissmann et al., 2006). Despite the deﬁciency in detailed aquifer data, we
were successfully able to adapt a pre-existing groundwater model to incorporate the
geologically-based, geometrical relationship of the relatively coarse-grained IVF. The
IVF is added using techniques which preserve the valley geometry and the internal
stratigraphy to asses the inﬂuence of this discrete geologic structure on the regional

groundwater ﬂow and contaminant transport.

PURPOSE OF STUDY

The main purpose of this study is to understand the impact IV F deposits in the
Tuolumne River ﬂuvial fan aquifer around Modesto, California, can have on regional
groundwater ﬂow, contaminant transport, and artiﬁcial aquifer recharge. This was
accomplished by (1) assessing the stratigraphic character of the study area with well logs
and core, (2) constructing model domains that capture this stratigraphic character, and (3)
simulating groundwater ﬂows and solute tracers through these models to evaluate the
inﬂuence IVFs have on groundwater and contaminant transport. This work provides the
foundation for future studies that will better deﬁne and model the IVF.

This study area along the Tuolumne River was chosen because geophysical well
logs revealed the presence of a relatively coarse grained IV F deposit within Tuolumne
River ﬂuvial fan deposits directly beneath the city of Modesto, California (Burow et al.,
2004). Groundwater ﬂow and contaminant transport models in previous studies of similar

IVF deposits of the Kings River near Fresno, California, showed that IVFs signiﬁcantly

inﬂuence groundwater ﬂow and contaminant transport (Weissmann et al., 2004).
Speciﬁcally, results indicated that the Kings River IVF provides potential for (1)
increased groundwater ﬂow and production rates (2) rapid contaminant transport within
the IVF sediments and (3) rapid contaminant movement from the IVF into the contiguous
aquifer sediments. If the identiﬁed IVF beneath the Modesto area has a similar effect,
then it may have implications for artiﬁcial aquifer recharge and regional water quality in
that area.

Current models of the hydrogeology in the Modesto, California, area are being
developed by the USGS. The main source of model inputs is previously collected data
(driller’s logs, geophysical logs, and a few continuous cores) which are used to
characterize the hydrogeology of the Modesto area (Burow et al., 2004) and create a
regional scale model of the geology (Burow et al., 2004) and groundwater ﬂow (S.
Phillips, unpublished model, 2005). While the model that has been developed is a
reasonable representation of the region’s geology and hydrologic regime, it does not
implicitly include the IVFs. The USGS model may be largely improved with the
additional application of sequence stratigraphic concepts, described by Weissmann et al.
(2002b, 2006), to depict and include IV F geometry and character into the groundwater
ﬂow model. An understanding of sequence stratigraphy in the area can aid in more
accurately modeling the geology to illustrate various hydro facies and their inﬂuence on
the groundwater ﬂow.

The model developed for this study utilizes an understanding of the area’s
sequence stratigraphy to develop multiple conceptual models to test the inﬂuence of

IVFs. Using the same data the USGS used to develop models of the Modesto area, four

geologic realizations of various combinations of IVF locations were developed for this
study. A comparison among groundwater models of these realizations and the recently
developed USGS groundwater model will improve the current understanding of regional

groundwater and contaminant transport trends in the Modesto area.

THESIS OUTLINE
This thesis is divided into three main sections with subsequent appendices:

1. The ﬁrst section, chapter 2, discusses the development and results of the Modesto
area groundwater model and ﬂow and transport simulations. I plan to submit this
chapter for publication.

2. The ﬁnal section of this thesis, chapter 3, is the conclusion where I describe major
results of this work and suggest ideas for future work.

3. Subsequent appendices that support this work include:

Appendix A: Delineating the IVF and study area geology

Appendix B: Unconsolidated Sediment Core Description

Appendix C: Driller’s logs

Appendix D: Sequence Boundary Surface Generation

Appendix E: Opening USGS MODFLOW Model In GMS

Appendix F: Adapted Model Generation

Appendix G: IVF Fortran Code

Appendix H: Vertical Hydraulic Conductivity and Anisotropy Ratio
Fortran Code

Appendix I: General Head Boundary Fortran Code

J. Appendix J: Well Fortran Code

Fact-99???

o -
o

Chapter 2: Inﬂuence of a Coarse-Grained Incised-Valley Fill
on Groundwater Flow in Fluvial Fan Deposits, Stanislaus
County, Modesto, California, USA

INTRODUCTION

A relatively coarse grained incised-valley ﬁll (IV F) deposit was identiﬁed in
geophysical well logs beneath the city of Modesto, California (Burow et al., 2004). A
recent investigation by Weissmann et al. (2004), showed the impact of a similar IVF in
the King’s River area. To gain insight to the potential impact the IVF can have on
regional groundwater ﬂow and contaminant transport in the Modesto area, an
investigation of the subsurface geometry, position, and hydrogeologic signiﬁcance of the
valley ﬁll was conducted. Recommendations based on this research are made for further,
higher resolution studies. The investigation includes : (1) development of multiple
geologic realizations which test different potential locations of the IVF, (2) production of
a corresponding number of ﬂow and transport groundwater models, (3) particle pathline
analyses that examine the potential for artiﬁcial recharge in the IV F, and (4)
recommendations for further investigation in the area.

To test the impact of the IVFs on the regional groundwater ﬂow and illustrate the
importance of understanding and using sequence stratigraphic concepts at this site, this
chapter reports on comparisons of realizations that include various scenarios of IV F
locations, as described in Appendix A, with an adaptation of a model generated by the
USGS that does not implicitly include the IV F deposits (Burow et al., 2004; S. Phillips,
pers. com., 2005). In this chapter, the development of the USGS groundwater ﬂow
model, the modiﬁcations made to the model (inclusive of the addition of the IVFs), and

various simulations of solute transport are described.

STUDY AREA

Regional and Local Geology

The San Joaquin Basin is located within the California’s Great Valley, an
approximately 700 km long (north to south) by 100 km wide (east to west) valley that is
bound on the east by the Sierra Nevada and on the west by the Coast Ranges. The valley
is divided into two sub-basins by the Stockton Arch, a buried transverse arch, with the
Sacramento Basin in the north and San Joaquin Basin in the south. The basin is underlain
by crystalline basement rock and approximately 9 km of Mesozoic and Cenozoic
sedimentary rocks and sediments (Bartow, 1988). Structurally, the basin is asymmetric
with a gently sloping eastern margin that abuts the Sierra Nevada, and the more steeply
sloping western edge adjacent to the Coast Ranges. This study focuses on the Tuolumne
River fan located in the northeast portion of the San Joaquin Basin (Figure 1 *NOTE:
Some of the ﬁgures included in this thesis are presented in color.*). The fan was formed
where the Tuolumne River ﬂows west out of the Sierra Nevada into the San Joaquin
Basin.

Quaternary ﬂuvial fan deposits from numerous rivers along the eastern San
Joaquin Valley, including the Tuolumne River fan, preserve evidence of past phases of
aggradation and degradation from varying amounts of sediment supply and discharge in
response to recurring glacial periods in the Sierra Nevada (J anda, 1966; Marchand, 1977;
Huntington, 1980; Marchand and Allwardt, 1981, Lettis, 1988; Weissmann et al. 2002b,

2006). Fluvial fans are differentiated from alluvial fans in this study to emphasize that

ﬂuvial fans are characterized by perennial ﬂuvial processes, while alluvial fans are
characterized more by ephemeral debris ﬂows or sheetﬂoods.

To understand the climatically induced cycles of deposition in the ﬂuvial fans,
Weissmann et al. (2002b) investigated the ﬂuvial fan deposits on the Kings River fan and
applied a sequence stratigraphic model. This sequence stratigraphic model describes
changes in accumulation space and preservation space (after Blum and Tornqvist, 2000)
that resulted in the cyclic deposits in the area. Accumulation space is deﬁned as one
component of accommodation space and refers to the volume of space available to be
ﬁlled with sediment on a process-scale. It is dependent on the balance of sediment
supply and stream discharge as well as channel geometry. Preservation space is also a
component of accommodation space, and, in this case, is a more long-term, net
accommodation and is controlled by the subsidence rate. It is the space below the lowest
level of sediment removal.

On the Kings River fan, packages of relatively rapidly deposited ﬂuvial fan
deposits, or open fan deposits, separated by paleosols indicate periods of aggradation
across the fan, or increased accumulation space, punctuated by periods of degradation
and fan incision, or restricted accumulation space and quiescence on the upper parts of
the fan. Preservation space was created by constant subsidence in the area, which
lowered the deposits below the lowest level of erosion. Sequence boundaries in this
model are identiﬁed as the paleosol surface and respective IVF base that divide the
ﬂuvial fan deposits into ﬁve stratigraphic units.

The ﬁve sequences mark distinct periods of regional aggradation and degradation.

Speciﬁcally, ﬂuvial fan aggradation or degradation on the Kings River fan occurred as

sediment supply to discharge ratios increased or decreased with changes from glacial to
interglacial climate. Interglacial periods are marked by limited aggradation, a low
accumulation space, and an intersection point located distally on the fan (Figure 2)
(Weissmann et al., 2002b). This portion of the cycle is when deposition occurred only at
the distal end of the fan allowing paleosols to form in the exposed upper fan outside the
incised valleys. Glacial periods are characterized by a large amount of laterally extensive
aggradation on the fan, a high accumulation space, and an intersection point proximally
located to the apex of the fan. Weissmann et al. (2002b) suggested that this sequence
stratigraphic model can assist in the prediction of facies distributions and stratigraphic
relationships in areas exposed to similar conditions.

Due to the similar cyclic depositional character among the fans in the eastern San
Joaquin Basin, the sequence stratigraphic model described in the Kings River fan study
can be applied to other fans in the basin (Weissmann et al., 2006). However climate and
sediment supply to stream discharge ratios are only two of the controls on the deposition
of the ﬂuvial fans in the valley (Weissmann et al., 2006). Factors that control the overall
amount of accumulation space available during periods of climate ﬂuctuation and hence
sequence geometry development are: (1) the sediment supply to stream discharge ratio,
(2) the rate of basin subsidence, (3) the amount of local base level change, and (4) the
basin width (Weissmann, et al., 2006). These controls vary within the San Joaquin Basin
and inﬂuence the sequence geometry on individual fans.

Controls on the Tuolumne River sequence geometry in particular are (l) glacial
inﬂuence in the drainage basin (2) relatively low subsidence rates (approximately 30cm/

1000 yrs, Lettis, 1988) due to the river’s location in the northern portion of the San

Joaquin Valley, and (3) the San Joaquin River local base level control on Tuolumne
River elevation (Weissmann et al., 2006). The Tuolumne River drainage basin in the
Sierra Nevada was glaciated during the Quaternary, which resulted in cycles of
signiﬁcant ﬂuctuations in the sediment supply and stream discharge. Because the
Tuolumne River is in the north, where the valley is narrower and subsidence rate is
relatively low, sequence thickness and lateral extent were thinner and smaller than
observed in southern portions of the valley. This resulted in an overall reduction in
accumulation and preservation space, which caused the lateral progression of apexes and
the thinner ﬂuvial fan units deposited along the Tuolumne River than were seen along the
Kings River (Weissmann et al. 2006; Bennett et al. in press) (Figure 2). The local base
level is connected to the San Joaquin River and ultimately sea level. This resulted in
deeper incision and sediment bypass in the distal portions the fan during interglacial
periods, also reducing the overall amount of accommodation space available to be ﬁlled.
The area within the scope of this study is delineated by the Sierra Nevada and the
San Joaquin River to the east and west, respectively, and by the Stanislaus and Merced
Rivers to the north and south (Figure 3). The study area is approximately 48 km (30
miles) long (west to east) and 19 km (12 miles) wide (north to south). Dissecting the site
is the Tuolumne River, which ﬂows east to west through the middle of the study area
(Figure 3). Deposits at the site are composed mainly of Cenozoic sedimentary deposits
(Marchand and Allwardt, 1981). These thick, slightly westward dipping ﬂuvial fan
deposits along the Tuolumne River decrease in age to the west, toward the San Joaquin
Basin center (Figure 1). Uplift and erosion have exposed older fan units in the eastern

portion of the basin. This study of the Modesto area will focus on the Quaternary

Pleistocene ﬂuvial deposits: the Turlock Lake Formation, the Riverbank Formation and
the Modesto Formation. A more comprehensive description of the older units in the area

can be found in Marchand and Allwardt (1981).

GENERATING GEOLOGIC REALIZATIONS TO BE MODELED

Each stratigraphic unit of open-fan deposits is bound at the top and bottom by a
paleosol and is composed of silty, sandy, and clayey sediments with discrete coarser-
grained channel deposits (Figure 4). Nestled among the relatively ﬁne-grained open-fan
sediments are the relatively coarse-grained IVFs, characterized by a thick basal gravel lag
(5 to 9 meters) that gradually ﬁnes up to the surface (Figure 5).

Characterization of hydrofacies within the ﬂuvial fan deposits was done by using
methods similar to those described in Weissmann et al. (2002b) where we utilized several
sources of geologic data, including driller’s well logs, geophysical logs, and lithology
from continuous core samples. Cooperation with the US. Geological Survey (U SGS) in
Sacramento, California afforded access to several thousand paper copies of the
Department of Water Resources (DWR) driller’s logs within the study area, geophysical
well logs from city wells, and recently obtained, relatively-continuous, soﬁ sediment
core. Also available from the USGS were digitized spatial data of the area hydrology,
geology, soils, topography, and approximate pumping rates of the city of Modesto
municipal wells (S. Phillips, unpublished data, 2005).

Multiple geologic scenarios were created using two soft sediment cores (a total of
155 meters (509 it) of core—Appendix B) and lithologic descriptions from

approximately 10,000 driller’s well logs. The lithology from the soft sediment core

10

samples was used to better understand the overall character of the ﬂuvial fan deposits,
and the driller’s well logs were used to identify the IVF in the area.

Open Fan Deposit Character

Continuous core samples collected by the USGS from two well locations were
assessed: one located in a proximal fan position and the other on a distal portion of the
fan (Figure 3). The proximal well, MREA, was drilled to a depth of 96 m (315 ft) and the
distal well, MRWA, was drilled to a depth of 59 m (195 ft).

Hydrofacies observed in these cores include: (1) gravel channel deposits (not
recovered in these cores but observed in drillers’ logs and described by Weissmann et al.,
2002b) (2) sand channel deposits (3) silty sands, silty clays, and clay overbank deposits,
and (4) pedogenically altered deposits. The core descriptions show that the western
extent of the study area contained more ﬁne-grained sediments of the distal ﬂuvial fan
deposits, while the eastern extent of the study area contained the more coarse-grained
proximal ﬂuvial fan sediments. More detailed descriptions of these hydrofacies are in
Appendix A.

Incised- Valley Fill Geometry

Through the interpretation of approximately 10,000 drillers’ well logs and
geophysical well logs, we were able to locate the originally identiﬁed IVF along with
several other plausible additional valley ﬁlls. Further analysis of the location and depth
of the IVFs was completed by mapping selected well logs in ArcMap.

The well logs that note gravels of sufﬁcient thickness (> 3 meters) were assigned
a rank of 1 to 4 in 0.5 increments based on how accurately the log’s description of the

stratigraphy resembled the ﬁning-upward prototype characterization of the IVF, as

11

observed in the geophysical well logs by Burow et al. (2004) and described for the Kings
River ﬂuvial fan by Weissmann et al. (2002b) (See Appendix C). A well assigned a rank
of 1 denotes the best representation of the IVF, with a thick gravel base ﬁning upward to
sand then silt and clay, while a well ranked 4 contains thick gravel but does not resemble
the ideal ﬁning-upward succession contained within the IVF deposits. The drilling
method and the consistent quality of individual drillers were also used to determine the
rank of the well.

The geometry of the IVF was approximated through the analysis of well logs and
assessment of the modern Tuolumne River valley geomorphic conﬁguration in
topographic maps, which is assumed analogous to the interglacial character of the
paleovalleys to be modeled. The IV F is estimated to (1) be 0.7 to 1.6 kilometers wide,
(2) range from approximately 30 meters thick at the apex of the fan to approximately 3
meters thick at the toe of the fan, giving it a wedge-shaped appearance, and (3) have a 5
to 9-meter-thick gravel base near the fan apex that thins down fan (Figure 5).

Using the database of well logs, a map of the various ranked wells was created
within ArcGIS. Because each plotted well does not represent the IV F, a subsequent map
exclusively of the highest ranked wells, 1 through 2, was generated to emphasize any
elongate channel patterns (F igure6A). General trends indicate presence of (1) a
paleovalley trending northwest from beneath the city of Modesto in the west side of the
site, (2) a paleovalley trending just north of and paralleling the current Tuolumne River,
(3a) a shallow paleovalley as a short meander loop in the eastern portion of the site, and
(3b) a shallow paleovalley trending to the southwest, also in the eastern extent of the

study area.

12

Further analysis to determine which geologic formation these valleys correlate to
was conducted by looking at the basal gravel depths (Figure 6B). The gravel depths were
mapped in intervals of the expected depths of the IVF base. A 6 m (~20 ft) resolution was
allotted for possible error or variation in accuracy among drillers. A depth of 24 to 38 m
(80 to 125 it) was expected for the Modesto Formation IVF basal gravel, and a depth of
43 to 61 m (~140 to 200 it) was expected for the Riverbank Formation IV F basal gravel
depth. From this map, the previously identiﬁed four IVF trends are still prevalent;
however four valleys can be segregated. The possible valleys interpreted to exist from
this map are: (1) a Riverbank Formation IVF from the Stanislaus River to the north that
may trend beneath the city of Modesto, (2) a Riverbank Formation IVF from the
Tuolumne River that appears to be clearly marked adjacent to the current river valley and
may potentially cross the modern river valley in the eastern reach of the river, and (3) the
two potential positions for the Modesto Formation IVF—(A) a small loop adjacent to the
modem Tuolumne River in the eastern reach of the river (identiﬁed in an aerial photo of
the current surface—See Figure 7) and (B) a southwestern trending IV F projecting from
the same location as position A. These trends are reemphasized when slightly lower
ranked wells (rank 2.5) were added to the map.

Because signiﬁcant uncertainty exists as to whether these trends are real, this .
study focuses on addressing multiple conceptual scenarios of valley ﬁll orientation and
geometries. Initial interpretations of the various locations for the Modesto IVF (positions
A and B) and the potential Stanislaus River IV F inﬂuence from the north provide a
foundation for the following four geologic scenarios that test the combinations of these

channel positions. The resulting four realizations are summarized in Table 1, and

13

include: (a) Riverbank IVF from Tuolumne River and the Modesto IV F A, or the small
loop adjacent to the modern Tuolumne River(RB/MS), (b) Riverbank IV F from
Tuolumne River and the Modesto IVF B the large IVF that projects southwest ﬁ'om the
apex (RB/ML) (c) Riverbank IVF from the Stanislaus River and the Tuolumne River and
the Modesto IVF A the small loop (RB/ST/MS) and (d) Riverbank IV F from the

Stanislaus River and the Tuolumne River and the Modesto IVF B (RB/ST/ML).

GROUNDWATER MODEL DEVELOPMENT

Steady—state groundwater and transport models were produced using Groundwater
Modeling System 5.1 (GMS 5.1) and were modiﬁed from a MODF LOW 2000 model
developed by the US Geological Survey (S. Phillips, unpublished model, 2005, Burow et
al, 2004). The Stanislaus, Merced, and San Joaquin Rivers provide relatively natural
hydrologic boundaries on the north, south and west sides of the model domain,
respectively. The Sierra Nevada is approximated as a no—ﬂow boundary to the east. The
steady-state model includes the inﬂuence of pumping in the city of Modesto and is
calibrated to match measured water levels in the region. Along with the groundwater ﬂow
and contaminant transport models of the multiple geologic realizations, simulations
testing the artiﬁcial recharge potential of the IV F scenarios were also assessed. Based on
the results of the ﬂow and transport models and the artiﬁcial recharge model,
recommendations are made on where future more comprehensive investigations should
be conducted. Methods for generating numerical models of these realizations are

described below.

14

US GS Model

The framework for the model used in this study of the inﬂuence of the IVF was
generated by the USGS (8. Phillips, unpublished model, 2005). The ﬁnite difference
steady-state model (produced in MODFLOW) was constructed based on the
hydrogeologic characterization of the Modesto area described in Burow et al. (2004).
Included in the report by Burow et al. (2004) is a description of how the model area
geology was characterized using existing data and how the area water budget was
calculated using information about water deliveries, pumping, and recharge within
several subwatersheds.

A coarse-ﬁne geologic model was produced by Burow et al. (2004) using
geophysical logs and lithologic descriptions from driller’s well logs to develop
representative percent coarse sediment fractions (Fcome) for the model area (F ﬁnc was
subsequently calculated as the remaining percent). To determine F come, a binary texture
classiﬁcation of “coarse grained” or “ﬁne grained” was used to assign either 100 or 0
percent coarse grained: gravels and sands were assigned 100 percent and silts and clays
as well as more consolidate sediments were assigned 0 percent. The percent coarse
fractions were then determined using a weighted average for l-meter (3.3 feet) depth
intervals. Horizontal and vertical hydraulic conductivities (Kh and Kv) across the study
area were calculated with these percent coarse fractions and various combinations of end-
member conductivities (a Kcoarse and a Kﬁne). Equivalent conductivities were calculated
using the arithmetic mean for Kh and the harmonic mean for Kv shown below in
equations 1 and 2.

Arithmetic Mean for Kh
Kequiv = (Fcoarse X Kcoarse) + (Fﬁne X Kﬁne) (1)

15

Harmonic Mean for Kv

K _ 1
equiv — F coarse + Fﬁne (2)

 

 

Kcoarse Kﬁne

The groundwater model was run systematically with equivalent conductivities for several
combinations of end-member K values and calibrated to wells in various locations of the
model (wells below the Corcoran Clay, above the Corcoran Clay but below the water
table, and east of the Corcoran Clay as well as some shallow wells in the western portion
of the model) (S. Phillips, pers. comm, 10/4/05). The root-mean squared error (RMSE)
was determined and plotted for each end-member combination to determine the
combination with the least amount of error. The ﬁnal end-members used to assign K
values are 80 m/day (262 ﬂ/day) for Kcoarse and 0.008 m/day (0.02624 ﬁ/day) for Kﬁne-

The 1 meter interval data set of equivalent K values was smoothed within a 10
meter (32.8 feet) vertical window (+ or -5 meters (16.4 feet) from the point being
sampled) and then resampled every 5 meters (16.4 feet). The 10 meter smoothed data set
was kriged using two nested structures: (1) Gaussian variogram model and (2)
exponential variogram model. This kriged grain size-to-K model implicitly includes the
IVFs, which is evident by regions of coarse grained deposits in various locations,
especially south of the Tuolumne River.

From the data included in the report by Burow et al. (2004), a 16-layer model was
created. The model area is approximately 62 km long and 55 km wide and has a

maximum thickness of ~420 meters. The model uses the Block Centered Flow (BCF)

16

package and the PCG2 (preconditioned conjugate gradient 2) solver. The vertical
discretization of the USGS model was developed as various percentages of the total
thickness between two halves of the model. The top half was discretized between the top
of layer 8 (the Corcoran Clay) and the top of the model (the land surface). The bottom
half was discretized as percentages of thickness from the bottom of layer 8 to the bottom
of the model (an artiﬁcially generated surface). Layer 8 top and bottom in the western
portion of the model were assigned based on thickness estimates of the Corcoran Clay
from Page (1986).

Boundary Conditions

The boundary conditions in the USGS model are deﬁned by general head
boundary (GHB) cells. The boundaries along the perimeter of the northern, southern, and
western portion of the model represent the expected vertical gradient in the aquifer. In the
top model layer, the rivers are GHBs. The San Joaquin River is denoted completely by
GHB cells. The other three westward ﬂowing rivers (Stanislaus, Tuolumne, and Merced
Rivers) in the area are represented as a combination of GHB cells and speciﬁed ﬂux cells.
In the western portion of the model area, these westward ﬂowing rivers are GI-IBs
because they are connected to the water table, where the depth to the water table is
relatively low. However, in the east, the rivers are no longer connected to the water table
and are denoted with a speciﬁed ﬂux cell (described below in recharge). Each general
head boundary cell requires two inputs: (1) a head value and (2) a conductance value.

Initial head values for the GHB representing rivers were determined from stream
gage data and the area topography. The lateral boundary GHB heads that represent the

expected vertical gradient were estimated from water-level data (S. Phillips, pers. com.

17

8/1/05 and 12/5/05). A vertical gradient of 0.05 meters of head per meter of depth
(thickness) was applied to the initial head values to produce the head elevations for the
GHB (ghbhead). The gradient applied was based on vertical gradients observed in nested
wells northeast of Modesto and generalized ﬂow directions based on the approximate
average elevation of the perforated interval of deep production wells in the area (S.
Phillips, unpublished data, 9/26/05 and 9/20/05).

The average elevation of the perforated interval of deep production wells is
estimated to be within layer 9 of the model. This heavy pumping generates a complex
vertical gradient with downward ﬂow from layer 1 to layer 9 and upward ﬂow from layer
16 to layer 9. To recreate the inﬂuence of deep production wells on the ﬂow regime, a
gradient of 0.05 meters of head per meter of depth was multiplied by the depth to the cell
center. This value was then subtracted from head levels in layer 1 for layers 2 through 9
(the layer below the Corcoran Clay), reducing the head elevations with depth and creating
a downward ﬂow. Conversely, the gradient was multiplied by the depth of the center of
the cell and added to the head assigned to layer 9 for layers 10 through 16, increasing the
head elevations with depth creating upward ﬂow toward layer 9. Additionally, to prevent
the head value calculated for layers 10 through 16 from exceeding the head value in layer
1, mathematically possible but physically implausible, head levels that were calculated to
be greater than the head elevation in layer 1 were set equal to the head elevation in layer
1.

The conductance (ghbcond) through the GHB cells was determined depending on
the location of the cell in the model: the top layer river cells had different conductance

calculations than the rest of the GHB cells. In general, conductance is calculated using

18

two variables: (1) the horizontal hydraulic conductivity assigned to the cell and (2) the
cell thickness. Constants also included in the calculation are: (1) cell size (400m) and (2)
general head boundary distance (400m).
ghbcond = KH*thick*cellsize/ ghbdist (3)

For the GHB cells that delineate rivers in layer 1, this boundary allows a small
amount of vertical ﬂow, while below layer 1 along the western model edge and for all
layers along the north and south extent of the model, the boundary reﬂects the overall
lateral ﬂow from the east. To implement this condition, the conductance through layer 1
GHB cells at the location of the rivers was calculated using the vertical hydraulic
conductivity, riverbed thickness (1m), river width (25m), and a vertical conductivity
multiplier of 10.

ghbcond = KV * ((cellsize*rivwidth) /bedthick)* vkrivmult (4)

Recharge

Recharge to the aquifer was assigned from the 2000 water budget reported by
Burow et al. (2004), and was determined with a land use approach. Burow et a1. (2004)
divide the study area (same as the model area) into the 47 sub-regions (the smallest
possible) where the amount of surface water deliveries could be evaluated. Separate
water budgets were evaluated for each of the 47 sub-areas containing non-urban (crop
and vegetation) and urban settings. Recharge in non-urban settings was estimated with
crop demand (calculated from National Oceanic and Atmospheric Administration
WOAA) and the California Department of Water Resources data), surface water
deliveries (from local irrigation district data), and precipitation (from NOAA data) in

combination with land use surveys for Stanislaus and San Joaquin counties. Urban area

19

recharge was approximated using estimates of applied water, leakage from distribution
lines, and precipitation. Recharge from rivers is assumed to be 0.005 m/d in locations
where the river was not connected to the water table. Although this recharge value is the
best current approximation, recharge rates over the rivers could not accurately be
estimated with the available data, and calibration of this parameter was poorly
constrained (S. Phillips, pers. comm, 08/01/05).

Base-soil Evaporation

Transpiration for this model was accounted for separately in the water budget

from Burow et al. (2004). Thus, the evapotranspiration package (ET) was used in this
groundwater model to account for base-soil evaporation with a maximum evaporation
rate of 1.6 m/yr at land surface. This rate was determined from pan evaporation
estimates; however signiﬁcant uncertainty exists around this value. The maximum
extinction depth is 2.1 meters below the land surface.

M

Pumping from three types of wells (urban-supply, agricultural, and water-table-
control, or have known "drainage") is accounted for in the model. (Water table control
wells are shallow pumping wells located mostly in the western portion of the model that
prevent the water table from rising to a point where it can interfere with crop roots.)
Although domestic wells are numerous in the area, pumpage from wells in this category
is extremely small compared to pumpage from urban supply, agriculture and water-table-
control wells, so it was ignored.

Pumpage was distributed by dividing the three types of wells in the model to one

of two categories: (1) actual wells with measured pumpage values and (2) imaginary

20

wells that account for unmeasured private agricultural pumpage. The measured wells
were assigned an annual pumping rate for the water year 2000 (i.e. from October 1, 1999
to September 30, 2000). The imaginary wells representing the private agriculture wells
were assigned annual pumping rates estimated from the water budget described in Burow
et al. (2004), where this rate is generally estimated as the residual between sources and
demand for crop water assumed to be met by private pumpage. The amount of pumpage
was distributed among imaginary wells within each sub-area deﬁned in the water budget
calculation (Burow et. al., 2004).

Reservoirs

This model also incorporates the recharge from local reservoirs through use of the
reservoir package. This package is similar to the river package. The reservoirs included in
this model are the Wood Reservoir (to the northeast), the Modesto Reservoir (to the east),
and Turlock Lake (to the southeast) (Figure 8). Turlock Lake is only partially within the
model boundaries, but still contributes leakage to the region and was included.

Leakage from the reservoirs is calculated by the reservoir package as the product
of the hydraulic conductance of the reservoir bed sediments and the difference between
the stage of the reservoir and the head in the groundwater system. However, once the
reservoir and groundwater are no longer connected, the recharge rate from the reservoir is
constant, not head-dependent. Reservoir bed conductance is calculated from estimates of
vertical hydraulic conductivity of the reservoir bed (In/day), thickness of the reservoir
bed (m), and the model cell’s row and column dimensions. The modeled total inﬂow
volume of 192,701 In3 (6,805,171 113) from all three reservoirs was based on an estimate

made by the Modesto Irrigation District (MID) for the Modesto Reservoir, and the

21

assumption that the other reservoirs had similar leakage rates (S. Phillips, pers. comm,
8/31/05).

Modiﬁcations to the USGS Model

The USGS model of the Modesto area was generated to obtain a better
understanding of the regional ﬂow and water budget. The model for our study aims to
speciﬁcally show the inﬂuence of the IV F within this regional approximation of
groundwater ﬂow. Alterations made to the original USGS model, described in more
detail below, include: (1) the addition of the IVFs, (2) altered slope of the uppermost
layers in the model, (3) increased vertical discretization, (4) use of the Layer Property
Flow (LPF) package, (5) addition of GHB conditions along the upper reaches of the river,
(6) removal of GHB cells from the northeastern portion of the model, (7) slight alteration
of the vertical head gradient calculation in the GHB, and (8) reduced vertical conductance
of the reservoirs.

Addition of the IVF

Each of the four IVF scenarios (RB/ML, RB/MS, RB/ST/ML, and RB/ST/MS)
was modeled in an adaptation of the USGS model (Figure 9). To add these IVFs to the
model, a code was developed to assign IVF hydraulic conductivity values to the cells
within the IVF (Appendix G). The code uses the elevation at the center of the cell and
the lateral location of the cell to assign either the original USGS hydraulic conductivity or
an IV F hydraulic conductivity. The code also accounts for the ﬁning upward character of
the valley by assigning K values that differentiate between cells that represent basal
gravel, sand, or the uppermost ﬁnes (see Appendix F and G). This internal IVF

stratigraphy was modeled as an elevation percentage between the top and bottom of the

22

valley (total valley thickness). Gravel represented the basal 40% of the IVF, sand the
middle 45% of the IV F, and ﬁnes the top 15% of the total valley thickness. The Kh
values for the gravel and the ﬁnes sections of the IVF with values used in Weissmann et
al. (2004), with gravel = 864 m/d and ﬁnes = 0.0864 m/d. The sand in the IVF was
assigned a K of 220 m/d, instead of the 86.4 m/d used by Weissmann et al. (2004). We
deviated from the values in Weissmann et al. (2004) and chose a K of 220 m/d to
maintain consistency with our conceptual model which asserts that the IVF is more
coarse-grained than the surrounding sediments. The reasons for increasing the K of the
sand in the IVF are discussed in more detail in the results.

Slope of Upper Model Layers gnd Vertical Discretization

To best preserve the continuous nature and ﬁning upward character of the IVF
deposits to test their impact on the groundwater ﬂow, the vertical discretization and slope
of the layers in the upper portions of the model were changed from the original USGS
model. The original 16 layer model was converted to a 27 layer model, described in
detail in Appendix F. The top of USGS layer 1 and layers 12 through 16 (23 to 27 in the
new model) were not changed in the modiﬁed version of the model. Layers 2 through 16
in the modiﬁed model (USGS layers 2 through 7) have slight to drastic variations in cell
thickness and/ or slope of the layer elevations. The slope of these upper layers matches
the gradient of the base of the IVF (Figure 10). In this modiﬁed model, the slope of the
layers 2 through 5 match the Modesto small IVF base and 6 through 16 match the slope
of the Riverbank IVF base.

Adaptations to the vertical discretization and the slope of the USGS model for use

in this study serve to improve the model’s ability to simulate the inﬂuence of the IVF

23

while preserving as much of the original model as possible. The ﬁner vertical
discretization in the modiﬁed model best preserves the IVF stratigraphic character and
geometry in the model (See Appendix F). The initial coarser vertical discretization of the
model would not allow for accurate representation of the IVF ﬁning upward stratigraphy,
which is an important geologic attribute to maintain because it is vital to assessing the
inﬂuence of the IV F s on groundwater ﬂow. Additionally, because the layers that contain
the IVF have the same gradient as the IVF in this model version, the Kh data imported
from the IV F code preserves the continuous nature of the IV F .

Layer Property Flow (LPF) versus Block Centered Flow (BCF)

Another difference between the model generated for this study and the USGS
model of the Modesto area is the ﬂow process package used. The model generated for
this study uses “Layer Property Flow” package (LPF). The LPF simpliﬁes parameter
input because it utilizes the cell elevations, as speciﬁed in the discretization ﬁle, to
calculate cell thickness and ultimately the ﬂow through each cell. The Block Centered
Flow package (BCF) does not use the cell elevation values to calculate the cell thickness
and ﬂow through each cell.

For example, the LPF package only requires model inputs of hydraulic
conductivity for each cell and will use cell top and bottom elevations to calculate
thickness and then transmissivity values for conﬁned model layers prior to running the
model. Using the BCF package, cell thickness are not be calculated prior to the model
run; therefore, model inputs for use with the BCF package require the thickness
component already be incorporated in parameter values. In practice, this means that the

BCF requires a combination of hydraulic conductivities and transmissivities be assigned

24

to the model cells. While both methods of ﬂow calculation offer comparable results, the
LPF allows more ﬂexibility for adaptations to the model discretization and parameters
(such as changing conﬁned/ unconﬁned layers and vertical conductivities).

Another example of the beneﬁts of using the LPF in this case is illustrated in the
use of an anisotropy factor instead of a leakance value. The leakance term is required as
an input in the BCF package, and is the product of vertical hydraulic conductivity and the
thickness of the cell. In the LPF, an anisotropy factor (Kh/Kv) is used instead of leakance
and does not include the cell thickness, which means that these values can remain the
same, despite any subsequent changes in cell thickness. For the modiﬁed model, Kh/Kv
was calculated using a FORTRAN code (See Appendix H).

Addition of GHB Conditions along the Upper Reaches of the River

The northern, southern, and western general head boundary cells in the modiﬁed
model are the same as those in the USGS. However, the speciﬁed ﬂux cells that
represent the Stanislaus, Tuolumne, and Merced Rivers in layer 1 of the USGS model
were changed to GHB cells in the modiﬁed model (Figure 8). Despite the USGS
justiﬁcation that the rivers are disconnected from the aquifer in the area, the lack of a
boundary in this portion of the model causes unrealistic volumes of water to collect
within the river valleys. Water ﬁlled the valley up to 10 meters above the valley base.
Addition of the GHB conditions in the river valley was implemented to rectify this
problem. Head elevations at these new GHB cells were assigned based on the ground
surface elevation, using National Geographic TOPO! California.

To preserve the condition that the rivers are disconnected from the aquifer in the

eastern portion of the model even with the addition of the GHB cells, ﬂow between the

25

river and the aquifer was reduced by assigning lower conductance values to the new GHB
cells. The lower conductances were calculated with the same equation as the original
river cells (explained above), but the vertical-K river multiplier (vkrivmult) of 10 was not
included. Additionally, the uppermost reaches of the river near the reservoir were
assigned even lower cell conductance values to reﬂect the lower conductivities of the
more consolidated geologic deposits in that area. These cells were also calculated without
the vertical K river multiplier and then divided by 2 as well. Reducing the conductance
still allows unrealistic volumes of water to ﬁll the river valley, 5 to 8 meters (16 to 26 it)
at most; the river still maintains losing and gaining reaches, which is the most
parsimonious condition that could be achieved for the model in this study.

Removal of GHB Cells from the Northeastern Portion of the Model

Along the northeastem-most edge of the USGS model, GHB cells are dry down to
an elevation of 10 to 17 meters (33 to 56 feet). While a solution can still be calculated
despite the presence of dry boundary cells, it is not practical to set a boundary condition
artiﬁcially high and allow the cell to dry. To avoid the drying of the peripheral GHB
cells, GHB conditions were removed from layer 1 through layer 4 (Figure 8). The exact
cells where the GHB condition was removed include:

Layer 1: I 1; J 76 to 153
Layer 2: I 1; J 81 to 153
Layer 3: I 1; J 96 to 153
Layer4: I 1;J 111 to 153

Alteration of Vertical Head Gradient in GHB
General head boundaries in the modiﬁed model were also assigned around the

perimeter of the model to represent the vertical gradient in the area that is caused by deep

26

production wells. The head and conductance values required as input for the GHBs were
calculated in a manner similar to the method used for the USGS model. The vertical head
gradient applied to the GHB cells, however, was calculated using slightly different
methods. This modiﬁed approach produced a comparable vertical gradient.

Similar to the USGS vertical head gradient, downward ﬂow was produced by
reducing the set head elevations in layer 1 by a gradient of 0.05 meters per meter of depth
(thickness) for layers 2 through layer 17 (the Corcoran Clay). Initial set head values for
layer 1 GHB were determined from gage data and the area topography (S. Phillips, pers.
comm, 8/1/05). One variation between the USGS GHB gradient and mine is that layer 18
heads were set equal to the heads in layer 17. This was changed to prevent an unrealistic
(at this model scale) head change between the low conductivity Corcoran Clay (layer 17)
and the layer below it (18). The upward ﬂow gradient was applied to the head assigned to
layers 19 through 27. To generate upward ﬂow in these layers, the gradient of 0.05
meters of head per meter of depth (thickness) multiplied by the depth of the cell center
from was added to the head values from layer 18. In a manner similar to the USGS
approach, this increasing head value was constrained with the condition that the
calculated head value could not exceed the assigned head value in layer 1. If this
condition occurred, the calculated head value for that layer would be ignored and the cell
would be assigned the layer 1 head value instead.

Conductance calculations were made with the same methods used in the USGS
model. Variations in the actual conductance values between the USGS model and the
model used in this study are a result of thinner layers in areas with ﬁner discretization or

the presence of the IVF that intersects the westem-most boundary.

27

Reduced Vertical Conductance of the Reservoirs

Another method used to reduce the volume of water that collects within the river
valleys in the model, the vertical conductivities of the reservoir bed sediments were
lowered in the reservoir package. Manual trials of various vertical conductivities were
used to constrain the value that not only best matched the USGS estimate of the volume
of inﬂow from the reservoirs, but also reduced the volume of water within the river
valleys. The values of 0.003 (for the Wood and Modesto Reservoirs) and 0.006 m/day
(for Turlock Lake) assigned to the reservoir bed sediments in the original USGS model
were divided by 1.2. The resulting vertical conductances used in the adapted model are
0.0025 (for the Wood and Modesto Reservoirs) and 0.005 m/day (for Turlock Lake).

Flow Model Simulations, Particle Tracking, Solute Transport Simulation

Six steady-state ﬂow models were run in MODFLOW 2000 for this study. They
include the USGS model, the modiﬁed model without the IVFs, and a model for each of
the four geologic realizations. Comparison of the head solutions, particle pathways, and
transport simulations for each of these realizations gives insight into the inﬂuence the

addition the IV Fs have on the groundwater ﬂow in the Modesto area.

RESULTS AND DISCUSSION

Results of the ﬂow model simulations for this study ﬁrst examine the impact that
changes made to the original USGS model have in the ﬂow solution by comparing the
USGS model and the modiﬁed model without the IVFs. Once the impact of changes
made to the original model is evaluated, the inﬂuence of the IVFs will be assessed with

particle tracking and solute transport simulations. Although models and results were

28

generated for each geologic realization, model results are similar for the four different
IVF realizations; therefore we illustrate the inﬂuence of IVF deposits using results from
the RB/ST/ML, and brieﬂy describe variability between IV F realizations at the end of
this section. The RB/ST/ML realization was chosen to illustrate the inﬂuence of the IVF,
because it incorporates the inﬂuence of three potential locations of the IVFs and most
clearly shows the degree of inﬂuence of the IVFs may have on the groundwater ﬂow.

Groundwater Modeling — Comparison of models without the I VF deposits

Changes made to the USGS model (altered slope of the uppermost layers in the
model, increased vertical discretization, use of the Layer Property Flow (LPF) package,
addition of GHB conditions along the upper reaches of the river, removal of GHB cells
from the northeastem portion of the model, and slight alteration of the vertical head
gradient calculation in the GHB) appear to have little impact on the model solution.

Both models show the general trend of groundwater ﬂow is to the west-southwest
with the highest hydraulic head elevations in the east (Figure 11A and B). For the
modiﬁed model without the IVF, the head solution has slightly lower heads in the
reservoir area, and the head gradient appears slightly more gradual in the central portion
of the model near the Tuolumne River. The large depression in the water table in the
southeast comer is observed in both models. This depression is caused by a heavy
dependency on local groundwater and limited recharge through application of non-local
irrigation water in this area (a signiﬁcant source of recharge in other areas of the model)
(Burow et al., 2004; S. Phillips pers. comm., 11/9/05).

Though the USGS and the modiﬁed ﬂow model solutions are similar, some minor

variation in the ﬂow statistics was observed. The volumetric budgets are comparable

29

with a total volume in and out of the USGS model at approximately 5.40 million meters3
(190.7 million feet3) and the modiﬁed model at 5.42 million meters3 (191.4 million feet3).
The discrepancy between the inﬂow and outﬂow of the model is smaller in the modiﬁed
model relative to the USGS model, but both discrepancy values are less than a tenth of a
percent (<<0.01%) of the total budget. The USGS model has a difference of 14 m3 (494
1’13) while the modiﬁed model has a difference of 0.34 m3 (12 ﬁ”).

More speciﬁc comparison shows the USGS model has a maximum head value of
70.9 meters (232.6 feet) at the Modesto Reservoir and a minimum of -2.4 meters (-7.9
feet). The modiﬁed model has a slightly lower maximum and a similar minimum head
value: a maximum of 60.2 meters (197.5 feet) at the Modesto Reservoir and a minimum
of -2.2 meters (-7.2 feet). The maximum head values have a 15% difference and the
minimum values ~8.9%. The mean simulated head in the USGS model is slightly lower
at 19.7 meters (64.6 feet) relative to the modiﬁed model’s mean head of 21 .5 meters
(70.5 feet) (Table 2).

Although the volumetric budget and regional ﬂow appear similar, local variation
between the two models does exist. The addition of the GHB to the upper reaches of the
Tuolumne River in the modiﬁed model did not eliminate ﬂooding within the river valley.
In the USGS model, because the river was not denoted as a boundary, less water
accumulated within the channel speciﬁcally in the uppermost (eastern) reaches. Along
the entire length of the river valley, however, comparable ﬂooding is observed within
both models. While the Tuolumne River is thought to be losing water to groundwater in
the upper reaches, as are the Stanislaus and Merced Rivers, (based on water-level contour

maps developed by the CA Dept. of Water Resources, S. Phillips, pers. Comm, 11/1/05),

30

model results indicate that the Tuolumne River may have both gaining and losing reaches
(see Advective Pathline Analysis section below). However, further reﬁnement of our
groundwater model may also be necessary in order to more accurately capture the surface
water — groundwater interaction. Aside from the changes within the river, the results
from both of these models indicate that the changes made to the modiﬁed model without
the IVFs did not impact the overall ﬂow model signiﬁcantly as the ﬂow budgets only
varied by 0.4% and the regional head solution is approximately the same.

Inﬂuence of the I VF Deposits on Groundwater Model Results

The results from the Modesto area model that includes the wedge-shaped, coarse-
grained IVFs indicate that the IVFs have a hydrogeologic signiﬁcance and are capable of
acting like a regional “pipeline” for groundwater ﬂow and contaminant transport in the
area. The inﬂuence of the IVF on the groundwater ﬂow is illustrated by comparing the
modiﬁed model without the IVFs (i.e. the model without the IVFs) to the model with the
IVFs (speciﬁcally, the RB/ST/ML model, which is representative of results seen in the
other geologic scenarios) using three methods: (1) a calculation of the head difference
between the model with and without the IVF, (2) advective pathline analysis, and (3)
solute transport simulation.

Head Difference

The head difference between the model with and without the IVF highlights the
inﬂuence the IVF may have on the regional head distribution. The head difference was
obtained by subtracting the head solution of the model with the IV F5 from the head

solution of the model without the IVFs (Figure 12), and allows us to visualize the extent

31

as well as the magnitude of the inﬂuence of the IVF. It also highlights the nature of the
head difference: positive or negative.

As expected, the magnitude of the head difference decreases with distance from
the IVF and is illustrated in ﬁgure 12. The areas signiﬁcantly inﬂuenced by the presence
of the IVFs, a head difference > 10 cm, are adjacent to the IVFs. In areas farther away
from the IVFs, the head difference, and therefore the inﬂuence of the IVFs on the head
elevation, decreases.

The location of positive and negative head difference values also provides
signiﬁcant insight into the inﬂuence of the IVFs. Areas with a positive head difference
mark locations where the model without the IVF has higher head elevations than the
model with the IVFs. A negative head difference indicates areas where the model
without the IV Fs has lower head elevations than the model with the IVFs.

In the eastern portion of the model domain, the head difference is positive.
Farther west, from the location where the RB and ST IVFs (Tuolumne River and
Stanislaus River Riverbank Formation IVFs) intersect to the western extent of the model,
the head difference is negative. Positive head differences in the eastern portion of the
model are high, approximately 1 to 4 meters, where the RB and ST IVFs are located
(Figure 12). The head difference is positive all along the eastern portion of the model,
but the magnitude of the head difference decreases radially with distance from the IVFs.

In the western portion of the model domain, negative head differences radiate out
from the intersection of the RB and ST IV F s. The most negative head differences

(approximately -2 meters) are located in the area where the IVFs intersect (Figure 12).

32

Farther west, the inﬂuence of the IVF remains, but the extent and magnitude of the
negative head difference surrounding the RB IVF decreases.

This pattern of head differences, positive in the east transitioning to negative
toward the west along the IV F, indicate that the RB and ST IVFs create areas of
convergent and divergent ﬂow. Thus, at the head of the valley, hydraulic head elevations
are lower (where head difference is positive) due to the addition of the thick, coarse-
grained IVFs that allow a signiﬁcant amount of ﬂow through the area. Farther down the
IVFs to the west, head elevations are higher (where head difference is negative) than
were observed in the model without the IVF, indicating ﬂow is being diverted from the
IVFs. In the steady-state conditions speciﬁed in this model, water ﬂows into the IVFs at
the head, but once the maximum volume of water capable of ﬂowing through the IVF
ﬁlls the valley, the water “backs-up” within this coarse “pipeline”, and raises the
hydraulic heads in the western portion of the model. This in turn causes ﬂow that is
initially following the IV F to divert into the contiguous aquifer sediments.

The convergent and divergent ﬂow regions can be inferred to represent an
equilibrium plane. The transition from convergent to divergent ﬂow along this plane
shows the location in the IV F where the hydraulic conductance has reached a balance
with the volume of water that can pass through. Above this point, the hydraulic
conductance of the IVFs is sufﬁciently high to allow the volume of water entering the
IVFs to pass through. Below the transition point, the hydraulic conductance of the IV F3
is not large enough to allow the entire volume of water entering the IVF to pass through
and the water “backs-up” and is pushed out of the IVF deposits into the surrounding

aquifer sediments.

33

One limitation on testing the inﬂuence of the IVFs on groundwater ﬂow in the
model is the presence of artiﬁcially generated coarse areas in the USGS grain size to K
model, which are a result of the implicit inclusion of the IV PS. The impact of the
shallower Modesto Formation IVF (ML), in particular, on groundwater ﬂow is dampened
by surrounding coarse grained regions across the area south of the Tuolumne River.
Here, the Kh of the gravels in the IVF are almost an order of magnitude greater than the
surrounding deposits, while the sand is approximately the same Kh as the deposits around
it. The result of this is a more localized impact on the hydraulic heads. Head elevations
do not show a distinct area dividing convergent and divergent ﬂow along the valley as
can be seen with both of the Riverbank Formation lVFs (RB and ST). Instead, this valley
appears to highlight local areas where the connectivity of coarse sediments is improved
(Figure 12).

In some areas along the ML IV F, there is only a small positive head difference.
These areas highlight where there was little improvement in the connectivity of the
aquifer with the addition of the IV F and head difference varies only slightly. Areas that
show little improvement in aquifer connectivity were already well connected due to the
presence of coarse sediments prior to the addition of the IVF.

In other areas along the ML IVF, the head difference is negative. These areas
highlight where the connectivity of the aquifer is improved locally by the addition of the
IV F. Replacement of relatively ﬁner sediments with the addition of the coarser ML IVF
creates a local area of preferential ﬂow, which displays the “back-up” of water.

The presence of the IVF within both relatively coarser and ﬁner sediments is

contrary to our conceptual model, where the IVF is thought to be relatively coarse

34

compared to the surrounding sediments. The USGS grain size-to-K model data indirectly
preserve the coarse grained nature of the IV F. Because this data set is kriged across the
model, the coarse grained nature of the IV F was probably interpolated across a large area,
artiﬁcially increasing K values. Thus, the USGS method of generating K values ﬁ'om
grain size does not exclude the IVFs and may not provide the best representation of the
deposits around the IVFs to illustrate the inﬂuence of IVFs on groundwater ﬂow.

Along the deeper Riverbank Formation IVFs to the north of the Tuolumne River
(the RB and ST IVFs), the IV F is surrounded by signiﬁcantly ﬁner sediments (low K
values) relative to the coarse grained sediments of the IV F (high K values). This allows
for a more regional increase in aquifer connectivity with emplacement of an IVF and
shows a single point of transition from convergent ﬂow into the IVF to divergent ﬂow
out of the IVF. Implications of the varying inﬂuence on groundwater ﬂow are described
below with the results of advective pathline analysis in the model.

Advective Pathline Analysis

Simple pathline tracking in the ﬂow models with and without the lVFs were used
to show the impact the IVFs have on the groundwater ﬂow in the Tuolumne River area.
Pathline tracking allows an analysis of the advective ﬂow pathways within the
groundwater model.

Using MODPATH within GMS 5.1, 1000 particles were assigned and released
from the same 4 cells in layer 1 of each model (the model without the IVF and the model
with the IVF) (Figures 13, 14, 15). The 4 cells were chosen because they are located in

each of the IVFs: 1 cell in the RB, 1 cell in the ST, and 2 cells in the ML (proximal and

35

distal). The particles were tracked in both models to their ultimate fate within the steady
state ﬂow conditions.

In the model without the IVF, pathlines from particles released in the central and
southern portion of the model tend to be located in the upper unconﬁned portions of the
aquifer, while other pathlines from particles released farther north, follow the vertical
gradient and trend down into the conﬁned portions of the aquifer (Figures 13A, 14A, and
15A). The addition of the IVF shows that pathlines extend farther westward into the
basin as well as deeper into the aquifer (Figures 13B, 14B, and 15B). As illustrated by
the pathlines that follow the IVF trend, the IV F provides a conduit for water to ﬂow
through. The addition of the IVF also allows pathlines to spread into aquifer sediments
that they previously would not reach. The vertical ﬂow gradient from heavy pumping in
the area contributes to the spread of pathlines, or water, throughout the area.

The implications of changes in head gradients through the addition of the IV F are
discernible from the results of the pathline analysis. The addition of the IVF sufﬁciently
changes the head gradients to generate areas of convergent and divergent ﬂow. This ﬂow
pattern redirects pathlines or groundwater ﬂow into the IV F and then diverts ﬂow out of
the IVF, thus causing the pathlines (or groundwater) to spread into contiguous aquifer
sediments.

Advective pathline analysis was also used to illustrate the impact of the IVF on
recharge to the aquifer from the rivers. Figures 16A and B shows a map view of
pathlines from particles released within the general head boundary cells that delineate the
Tuolumne River. Losing and gaining reaches of the stream can be identiﬁed by the

trends of the pathlines. The Tuolumne River shows intermittent losing and gaining

36

reaches along the stream in this model. A cross-section through the area (Figure 17A and
B) shows pathlines from the losing part of the stream, at the head of the stream in the
model with and without the IVF. In the model with the IVFs, the pathlines illustrate the
ﬂow into the IVF and westward (Figure 16 and 17B). In the model without the IVFs, the
pathlines have very little lateral movement and extend downward into the aquifer with
the vertical gradient (Figure 16 and 17A). Particle pathlines released from along the
losing portion of the stream near the Modesto area, in the model with the IVFs, show
ﬂow into the IVF and under the city (Figure 17B). Pathlines in the same area in the
model without the IVF show some lateral movement beneath the city, but not to the
extent shown in the model with the IV F s (Figure 17A). Comparison of pathlines in the
models with and without the IVFs illustrates that the presence of the IV F near the river
allows the IVF to gain recharge from the river. This is important, especially at the head
of the valley where artiﬁcial recharge to the aquifer could provide a water supply for
wells pumping in the Modesto area.
Solute Transport Simulation

Transport simulations were conducted using MT3DMS to run a potential transport
scenario in the modiﬁed model without the IV F8 and the model with the IVFs. A
constant concentration of 100 mg/L of an unspeciﬁed conservative solute was released
from the same location in layer 1 in simulations for both of the models (Figure18A and
B). Data requirements for the transport simulation include groundwater ﬂow solution
from the MODFLOW simulation, longitudinal dispersivity (01L), ratio of transverse to
longitudinal dispersivity, ratio of vertical to longitudinal dispersivity, effective molecular

diffusion (DI), and effective porosity. The longitudinal dispersivity was assigned a value

37

of 1 meter, which is based on work by Gelhar et al. (1992) who indicate that 1 meter is a
reasonable value for a 400 meter cell size. The ratios of transverse and vertical
diffusivity to longitudinal were both set equal to 1. Effective molecular diffusion was
assigned a value of 5.9616 x 10’5 m2 /day (Weissmann et al., 2002a). The effective
porosity assigned to these transport simulations was 33% (0.33). This porosity was
chosen because it provides a good average estimate for the porosity in the unconsolidated
ﬂuvial fan sediments (Weissmann et al., 2002a, c). To maintain simplicity, the
consolidated deeper, conﬁned aquifer cells were assigned the same porosity. Transport
simulations were run in the model with the IVF and without the IV F for 100 years.

The variation in plume morphology in these transport simulations clearly depicts the
inﬂuence the IVF can have on solute transport. The model without the IVF shows a more
uniform plume front that spreads laterally and vertically over a smaller area, while the
model with the IVF, displays preferential lateral and vertical movement through the IV F.
An interesting observation in Figure 18A is the southern most plume front shows some
preferential spreading in the coarse grained areas south of the Tuolumne River. This is
the area that likely has artiﬁcially high conductivity values due to the implicit inclusion
of the IVF in the USGS grain size-to-K model.

Because of the vertical gradient, both models show a signiﬁcant amount of vertical
movement of the plume. However, in the model with the IV F, a distinct elongate lobe of
the plume develops in the location of the IV F which spreads areas of the plume farther
westward than they do in the model without the IVF (Figure 183). The results show the
contaminant plume front follows the IVF and moves farther to the west in the model with

the IVF.

38

This relatively rapid plume movement within the IVF indicates that the IVF will have
a large role in water quality and remediation schemes. The presence of these valleys will
signiﬁcantly increase contaminant residence times, which could drastically affect efforts
to develop effective remediation schemes in the area. These results also indicate the
aquifer is more susceptible to widespread contamination due to the presence of the IVFs.
These results are similar to those observed by Weissmann et al. (2004) on the Kings
River.

Comparison among Geologic Realizations

Head differences among the IVF realizations show that that addition of IVFs in
different spatial locations can alter the head solution. Table 3 shows that while the overall
statistics of the head solution appear similar, very different local head variations are
apparent when the difference between heads solutions is calculated.

Comparing head differences of the various geologic scenario head solutions allows
for analysis of the impact each additional IV F has on the groundwater ﬂow. The
resulting head difference plots show the same pattern observed by adding the IV F s to the
model without the IVF: lower hydraulic heads at the head of the fan and higher heads
where the ﬂow has “backed-up” in the IV F . The location of the positive and negative
head differences varies depending on the IVF location. The addition of the ST valley
reduces the head elevations more over a larger area in the eastern portion of the model
than in models without the ST IVF. Figure 19A and B show a smaller positive head
difference in the northeastern portion of the model without the ST valley. The addition of
the MS valley instead of the ML valley results in a similar pattern of patchy areas of

positive and negative head differences highlighting where the IVF lowers or raises the

39

local head elevation instead of continuous regions of convergent and divergent ﬂow
along the IVF (Figure 19A and B).

While the results are similar for each of the IVF scenarios, the various locations of the
IVF do highlight the potential for drastic local variation in groundwater ﬂow. The
location of the IV F will have implications for how the IVF can best be utilized for
artiﬁcial recharge (i.e. where to drill wells to obtain the maximum groundwater
production rates). The areas of the aquifer most susceptible to rapid and widespread
contaminant transport through the IVF will be controlled by its location. As an essential
component of the groundwater system, the exact location of the IVF should be integrated

into remediation schemes.

CONCLUSION

From this investigation into the inﬂuence of the IVF, we can make several
conclusions about this work, including the methods used to delineate the IVF and the
impact the addition of the IVF has on the groundwater ﬂow in the Modesto area.

Our results indicate that the IVF has a signiﬁcant inﬂuence on the ground water
ﬂow in the Modesto area. We found that the IVF signiﬁcantly inﬂuences regional
hydro geolo gy by:

0 acting as a regional “pipeline” of coarse sediment for groundwater ﬂow,
0 providing a preferential pathway that has the potential to provide artiﬁcial
recharge to the Modesto area as well as create a conduit for contaminants to

follow,

40

0 increasing the dispersion of particle pathlines into the aquifer sediments
surrounding the IVF, and
o enabling solute plumes to move farther distances more rapidly through the IVF
making the aquifer susceptible to widespread contamination.
These results are consistent with those observed by Weissmann et al. (2004). They found
that the IVF geometry allows it to signiﬁcantly impact the ground water ﬂow and solute
transport due to the continuous and relatively thick basal cobble unit. The continuous,
course-grained nature of the valley ﬁll creates a highly conductive conduit for
groundwater ﬂow, cutting across any laterally bounding conﬁning units.

The four geologic scenarios developed in our study from the drillers’ well logs
allowed the inﬂuence of the IV F to be assessed along with the impact of uncertainty in
geologic model. Comparisons among these four scenarios give insight to the impact of
incorrectly developing a conceptual model and substantiate the need to better constrain
the location of these IV Fs.

Future Recommendations

While these models provide a good preliminary understanding of the inﬂuence of
the IV F, they should be used as a basis for further investigation into the area stratigraphy
and hydrologic regime. The geologic model could use some improvement by (1) using
more reliable methods for identifying the location of the IVF (2) obtaining core through
several locations of the IVF to better understand IVF lithologic character, (3) collecting
additional continuous core in the open fan deposit areas, and (4) more studies of the

Tuolumne River's ﬂow differential down-stream during stable ﬂow conditions.

41

Further investigation into the location of the IV F should be conducted. Although
the driller’s logs were successfully applied, this method of locating and deﬁning the IVFs
could be signiﬁcantly improved. Although multiple potential IVFs within the same
stratigraphic range (i.e. two potential Modesto Formation IV Fs) were identiﬁed, it is
unlikely that they both exist. While driller’s well logs provide a good approximate
location of the IV F, to more effectively model the TV F, more reliable geophysical data
should be acquired. Seismic data across the area will help better constrain the location
and geometry of these features, providing more dependable estimates of the width, depth,
and length of the IVF.

In this study, no core was collected within the IVF. This made modeling of the
lithologic character more challenging. The character of the proximal and distal portions
of the IVF need to be investigated as these are the principle areas of recharge and
discharge for groundwater and will impact the location of the divergent ﬂow from the
valley. Investigation into the toe of the fan may provide insight into the inﬂuence of
local base level (sea level) and the basin width on the preservation (or lack of) of the
distal fan deposits. Currently, the IVF code does not account for the fact that gravels are
not likely present in the distal portion of the model. With further constraints on the distal
character in the IV F, this portion of the model could be improved. Additional studies
near the apex of the fan will provide further insight into the geometry of the stacked IV F
deposits believed to exist. This may have a signiﬁcant inﬂuence on the ability to
artiﬁcially recharge the aquifer.

Additional continuous core data across the fan will also help to better capture the

aquifer heterogeneity outside of the IVF. One signiﬁcant improvement in this area would

42

be to add the sequence bounding paleosols. While this model does not include the
paleosols, they may have an impact on the regional or local groundwater ﬂow, acting as
no or low ﬂow barriers which could potentially increase the inﬂuence of the IVF and
their ability to inﬂuence the aquifer connectivity (Weissmann et al., 2004).

Studies to better constrain the differential ﬂow during stable ﬂow conditions
along the Tuolumne River will give insight into the true potential for artiﬁcial recharge
into the aquifer through the IVF. Detailed characterization of reaches along the
Tuolumne River where recharge occurs is needed in order to understand the nature of
groundwater-surface water interaction and conductance between the river and
groundwater system. While our model highlights ﬂow from the river as a potential
pathway for recharge to access the IVF, further studies would be needed to conﬁrm this
potential.

The groundwater and contaminant transport models produced enhance
understanding of the groundwater ﬂow and the potential for contaminant movement
through the Modesto area. The models also provide a preliminary understanding of the
potential inﬂuence these incised-valley ﬁlls may have on artiﬁcial recharge in the

incised-valley ﬁll.

43

Tables

Table 1: Summary of the geologic scenarios developed from the locations of thick

gravels in the drillers’ well logs delineating potential IVF locations. These scenarios will

be incorporated into multiple, steady-state, saturated groundwater ﬂow models.

 

 

 

 

 

 

 

 

IVF Age Riverbank lncised-Valley Fill (IVF)
Tuolumne River (RB) and Tuolumne River
Stanislaus River (81) (RB)
Modesto Large IVF RB/ST/ML RB/ML
lncised- (ML)
Valley Fill
(IVF) Small IVF RB/ST/MS RB/MS
1M8)

 

Table 2: Summary of the hydraulic head maximum, minimum and mean elevations for
the USGS model and the modiﬁed model without the IVF. Percent differences between
the USGS model and the adapted model and between the adapted model and the model

with IVFs are also included.

Head Elevation Statistics

 

 

 

 

 

 

 

 

 

 

 

% difference
% from the
difference model
Hydraulic Head from IVF without the
Elevations USGS No IVF USGS (RBISTIM L) IVF
Minimum Value (m) -2.37 -2.08 12.2 -2.06 1.0
Maximum Value (m) 70.94 60.37 14.9 60.06 0.5
Mean Value (m) 19.68 22.15 12.6 22.03 0.5

 

Table 3: Summary of the hydraulic head maximum, minimum and mean elevations for

the four geologic scenario models. Percent differences between the USGS model and the

adapted model and between the adapted model and the model with IVFs are also

 

 

 

 

included.
Modified % difference

Hydraulic Average Model from Modified
Head RBI RBI RBISTI RBISTI IVF without Model without
Elevations ML MS ML MS Scenarios the IVF the IVF
Minimum
Value (111) -2.07 -2.07 -2.06 -2.07 -2.07 -2.08 0.60
Maximum
Value (m) 60.10 60.12 60.06 60.07 60.09 60.37 0.47
Mean
Value (m) 22.07 22.07 22.03 22.03 22.05 22.15 0.60

 

 

 

 

 

 

 

 

 

44

 

 

Figures

45

Figure l: Generalized stratigraphy of the Great Valley of California, with the white and
black dashed boxes showing the approximate study area of the Tuolumne River area near
Modesto, California in the Eastern San Joaquin Valley. The geologic map, based on soil
surveys, shows the ﬂuvial fan deposits in the eastern portion of the San Joaquin Valley
(adapted from Weissmann et al., 2006). Thick, slightly westward dipping ﬂuvial fan
deposits along the Tuolumne River decrease in age to the west, toward the San Joaquin
Basin center. Uplift and erosion have exposed older fan units in the eastern portion of the
basin. This study focuses on the Turlock Lake, Riverbank, and the Modesto deposits.
Geologic map shown in color.

 

  
  
     
  
  

Mokelumne River Fan

San Joaquin Basin
Fluvial Fans

Calaveras River Fan

..m-Mt‘l! Stanislaus River Fan
I

l ’2‘” «”5"
,

Tuolumne River Fan

Merced River Fan

    

sits

dock Lake eposn

- Undrfferenhated Plio-
- Older Units Outside Basi
West Side Alluvral Fans

I n ea:

I Rivers and Lakes
0 510 20 30 4o 50
Kilometers

 
 
  

>2

 

 

 

46

Figure 2: Schematic proﬁle of the Tuolumne River fan stratigraphy showing the
westward stepping, thin, stacked, ﬂuvial fan units (Weissmann et al. 2006).

Dip Section

 

 

Ground Surface

   
 

 

Modesto Paleosol

 

 

Riverban -   . _ _... ..
Upper Turloc ‘

Paleosol
I” Lake Paleosol

Lower Turlock

r Lake Paleosol

 

47

 

Figure 3: The Tuolumne River area near Modesto, California in the Eastern San Joaquin
Valley. The black solid box denotes the entire study area. The dashed line outlines the
area shown in Figure 13.

 

 
  
 
   
  

"b-
1.33%..

Wood Reservoir

  
   
 

Stanislaus River

Ni m; \

 

       
 
   
 

. .- . (proleal we”) Modesto Reservoir
U NRWA WES?) I [to t‘
(uenuwem ... ”"
I

@ Tuolumne River Y“?! ll

\ Turlo‘ék Lair e
\ San Joaquin River 3;
\ Q.

Merced River

 

 

 

   
  

 

Legend
I Core Locations

Urban Area

 

 

 

 

T"? Reservoir

1:] Model Area

0 5 10 20 30 *\\ I
— — Kilometers \/ +

 

 

48

Figure 4: Each cyclic Quaternary deposit contains a relatively coarse-grained IVF
nestled within ﬁner open fan deposits of silt, clay and some sand. The IV F has a thick

gravel base that ﬁnes upward to sand then ﬁnes.

 

Modesto Paleosol Ground Surface

88 Si 81 58151 8 8338881 $33 838138 18531331 $8585 5% 33883888388 Sliill

Mdo eSt O Riverbank Paleosol
Formation 1 to 1.5 m (~3 to 5 ft)

Tea-— - {ff—Cd. :?_ -:O'o-_ .. 61:1“? —oo”"-3'oé-7

Upper Turlock Riverbank

Lake Paleosol ave as F .
‘ 15 to 30 m (~50 to 100 ft) tsp->97. ° ormatlon

 

 

—- -— u..- o ”I- I': -‘i- _..E—izzo —: S
0:...” . .

"." .. . v..‘.',i‘?:v.'1-'.,w. : . . .
Lower Turlock \r :7 it. 1‘-
Upper Turlock
, Lake Paleosol €59; it;
Lake Formation 49 to 55 m p 150 to 180 ft)

'55-‘53. ‘:!::-'::—'::.°::..'.":‘61'-' I I SI SI
. {‘1‘00’ .
‘._;-.;:.-gg-:.':.-.-.¥3;,= Lower Turlock

"iiog‘v'via'u‘s'erl Lake Formation

Ill“ Paleosol Basal Gravel

*NOT DRAWN TO SCALE.*

 

 

 

 

 

 

 

 

49

Figure 5: Conceptual model of the Riverbank IVF. The valley ﬁll is 0.7 to 1.6 km wide
and ranges from approximately 30 meters thick at the apex to 0.3 meters at the toe. It has
a 5 to 9 m thick gravel base that ﬁnes down fan.

ModéSto 7 N, River,;:.‘7 ,1

     

*NOT DRAWN

’ ‘ ' ' TO SCALE*
5 to 9m ”LISP—1&9“
thick ~ . ,
30m gravel

ﬁning
downfan c

 

50

Figure 6: Wells assigned a rank of l and 2, plotted spatially, roughly show a few east-
west elongate trends. The areas of particular interest are (l) the northwest trending line of
wells that runs beneath Modesto, (2) the line of wells that trends along just north of the
modern Tuolumne River, (3) a short string of wells that create a small loop to the south in
the eastern portion of the river, and (4) the line of wells that begins on the southeastern

part of the river and trends to the southwest.

\
\

/’ \\
// Q Stanlslaus Rlver

 

   
  
 

 

\

    

 

* & Turlock Lake"
*
I

 

6:}? Rank 1 and 2 Well Logs

j: p toepmurosommmzom
'5 4;» Depth 371° 41 m (121-13911)

I Depth43t061 m (140-20011)

1:3 Urbm Area

if"? Reservoir

:1 Nbdel Area

 

 

 

 

   
 
  
  
 
 

,x/Stanislaus River . . , \
1: Stanislaus Rlver

RIverbank‘Formatlon
IVF (ST)
1:

 
 

Modesto Reservoir
1'.

  
 
 

2: Tuolumne River“, _ . ..
Rlverbank Formatlon ‘ L» ,.

I I Tuolumne -,
.C- River

 
 
 
  
   

\ ThriBEk lack a
3A: Tuolumne Riverag j"
Modesto Formation 3“

 

    

 

 

[I _5
3B: Tuolum ne River. *- Small Iv: (MSd)
‘ egen
Eliza}: I381 aﬂon Rank 1 and 2 Well Logs
9 *Depthurosommo-mm
"LJ' Depth :17 to 41 111 (121-139 a)
. . IDepthﬂtoanao-zoom
San Joa urn River
Q q gum-ma .
“‘"m Emu-u...

 

 

 

 

51

Figure 7: Aerial photograph showing the potential location of the Modesto small IVF
loop (outlined by the light gray dashed line). The solid white line represents the current
river valley.

 

52

Figure 8: The speciﬁed ﬂux cells that represent the Stanislaus, Tuolumne, and Merced
Rivers in layer 1 of the USGS model were changed to GHB cells in the adapted model.
The locations of the reservoirs are outlined with a thin black line. The GHB cells are
represented by the dark gray dots around the perimeter of the model and over the
locations of the rivers. The light gray squares represent wells.

 

 

 

 

 

LX o 6 12 24 36 48 Kilometers

 

53

Figure 9: Four geologic scenarios were created to test the inﬂuence of the IVF: (from top
left to bottom right) RB/ST/ML, RB/ML, RB/ST/MS, and RB/MS. Vertical exaggeration
of 100.

Riverbank: Tuolumne + Stanislaus I Riverbank: Tuolumne Valley
Valleys E y
>.
2
E
o
E’
3
§
8
8
2
3
E
i
E
(I)
3
‘15
O
8
E

 

54

Figure 10: Row 60 (I60) in A: the USGS model and B: the newly discretized model.
Vertical exaggeration is 50. Each cell is 400 meters wide.

 

 

 

 

 

 

 

 

    

_ .
.—

C reap- " l-
...L: .1 __ ~

: ::=~ +- -— : .._—u—*‘ " I‘

l- _..—m- _ ,1.- r-‘ " .1—

_ ““W—F: F: .. _u— - _v' l- v _ _... we“ ("_-.-4'
_____.r“ _ .- .. W‘ F J I‘NH—U-I- l-H-l ,_ ..o-v' r "' F' _..-1

TI. vf'm" .4” emu-w" "FTTT Tl _‘_‘,_...M"" “TI-TIT

_v~.ar "+_ l- “ mm" "‘ ..m"

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

West East

 

 

 

 

 

55

Figure 11: Comparison of (A) the USGS model and (B) the adapted model without the
IVF MODFLOW hydraulic head solutions. A: USGS head solution. The regional
groundwater ﬂow is to the west-southwest. The large depression in the water table in the
southeast corner of the model is due to a heavy dependency on local groundwater with
little recharge ﬁom application of irrigation water. B: Adapted model without the IVF
head solution. The general ﬂow direction is the same. The heads in the area of the
reservoir are slightly lower and the head gradient is slightly more gradual. The large
depression in the southeast comer is maintained in this adapted model. (Elevations are in
meters and the vertical exaggeration is 50). Shown in color.

 

 

 

Head Elevation
meters (feet)
70.9 (21 .6)

 

T 52.6 (16.0)
34.3 (10.5)
16.0 (4.9)

-2.37(-o.7)

ll

North

 

 

 

 

Head Elevation
meters (feet)

70.9 (21 .6)

52.6 (16.0)

34.3 (10.5)
I 16.0 (4.9)

-2.37(-o.7)

1

North

 

56

Figure 12: Head differences between the model without IVFs and the IVF model
(RB/ST/ML). Positive residuals (yellow to red) indicate areas of the model where the
head elevations are lower in the IVF model than in the model without the IVF. Negative
residuals (green to blue) indicate areas were the head elevation is higher in the IVF model
than in the model without the IVF. (Elevations are in meters and the vertical
exaggeration is 50). A: 63 isosurfaces. B: 20 isosurfaces (rotated for improved view).
Shown in color.

         
  

Head Difference
meters

21.0
0.625

0.25

Head Difference
meters
21 .0
0.625
0.25
-0.125

90.5

North '>

57

Figure 13: Pathline analysis was done using MODPATH. The light gray triangles
indicate where the particle started and the black circles indicate where the particle
ultimately ends up. A: Map view of the model without the IV F. Pathlines have the same
trend along the hydraulic gradient across the model. B: Map view of the model with the
IVF (RB/ST/ML). Pathline analysis in the model with the IVFs (RB/ST/ML) shows that
pathlines extend farther to the west and spread laterally to the north and south.

 

Figure 14: A cross-section of the particle pathlines displayed in the previous ﬁgure. The
yellow triangles indicate where the particle path started and the black circles indicate
where the particle pathline ultimately ends. Vertical exaggeration is 50. A: No IVF
cross-section showing a view of the pathlines looking north. Particles released in the
northern portion of the model have pathlines that trend downward near the bottom extent
of the model. The other pathlines remain relatively shallow, but do move from the
unconﬁned to the conﬁned portion of the aquifer. B: IVF (RB/ST/ML) cross-section
showing a view of the pathlines looking north. The pathlines from particles released in
the north are redirected into the IVF and do not ﬂow as deep into the aquifer as they do in
the model without the IVF. The other particle pathlines also follow the path of the IVF
then disperse into the aquifer. Shown in color.

WEST EAST

K (tn/day)

   

0 2 3 400 meters

K (mlday)
864

830.3
764.3
697.8

  

0 2 3 400 meters

59

Figure 15: Cross-section of the particle pathlines shown in the previous two ﬁgures
viewed obliquely from above the south edge. The yellow triangles indicate where the
particle path started and the black circles indicate where the particle path ultimately ends.
Vertical exaggeration is 50. A: In the model without the IVF, the particle pathlines follow
a similar trend through the aquifer and move deep into the conﬁned portion of the

aquifer. .B: In the model with the IVF (RB/ST/ML) the particle pathlines follow the IVF
then spread out displaying the various ﬂow paths into the deeper portions of the aquifer.
Shown in color.

K (In/div)
— 8

    

? i8 i8 30 meters

K (mlday)
884

830.8
764.3
697.8
631 .4
564.9
498.5
432.0
385.5
299.1
232.6
166.2
99.7

33.2

0.001

2

Y'sI/X

 

0 i8 iE 400 meters

60

Figure 16: Map view of particles released within the general head boundary cells that
delineate the Tuolumne River: A: In the model without the IVF the advective pathlines
are short and follow the hydraulic gradient and B: In the model with the IVF, the

advective pathlines are longer and follow the IVF. Vertical exaggeration is 50. Shown in
color.

 

Kilometers
0 6 12 36 46

 

 

61

Figure 17: Cross—section of particles released within the general head boundary cells that
delineate the Tuolumne Rivers. Vertical exaggeration is 50. A: In the model without the
IVF the advective pathlines are short and follow the hydraulic gradient and B: The model
with the IVF shows, in losing portions of the stream, the advective pathlines move from
the river into the IVF. Advective pathlines from particles released within the river also
migrate beneath the city of Modesto. Shown in color.

K (mlday)
884

    
    

i ‘8 i8 # "1219f!

 

? 2E 38 #0 meters

62

Figure 18A: Solute transport simulations were run for 150 years. A: Shows the uniform
plume morphology in the modiﬁed model without the IVF. NOTE: The IVFs are NOT in
this model. The IVF outline is shown only to facilitate reference between ﬁgures. B:
Shows the plume morphology in the model with the IVFs. The plume preferentially
follows the IVF and allows higher concentrations of the contaminant to move farther
distances and more deeply into the aquifer in the same amount of time as the modiﬁed
model without the NB. Shown in color.

7

 

 

A

 

—*-*—

 

 

 

 

 

\ \ ///

 

 

 

*NOTE: In ﬁgure A, the NF outline is only shown for reference.

 

 

63

Conservative
Solute
Concentration
(mg/L)

“100

76.3
52.5
28.8

5.0

4

North

Figure 18B: Solute transport simulations were run for 150 years. B: Shows the plume
morphology in the model with the IV F3. The plume preferentially follows the IVF and
allows higher concentrations of the contaminant to move farther distances and more

deeply into the aquifer in the same amount of time as the modiﬁed model without the
IVFs. Shown in color.

B

 

 

 

 

———*—

 

Conservative
Solute
Concentration
(mg/L)

@100

76.3

 

52.5
28.8

5.0

l

North

 

 

 

 

Figure 19: Head differences between the model without IVFs and the IVF model (RB/
MS). Positive residuals (yellow to red) indicate areas of the model where the head
elevations are lower in the IVF model than in the model without the IVF. Negative
residuals (green to blue) indicate areas were the head elevation is higher in the IVF model
than in the model without the IVF. (Elevations are in meters and the vertical
exaggeration is 50). A: 63 isosurfaces. B: 20 isosurfaces. Shown in color.

     

Head Difference

21.0
0.625
0.25
43.125
S-0.5

North ‘?

65

Chapter 3: Conclusions and Future Work

CONCLUSIONS

The primary purpose of this study was to understand the impact incised-valley
ﬁlls (IVFs) have on regional groundwater ﬂow, contaminant transport, and the potential
for artiﬁcial aquifer recharge in the Tuolumne River ﬂuvial fan aquifer around Modesto,
California. This task was accomplished by (1) assessing the stratigraphic character of the
study area with well logs and core in order to construct model domains that capture this
character and then (2) simulating groundwater ﬂows and solute tracers through these
models.

The most current regional scale groundwater model in the Modesto, California
area, developed by the USGS (S. Phillips, unpublished model, 2005), used previously
collected data (driller’s logs, geophysical logs, and a few continuous cores) to
characterize the area’s hydrogeology (Burow et al., 2004). While this model is a
reasonable representation of the area geology and hydrologic regime, it does not
implicitly include the IVFs.

By adapting the existing USGS model to incorporate the IVFs, this thesis shows
that the IVFs are critical to understanding the regional hydraulic regime. To include the
IVFs in the model, sequence stratigraphic concepts, by Weissmann et al. (2002b), were
applied to characterize IVF geometry and character for integration into the USGS
groundwater flow model. With a sequence stratigraphic model, a more accurate model of
the regional geology could be generated, even in areas where little subsurface data were
available. Addition of the IV F improves the geologic model of the Modesto area and in

turn the understanding of regional groundwater ﬂow and solute transport.

66

Assessment of Stratigraphic Character
Stratigraphic character assessment was completed through the analysis of two soft
sediment continuous cores and many (>l0,000) driller’s well logs. The continuous cores
were used to develop a better understanding of the fan deposits surrounding the IV F. The
use of a ranking scheme for driller’s well logs and subsequent analysis in this study
helped to ﬁnd several potential locations for the IVF. The thick gravel base of the IVF
provided a means to identify the location of the IVF.

While it would have been ideal to incorporate more core data into this model, the
two cores that were described in the study area did not provide a sufﬁcient amount of
data to generate statistical realizations of the area. The two cores were used to
supplement the understanding of the surrounding IVF deposits.

Two conclusions come from the investigation of driller’s logs: (1) such driller’s
logs can be used with reasonable success to generally identify the position of IVFs by
focusing on presence or absence of thick gravel units and (2) multiple (four) geologic
scenarios were developed to help assess the inﬂuence of the IV F on regional groundwater
ﬂow and assess the impact of model uncertainty.

While the quality of driller’s logs is often unreliable, there are certain instances
where these logs can be used with relative conﬁdence. The use of the driller’s logs as an
initial attempt to delineate the IV F and identify the thick, gravel base of the IVF provides
a good example of the appropriate application of driller’s logs. Because of the coarse
grained nature and signiﬁcant thickness of these deposits, the driller’s reliability to

prOperly identify the interval increases.

67

Although the driller’s logs were used in this study with some success, they should
be employed for subsurface analysis with caution. For example, I was able to identify
several IVF locations, which may or may not actually exist. The geometry of the IVF
was inferred from the current topography. These trends need to be validated with further
geophysical survey (e. g. seismic reﬂection) studies to better locate and describe the IVF
geometry and stratigraphy.

Simulation of Groundwater Flow and Solute Tracers

The groundwater models developed to test the inﬂuence of the IVF resulted in four
main conclusions. The addition of the IVF (1) changes regional hydraulic heads regimes
(2) allows pathlines (water) to move more rapidly to a greater lateral extent and deeper
into the aquifer (3) allows ﬂow (illustrated by particle pathlines) to spread into the aquifer
sediments adjacent to the IVF and (4) creates a preferential pathway for contaminant
plumes, decreasing solute transport times in areas where the IVF is located.

The creation of a preferential pathway from the addition of the IVF is not easily
identiﬁed simply through the measurement of head elevations. As was shown by the
slight variation in head elevations in the model with and without the IVF, measurements
of groundwater levels alone would not indicate the presence of these preferential
pathways. Examining the model head solutions alone also provided very little insight
into the inﬂuence of the IV F on groundwater ﬂow. To better illustrate the inﬂuence of
the IV F, the head difference between the model with and without the IV F was used to
show the areas of convergent and divergent ﬂow. Areas where the head levels are lower
in the model with the IV F indicate that the ﬂow in that area converges on the IV F. In

steady-state conditions represented by this model, the IV F is “funneling” the maximum

68

 

amount of water through the available effective pore space, which causes the water to
“back-up” and diverge from the IVF.

Pathline analysis experiments were used to asses the advective ﬂow in the model and
illustrate the impact of ﬂow changes along the IVF. Pathlines in the model without the
IVF have little lateral movement and vertically tend to follow with the local gradient.
Pathlines in this model do not disperse in various directions in the aquifer; rather, they all
have similar trends that reﬂect the regional head gradients. In the model with the IVF,
however, pathlines follow the IVF and are dispersed into the contiguous aquifer
sediments.

Transport experiments reafﬁrm the pathline analysis results. In the model without the
IV F, transport simulations reveal that the plume expands in a uniform radial pattern from
the constant contaminant source. In the model with the IVFs, the solute transport
simulations clearly illustrate the changes in the plume morphology with the addition of
the IVF. The contaminant plume preferentially follows the path of the IV F. The IVF
increases the distance the plume can travel in the same amount of time, thus resulting in a
transport time that would be shorter than predicted using the model without the IVF.

These results show that the IVF provides a pathway of preferential ﬂow for
groundwater as well as contaminants. In the terms of managing the regional water
quality, this is both beneﬁcial and detrimental. The IVF provides the beneﬁt of
potentially providing a conduit to artiﬁcially recharge the aquifer. Recharge will be
important to provide sustainable water supply to the city of Modesto. While the IVF

provides a good source for artiﬁcial recharge, unfortunately, it also creates a pathway for

69

contamination to enter and disperse into the aquifer, exposing the entire aquifer to

potential contamination.

RECCOMMENDATIONS FOR FUTURE INVESTIGATIONS

While the realizations generated in this study provide a good preliminary
understanding of the inﬂuence of the IVF, these realizations should be used as a basis for
further investigation into the area stratigraphy and hydrologic regime. The geologic
realizations could be improved by (1) using more reliable methods for identifying the
location of the IVF (2) obtaining core through several locations of the IVF to better
understand IV F lithologic character and (3) collecting additional continuous core in the
open fan deposit areas.

Further investigation into the location of the IV F should be conducted. While
analysis from driller’s well logs provides a good approximate location of the IV F more
reliable geophysical data should be accrued, to effectively model the IVF. Seismic data
across the area will help better constrain the location and geometry of these features,
providing more dependable estimations of the width, depth, and length of the IV F.

In this study, no core was collected within the IVF. This made modeling of the
lithologic character more challenging. The character of the apex and toe of the IVF need
to be investigated as these are the principle areas of recharge and discharge for
groundwater and will impact the location of the divergent ﬂow from the valley.
Investigation into the toe of the fan may provide insight into the inﬂuence of local base
level (sea level) and the basin width on the preservation (or removal) of the distal fan

deposits. Currently, the IVF code does not account for the fact that gravels are not likely

70

present in the distal portion of the model. With further constraints on the distal character
in the IVF, this portion of the model could be improved. Additional studies near the apex
of the fan will provide further insight into the geometry of the stacked IVF deposits
believed to exist. This may have a signiﬁcant inﬂuence on the ability to artiﬁcially
recharge the aquifer.

Additional continuous core data across that fan will also help to better capture the
aquifer heterogeneity. One signiﬁcant improvement in this area would be to add the
sequence bounding paleosols. While this model does not include the paleosols, they may
have an impact on the regional or local groundwater ﬂow, acting as no or low ﬂow
barriers, as noted on the Kings River ﬂuvial fan (Weissmann et al., 2004).

The groundwater and contaminant transport models generated in this study
enhance understanding of the groundwater ﬂow and the potential for contaminant
movement through the Modesto area. The models also provide a preliminary
understanding of the potential inﬂuence these incised-valley ﬁlls may have on artiﬁcial
recharge in the incised-valley ﬁll.

SUMMARY

The coarse-grained continuous nature of the IVFs indicates that the IV F in the
Modesto area is capable of acting like a regional conduit for groundwater ﬂow and
contaminant transport. Our results support those reported by Weissmann et al. (2004)
who created groundwater ﬂow and contaminant transport models in studies of similar
IVF deposits of the Kings River near Fresno, California. They showed that IV Fs
signiﬁcantly inﬂuence groundwater ﬂow and contaminant transport speciﬁcally by

providing the potential for (1) increased groundwater ﬂow and production rates (2) rapid

71

contaminant transport within the incised-valley ﬁll sediments and (3) rapid contaminant
movement from the incised-valley ﬁll into the contiguous aquifer sediments. Based on
the results of this study, the identiﬁed IVF beneath the Modesto area has a similar effect.
Continued and more detailed studies on the inﬂuence of the IVF will help to fully explore
the potential role the IVF plays in artiﬁcial aquifer recharge and regional water quality in
that area.

Furthermore, implications of these conclusions are directly applicable to the current
and continuing USGS investigation into the groundwater status and long term quality, for
which the original model was created. The eastern ﬂuvial fans in the San Joaquin valley
have been primarily agricultural land and subjected to substantial amounts of fertilizer
application and irrigation since the early 1900’s (Gronberg et al., 2004). An investigation
of the transport of anthropogenic and natural contaminants to community supply wells by
the USGS is already underway in the Modesto area. The main question motivating this
USGS investigation of the groundwater status is, “What are the primary man-made and
natural contaminant sources, aquifer processes, and well characteristics that control the
transport and transformation of contaminants along ﬂow paths to community supply
wells in representative water-supply aquifers?” (Gronberg et al., 2004, p.4). The speciﬁc
objectives of the study are to (1) characterize the regional geologic setting to determine
the preferential or most common, groundwater pathways to community supply wells and
(2) gain an understanding of the sources of contaminants in the aquifer and the processes
that impact the transport and potential transformation of the contaminants (Gronberg et
al. , 2004). The addition of the IVF into the USGS model will greatly improve the current

groundwater model’s ability to illustrate the dominant pathways for transport. Areas

72

where contamination of the Tuolumne River can migrate deep into the groundwater
would also be highlighted in a groundwater model that incorporates the IVFs. In terms of
remediation, it is vital to understand the sources and pathways of the chemicals to better
treat or remove the contaminant. The IVF provides a preferential path for water ﬂowing
downward through the river bed thus creating a potential conduit for runoff contaminants
like fertilizers that will degrade water quality and should be addressed. The realizations
developed in this study show that the IVFs signiﬁcantly inﬂuence the groundwater ﬂow
by acting as a regional “pipeline” of coarse sediment that creates the potential to provide
artiﬁcial recharge to the Modesto area and a conduit for contaminants to follow,
increasing the dispersion of particle pathlines into contiguous aquifer sediments and
enabling solute plumes to move farther distances more rapidly leaving the aquifer

susceptible to widespread contamination.

73

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J anda, R. J ., 1966, Pleistocene history and hydrology of the upper San Joaquin River,
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J ervey, M. T., 1988, Quantitative geological modeling of siliciclastic rock sequences and
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central San Joaquin Valley, California: US. Geological Survey, 82-526.

-, 1988, Quaternary geology of the Northern San Joaquin Valley, in SEPM, Paciﬁc
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Lettis, W. R., and Unruh, J .R., 1991, Quaternary geology of the Great Valley, California,
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Marchand, D. E., and Allwardt, A., 1977, The Cenozoic history of the San Joaquin
Valley and the adjacent Sierra Nevada as inferred from the geology and soils of
the eastern San Joaquin Valley, in Singer, M. J ., ed., Soil Development
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and Adjacent Areas, California, University of California Press, Guidebook for
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Page, R. W., Geology of the fresh ground-water basin of the Central Valley, California,
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Webb, B. K., and Anderson, M. P., 1996, Simulation of preferential ﬂow in three-
dimensional, heterogeneous conductivity ﬁelds with realistic internal architecture:
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Weissmann, G. S., and Fogg, G. E., 1999, Multi-scale alluvial fan heterogeneity modeled
with transition probability geostatistics in a sequence stratigraphic framework:
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Weissmann, G. S., Carle, S. F ., and Fogg, G. E., 1999, Three dimensional hydrofacies
modeling based on soil surveys and transition probability geostatistics: Water
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Weissmann, G. 8., Mount, J. F., and Fogg, G. E., 2002b, Glacially driven cycles in
accumulation space and sequence stratigraphy of a stream-dominated alluvial fan,
San Joaquin valley, California, USA: Journal of Sedimentary Research, v. 72, no.
2, p. 240-251.

Weissmann, G. S., Yong, Z., LaBolle, E. M., and Fogg, G. E., 2002c, Modeling
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Weissmann, G. S., Zhang, Y., Fogg, GE, and Mount, J .F., 2004, Inﬂuence of Incised
Valley-Fill Deposits on Hydrogeology of a Stream-Dominated Alluvial Fan, in
Bridge, J ., and Hyndman, D. W., eds., Aquifer Characterization, SEPM (Society
for Sedimentary Geology) Special Publication, p. 15-28.

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Appendix A: Geologic Setting and Stratigraphic Character

INTRODUCTION

Through this study, we intend to show the inﬂuence of relatively coarse-grained
incised-valley ﬁlls (IV F5) on groundwater ﬂow, contaminant transport, and potential
artiﬁcial recharge in the Modesto, California area. To assess the inﬂuence of the IVFs, an
investigation of the area geology was conducted to locate and characterize the IVFs and
the surrounding sediment.

In this appendix, I ﬁrst describe the regional geology and stratigraphy. Next, the
local geology is described with the ﬁndings of geologic investigations using sediment
core and driller’s well log data. The results of this investigation are then incorporated into

four geologic realizations.

BACKGROUND GEOLOGY

San Joaquin Basin and Regional Geologic Setting

The San Joaquin Basin is located within the California’s Great Valley, an
approximately 700 km long (north to south) by 100 km wide (east to west) valley that is
bound on the east by the Sierra Nevada and on the west by the Coast Ranges. The valley
is divided into two sub-basins by the Stockton Arch, a buried transverse arch, with the
Sacramento Basin in the north and San Joaquin Basin in the south (See Figure 1 from
Chapter 2). The basin is underlain by crystalline basement rock and approximately 9 km
of Mesozoic and Cenozoic sedimentary rocks and sediments (Bartow, 1988).
Structurally, the basin is asymmetric with a gently sloping eastern margin that abuts the

Sierra Nevada, and the more steeply sloping western edge adjacent to the Coast Ranges.

77

 

This study focuses on the Tuolumne River fan, located in the northeast portion of the San
Joaquin Basin (Figure 1). The fan was formed where the Tuolumne River ﬂows west out
of the Sierra Nevada into the San Joaquin Basin.

Climate Effects on Quaternary Deposits in the Eastern San Joaquin Valley

Quaternary ﬂuvial fan deposits along the eastern San Joaquin Valley preserve
evidence of past phases of aggradation and degradation from varying amounts of
sediment supply and discharge in response to recurring glacial periods in the Sierra
Nevada (J anda, 1966, Marchand 1977 , Huntington 1980, Marchand and Allwardt 1981,
Lettis 1988, and Weissmann et al. 2002b, 2006). Fluvial fans are differentiated from
alluvial fans in this study to emphasize that ﬂuvial fans are characterized by perennial
ﬂuvial processes, while alluvial fans are characterized more by ephemeral debris ﬂows or
sheetﬂoods. Similarities among the ﬂuvial fans along the eastern portion of the valley
have allowed for basin-scale comparisons of these fans (Weissmann et al., 2006).

Weissmann et al. (2002b) investigated the ﬂuvial fan deposits on the Kings River
fan and use a sequence stratigraphic model to understand the climatically induced cycles
of deposition. In the model, they apply the traditional deﬁnition of a sequence as “. . .a
relatively conformable succession of genetically related strata bounded at its top and base
by unconforrnities, or their correlative conforrnities” (Mitchum, 1977, p. 210). Sequence
boundaries in this model are identiﬁed as the paleosol surface and respective IVF base
that divide the ﬂuvial fan deposits into ﬁve stratigraphic units.

In this sequence stratigraphic model, concepts of accumulation and preservation
space (after Blum and Tornqvist, 2000) are used to understand the stratigraphy.

Accumulation space is deﬁned as one component of accommodation space and refers to

78

 

the volume of space available to be ﬁlled with sediment and is dependent on the balance
of sediment supply and stream discharge as well as channel geometry. Preservation space
is also a component of accommodation space and is the space below the lowest level of
sediment removal. In the Kings River and Tuolumne River areas, it is a long-terrn,
preservation space controlled by the local subsidence rate. The ﬁve sequences marked
distinct periods of regional aggradation and degradation, or accumulation space change.
Packages of relatively rapidly deposited ﬂuvial fan deposits separated by paleosols
indicate periods of aggradation across the fan, or increased accumulation space,
punctuated by periods of degradation and fan incision, or restricted accumulation space
and quiescence on the upper parts of the fan. Preservation space was created by constant
subsidence in the area, which lowered the deposits below the lowest level of erosion
(sequence stratigraphic model described in more detail in the next paragraph). Sequence
boundaries in this model are identiﬁed as the paleosol surface and respective IV F base
that divide the ﬂuvial fan deposits into ﬁve stratigraphic units.

Speciﬁcally, ﬂuvial fan aggradation or degradation occurred on the Kings River
fan as sediment supply to discharge ratios increased or decreased as a result of changes
from glacial to interglacial climate. Interglacial periods are marked by limited
aggradation, a low accumulation space, and an intersection point located distally on the
fan (Figure A1) (Weissmann et al., 2002b). During this portion of the cycle, paleosols
formed in the exposed upper fan outside the incised valleys. Glacial periods are
characterized by a large amount of laterally extensive aggradation on the fan, a high
accumulation space, and an intersection point proximally located near the apex of the fan

(Figure A2). Weissmann et al. (2002b) indicated that this sequence stratigraphic model

79

can serve as a means to predict facies distributions and stratigraphic relationships in areas
exposed to similar conditions.

Due to the similar cyclic depositional character among the fans in the eastern San
Joaquin basin, the sequence stratigraphic model described in the Kings River fan study
can be applied to other fans in the basin. However climate and sediment supply to stream
discharge ratios are not the only controls on the deposition of the ﬂuvial fans in the valley
(Weissmann et al., 2006). Factors that control the overall amount of accumulation space
available during periods of climate ﬂuctuation and hence sequence development are: (1)
the sediment supply to stream discharge ratio, (2) the rate of basin subsidence, (3) the
amount of local base level change, and (4) the basin width (Weissmann, et al., 2006).
These controls vary within the San Joaquin Basin and inﬂuence the sequence geometry
on individual fans.

Controls on the Tuolumne River ﬂuvial fan sequence geometry in particular are
(1) glacial inﬂuence in the drainage basin (2) relatively low subsidence rates
(approximately 30cm/ 1000 yrs, Lettis, 1988) due to the river’s location in the northern
portion of the San Joaquin Valley, and (3) the San Joaquin River aslocal base level
control on Tuolumne River elevation (Weissmann et al., 2006). The Tuolumne River
drains the Sierra Nevada which resulted in cycles of signiﬁcant ﬂuctuations in the
sediment supply and stream discharge from Quaternary glaciations. Because the
Tuolumne River is in the north, where the San Joaquin Valley is narrower and the
subsidence rate is relatively low (30 cm/ 1000 yrs, Lettis, 1988), sequence thickness and
lateral extent were thinner and smaller than found in southern portions of the valley. This

resulted in an overall reduction in accumulation and preservation space, which caused the

80

 

lateral progression of apexes and the thinner ﬂuvial fan units observed here (Weissmann
et al. 2006; Bennett et al. in press; See Figure 2 in Chapter 2). The local base level is
connected to the San Joaquin River and ultimately sea level. This resulted in deeper
incision and sediment bypass in the distal portions the fan during interglacial periods,

also reducing the overall amount of accumulation space available to be ﬁlled.

LOCAL GEOLOGIC INVESTIGATION

Study Area

The study area is delineated by the Sierra Nevada and the San Joaquin River to
the east and west, respectively, and by the Stanislaus and Merced Rivers to the north and
south (See Figure 3 in Chapter 2). It is approximately 48 km (30 miles) long (west to
east) and 19 km (12 miles) wide (north to south). Dissecting the site is the Tuolumne
River, which ﬂows east to west through the middle of the study area (See Figure 3 in
Chapter 2). Deposits at the site are composed mainly of Cenozoic sedimentary deposits
(Marchand and Allwardt, 1981). This study of the Modesto area will focus on the
Quaternary Pleistocene ﬂuvial deposits: the Turlock Lake Formation, the Riverbank
Formation and the Modesto Formation (See Figure 1 in Chapter 2). A more
comprehensive description of the older units in the area can be found in Marchand and
Allwardt (1981).

Stratigraphic Units

Each unit of open-fan deposits is bound at the top and bottom by a paleosol and is
composed of silty, sandy, and clayey sediments with discrete coarser-grained channel

deposits. Nestled among the relatively ﬁne-grained open-fan sediments is the relatively

81

 

coarse-grained IV F, characterized by a thick basal gravel lag (5 to 9 meters) that
gradually ﬁnes up to the surface (See Figure 4 in Chapter 2).

Characterization of hydrofacies within the ﬂuvial fan deposits was done by using
methods similar to those described in Weissmann et al. (2002b) where we utilized several
sources of geologic data, including driller’s well logs, geophysical logs, and lithology
from continuous core samples. Cooperation with the US. Geological Survey (U SGS) in
Sacramento, California afforded access to several thousand paper copies of the
Department of Water Resources (DWR) driller’s logs within the study area, geophysical
well logs from city wells, and recently obtained, relatively-continuous, soft sediment
core. Also available from the USGS are digitized spatial data of the area hydrology,
geology, soils, topography, and approximate pumping rates of the city of Modesto
municipal wells (S. Phillips, unpublished data, 2005).

Open-fan Deposit Character

Four hydrofacies were identiﬁed within the study area from two continuous soft
sediment cores collected by the USGS (See Figure 3 in Chapter 2) and well logs from the
California Department of Water Resources (DWR) (See Appendix B for detailed
descriptions). Included in the core descriptions are a visual estimation of grain size, grain
shape, grain sorting, color, primary sedimentary structures, and secondary alterations
(including pedogenic alteration). The core descriptions were used to better understand
hydrofacies in the proximal (eastern) and distal (western) open-fan deposits. The core
data were also used to help generate the fan surfaces used in the IVF ﬁll code in
Appendix G. The hydrofacies include: (1) gravel channel deposits (not recovered in

these cores but observed in drillers’ logs and described by Weissmann et al. , 2002b) (2)

82

 

sand channel deposits (3) silty sands, silty clays, and clay overbank deposits, and (4)
pedogenically altered deposits. The core descriptions show that the western extent of the
study area contained more ﬁne-grained sediments of the distal ﬂuvial fan deposits, while
the eastern extent of the study area contained the more coarse-grained proximal ﬂuvial
fan sediments.

Gravel Channel Deposits

Although the gravel channel deposits of the IV F were not seen in these cores,
descriptions by Weissmann et al. (2002b) indicate that the gravel is composed of clast-
supported gravel and cobbles and are commonly found at the channel base.

A few thin intervals of poorly sorted, matrix supported, gravel ﬂood deposits were
observed in the core (Figure A3). The matrix is coarse to very coarse sub-angular sand
with some granules. The gravel is 1 to 4 cm (0.4 to 1.6 inches). The deposits do not
show any primary structures and tend to be heavily iron stained. In both the proximal and
distal portions of the fan, these thin gravels are sparse and were located at depth: MREA
at 70 meters (230 it) and MRWA at 55 meters (180 ft). The proximal gravels overall are
more coarse than the distal deposits and have less matrix material. The gravel in the
distal deposits has fewer large clasts of gravel within the coarse matrix.

Sand Channel Deposits

The channel sand facies (Figure A4) are well sorted, mica rich quartz and
feldspathic arenites. The mica is present in large unweathered ﬂakes giving the core a
“glittery” appearance. They range in grain size from very ﬁne to coarse sand and are
sub-rounded to sub-angular. These channel sands were commonly noted to have a poorly-

sorted matrix-supported pebbles at the basal contact. These deposits are typically cross-

83

stratiﬁed but were also observed without any primary structures (massive). The channel
sand deposits also commonly display a ﬁning upward trend. Proximal fan deposits are
more coarsely grained (medium to coarse or very coarse sand) while distal deposits tend
to have a ﬁner grain size (very ﬁne to medium grained). Distal deposits showed less
evidence of secondary alteration and tended to show more distinct heavy mineral banding
along cross-bedding planes.

Overbank Deposits

Overbank deposits (Figure A5) characterized in the cores was silty ﬁne to very
ﬁne sand, silty clay, or clayey silt. They are gray to light brown in color with occasional
sandy lenses. Slight to no pedogenic alteration was observed. These deposits were
commonly thinly laminated, although some massive deposits were observed. Root traces
and/or burrows were also observed within the overbank deposits. Iron staining/ mottling
was also a very common characteristic among the overbank deposits. Proximal overbank
deposits have more silty sands a smaller fraction of silt and clay than was observed in the
distal deposits. The distal deposits are most commonly sandy or clayey silt.

Pedogenically Altered Deposits

Reddish colored pedogenically-altered sand with medium to thick clay coats was
also present in the cores (Figure A6). Hues range 2.5YR to 10YR in the proximal and
SYR to 10YR (mostly 7.5YR). Value ranges from 4 to 5 in the proximal and 5 to 6 in the
distal, and chroma ranges from 3 to 6 in the proximal and 2 to 4 in the distal. These
deposits are the most developed paleosols observed in the core samples. Slickensides and
root traces are also present in these deposits. Proximal paleosols are better developed

with thicker clay coats (thick to medium) and have a stronger reddish coloring. The

84

distal paleosols are less well developed with thinner clay coats (medium to light) and do
not always exhibit the distinct reddish coloring seen in the proximal paleosols.

Incised- Valley Fill Geometry

Core was not collected within the targeted IVF deposit. However, Weissmann et
al. (2002b, 2004) noted that the incised-valley ﬁll could be recognized in driller’s logs by
a thick gravel or cobble base with overlying sandy facies. These coarse-grained deposits
form approximately two-thirds of the ﬁll deposit. Burow et al., (2004) indicated that the
IVF could also be recognized in geophysical logs by a relatively high resistivity and a
correspondingly low gamma ray response. This method was adapted and applied to locate
and describe the IVF character in the Modesto area using available driller’s well logs and
geophysical well logs. .

Identiﬁcation of Incised-Valley Fill with Driller’s Well Logs

Approximately 10,000 well logs from the study area were examined. The well
logs used for this investigation are the same as those initially ﬁltered by the USGS to
develop a database (described in Burow et al. (2004)). Logs containing gravels of
sufﬁcient thickness (> 3 meters) were assigned a rank of 1 to 4 based on how well the
log’s description of the stratigraphy resembled the ﬁning-upward prototype
characterization of the incised-valley ﬁll. A well assigned a rank of 1 denotes the best
representation of the incised-valley ﬁll, with a thick gravel base ﬁning upward to sand
then silt and clay, while a well ranked 4 contains thick gravel but does not resemble the
ideal ﬁning-upward succession contained within the incised-valley ﬁll deposits.

Examples of driller’s well logs of various ranks are included in Appendix C. The drilling

85

method and the consistent quality of individual drillers were also used to determine the
rank of the well.

Once all of the wells had been ranked, a database of these well logs was generated
in ArcGIS to locate the incised-valley ﬁll (Figure A7). Wells were located using reported
addresses and descriptors on logs, and then well location coordinates were obtained by
plotting them as waypoints in National Geographic TOPO! California. Information that
was entered into the database of wells includes (1) the well identiﬁcation number (2) the
rank of the well, (3) the elevation of the well, (4) the depth of the gravel lag deposit, (5)
whether or not the well had previously been selected for a USGS database, (6) the
relative depth of the gravel (shallow or deep), and (7) general notes about the log. The
results of IV F delineation is described in Chapter 2 and Appendix C.

A potential source for uncertainty in this method of delineating the IVFs is
created by using driller’s well logs as the primary data set. There is signiﬁcant
uncertainty when identifying the basal gravel lag within the IV F through interpretation of
the driller’s logs. The quality and accuracy of the logs may be questionable and
somewhat unreliable for detailed subsurface correlation. Quality strongly depends on the
driller’s ability and conscientiousness, and even under the best circumstances, carries
signiﬁcant uncertainty. More easily drilled units, such as sand and clay, may not be noted
accurately during drilling, as there is no variation in the rig character or drilling
techniques required. However, well logs are successfully used in this study to identify
thick gravel deposits. These thick basal gravel lag deposits result in a distinct change in

the rig behavior. For this reason, it is assumed that the depth of the gravel units, as well

86

as their thicknesses, will be consistently more correctly described in the log than sand,
silt, or clay, thus allowing more reasonable identiﬁcation of the incised-valley ﬁlls.

Construction of Sequence Bounding Surfaces

Prior to generating the groundwater models, digital spatial data were used to
generate surfaces for the model layers and for use in FORTRAN codes to insert the IV F
in the model. Surfaces depicting the incised channel were developed by (1) adapting the
modem Tuolumne River valley from the digital elevation model (DEM) for the
Riverbank Formation fan surface and (2) by using ArcGRID to interpolate the older
Turlock Lake Formation fan surface (See Appendix D). The present ground surface was
assumed to be a sufﬁcient estimate of the Riverbank fan surface outside the IV F, because
the Modesto formation that covers the area is only a thin veneer over the fan surface
(Bennett, 2003; Weissmann et al., 2006; Bennett et al. in press), . The Turlock Lake
Formation surface was developed with data from well logs, digital soil maps, and core
data. Surfaces were combined to match the four geologic scenarios described in Chapter

2 (See Table 1 in Chapter 2).

CONCLUSION

Two conclusions come from the analysis of the IVF and surrounding open fan
deposits: (1) driller’s logs can be used with reasonable success to generally identify the
position of IVFs by focusing on presence or absence of thick gravel units and (2) multiple
(four) geologic scenarios were developed to help assess the inﬂuence of the IVF on

regional groundwater ﬂow and assess the impact of model uncertainty.

87

While the driller’s logs were successfully applied, there are still signiﬁcant
improvements that can be made to locating and deﬁning the IVFs. It is unlikely that
multiple potential IVFs within the same stratigraphic range (i.e. two potential Modesto
Formation IVFs) exist, although were identiﬁed. Using four geologic scenarios, the
inﬂuence of the IVF can be assessed as along with the impact of uncertainty in geologic
model. Comparisons among these four scenarios may give insight to the impact of
incorrectly developing a conceptual model.

To improve this analysis of the Tuolumne River ﬂuvial fan sediments and
stratigraphy, further collection of geologic data is needed. This data collection effort
might help constrain the location and the geometry of the IV F. Suggestions for
investigation include: seismic studies to delineate the extent of the IVF and additional
continuous core data from the area to provide a better deﬁnition of the character of the
IV F sediments and their variation proximally or distally on the fan. Supplementing the
current characterization of the open-fan deposits from more continuous core would be
vital to generate a geostatistical model to illustrate the distribution of hydrofacies and
supply information to parameterize the deposits that surround the incised-valley ﬁlls.
While it would have been ideal to use the core descriptions to generate geostatistical
realizations for this study, the data set (inclusive of the two wells described above) was
not large enough for proper statistical analysis.

The geologic analysis described in this appendix provides the framework for the
groundwater ﬂow and solute transport models described in Chapter 2 of this thesis. The
groundwater and contaminant transport models produced are expected to enhance

understanding of the groundwater ﬂow and the potential for contaminant movement

88

through the Modesto area. Additionally, the models are also expected to provide a
preliminary understanding of the potential inﬂuence these incised-valley ﬁlls may have

on artiﬁcial recharge in the incised-valley ﬁll.

89

Figure Al: Map and cross-sectional view of the balance between aggrading and
degrading reaches in deposits on the Kings River. Changes in accumulation space are
inferred by a shifting intersection point that marked the aggrading (lower fan) and
degrading reaches (incised channel) of the river. (adapted from Weissmann et al., 2002b).

Plan View

. --- '.'.l; .

   
 
  

Intersection

Active ____
Depositional ‘ ~
Lobe

Down-channel Cross Section

Adjacent

Fan Surface
\ k Intersection Point

Channel Profile-— .1 /

     

      

  

Negative Positive
Accumulation Accumulation
Space Space

90

Figure A2: Four diagrams of various depositional regimes though a full glacial to
interglacial cycle. The intersection point of the fan, or the transition between aggrading
and degrading reaches, will shift up and down the fan as sediment supply:discharge ratios

increase and decrease, respectively, with changes from glacial to interglacial episodes.
(adapted from Weissmann et al., 2002b).

    
 
 

A) .. .. .. . 3)
lncied - 7
Valley

. _. -. Intersection

ActiveN ‘ .. ' ' ‘ 2‘ ' Point

Lobe

Decreasing accumulation space- and -Low accumulation space- interglacial

of glacial
-valley incision -progradation below the intersection
-basinward shift of intersection point oint

-basinward shift of deposition
-soil development! eolian reworking on
upper fan surface

p
- continued soil development! eolian
reworking on upper fan surface

 

-lncreasing accumulation space-

glacial recession
-Ineised-valley fill followed by open -lntersectlon point near the apex
fan deposition

-H|gh accumulation space- glacial

-Open fan deposition

-intersection point moves toward Progredation below intersection point

the apex

91

Figure A3: An example of the infrequent matrix-supported gravel facies. This facies
represents only a small portion of the core samples and is presumed to be rare. Shown in
color.

DISTAL PROXIMAL

 

92

Figure A4: Typical distal and proximal fan examples of the channel sand facies. The
distal sediments are typically more ﬁne grained and display less iron staining. Shown in
color.

DISTAL PROXIMAL
i "

 

 

[_H _F'_'a~ﬁh‘—A—‘ “‘

i.
.1
i,
l
i
l
l

93

Figure A5: Typical distal and proximal fan examples of the overbank silty clay deposits.
These deposits are typically composed of silty ﬁne to very ﬁne sand, silty clay, clayey
silt. They are usually gray to light brown with some sandy lenses, slight to no pedogenic
alteration, and can be thinly laminated or massive. Some deposits displayed root traces
or burrows and iron staining or mottling. Shown in color.

PROXIMAL

l
I
l
l
l
g
i
it
E
:1
l
l
l

 

94

Figure A6: Typical distal and proximal fan examples of the pedogenically altered facies.
This facies is typically reddish in color with signiﬁcant pedogenic alteration (sand with
medium to thick clay coats). Some slickensides and root traces were also noted in
descriptions of this facies. Shown in color.

DISTAL PROXIMAL

i
l
!

5.1-t.

_ ,_,._..._...-.;.._. 4...“.-

 

95

Figure A7: All driller’s log wells entered in the ArcMap database ranked 1 through 4.

 

/ .‘ru :Fe crrovr . ‘
S‘mnrsmus Favor

 

n‘e Ffwcr

 

 

 

 

   
  

 

\\ v\a\ﬂla" 'Z'dt HR: cvﬁ
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° All Well Logs Rank 1 through 4
I Core Locations

5 m,,,$Approximate Extent of Well Logs
- “Urban Area

:7 Reservoir

{:1 Model Area

0 5 1o 20 30 /
— — Kilometers

 

 

 

 

 

 

96

Appendix B: Open Fan Deposit Character Core Description

In order to better understand the character of the open-fan deposits surrounding
the incised-valley ﬁll, continuous core, collected by the USGS from two well locations
was assessed—one located in a proximal fan position and the other on a distal portion of
the fan (See ﬁgure 3 in Chapter 2). The proximal well, MREA, was drilled to a depth of
96 m (315 ft) and the distal well, MRWA, was drilled to a depth of 59 m (195 ﬁ). Core
was collected in 2.5 cm (1 inch) diameter tubes in 1.5 m (5 ft) increments. The
cumulative 155 m (~500 ft) were described with visual assessments of grain size, sorting,
shape; lithologic composition; color; primary structures; and secondary alterations.
Descriptions verify the western extent of the study area contained more ﬁne-grained
sediments of the distal ﬂuvial fan deposits, while the eastern extent of the study area
contained the more coarse-grained proximal ﬂuvial fan sediments. See Table B1 and

Table B2 for detailed lithologic descriptions for MREA and MRWA, respectively.

97

Table B1: Lithologic descriptions of the proximally located soft sediment core MREA.
The facies column uses a number to denote the hydrofacies and areas where core was not
recovered. The numbers include: (0) no core recovered, (1) gravel ﬂood deposits, (2)
sandy channel deposits, (3) silty sands, silty clays, and clay overbank deposits, (4)
pedogenically altered deposits.

See table on following page.

98

 

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Table B2: Lithologic descriptions of the proximally located soft sediment core MRWA.
The facies column uses a number to denote the hydrofacies and areas where core was not
recovered. The numbers include: (0) no core recovered, (1) gravel ﬂood deposits, (2)
sandy channel deposits, (3) silty sands, silty clays, and clay overbank deposits, (4)
pedogenically altered deposits.

See table on following page.

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Appendix C: Drillers’ Well Log Analysis

Incised Valley Fill (IVF) Character: Driller’s Logs

A relatively coarse grained incised-valley ﬁll (IV F) deposit was identiﬁed beneath
the city of Modesto using geophysical logs (Burow et al. 2004; Figure C1). The logs
show a very coarse grained gravel base that gradually ﬁnes upward to sand then ﬁne

silt and clay. To further investigate the location of the identiﬁed IV F in the Modesto

 

area, I evaluated approximately 10,000 driller’s well logs from the California
Department of Water Resources (provided by the US Geological Survey). While well
logs are notorious for irregular quality and potential inaccurate subsurface
descriptions, drilling through thick gravel, such as that expected with the IVF basal
deposits, would cause a signiﬁcant change in the drill rig character and possibly
require a change in the drill bit. Thus, I assume that thick gravels are consistently
more accurately recorded than other deposits within driller’ well logs.

However, due to the potential variable quality of well logs, a subjective ranking
system from 1 (highest quality) to 4 (lowest quality) was created to ﬁlter out the best
quality logs. Ranks were also assigned in-between each of these four main
categories. The intermediate groups were reserved for the few logs that were slightly
greater in quality or slightly lower in quality than the category to which they would
be assigned. Subjective ranks were assigned to each well based on the following
factors that impact the quality of the logs and thus their rank:

- Gravel thickness

0 Gravel 5 meters or greater indicated possible IVF deposits present.

0 Drilling method
0 Cable tool drilling logs were considered more reliable than mud-rotary
logs because the cable tool drilling method produces a sample for the

157

driller that is not mixed in drilling mud and allows more detail to be
described.
Stratigraphy—character of the IVF
0 The ﬁning upward succession of thick gravel overlain by thick sand then
silt or clay was expected in IVF deposits (Weissmann et al., 2002b, 2004).
Driller
0 Some drillers consistently recorded more detail than others. Lack of
quality for a particular driller was also assessed by the uniqueness of each
log. Wells within the same section were noted to have exactly the same
stratigraphy recorded by the same driller, which is unlikely in the ﬂuvial
fan deposits.
Total depth of the log
0 Some of the logs did not record to the depth of expected incised valley ﬁll
bases. Logs from wells that were 60 meters deep or greater were given
preference. Some of the logs terminated within the basal gravel, and
therefore were not useful for deﬁning the absolute depth of the IVF.
Level of detail in the log
0 The level of detail in a log was measured by the lithologic description and
the amount of detail in the vertical succession of units. The presence or
absence of color and sediment size descriptors was noted in the ranking
process. Some of the driller’s produced logs that lacked very little detail
vertically. For example, a well with a depth of 100 meters or more was
described as 3 or 4 thick units of sand and clay. This resolution makes
identiﬁcation of the IVF much more difﬁcult.

It is important to note that the logs were assigned ranks on a combination of the

above factors. No one category outweighed the others consistently in importance. An

incomplete log that shows IV F stratigraphy, a log recorded by a driller that consistently

produced poor quality logs but that clearly shows the IVF stratigraphy, or a log where the

stratigraphy appears slightly irregular could all receive the same ranking. Although this

method is subjective, very few wells (< 100) that recorded thick gravel units were difﬁcult

to describe given the above guidelines. A description of wells ranked 1 through 4 is

included below.

REA 1 Wells

A well assigned a rank of 1 denotes the best representation of the incised-valley

ﬁll (Figure C2).

158

o A thick gravel base ﬁning upward to sand then silt and clay.
o The drilling method was mostly cable tool.

0 Several geophysical logs are also included in this category.

Rank 2 Wells

A well assigned a rank of 2 denotes a good representation of the incised-valley ﬁll

 

(Figure C3).
0 A moderately thick gravel base ﬁning upward to sand then silt and clay.
0 Wells in this category show a slightly less distinct ﬁning upward trend of
the IVF: thin sand or sand and clay alternating overlie the gravel.
- The drilling method recorded on the driller’s logs was mostly mud-rotary

and some cable tool.

Rank 3 Wells

 

A well assigned a rank of 3 denotes a poor representation of the incised-valley ﬁll
(Figure C4).
0 A thin sandy gravel base ﬁning upward to clay or sand then silt and clay.
0 Wells in this category show a less distinct ﬁning upward pattern of the
IVF: thin sand or thick clay overlie the gravel.
o The drilling method recorded on the driller’s logs was mostly mud-rotary

and some cable tool.

159

Rank 4 Wells
A well ranked 4 denotes the poorest representation of a possible incised-valley ﬁll
(Figure C5).
0 Contains relatively thin gravel and does not resemble the ideal ﬁning-
upward succession contained within the incised-valley ﬁll deposits.

0 The drilling method recorded on the driller’s logs was mud-rotary.

160

 

Figure C1: Resistivity log of selected test holes near Modesto, San Joaquin Valley,
California. (Adapted from Burow, et al., 2004)

DEPTH. IN NETERS BELOW LAND SURFACE

35195-33 3SI10E-30 3819525
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161

 

DEPTH. IN FEET BELOW LAND SURFACE

8
o

 

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Figure C2: Driller's Well Log: Rank 1 example log. This log was drilled with a cable
tool and includes detail as well as the expected IVF stratigraphy. NOTE: The well logs
record depth from the surface in feet.

 

(l2) WELL LOG: Total depth _WL Completed depth .254— hi
1 from {L to ft. Formation (Describe by color. clam. size or material)

 

 

 

 

0 — 5 tnnqnil

5 - 7 clav _ ,
7 — 10 311m mind
10 — 12 am ~ '

 

 

 

162

Figure C3: Driller's Well Log: Rank 2 example log.

(12) WELL LOG: Tum! depth 72 2 0...“. Depth of completed “TILELO ft.

 

 

163

      

   

C4: Driller's Well Rank 3
(I2) ”WELL LOG: ”Tom! deszEQéJc. Depth of conipleted deLiZirl

164

Figure C5: Driller's Well Log: Rank 4 example log.
C H) LOC I C l.()(;

  
 
 

  
 

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165

Appendix D: Sequence Boundary Surface Generation

The stratigraphy of the area was modeled by generating surfaces of the sequence
boundary paleosols. These boundaries include the continuous paleosol surface and the
incised valley sides and base.
Riverbank Paleosol with Modesto IVF

To generate the Riverbank paleosol, the ground surface was assumed to be a
sufﬁcient estimate of the Riverbank fan surface because the overlying Modesto formation
that covers the area is only a thin veneer across the fan surface (Bennett, 2003;
Weissmann et al., 2006; Bennett et al. in press). This assumption allowed the Riverbank
paleosol surface to be created using the modern 30-meter DEM.

l. The DEM raster ﬁle, or grid, was clipped manually in ArcMap with the N-Bands
Raster Clipper tool, which can be downloaded from the ESRI website (Multi-
Bands Raster Clipper v1.2 for ArcMap 9.x at
http://arcscripts.esri.com/details.asp?dbid=13474).

2. Because the ﬁle size of the 30-meter DEM grid was very large, the 30-meter
resolution of the DEM was increased using the AGGREGATE expression in
ArcMap Spatial Analyst.

a. In spatial analyst, select raster calculator and type in the following map
algebra syntax: aggregate ([name of grid], [cell factor], [aggregation type],
truncate, data).

b. The cell factor (13.333) was calculated to make the cell size 400 meters

(400/30=l3.333).

166

c. The aggregation type “mean” was chosen as the method for calculating the
output value of each cell.

(1. “Truncate” refers to how the calculator will manage boundaries. The
truncate option reduces the number of rows and/or columns by one. This
option allows truncation of the input grid at the bottom and right

boundaries so that the number of rows and columns will be a multiple of

 

the cell factor. This means that the output grid may be smaller then the
input. In the case of the input 30-meter DEM, the grid was clipped
sufﬁciently large enough to account for the potential shortening by this
function.
e. The “data” parameter speciﬁes that if the 30—meter DEM had a NoData
value, it would be ignored in the calculation of the new output grid.
3. The new coarser grid was converted to a point shapeﬁle in Arc GRID with the
GRIDPOINT SHAPE function or in ArcMap Spatial Analyst.
a. The syntax required for this is: GRIDPOTNTSHAPE ([name of grid to be
converted]).
b. An alternate method is available in ArcMap to convert the raster ﬁle to a
point shape ﬁle. In the Spatial Analyst menu, select “Convert” then select
“Raster to Feature”. Be sure the “Output geometry type” is point. The
“ﬁeld” speciﬁed should be the column with the elevation data.
4. Using the shapeﬁle of rank 1 and 2 wells delineating the presence of the Modesto
Formation IVF basal gravel between 24 to 38 m (80 to 125 ft) (Appendix A),

polygon shapeﬁles of the proposed incised valleys (both the IVF scenarios, the

167

small Modesto Formation valley, and the larger valley) were created. The width
of the valley was approximated from measurements of the modern valley: 0.7 to
1.6 kilometers. Because there was little information on the actual IVF width, we
assume that the current valley is a good representation of the geometry of past
interglacial incised-valleys.

5. The polygon shapeﬁles were then converted to point shapeﬁles.

a. To do this, the polygons were ﬁrst converted to grids (raster ﬁles). In

 

Spatial Analyst, select “Convert” then “Feature to Raster”. The grid cell
size speciﬁed was 200 meters.

b. To convert the raster ﬁle to a point shape ﬁle, return to the same menu,
only this time select “Raster to Feature”. Be sure to convert the ﬁle to a
point shapeﬁle. The “ﬁeld” speciﬁed for the conversion does not matter
because raster ﬁle did not have any relief. The elevation of the valley
bottom will be manually added in the following steps. The result is a grid
with rows and columns of points (or ghost wells) spaced 200 meters apart
delineating the location of the IVF.

6. Once the IV Fs were delineated as a ZOO-meter spaced point shapeﬁle, elevations
of “ghost” wells were assigned to the valley bottom with a gradient of 0.0004
(“ghost” wells were used to enforce our conceptual model of the incised valley
geometry). The gradient value was obtained from measurements of the modern
interglacial river gradient in Weissmann et al. (2006). To assign the elevations,
the rank 1 and 2 well shapeﬁle was plotted with the ghost well point shapeﬁle.

Rank 1 and 2 wells with elevations that matched the expected gradient of the

168

basal gravel were used as anchors to calculate and then assign elevations to the
“ghost” wells situated between them (see description of RB valley below and
Figure D1 for image of implementation of “ghost” wells).
To insert the IV F point into the Riverbank paleosol surface, the polygon coverage
was used to select all points in the DEM within the area of the Modesto IVF
polygon.
a. In ARC Map the “Select by Location” feature was used to select all points
that “are contained by” the polygon. A buffer of 50 meters was applied.
Once the points were selected, they were removed.
The Modesto IVF point shapeﬁle was then used to ﬁll in the valley ﬂoor elevation
points.
The point shapeﬁle that resulted from the merging of the DEM and the Modesto
IVF was then used to generate a grid of the surface in ArcMap Geostatistical
Wizard using the Local Polynomial Function A cell size of 300 meters was

chosen. The resulting surfaces are shown below in ﬁgures 1 through 4.

Upper Turlock Lake Paleosol with Riverbank IVF

The Upper Turlock Lake (UTL) surface was not generated from a preexisting

surface, and therefore was created in a series of steps with multiple sources of data.

1.

First, the elevation of the Corcoran clay was used to estimate the elevation of the
Turlock Lake surface. The Corcoran clay elevation was estimated by sorting out
"blue clay" or "blue silt" from the USGS texture database (described in Burow et
al., 2004). These data were saved in an individual database and plotted in

ArcMap using the plot XY data option. In ArcMap, points were selected by depth

169

("Select by attributes"). An estimated depth of 60 meters (~200 feet) for the
Corcoran Clay was obtained from Burow et al. (2004), so a range of 55 to 67
meters (180 to 220 feet) was applied to select the Corcoran Clay. The elevations
were calculated from the "land surface elevation" minus the "top depth" of the
Corcoran clay. Because this database search limited the depth range for the
Corcoran clay, most of the points plotted were clustered in a band that runs the
length of the study area approximately 100km east of the San Joaquin River.

. To determine the elevation of the upper Turlock Lake (UT L) paleosol, the
thickness between the Corcoran Clay and the Turlock Lake paleosol was added to
each of the points in the Corcoran Clay database, using the "calculate values"
feature in the ARC Map attributes table. MRWA, the core drilled in the distal
portion of the fan, is located in the area where most of the Corcoran Clay points
Were identiﬁed in the database; therefore the thickness added to the Corcoran
Clay points was obtained from the MRWA core descriptions. The MRWA
thickness between the UTL top and the top of the Corcoran Clay was estimated to
be 23.5 meters (77 feet). Estimates from Lettis (1982) verify that this is a
reasonable value.

. Once all of the Corcoran Clay data points had been converted to UTL top
elevations, other data points were added. Supplementary points added to the
database of UTL elevations include (1) the land surface elevations of the
outcropping UTL from soil maps (Weissmann et al., 2006) and (2) the elevation

of the UTL approximated in the two cores described (MREA and MRWA). The

170

combination of these data in a point shapeﬁle (Figure D2) was used as the input to

generate the UTL surface in Arc GRID.

. In Arc GRID, the TREND surface interpolation function was used to make the

UTL surface, which uses a polynomial regression technique to ﬁt a least squares

surface to the data set. This function was chosen because it creates a smooth

surface in a raster ﬁle format. Because this method attempts to ﬁnd a best ﬁt for

the entire surface, the resulting surface may not go through many of the input data

points. A low root-mean square (RMS) error (~7.928) and general shape of the

surface were used as indicators of the generated surface’s accuracy.

a. The syntax used in ARC GRID is as follows:

i.

ii.

iii.

iv.

TREND (<point_cover I point_file>, {item}, {order}, {xmin,
ymin, xmax, ymax})

The <point cover> is the point shapeﬁle of UTL elevations that
was used to generate the surface.

The item is the elevation of the UTL surface in the shapeﬁle that
will be used to make the surface.

The order used was 2. This was used after experimentation with
other orders, which produced unrealistic representation of the
surface.

The coordinates for the area to be interpolated were (-125696,

1564302, -35442, 1663440) in NAD 1983 Albers projection.

b. The elevation of the TREND surface/ grid was checked with the

Riverbank surface to be sure that they do not intersect, as realistically, the

171

UTL would not overlie the younger Riverbank Formation. To ensure that
the UTL was not above the Riverbank in any locations, ArcMap’s “Raster
Calculator” in Spatial Analyst was used.

i. In Raster Calculator, the following expression was used:

1. con ([UTL grid] <= [Riverbank grid], [UTL grid],
[Riverbank grid]).

2. This conditional statement says that if the UTL grid is less
than or equal to the Riverbank, then the output grid should
be assigned the UTL grid elevation, if not, the output grid
should be assigned the Riverbank elevation.

5. The raster ﬁle of the UTL fan surface that was created in TREND was then
converted to a point shapeﬁle in ArcMap in Spatial Analyst (see above in step 3b
of the Riverbank Paleosol with Modesto IVF).

6. Similarly to the methods described above for the Riverbank paleosol surface,
polygon shapeﬁles of the proposed Riverbank incised valleys were used create
“ghost” wells assigned elevations of the IVF bottom gradient (Figure D1). The
IVFs were added to the UTL fan surface (See steps 4-9 in Riverbank Paleosol
with Modesto IVF).

a. One difference between the generation of this valley bottom and the
Modesto valley is the correction for subsidence. Because the Riverbank
Formation is older (~130 to 450 ka) (Lettis, 1988; Weissmann et al.,
2002b), the impacts of subsidence were calculated into the gradient. The

valley bottom was assigned a gradient of 0.001 7. Subsidence was

172

calculated from a rate of 0.2 mm/yr, or 0.0002 m/yr (Lettis, 1988) over a
span of approximately 330 ka. This results in about 66 meters of
subsidence, which increases the slope by 0.0013. When added to the
modern valley gradient of 0.0004 is equal to 0.0017.

7. The resulting surfaces are shown below in ﬁgures D3 through D6.

173

Figure D1: This ﬁgure shows a map view of the use of ghost wells used to delineate the
IVF. In this example, the black dots show the ghost wells used to delineate the RB IV F.
The surrounding gray dots represent the elevation of the paleolsol surface. This point
data was used to interpolate the Turlock Lake sequence boundary surface.

 

   
  

     
 
 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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174

Figure D2: This map shows the location of the Corcoran Clay wells found in the USGS
database. The dense clusters of points in the eastern portion of the map mark areas where
the Turlock Lake formation outcrops. These points were used to interpolate the UTL
sequence boundary surface.

 

  

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Stanislaus River

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175

Figure D3: The Riverbank Formation surface with the Tuolumne River large Modesto
IVF above the upper Turlock Lake Formation surface with the Tuolumne River
Riverbank IVF (RBML). NOTE: Surfaces are exploded 100 meters.

 

 

 

176

Figure D4: The Riverbank Formation surface with the Tuolumne River small Modesto
IVF above the upper Turlock Lake Formation surface with the Tuolumne River
Riverbank IVF (RBMS). NOTE: Surfaces are exploded 100 meters.

 

 

 

177

Figure D5: The Riverbank Formation surface with the Tuolumne River large Modesto
IVF above the upper Turlock Lake Formation surface with the Tuolumne River
Riverbank IVF and the Stanislaus River Riverbank IVF (RBSTML). NOTE: Surfaces
are exploded 100 meters.

~'

 

 

 

178

Figure D6: The Riverbank Formation surface with the Tuolumne River small Modesto
IVF above the upper Turlock Lake Formation surface with the Tuolumne River
Riverbank IV F and the Stanislaus River Riverbank IVF (RBSTMS). NOTE: Surfaces are
exploded 100 meters.

 

 

179

Appendix E: USGS Model
Details of the USGS model are described in Chapter 2 of this thesis. The

following appendix details how the original USGS MODF LOW model was opened. To

open the original USGS MODFLOW model:

Open the MODF LOW model from the USGS one layer at a time.
Alterations to the original model ﬁle include:

(1) changing number format : mostly done in Excel or by using the
“Search and replace” feature in EditPadPro

a. reducing the number of digits (0.990000E0002 to 0.0099)

b. also adding a 0 after ﬂoating points (123. to 123.0—
CAREFUL: some values lost spaces between them by doing
this. To rectify problem search for “.00.” and replace with “.0
.0”)

(2) reducing the number of spaces in between numbers: done in Excel and
the “Search and replace” feature in EditPadPro--(save as a comma
delimited ﬁle and then replace commas with spaces)

a. e.g.- from 5 or more to just one

(3) Reducing the number of hard returns within a row of data (files are set
up so that they list data by row (153 rows) for each layer (16 layers).
There are also 137 columns. To complete step 3, I used a script that
Anthony wrote in Matlab—see “dat_read.m” in
C :\users\Amy\May3 l2005\USGSModelFiles\TuolumneO60605\USGS

_used_4_ﬁnal_model.

180

(4) ALSO MAKE EXTERNAL FILES INTERNAL—I cut and pasted all
of the external ﬁles into the .bcf ﬁle. GMS would not read the external

ﬁles.

To generate Kdatasets.gpr

Once the model will read the discrete (.dis) and (.bcf) ﬁle by using the above methods,
the K values must be imported separately. GMS would only read one layer of K data at a
time. (I think this is because they are read in as 3D data sets, which makes K values in all
other layers zero.)

(1) Open the MODF LOW model in GMS with the K, Leakance, and Wetdry
values for ALL layers “INTERNAL”. MODF LOW will only read the ﬁrst
layer with values into the model. This means that ONLY the K, leakance, and
wetdry values for layer 1 will be read into GMS when it opens.

(2) Once GMS opens, save this model as “Kdatset.gpr”—CLOSE THE MODEL

(3) Go into the “Kdatsetmfs” ﬁle using Microsoft Explorer to set the origin
(ORG -83756.2 1573010 0.0) and the rotation angle (ROT 37).

(4) Re-open the model “Kdatasetgpr”.

(5) Click on the 2D grid module.

(6) Click on the “Grid” dropdown menu and “Create grid”. Enter the following:

a. X origin: -83756.2 Y origin: 1573010 Z origin: 0.0
b. Length: 54800 Length: 61200
c. Number of cells: 137 Number of cells: 153

(1. IN BOTTOM RIGHT CORNER, FILL IN ROTATION ANGLE: 37

181

(7) Go into the 3D grid module and click on the “MODFLOW” dropdown menu.
Select “BCF Package” and view AND export the K, leakance, and wetdry
values for Layer 1. EXPORT the values from “Layer to 2D data set”—File
names should be something like “layer1_k”, “layerl_leak”, and
“layer1_wetdry”.

(8) Go into the 2D grid module and right click on each of the 2D data sets just
generated, and EXPORT the data as and ASCII ﬁle (CAREFUL—the default
ﬁle type is Binary—BE SURE TO CHANGE THIS! ! !).

(9) Once the data have been saved as ASCII ﬁles they can be imported into any
model later (see step 13). After data has been saved, SAVE “Kdatasetgpr”
and CLOSE out of the model.

(10) Open the original MODFLOW .bcf ﬁle. This time, delete all of the values
for layer 1 ( including K, leakance, and wetdry). Fill in “CONSTANT 0.0
(free) 0” for each of those data sets. Now Layer 2 data should be the ﬁrst data
set visible in the .bcf ﬁle. Save and close this ﬁle.

(11) Open a new GMS ﬁle. Open the original MODFLOW ﬁle (.nam) . (This
step is similar to step 1).

(12) Repeat steps 7-10 for each successive layer. NOTE: Only Layers 1
through 7 have “wetdry” data. Layers 8 through 16 are conﬁned and have
transmissivity values, NOT hydraulic conductivity values.

(13) Once all of the ASCII ﬁles have been generated, they can be opened in

GMS as 3D data sets.

182

(14) Open “Kdatasetgpr” then go to “F ile—Open” and select the ASCII ﬁle to
import. Once all ﬁles are open as 3D data sets, they will need to be converted
into 2D data sets (next step). *This is necessary because importing a 3D data
set into the BCF package will make all values in other layers zero.
Transforming to a 2D ensures that only the values of the 2D layer to which
you import the data will be changed.*

(15) To convert from 3D to 2D, click on the ﬁle to be converted so that the text
is bolded.

(16) Go to the “Data” dropdown menu and select “3D data to 2D data”.

(17) BETTER WA Y: Go into the 3D grid module and click on a data set.
Go to the DATA drop down menu. Select “3D data to 2D data”. In
dropdown box, select “Value from k layer” then in the adjacent box,
match the layer of K values to the layer specified in the data set to be
converted. Save the file as, for example: “layl_layer1_k” or
“lay8_layer8_trans”. This results in a 2D array ASCII file when
exported. OLD WA Y—DID NOT WORK: Make sure the ﬁle you want to
convert is selected, and that the ”Maximum value in the ij column " is selected
and click “OK When a 2D data set is saved as a 3D data set, a value of zero
is ﬁlled in for all unknown numbers. Any K, leakance, or wetdry number for a
given layer will be greater than 0, therefore the maximum number in the ij

column will be K (or leakance, or wetdry) for the layer file being converted.

(18) SAVE the new 2D ﬁle with the old 3D ﬁle name.

183

(19) Once all of the layers have been converted into 2D grid, go back to the 3D
grid module and click on the MODFLOW dropdown menu. Go to the “BCF
Package” and begin importing the 2D data to each layer.

a. NOTE: to do this go into each layer (1-7) and (1) click “hydraulic
conductivity”, “leakance” or “wetdry” (2) select the appropriate layer
(3) click “2D data set to layer” and pick the data set you want to ﬁll in.
DO NOT FILL IN HYDRAULIC CONDUCTIVITY FOR LAYERS 8
THROUGH 16.

b. FOR (8-16) ﬁll in the “transmissivity” and the “leakance” ONLY

(20) SAVE ALL WORK PERIODICALLY.

To open sequence boundary surfaces in GMS

(l) in 2D scatter point module
(2) Select “File” then “Open”
(3) “Import as scatter points”
(4) assign scatter point ﬁle a name
(5) with this data set active, go to “Interpolation” drop down menu
(6) Select “Interpolate -> 2D grid”
(7) Select interpolation method
a. “Inverse distance weighted” and leave default options

i. “Nodal Function” should stay as “Gradient plane”

184

ii. “Computation of nodal ﬁmction coefﬁcients” should stay as
“Use subset of points”
iii. “Computation of interpolation weights” should stay as “Use
subset of points”
(8) repeat until all data ﬁles have been opened in GMS
(9) Next, export the 2D grid data to an ASCH ﬁle and save for use within the

code.

185

Appendix F: Model Generation

The following documentation details the inputs for the model adapted from the
USGS. The initial model from the USGS was developed by Steve Phillips (Steve
Phillips, Hydrologist; US. Geological Survey, 6000 J Street, Placer Hall, Sacramento,
CA 95819-6129; sphillip@usgs.gov; phone: 916-278-3002; fax -307l).
A. Flow Package
The Layer Property Flow (LPF) was chosen to allow more ﬂexibility in entering
parameters (the Block-Centered Flow (BCF) package was used in the USGS model).
Values such as the vertical hydraulic conductivity were incorporated into the model
without conversion of the original “vcont” values (or Kv/ thickness), but rather through
the Kh/Kv ratio that calculates the vertical conductance based on the layer elevations.
B. Solver
Preconditioned Conjugate Gradient 2 (PCG2) solver was used for this model (the
USGS model also used the PCG2 solver). The initial solution for the adapted model was
generated using the geometric multigrid (GMG) solver and starting heads all equal to the
elevation of layer 1. The solution generated using the GMG was then used as a starting
head and allowed the rest of the models to be solved with the PCG2 solver.
C. Discretization
Changes to the discretization of the USGS model were made in layers where the
IVFs are located (approximately layers 2 through 7 in the old model) (See Figure 10 in
Chapter 2). The new discretization is ﬁner and better models the IVFs. The adapted
model layers 2 through 16 (old layers 2 through 7) use the IV F base as a layer surface.

The original layers 2 through 7 in the USGS model were assigned as a percent thickness

186

between the ground surface elevation and the top of the Corcoran Clay layer. In the
adapted model, the slope of the layers 2 through 5 match the Modesto small IVF base and
6 through 16 match the slope of the Riverbank IVF base.
One problem that arose with the new discretization was the new layers intersected
with some of the older layers due to varying gradients. To ﬁx this, layers 17 through 22
(USGS model layers 8 through 1 1) were slightly altered in the eastern portion of the
model to prevent intersection of layer elevations. Layers 23 through 27 (U SGS layers 12
through 16) were well below the IV F and were not affected by the gradient of the new
surfaces, thus the original USGS top and bottom elevations were preserved in the adapted
model.
The following instructions describe how the modiﬁed model was discretized.
1) Open a new model in GMS 5.0.
a) Create a 3D grid with at least 8 layers.
b) In the MODFLOW super ﬁle, be sure to set the correct origin and rotation
i) Rotation 37 degrees,
ii) ORIG
(1) (x) -83795.7
(2) (y)1572727.3
(3) (z)0.0 (from well code)
iii) length
( 1) (x length) 54800
(2) (y length) 61200

iv) Cells 400 by 400m

187

v) (x) 137 coltunns (y) 153 rows

c) Be sure the LPF package is activated.

d) Import the top and bottom elevation from the old model. Import layers 11-16 of
the old model as the basal layers of this model. (export data and import to my
model)

2) Use the base of IVF to determine the dip of the upper most grid layers.
i) To do this, use the IVF base grid surface (same as the data used for the
calculations of the gravel, sand and ﬁnes top elevation of the IVF).
(1) Generate in Arc GRID using the TREND function
(2) Assign coordinates so that the surface generated will cover the entire

model area.

 

-121278.992157, 13.633231211375223
1655181976558 -

Coordinates of
the IV base (RB
and ModS)

-39437.1oo741
421278992157, .
1572229432982 1572229432982

 

 

 

(a) RIVERBANK VALLEY and MODESTO VALLEY surface
(i) Resarnple original 12m cell surface from initial IV base (used in

code and to make gravel, sand , and ﬁnes surfaces) to 50m cells

188

1. In ArcMap Toolbox—Data Management—Raster—Resample

(ii) Convert raster to point ﬁle in Arc GRID

(iii)Make TREND surface with 50m cells in Are GRID

(iv) Resarnple trend surface to 300m cells
1. In ArcMap Toolbox go to—Data Management—Raster—

Resarnple

(v) Check Riverbank surfaces using the raster calculator in ArcMap
Spatial Analyst to ﬁnd the absolute difference between the surfaces
to be sure there is not a signiﬁcant difference.

1. The maximum difference between the initial 12 m and the
resampled 50m grid is minimal (calculates 0).

2. The maximum difference between the 50m grid and the trend
surface is 0.012m.

3. The maximmn difference between the 50m trend surface and
the resampled 300m surface is 0.095m.

4. Difference between the original 12m cell surface and the ﬁnal
300m surface was ~0.01m.

(vi)Check MODESTO surfaces using the raster calculator in ArcMap
Spatial Analyst to ﬁnd the absolute difference between the surfaces
to be sure there is not a signiﬁcant difference.

1. The maximum difference between the initial 12 m and the

resampled 50m grid is 0.05m.

189

2. The maximum difference between the 50m grid and the trend
surface is 0.017m.

3. The maximum difference between the 50m trend surface and
the resampled 300m surface is 0.019m.

4. Difference between the original 12m cell surface and the ﬁnal
300m surface was ~0.053m.

ii) INTERSECTION OF SURFACES: The Riverbank valley base and Modesto
valley base surfaces intersect in the southern portion of the model grid. To ﬁx
this, there are two steps using the raster calculator in ArcMap will be used.
(1) Initial adjustment: The following conditional statement was used to

calculate the new surface:
(a) con( RIVERBANK>= MODESTO, MODESTO — 5, RIVERBANK)
(2) Thin cells-- To ﬁx thin areas between the Modesto IV base surface and the
Riverbank IV base surface...
(a) m-r = [MODESTO]- [RIVERBANK]
(b) RIVERBANK below MODESTO ﬁx thin cells = con([m-r] < 5,
[MODESTO] - 5,[RIVERBANK])

iii) Convert raster ﬁle to ASCH ﬁle in ARC toolbox then import to GMS as
scatter points and reinterpolate to the 2D grid using natural neighbor.

iv) Add layers between the Modesto IVF base and the Riverbank IVF base to
make discretization ﬁne enough to capture IVF character (minimum 0.5m

thick cells)

190

v) Both of the IVF base surfaces intersect the Corcoran clay and layers 9 through
11. Use the GMS model checker to auto ﬁx layer errors. In GMS model
checker:
(1) Set a minimum thickness of 0.5 m for cell thickness.
(2) Fix layer errors from the top down and preserve the top layers (this is the
best way to preserve the known data surfaces: the Modesto IVF, the
Riverbank IVF and the Corcoran Clay)
D. Boundaries
The southern and western general head boundary cells in the modiﬁed model are
the same as in the USGS model. Changes in the GHB conditions were made to the rivers
in the central portion of the model and the northeastern boundary. The upper reaches of
the Stanislaus, Tuolumne, and Merced Rivers in layer 1 where changed from speciﬁed
ﬂuxes to general head cells. The GHB condition was removed in several cells (~250) in
the northern portion of the model.
The speciﬁed ﬂux cells that represent the Stanislaus, Tuolumne, and Merced
Rivers in layer 1 of the USGS model were changed to GHB cells in the adapted model
(See Figure 8 in Chapter 2). The head elevations at these new locations were assigned
based on the ground surface elevation. Using National Geographic TOPO! California,
waypoints were marked along the river and imported into ArcMap and plotted as XY
data. This point ﬁle was then opened in GMSS.O and used to assign head elevations to
the new GHB cells delineating the upper reaches of the rivers. These new head

elevations were then added to the input ﬁle for the GHB code.

191

In the USGS model, lack of boundary cells along the rivers was justiﬁed because
the rivers are disconnected from the aquifer in the area. However, the lack of a boundary
in this portion of the model causes unrealistic volumes of water to collect within the river
valleys. To maintain the condition that the rivers are not connected to the aquifer in the
eastern portion of the model, conductances of the newly assigned GHB cells were
reduced. Conductances were assigned using the same calculations as the original river
cells (explained below), but without the vertical K river multiplier (vkrivmult) of 10,
which lowered the cell conductances to reﬂect the disconnected surface and groundwater
in the area. The cell conductances that were calculated this way include all of the GHB
cells delineating the rivers to the eastern extent: cell 1 20 J78 on the Stanislaus River, I 53
J 61 on the Tuolumne River, and I 136 J73 on the Merced River (Table F 1). The eastern
most GHB cells along the river have even lower conductivities to represent the more
consolidated geologic deposits in that area. These cells were not only calculated without
the vertical K river multiplier but also divided by 2 (Table F2). This allows unrealistic
volumes of water to ﬁll the river valley, 5 to 8 meters at most. This condition is also
present in the USGS model. To attempt to rectify this unrealistic situation, ﬁve cells
were added to the head of the Tuolumne River, extending the GHB to the edge of the
model. These cells are in Table 2. This helped to reduce the amount of water gained by
the stream in the model, but did not correct the problem completely. The river in the
adapted model still maintains losing and gaining reaches which is the most parsimonious
condition that could be achieved for the model in this study.

In the northeastem-most portion of the USGS model, GHB cells are dry down to

an elevation of 10 to 17 meters (33 to 56 feet). To avoid the drying of boundary cells,

192

some of the boundary cells assigned in the original USGS model were removed from
layer 1 through layer 4 in the adapted model. The exact cells where the GHB condition
was removed include:

Layer 1: I l; J 76 to 153
Layer 2: I 1; J 81 to 153
Layer 3: I 1; J 96 to 153
Layer4: I 1;J 111 to 153

General head boundaries were assigned using an adaptation of a code used to
develop general head boundaries for the USGS model. Head elevations and cell
conductances were assigned using a code adapted from the 16 layer USGS model to the
27 layer model used in this study. This code accounts for the location of the boundary
(i.e. north and south lateral boundary, the San Joaquin River in the west, or the river cells
in the top active layer).

Head was calculated with a vertical gradient of 0.05 meters of head per meter of
depth. The head decreases from layer 1 to layer 17. Initial head values for layer 1 GHB
were determined from stream gage data and the area topography (S. Phillips, pers.
comm., 8/1/05). Layer 18 was set equal to layer 17 to prevent an unrealistic (at this
model scale) head change between the low conductivity Corcoran Clay layer and the
layer below it. The head was set to increase from layer 19 down to layer 27 in order to
generate upward ﬂow in these layers. The gradient of 0.05 meters of head per meter of
depth (thickness) was added to the initial start head (values from layer 18). In a manner
similar to the USGS approach, this increasing head value was constrained with the sub-
condition that the calculated head value could not exceed the assigned head value in layer
1. If this condition occurred, the calculated head value for that layer would be ignored

and the cell would be assigned a head equal to the head in layer 1).

193

Downward ﬂow
ghbhead =ghbhead-(totthick*gradient) (1)
[for layers 1 through18 where head in layer 18 =head in layer 17]
Upward flow
ghbhead =ghbhead+(totthick*gradient) (2)
[for layers 19 through 27]

where ghbhead is the head assigned to the GHB cell, and totthick is the thickness from
the center of the cell in layer 1 (for the downward ﬂow) or layer 19 (for the upward ﬂow)
to the center of the layer for which the GHB head value is being calculated.

The conductance (ghbcond) through the GHB cells was determined depending on
the location of the cell in the model: the top layer river cells had different conductance
calculations that the rest of the GHB cells. In general, conductance is calculated using
two variables: (1) the horizontal hydraulic conductivity assigned to the cell and (2) the
cell thickness. Constants also included in the calculation are: (1) cell size (400m) and (2)
general head boundary distance (400m).

Preserve Regional Lateral F low from the East
ghbcond = KH*thick*cellsize/ ghbdist (3)
(For GHB cells assigned to northern and southern boundaries and western boundary below layer 1)

GHB cells that delineate rivers in layer 1 allow a small amount of vertical ﬂow,
while below layer 1 along the westem model edge and for all layers along the north and
south extent of the model, the boundary reﬂects the overall lateral ﬂow from the east. To
implement vertical ﬂow, the conductance through layer 1 GHB cells at the location of the
rivers was calculated using the vertical hydraulic conductivity, riverbed thickness (1m),

river width (25m), and a vertical conductivity multiplier of 10.

194

Preserve Local Vertical F low from Rivers

ghbcond = KV * ((cellsize*rivwidth) /bedthick)* vkrivmult (4)
(For GHB cells assigned to rivers in layer 1)

E. Hydraulic Conductivity data
See IVF F ortran code and Vertical Hydraulic Conductivity and Anisotropy Factor Fortran
code
Horizontal Hydraulic Conductivities

Horizontal hydraulic conductivities were assigned using the IVF Fortran code
(See Appendix G). This code assigns the IV F of the various scenarios based on the
elevation at the center of a cell. The output ﬁle from this code preserves the general
geometry and slope of the valley; however, some manual editing was needed for the
hydraulic conductivity of the IV F F ortran output ﬁle. The main areas where edits were
made are at the head and the mouth of the valley: the head of the valley from j 137 to
approximately jl 16 and the end of the valley from j49. The mouth of the valley, in
particular lack ﬁnes at the top of the valley. General summary of where and why changes
were made to the IV F HK ﬁles include:

o The stratigraphy at head of the valley was not as well deﬁned as it was
farther down valley. For example, the sand pinched out in some areas of
the valley (e. g. J 134 in the RBMS and RBSTMS model). The upper
reaches of the IVF were edited to preserve the stratigraphy and lateral
continuous nature of the valley.

0 Lateral valley continuity was disrupted most commonly at bends in the
river valley. In this case, the valley needed to be widened by a cell width
(or 2 cell widths at the most) in areas to maintain connectivity (e. g. J116 in
the Modesto small IVF).

o In all of the models, the Tuolumne River Riverbank IVF (RB) has ﬁnes
largely nonexistent. Fines were added to the areas where they did not
exist. The ﬁnes are likely under represented because the lower limit

elevation of the ﬁnes may be slightly higher than the center of the cell.
The unit may be too thin to be preserved with the discretization.

195

The need for manual edits of the IVF Kh ﬁle may have been caused by using a
different surface generation method for the sequence boundary surfaces (surfaces
including the IVF used in the IVF code) than was used to make the IVF base surface
(which was used to make the upper layers of the model and in the IV F code to designate
the location and Kh of cells in the IV F). Due to limited data, Arc GRID TREND was
chosen as the surface generation tool for the IVF bottom surface that was entered in the
model discretization. TREND was chosen over the ArcMap Geostatistical Wizard, which
was used to create the sequence boundary surfaces, to allow manual designation of the
output surface extent (step 2i2 above). The ArcMap Geostatistical Wizard will only
extrapolate a surface to a predetermined extent outside of the input data points. Manually
assigning the extent of the output surface provided the ﬂexibility to create a surface large
enough to cover the model extent. Using the same method of surface generation for both
the sequence boundary surface and the IVF base surface for the new model discretization
would be ideal, but it was not possible in this case due to a lack of input data points,
which prevented the ArcMap Geostatistical Wizard from generating a surface large
enough to cover the model area. Minimal lateral and vertical shifting of the valley mostly
in the eastern portion of the model was the result of this discrepancy.

Vertical Hydraulic Conductivities

Because the LPF package is used for this model, a leakance value (vcont =
Kv/thickness) is not speciﬁed. Instead, this model uses an anisotropy factor (Kh/Kv). To
calculate this number for the model, a FORTRAN code (See Appendix H) was generated
to ﬁrst, calculate the vertical conductivity from the leakance parameter in the USGS data.

This vertical conductivity was assigned to cells in the newly discretized model using the

196

same method as the horizontal hydraulic conductivity: if the new model’s cell centered
elevation falls between the top and bottom layer elevations of the coarser USGS model
the KV from the USGS model will be assigned to the adapted model. Then, the vertical
hydraulic conductivity was used to calculate the anisotropy factor (Kh/Kv).
F. Recharge
All of the original recharge values from the USGS land-use approach were
maintained in the model (Burow et al., 2004). Recharge values range from 0 to 0.005
m/d or 1.825 m/yr (0.0164 ﬁ/day or 5.986 ﬁ/yr).
G. Evapotranspiration
The original ET values were maintained in the new model: maximum evaporation
rate of 1.6 m/yr at land surface and the maximum extinction depth is 2.1 meters below the
land surface. These values were assigned based on the water budget in Burow et al.
(2004)
H. Wells
Pumping wells in this model are the same wells used for the USGS model and
include: urban-supply, agricultural, and water-table-control, or have known "drainage".
Pumping from the wells was calculated using the same FORTRAN code as was used for
the USGS model. Appropriate layer numbers were changed for the newly discretized
model (see code). The well code had to be run to produce a separate well ﬁle for each
model scenario, as several wells intersect the IVF.
I. Reservoirs
Wood Reservoir, Modesto Reservoir, and Turlock Lake are all preserved in the

adapted model. The reservoir ﬁle was used with the adapted model without any changes

197

to the ﬁle format. The vertical conductance of the reservoir, however, was reduced. The
values of 0.003 and 0.006 mz/day were divided by 1.2 (value obtained by manually
testing various vertical conductances to constrain the best value). The resulting vertical
conductances are 0.0025 and 0.005 mZ/day.

The only other small alteration made in the ﬁle is in the heading. The unit
number that speciﬁes where the cell—by-cell ﬂow should be recorded was changed to

zero. This is the second number in the initial line of the reservoir data array.

198

Table F1: Cells where the conductance in the upper reaches of the Tuolumne River were

reduced by 10 and then 2. It is assumed that the lithologic units here are more
consolidated, therefore their conductance is lower.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

original New Conductance
i j k head conductance (Divide by 10 then 2)
87 121 1 28 163.93 81 .96500245
87 122 1 28 233.15 116.575
88 123 1 28 391.37 195.6849976
89 124 1 29 438.96 219.4800049
89 125 1 29 128.64 64.3200012
90 126 1 30 277.76 138.8799927
91 126 1 30 204.3 102.15
92 127 1 31 153.32 76.66000365
93 128 1 31 130.46 65.2299988
94 129 1 32 122.12 61 .05999755
95 130 1 33 1 19.37 59.68499755
96 131 1 33 127.74 63.8700012
97 131 1 34 122.28 61 .14000245
98 132 1 34 125.52 62.75999755
99 133 1 35 249.4916 124.7457861
99 134 1 36 269.4419 134.7209548
100 135 1 37 227.6779 113.8389331
100 136 1 37 304.0255 152.0127616
101 137 1 37 266.2508 133.1253762

 

Table F2: Table listing the cells added to the head of the Tuolumne River to attempt to
reduce the amount of water within the river valley.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

i j k head Conductance Kh Kh/Kv Kv con
99 133 1 35 249.4947 86.4 3463 0.024949 249.4947
99 134 1 36 269.4443 86.4 3206.6 0.026944 269.4443
100 135 1 37 227.674 86.4 3794.9 0.022767 227.674
1 00 136 1 37 304.022 86.4 2841 .9 0.030402 304.022
101 137 1 37 266.2558 86.4 3245 0.026626 266.2558

 

 

199

 

Appendix G: Incised-Valley Fill FORTRAN Code

A. IVF Code Information
1. Use IVF_CODE to assign the original Kh from the USGS model to the cell in the
adapted model with the same elevation as the USGS model cell. Be sure to use the
code speciﬁc to the geologic scenario: without ST or with ST.
2. Files Needed

a. Elevation data exported from the GMS 5.0.

b. Kh data from the USGS in ASCII form exported from GMS (no IVF, this
code will add them)

c. Gravel, sand, and ﬁnes top elevations for the IVF

(1. File to delineate where the Tuolumne River Riverbank IV F and Stanislaus
Riverbank IVF are located. This ﬁle assigns the Stanislaus IVF a value of
3 and the Tuolumne River IVF a value of 2.

3. Writes
a. This will output a new Kh data set for each geologic scenario.
B. IVF Developing Stratigraphy and Assigning Hydraulic Conductivities
1. Development of Top Surfaces of gravel, sand, and ﬁnes.

a. This code relies on the stratigraphy of the area to identify a cell located
within the IVF. The code uses a series of logical statements to determine
whether the IVF is within the IVF, and if it is, what part of the IV F is the
cell located in based on elevation. To do this, the code uses the grid
surfaces developed in Appendix D, as well as grid surfaces of the top of

the gravel, sand, and ﬁnes fractions of the IVF.

200

b. These surfaces were generated by:
i. First, creating grid surfaces for the IV base.
1. Use the point shapeﬁle of each IVF with the valley gradient
assigned to generate a surface in Arc GRID using TREND.
2. The syntax used is:
a. TREND (<point_cover I point_file>, {item},
{order}, {xmin, ymin, xmax, ymax})
b. The <point cover> is the point shapeﬁle of IV basal
elevations that was used to generate the surfaces in
Appendix D.
c. The item is the elevation of the IVF base in the
shapeﬁle that will be used to make the surface.
(1. The order used was 1.
e. The coordinates for the area to be interpolated were
(-125695, 1607364, -35303, 1632550) in NAD
1983 Albers projection.
ii. Obtain the entire thickness of the valley
3. Using the Raster Calculator function in ArcMap’s Spatial
Analyst, the thickness of the valley was calculated with the
following equation:
a. Total IV Thickness = abs ([fan surface (UTL or

RB)] —[bottom of IV surface])

201

b. The fan surface was either the Riverbank or the
UTL fan surface WITHOUT the IV F .

c. The bottom of the IV surface was the Riverbank
Tuolumne River, Riverbank Stanislaus River,
Modesto Large, or Modesto Small gravel base

surface generated in step “i”.

4. Percent thickness of each fraction of the IVF (gravel, sand,
and ﬁnes) were calculated using the grid ﬁles in the raster
calculator:

a. Gravel thickness= 0.4* [valley thickness]
b. Sand thickness= O.45* [valley thickness]
c. Fines thickness = 0.15* [valley thickness]

5. Using the thickness calculated above, the elevation of the
top of the gravel, sand, and ﬁnes surfaces were created by
adding that thickness to the valley bottom (for the gravel)
or to the elevation of the top of the unit below (for the sand
and the ﬁnes)

a. Gravel top surface elevation = [bottom of IV
valley] + [gravel thickness]

b. Sand top surface elevation = [gravel top elevation]
+ [sand thickness]

c. Fines top surface elevation = [sand top elevation]

+ [ﬁnes thickness]

202

2. Hydraulic Conductivity Values
b. The hydraulic conductivity values assigned to the IV F were obtained from
Weissmann et al. (2004).
i. Gravel was assigned a Kh of 864 Md (or 1x10'2 m/s)
ii. Sand was assigned a Kh of 86.4 m/d (or 1x10"3 m/s)
iii. Fines were assigned a Kh of 0.0864 m/d (or 1x10'6 m/s)
C. Preparing the ﬁles for use in the FORTRAN code
The resulting Riverbank and UTL surfaces with IVFs were used in a FORTRAN
code to assign the hydraulic conductivities of the IVF. To preserve a consistent ﬁle
format and interpolate the ArcMap generated grid to the model grid for the FORTRAN
code, the surfaces were opened in GMS 5.0 and interpolated to an existing model grid.
1. To open the surfaces in GMS 5 .0, the raster ﬁles were converted into an ASCH
ﬁle.
a. In Arc Toolbox, look in Conversion Tools, then select “Raster to ASCII”
tool.
2. In GMS 5.0, the USGS grid must be deﬁned.
a. The grid is 153 rows by 137 columns of 400 meter2 cells.
b. In the 2D grid module, select the Grid dropdown menu, then “create
Grid”.
i. Origin (x) -83795.7 (y)1572727.3
ii. Length (x length) 54800 (y length) 61200
iii. Number of Cells: (x) 137 columns (y) 153 rows

iv. Rotation: 37

203

3. Next, the ASCII ﬁle was opened in GMS 5.0 as 2D scatter points.
4. The 2D scatter points were then interpolated to the grid generated by the USGS.

a. In the 2D grid module, go to the “Interpolation” drop down menu, and
select interpolate to 2D grid.

b. Inverse distance weighted was used to interpolate the surface. A
maximum of 1 meter difference in the actual versus interpolated surface
was noted based on a random sampling of points.

5. The newly interpolated surfaces were then exported as ASCII ﬁles to be used in
the IVF FORTRAN code to assign hydraulic conductivity values.

a. Some ﬁle manipulation was necessary prepare the ASCII ﬁles from GMS
for the code. The exported GMS ASCH ﬁles each contain a header
providing information the type of data set the ﬁle is, the ﬁle name, and the
number of data points (see sample below). This as well as the list of 1’s
and 0’s, denoting active and inactive cells respectively, needed to be
removed.

b. Sample GMS Header

DATASET
OBJTYPE "grid2d"
BEGSCL
ND 20961
NC 20961
NAME "elevation2"
TS 0 0

AND for 3 dimensional grids
DATASET
OBJTYPE "grid3d"
BEGSCL

ND 565947
NC 565947

204

NAME "gmsﬁl"
TS 0 0

D. IVF FORTRAN CODE

1.

General Description of Code

a. The IVF code uses a series of conditional statements to assign hydraulic

conductivity values to cells of the model located within the IV F. Figure
G1 shows a ﬂow chart of conditional statements that must be met to assign
the predetermined Kh values for the IV F. The elevation of the center of
the cell was used to determine (1) if the cell was in the IVF and (2) based
on the location, which Kh value it should be assigned. Cells outside of the
IVF were assigned Kh values from the USGS model. Cells within the IVF
were assigned a Kh value for gravel (864 m/d), sand (86.4 m/d), or ﬁnes
(0.084 m/d) (Figure G2). For example, if the cell were above the Turlock
Lake surface, below the Riverbank surface, above the top of the gravel
elevation and below or equal to the bottom elevation of the top of the
sand, then the cell is in the IV F and would be assigned a Kh of sand. As is
illustrated in ﬁgure G2, the ﬁnes represent a small fraction of the total IVF
thickness and the likelihood of the center of the cell falling either below or
above this relatively thin interval is high, thus the ﬁnes are under

represented in this model.

205

Figure G]: A ﬂow chart of the conditional statements used to deﬁne the Kh of the IV F
based on its stratigraphic position. This example uses the Modesto large IVF and the

Tuolumne River Riverbank IVF (RBML).

Assign USGS model
open fan deposit (OFD) K

if cell in R8

THEN

if elevation of center of
cell is above UTL

if elevation less
than or equal to RB
ﬁnestop l

I
v
if elevation is less

than or equal to R3
sand top AND
greater than gravel

l

v
if elevation is less
than or equal to RB

   

  
  

if elevation is less than
or equal to ModL

if elevation of center of
cell is above RB surface

if elevation is less
than or equal to
ModL fines top
l .
l

l
v

if elevation is less
than or equal to
ModL sand top AND
greater than gravel

l
l
i
v

 

 

 

 

v i

< assign ﬁnes K )
x /

\-—-—’

". or“... sun—'-

 

assign sand K

206

\ l \ \\
w’“ "" M" ”Ms.

1’ assign gravel K :5.

M w...

“M

Figure GZ: Cells located within the IVF were assigned a Kh based on the elevation of
the center of the cell relative to the sequence boundary surfaces (in red), gravel (in
orange), sand (in yellow), and ﬁnes (in blue) surfaces within the IVF. Shown in color.

 

OOOOQOOOOOOOOOOO

 

2. No IVF
a. FORTRAN code

program noIVF

Program to create an output file to be

used in 30 realization for a GMS 5.1
groundwater model.

DO NOT USE WITHOUT ADJUSTMENT FOR SPECIFICS

PARAMETERS:
gk = gravel hydraulic conductivity**;
sk = sand conduct; fk = fines conduct**;
pk=paleosol conduct**
**from King's River model Weissmann, Zhang, Fogg, Mount (2004)
HK1_4O = open fan deposit conduct from USGS model; nzusgs= vertical
iscretization of USGS model;
nx= number of rows
ny= number of columns
nz= my vertical discretization;

207

Elevation from newly discretized USGS model (40 layers): elevfil
Riverbank top : rsfil

Riverbank bottom of paleosol : rpfil

Upper Turlock Lake top : usfil

UTL base of paleosol : upfil

Mod IV (large or small) fines top surface: mfsfil; mssfil : sand;
mgsfil : gravel

c Mods IV fines top surface: mfsfil (ONLY CHANGE TO MODESTO SMALL WHEN
FILE CHANGED IN PARFIL); mssfil : sand; mgsfil : gravel

c RB IV fines top surface: rfsfil; rssfil = sand; rgsfil = gravel

c ST IV fines top surface: stffil; stsfil : sand; stgfil gravel (ONLY
WHEN PRESENT IN PARFIL)

c HK1_40fi1: 3D array file of all the K values from newly discretized
USGS model

c HK_IVF : new file of horizontal hydraulic conductivities with IVF K
values inclusded

c

c

c EDIT 080905 Changed the HK1_40 to HK1_36. nzz=36 Using newly
discretized HK data...

c ...nz:33 FInal model will have 33 layers.

c

c

OOOOOO

c Declaration of variables
c integer nznewd, nxy, nxyz, nxyzusgs

parameter nx=153
parameter ny=137
parameter nz:27
parameter nzz=16
parameter nxy=nx*ny
parameter nxyz:nx*ny*nz
parameter nxyzz:nx*ny*nzz

real elev(nxyzz)
real e1ev2(nxyz)
real HK1_16(nxyzz)
real HK1_27(nxyz)

character*40 elevfil
character*40 elev2fil
character*40 HK1_16fil
character*40 HK1_27fi1
character*40 parfill

dx=400.
dy=400.
dz:0.5

c Where are the data???????

208

O

0000

print*,'input par file name?:' ! Open input and output files
read(5,'(a40)') parfill

print*,'input file name:', parfill
open(1,file:parfi11,status:'old')

read(1,'(a40)') elevfil

read(1,'(a40)') elev2fil
read(1,'(a40)') HK1_16fil
read(1,'(a40)') HK1_27fil

closell)

open(18,file:'dbg.txt',status='unknown')
open(19,file:'elevations',status:'unknown')

Read data from ASCII file to fill arrays. Sequence boundaries!
print*,'reading Parfil including ASCII files from GMS--read elev'
read(*, '(a40)') eleva
open(l,file=elevfil,status='old', form:'formatted')
print*, 'lst doloop'

do i=1, 7
read (1,'(a40)') junk
enddo
do k=1,nzz
do j:1,ny
do i=1,nx
ijk: i+((k-1)*nx*ny)+((j-1)*nx)
read (1,*) e1ev(ijk)
enddo
enddo
enddo
close(1)

print*, 'open elev2fil'
open(2,file=elev2fil,status:'old', form:'formatted')

do k=1,nz
do j:1,ny
do i=1,nx
ijk: i+((k-1)*nx*ny)+((j-1)*nx)
read(2,*) elev2(ijk)
enddo
enddo
enddo
close(2)

print*, 'open HK1_16fil'
open (17,file:HK1_16fil,status:'old', form:'formatted')

do k=1,nzz

do j:1,ny
do i:1,nx

209

ijk:i+((k-1)*nx*ny)+((j-1)*nx)
read(l7,*) HK1_16(ijk)
enddo
enddo
enddo

cloee(17)

c fill in gms final grid array with proper hydraulic conductivity

do k=1,nz
do j:1,ny
do i=1,nx
ij=i+((j-1)*nx)
ijk:((k-1)*nx*ny)+i+((j-1)*nx)

c Fill in USGS K values for bottom cells (USGS cells 12 through 16)
i£(k.ge.23) then
kusgs:k-11
ijkusgs:((kusgs—1)*nx*ny)+i+((j-l)*nx)
HK1_27(ijk)=HK1_16(ijkusgs)

else
c Calculate elevation of cell and fill in USGS value as 'default'
c ijkz : cell number located below ijk
c elevation : elevation of center of cell for rediscretized grid
ijk2:(k*nx*ny)+i+((j—1)*nx)
elevation : ((elev2(ijk)-

elev2(ijk2))/2)+elev2(ijk2)

c Find out which usgs cell we are in and assign usgs K value
do kk=1,12
ijkk:((kk-1)*nx*ny)+i+((j-1)*nx)
ijkk2:(kk*nx*ny)+i+((j-1)*nx)
if(elevation.1e.e1ev(ijkk).and.e1evation.gt.elev

* (ijkk2ll than
HK1_27(ijk)=HK1_16(ijkk)
writel15,'(4i4)') i,j,k,kk
endif
enddo
endif
enddo
enddo
enddo

c Prepare the gms input files

print*, 'printing gms files'
lopen(20,file=HK1_27fil,status:'unknown')

c writing GMS full grid file

210

O O

O 0 O O O O 0 0 O QaO O 0 O O O O O 0

do k=1,nz
do j:1,ny
do i:l,nx
ijk:i+((j-1)*nx)+((k-1)*nx*ny)
write(20,'(E9.4)') HK1_27(ijk)
enddo
enddo
enddo

print*,'gms files noIVF completed'
print*,'Goodbye!'

cloae(20)
closelle)
cloee(19)

print*,'****************************'

print*,'GMS files written.’

print'k’ I****************************'

print*,'(:'

stop
and

3. With the Riverbank IVF ﬁ'om the Stanislaus River
a. FORTRAN code

program IVF code wST

Program to create an output file to be
used in 3D realization for a GMS 5.1
groundwater model.

.DO NOT USE WITHOUT ADJUSTMENT FOR SPECIFICS

PARAMETERS:
§ﬂ< = gravel hydraulic conductivity**;
sk = sand conduct; fk : fines COHdUCt**;

‘pk:pa1eosol conduct**
**from King's River model Weissmann, Zhang, Fogg, Mount (2004)
HK1;27 : open fan deposit conduct from USGS model; nzusgs: vertical

.iscretization of USGS model;

nx: number of rows
ny= number of columns
:nz: nw'vertical discretization;

Iilevation from newly discretized USGS model (40 layers): elevfil
Riverbank top : rsfil

Itiverbank bottom of paleosol : rpfil

Upper Turlock Lake top : usfil

UTL base of paleosol : upfil

211

c Mod IV (large or small) fines top surface: mfsfil; mssfil : sand;
mgsfil : gravel

c Mods IV fines top surface: mfsfil (ONLY CHANGE TO MODESTO SMALL WHEN
FILE CHANGED IN PARFIL); mssfil : sand; mgsfil : gravel

c RB IV fines top surface: rfsfil; rssfil : sand; rgsfil = gravel

c ST IV fines top surface: stffil; stsfil : sand; stgfil : gravel (ONLY
WHEN PRESENT IN PARFIL)

c HK1_27fil: 3D array file of all the K values from newly discretized
USGS model

c HK_IVF : new file of horizontal hydraulic conductivities with IVF K
values inclusded

c

C TO USE...

c (1) If changing vertical discretization: Check Lines 361 and 362

c (2) Edit 'parfil.par' to edit input files.

c

c Declaration of variables
c integer nznewd, nxy, nxyz, nxyzusgs

parameter nx:153
parameter ny=137
parameter nz:27
parameter nzz=16
parameter nxy=nx*ny
parameter nxyz:nx*ny*nz
parameter nxyzz:nx*ny*nzz

c nxy:nx*ny
c nxyz:nx*ny*nz
c nxyzusgs=nx*ny*nzusgs

real rs(nxy), rp(nxy), mfs(nxy), mss(nxy), mgs(nxy)
real us(nxy), up(nxy), rfs(nxy), rss(nxy), rgs(nxy)
real st3rb2(nxy)

real stf(nxy)

real sts(nxy)

real stg(nxy)

real e1ev(nxyzz)

real elev2(nxyz)

real HK1_16(nxyzz)

real HK_IVF(nxyz)

character*40 elevfil
character*40 elev2fil
character*40 usfil
character*40 upfil
character*40 rfsfil
character*40 rssfil
character*40 rgsfil
character*40 stffil
character*40 stsfil

212

character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40

character*40

stgfil
rsfil
rpfil
mfsfil
mssfil
mgsfil

st3rb2fil
HK1_16fil
HK_IVFfil
parfill

parfill,

junk

K values for facies from Weissmann junk

gk:864
sk=86.4
fk=0.0864
pk:0.0000001

dx=400.
dy=400.
dz:0.5

Where are the data???????

print*,'input par file name?:'
read(5,'(a40)') parfill

print*,'input file name:', parfill

open(1,file=parfi11,status:'old')

read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')
read(1,'(a40)')

close(1)

elevfil
elev2fil
usfil
upfil
rfsfil
rssfil
rgsfil
stffil
stsfil
stgfil
rsfil
rpfil
mfsfil
mssfil
mgsfil
st3rb2fil
HK1_16fil
HK_IVFfil

213

Open input and output files

open(18,file:'dbg.txt',status:'unknown')
open(19,file:'e1evations',status:'unknown')

C Read data from ASCII file to fill arrays. Sequence boundaries!
print*,'reading Parfil including ASCII files from GMS--read elev'

c read(*, '(a40)') eleva
open(l,file=elevfil,status:'old', form:'formatted')
c print*, 'lst doloop'
c do i=1, 7
c read (1,'(a40)') junk
c enddo
do k=1,nzz
do j:1,ny
do i=1,nx
ijk: i+((k—l)*nx*ny)+((j—1)*nx)
read (1,*) e1ev(ijk)
enddo
enddo
enddo
close(1)
print*, 'open e1ev2fil'
open(2,file=elev2fil,status:'old', form:'formatted')
do k=1,nz
do j:1,ny
do i:l,nx
ijk: i+((k-1)*nx*ny)+((j-1)*nx)
read(2,*) e1ev2(ijk)
enddo
enddo
enddo
close(2)
c Open and read Upper Turlock Lake surface with Riverbank Valley
print*, 'open usfil'
c read(*, '(F15.5)') utl_rb1_300

open(3,file=usfil,status='old', form:'formatted')

do j:1,ny
do i:1,nx
ij=i+((j—1)*nx)
read(3,*) us(ij)
enddo
enddo
cloee(3)

print*, 'open upfil'
open(4,file:upfi1,status:'old', form:'formatted')

do j:1,ny
do i=1,nx
ij=i+((j—1)*nx)
read(4,*) up(ij)
enddo
enddo

214

close(4)

print*, 'open rfsfil'
‘ open(S,file:rfsfil,status:'old', form:'formatted')

do j:1,ny
do i:1,nx
ij=i+((j-1)*nx)
read(5,*) rfs(ij)
enddo
enddo

cloee(5)

print*, 'open rssfil'
open(6,file:rssfi1,status='old', form:'formatted')

do j:1,ny
do i:l,nx
ij:i+((j-1)*nx)
read(6,*) rss(ij)
enddo
enddo

cloael6)
print*, 'open rgsfil'
open(7,file=rgsfil,status:'old', form:'formatted')

do j:1,ny
do i=1,nx
ij:i+((j-1)*nx)
read(7,*) rgs(ij)
enddo
enddo

cloee(7)

print*, 'open stffil'
open(8,file:stffil,status:'old', form:'formatted')
do j:1,ny
do i=1,nx
ij=i+((j—1)*nx)
read(8,*) stf(ij)
enddo
enddo

cloee(8)

print*, 'open stsfil'
open(9,file=stsfil,status:'old', form:'formatted')
do j:1,ny
do i:1,nx
ij=i+((j-1)*nx)
read(9,*) sts(ij)
enddo

215

enddo
close(9)

print*, 'open stgfil'

open(lo,file:stgfil,status='old',

do j:1,ny
do i:1,nx

form:'formatted')

ij=i+((j-1)*nx)

read(10,*)

enddo
enddo

close(10)

print*, 'open rsfil'
open(ll,file:rsfil,status:'old',

do j:1,ny
do i=1,nx

stg(ij)

form:'formatted')

ij=i+((j—1)*nx)

read(11,*)

enddo
enddo
cloee(11)

print*, 'open rpfil'
open(lz,file:rpfil,status='old',

do j:1,ny
do i=1,nx

rs(ij)

form:'formatted')

ij=i+((j-1)*nx)
read(12,*) rp(ij)

enddo
enddo

close(12)

print*, 'open mfsfil'

open(13,fi1e=mfsfil,status='old',

do j:1,ny
do i=1,nx

form:'formatted')

ij=i+((j-1)*nx)
read(13,*) mfs(ij)

enddo
enddo

close(13)

print*, 'open mssfil'

open(14,file:mssfil,status='old',

do j:1,ny
do i:1,nx

216

form:'formatted')

ij:i+((j-1)*nx)
read(14,*) mss(ij)
enddo
enddo

cloee(14)

print*, 'open mgsfil'
open(lS,file:mgsfil,status:'old', form:'formatted')

do j:1,ny
do i=1,nx
ij=i+((j—1)*nx)
read(15,*) mgs(ij)
enddo
enddo

cloae(15)

print*, 'open st3rb2fil'
open (16,file: st3rb2fil, status:'old', form:'formatted')

do j:1,ny
do i:1,nx
ij:i+((j-1)*nx)
read(16,*) st3rb2lij)
enddo
enddo

close(16)

print*, 'open HK1_16fil'
open (17,file=HK1_16fi1,status:'old', form:'formatted')

do k=1,nzz
do j:1,ny
do i=1,nx
ijk:i+((k-1)*nx*ny)+((j-1)*nx)
read(l7,*) HK1_16(ijk)
enddo
enddo
enddo
cloee(17)

<2 fill in gms final grid array with proper hydraulic conductivity

do k=1,nz
do j:1,ny
do i=1,nx
ij:i+((j-1)*nx)
ijk:((k—1)*nx*ny)+i+((j-1)*nx)

217

c Fill in USGS Kh values for bottom cells (USGS cells 12 through 16)
if(k.ge.23) then
kusgs:k-11
ijkusgs:((kusgs-l)*nx*ny)+i+((j-1)*nx)
HK_IVF(ijk)=HK1_16(ijkusgs)
else

c Calculate elevation of cell and fill in USGS value as 'default'
ijk2 : cell number located below ijk
c elevation : elevation of center of cell for rediscretized grid
ijk2:(k*nx*ny)+i+((j—1)*nx)
elevation : ((elev2(ijk)—elev2(ijk2))/2)+elev2(ijk2)

O

c Find out which usgs cell we are in and assign usgs K or T value
do kk:1,12
ijkk:((kk-1)*nx*ny)+i+((j-1)*nx)
ijkk2:(kk*nx*ny)+i+((j-1)*nx)

if(elevation.1e.elev(ijkk).and.elevation.gt.elev
* (ijkk2)) then
HK_IVF(ijk):HK1_16(ijkk)
write(15,'(4i4)') i,j,k,kk
endif
enddo

c Fill in UTL Formation Paleosol
c if(e1evation.le.us(ij).and.elevation.ge.up(ij)) gms(ijk): pk

c Fill in Riverbank Formation Paleosol ModL IV
c if(elevation.le.rs(ij).and.elevation.ge.rp(ij)) gms(ijk): pk

c fill in Riverbank formation with RB IV (st3rb2 : 2 = inside tuol rb
valley)
1£(st3rb2(ij).eq.2) then
i£(elevation.lt.rfs(ij).and.elevation.gt.us(ij)) then
if(elevation.le.rfs(ij).and.elevation.gt.rss(ij)) then
HK_IVF(ijk): fk
else if (elevation.1e.rss(ij).and.elevation.gt.rgs(ij))
then
HK_IVF(ijk): sk
c write(15,'(a6,e9.4)')'sands ',gms(ijk)
else i£(elevation.1e.rgs(ij)) then
HK_IVF(ijk)= gk
endif
endif
endif

c Fill in the Riverbank formation with ST IV
if(st3rb2(ij).eq.3) then
i£(elevation.lt.stf(ij).and.elevation.gt.us(ij)) then
if (elevation.1e.stf(ij).and.e1evation.gt.sts(ij))
then
HK_IVF(ijk): fk
else if (elevation.1e.sts(ij).and.elevation.gt.stg(ij))

then
HK_IVF(ijk)= sk

218

else if (elevation.1e.stg(ij)) then
HK_IVF(ijk)= gk
endif
endif
endif

c Fill in the Modesto formation with Mod IVF
if(elevation.gt.rs(ij)) then
if (elevation.1e.mfs(ij).and.elevation.gt.mss(ij)) then
HK_IVF(ijk)= fk
else
if(elevation.le.mss(ij).and.e1evation.gt.mgs(ij))then
HK_IVF(ijk)= sk
else if (elevation.1e.mgs(ij)) then
HK_IVF(ijk)= gk

endif
endif
endif
enddo
enddo
enddo

c Prepare the gms input files

print*, 'printing gms files'
open(20,file=HK‘IVFfil,status='unknown')

c writing GMS full grid file

do k=1,nz
do j:1,ny
do i=1,nx
ijk:i+((j-l)*nx)+((k-1)*nx*ny)
write(20,'(E9.4)') HK_IVF(ijk)
enddo
enddo
enddo

print*,'gms files completed'
print*,'Goodbye!'

close(20)
close(18)
close(19)

print*’ I****************************'

print*,'GMS files written.‘
print*,'****************************'

print*,'(:'

219

stop
end

4. Without the Riverbank IVF from the Stanislaus River
a. FORTRAN code

program.IVF code noST

Program to create an output file to be
used in BD realization for a GMS 5.1

groundwater model.
DO NOT USE WITHOUT ADJUSTMENT FOR SPECIFICS

PARAMETERS :

gk : gravel hydraulic conductivity**;

sk : sand conduct; fk : fines conduct**;

pk=pa1eosol conduct**

**from King's River model Weissmann, Zhang, Fogg, Mount (2004)
HK1_40 : open fan deposit conduct from USGS model; nzusgs: vertical
iscretization of USGS model;

: number of rows
ny: number of columns
nz: my vertical discretization;

Elevation from newly discretized USGS model (40 layers): elevfil
Riverbank top : rsfil

Riverbank bottom of paleosol : rpfil

Upper Turlock Lake top : usfil

UTL base of paleosol : upfil

Mod IV (large or small) fines top surface: mfsfil; mssfil : sand;
mgsfil : gravel

C bkmds IV fines top surface: mfsfil (ONLY CHANGE TO MODESTO SMALL WHEN
FIIJZ CHANGED IN PARFIL); mssfil : sand; mgsfil : gravel
c RB IV fines top surface: rfsfil; rssfil = sand; rgsfil
<3 ST IV fines top surface: stffil; stsfil : sand; stgfil
WHEN PRESENT IN PARFIL)

c HK1;40fil: 3D array file of all the K values from newly discretized

‘USGS model
c PHC_IVF — new file of horizontal hydraulic conductivities with IVF K

OOOOOOOOOOQOOOOOOOOOOO

gravel
gravel (ONLY

‘values inclusded

C

c
(3 EDIT‘080905 Changed the HK1_40 to HK1_36. nzz=36 Using newly

ciiscretized HK data...
c . ..nz:33 FInal model will have 33 layers.

C
C

c Declaration of variables

220

c integer nznewd, nxy, nxyz, nxyzusgs
nx=153

ny=137

nz:27

nzz=16

nxy:nx*ny

nxyz=nx*ny*nz
nxyzz=nx*ny*nzz

parameter
parameter
parameter
parameter
parameter
parameter
parameter

c nxy=nx*ny
nxyz:nx*ny*nz
c nxyzusgs=nx*ny*nzusgs

O

rs(nxy), rp(nxy), mfs(nxy), mss(nxy), mgs(nxy)
us(nxy), up(nxy), rfs(nxy), rss(nxy), rgs(nxy)
st3rb2(nxy)

stf(nxy)

sts(nxy)

stg(nxy)

e1ev(nxyzz)

elev2(nxyz)

HK1_16(nxyzz)

HK_IVF(nxyz)

real
real
real
c real
real
c real
real
real
real
real

 

0

character*40
character*40
character*40
character*40
character*40
character*40
character*40
c character*40
c character*40
c character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40
character*40

c character*40

elevfil
elev2fil
usfil
upfil
rfsfil
rssfil
rgsfil
stffil
stsfil
stgfil
rsfil
rpfil
mfsfil
mssfil
mgsfil
st3rb2fil
HK1_16fil
HK_IVFfil
parfill

parfill, junk

c K values for facies from Weissmann junk

gk:864
sk=86.4
fk:0.0864

pk:0.0000001

221

C

O

O

O O O O

dx=400.
dy=400.
dz:0.5

Where are the data???????

print*,'input par file name?:' 1 Open input and output files
read(5,'(a40)') parfill
print*,'input file name:', parfill

open(1,file:parfill,status='old')

read(1,'(a40)') elevfil
read(1,'(a40)') elev2fil
read(1,'(a40)') usfil
read(1,'(a40)') upfil
read(1,'(a40)') rfsfil
read(1,'(a40)') rssfil
read(1,'(a40)') rgsfil
read(1,'(a40)') stffil
read(1,'(a40)') stsfil
read(1,'(a40)') stgfil
read(1,'(a40)') rsfil
read(1,'(a40)') rpfil
read(1,'(a40)') mfsfil
read(1,'(a40)') mssfil
read(1,'(a40)') mgsfil
read(1,'(a40)') st3rb2fil
read(1,'(a40)') HK1_16fil
read(1,'(a40)') HK_IVFfil

close(ll

open(18,file:'dbg.txt',status='unknown')
open(19,file='elevations',status='unknown')

Read data from ASCII file to fill arrays. Sequence boundaries!

print*,'reading Parfil including ASCII files from GMS--read elev'
read(*, '(a40)') eleva

open(1,file=elevfil,status:'old', form:'formatted')

print*, 'lst doloop'

do i=1, 7
read (1,'(a40)') junk
enddo
do k=1,nzz
do j:1,ny
do i:1,nx
ijk: i+((k-1)*nx*ny)+((j-1)*nx)
read (1,*) e1ev(ijk)
enddo
enddo
enddo
close(l)

222

print*, 'open elev2fil'
open(2,file=elev2fil,status='old', form:'formatted')

do k=1,nz
do j:1,ny
do i=1,nx
ijk: i+((k—1)*nx*ny)+((j-1)*nx)
read(2,*) e1ev2(ijk)
enddo
enddo
enddo
close(2)

Open and read Upper Turlock Lake surface with Riverbank Valley
print*, 'open usfil'

read(*, '(F15.5)') utl_rb1_300

open(3,file:usfil,status='old', form:'formatted')

do j:1,ny
do i=1,nx
ij=i+((j-1)*nx)
read(3,*) us(ij)
enddo
enddo
close(3)

print*, 'open upfil'
open(4,file:upfil,status:'old', form:'formatted')

do j:1,ny
do i:l,nx
ij=i+((j—1)*nx)
read(4,*) up(ij)
enddo
enddo

close(4)

print*, 'open rfsfil'
open(S,file=rfsfil,status:'old', form:'formatted')

do j:1,ny
do i=1,nx
ij=i+((j—1)*nx)
read(5,*) rfs(ij)
enddo
enddo

close(S)

print*, 'open rssfil'
open(6,file=rssfil,status='old', form:'formatted')

do j:1,ny

do i:1,nx
ij=i+((j-l)*nx)

223

read(6,*) rss(ij)
enddo
enddo

close(6)
print*, 'open rgsfil'
open(7,file=rgsfil,status='old', form:'formatted')

do j:1,ny
do i:l,nx
ij=i+((j-1)*nx)
read(7,*) rgs(ij)
enddo
enddo

close(7)

print*, 'open rsfil'
open(ll,file:rsfil,status='old', form:'formatted')

do j:1,ny
do i:1,nx
ij=i+((j-1)*nx)
read(11,*) rs(ij)
enddo
enddo

close(ll)

print*, 'open rpfil'
open(12,file=rpfil,status='old', form:'formatted')

do j:1,ny
do i=1,nx
ij=i+((j-1)*nx)
read(12,*) rp(ij)
enddo
enddo

close(12)

print*, 'open mfsfil'
open(13,file=mfsfil,status='old', form:'formatted')

do j:1,ny
do i:1,nx
ij:i+((j-1)*nx)
read(13,*) mfs(ij)
enddo
enddo

close(13)

print*, 'open mssfil'
open(14,file=mssfil,status:'old', form:'formatted')

224

do j:1,ny
do i=1,nx
ij:i+((j-1)*nx)
read(14,*) mss(ij)
enddo
enddo

close(14)

print*, 'open mgsfil'
open(15,file:mgsfil,status='old', form:'formatted')

do j:1,ny
do i:1,nx
ij=i+((j-1)*nx)
read(15,*) mgs(ij)
enddo
enddo

close(IS)

print*, 'open st3rb2fil'
open (16,file: st3rb2fil, status:'old', form:'formatted')

do j:1,ny
do i=1,nx
ij:i+((j-1)*nx)
read(16,*) st3rb2lij)
enddo
enddo

close(16)

print*, 'open HK1_16fil'
open (17,file:HK1_16fil,status:'old', form:'formatted')

do k=1,nzz
do j:1,ny
do i:1,nx
ijk=i+((k-1)*nx*ny)+((j-1)*nx)
read(l7,*) HK1_16(ijk)
enddo
enddo
enddo

close(17)

c fdﬂll in gms final grid array with proper hydraulic conductivity

do k=1,nz
do j:1,ny
do i=1,nx
ij=i+((j-1)*nx)
ijk:((k-1)*nx*ny)+i+((j-1)*nx)

225

c Fill in USGS K values for bottom cells (USGS cells 12 through 16)
1f(k.ge.23) then
kusgs:k—11
ijkusgs:((kusgs-1)*nx*ny)+i+((j-1)*nx)
HK_IVF(ijk)=HK1_16(ijkusgs)
else

c Calculate elevation of cell and fill in USGS value as 'default'

c ijk2 : cell number located below ijk

c elevation : elevation of center of cell for rediscretized grid
ijk2:(k*nx*ny)+i+((j-1)*nx)
elevation : ((elev2(ijk)-elev2(ijk2))/2)+elev2(ijk2)

c Find out which usgs cell we are in and assign usgs K or T value
do kk:1,12
ijkk:((kk-1)*nx*ny)+i+((j-1)*nx)
ijkk2:(kk*nx*ny)+i+((j-1)*nx)

if(elevation.1e.elev(ijkk).and.e1evation.gt.elev

* (ijkk2)) then
HK_IVF(ijk):HK1_16(ijkk)
write(15,'(4i4)') i,j,k,kk
endif
enddo

c Fill in UTL Formation Paleosol
c if(e1evation.le.us(ij).and.e1evation.ge.up(ij)) gms(ijk): pk

c Fill in Riverbank Formation Paleosol ModL IV
c if(e1evation.le.rs(ij).and.e1evation.ge.rp(ij)) gms(ijk): pk

c fill in Riverbank formation with RB IV (st3rb2 : 2 : inside tuol rb
‘valley)
if(st3rb2lij).eq.2) then
if(e1evation.lt.rfs(ij).and.e1evation.gt.us(ij)) then
if(e1evation.le.rfs(ij).and.e1evation.gt.rss(ij)) then
HK_IVF(ijk)= fk
else if (elevation.1e.rss(ij).and.e1evation.gt.rgs(ij))
then
HK_IVF(ijk)= sk
c write(15,'(a6,e9.4)')'sands ',gms(ijk)
else if(e1evation.le.rgs(ij)) then
HK_IVF(ijk): gk
endif
endif
endif

(2 Fill in the Modesto formation with Mod IVF
if(e1evation.gt.rs(ij)) then

if (elevation.1e.mfs(ij).and.e1evation.gt.mss(ij)) then
HK_IVF(ijk)= fk
else

if (elevation. le.mss (ij) .and.e1evation.gt .mgs (ij) ) then
HK_IVF(ijk)= sk
else if (elevation.1e.mgs(ij)) then

226

HK_IVF(ijk)= gk

endif
endif
endif
enddo
enddo
enddo

c Prepare the gms input files

print*, 'printing gms files'
open(20,file=HK;IVFfil,status='unknown')

c writing GMS full grid file

do k=1,nz
do j:1,ny
do i=1,nx
ijk:i+((j—1)*nx)+((k-1)*nx*ny)
write(20,'(E9.4)') HK;IVF(ijk)
enddo
enddo
enddo

print*,'gms files completed'
print*,'Goodbye!'

close(20)
close(18)
close(19)

I l
print‘k’ ****************************

print*,'GMS files written.‘

print*’ '****************************I

print*,'(:'

stop
end

227

Appendix H: Code to Calculate Kv and Kh/Kv

program KVcalc

c Program to create an output ﬁle to be

c used in 3D realization for a GMS 5.1

c groundwater model.

c DO NOT USE WITHOUT ADJUSTMENT FOR SPECIFICS

c

c NOTE: This code uses a parameter ﬁle.

c

c PARAMETERS:

c

c nx= number of rows

c ny= number of columns

c nz= my vertical discretization;

c nzusgs= number of layers in original USGS model from Steve Phillips in July of 2004

c

c Elevation from original USGS 16 layer model= elevﬁl

c Elevation from rediscretized 27 layer model= elev2ﬁl

c

c This program is intended to generate KV and Kh/Kv parameters for the 27 layer model generated
c * from the rediscretization of a 16 layer USGS model.

c kvert.dat = vertical K calculated from the vcont values in the original USGS model (kvert= vcont/

thickness)

c thickness= the distance between the center of the two cells for which kvert is being calculated

c khkv.dat = kvert from original USGS reassigned to my layers (kvert is based on the material, not the cell
thickness)

c vcontdat = leakance from the original USGS model

c Declaration of variables

parameter nx=153

parameter ny=137

parameter nzusgs=l6

parameter nz:27

parameter nxy=nx*ny

parameter nxyz=nx*ny*nz
parameter nxyzusgs=nx*ny*nzusgs

real e1ev(nxyzusgs)
real elev2(nxyz)
real vcont(nxyzusgs)
real corc01(nxy)
real thick(nxyzusgs)
real kh(nxyz)
real hlfthick(nxyzusgs)
real kv l_l 6(nxyzusgs)
real kvl_27(nxyz)
real khkv(nxyz)

228

character‘40 elevﬁl
character*40 elev2ﬁl
character*40 vcontﬁl
character*40 corc01ﬁl
character‘40 thickﬁl
character*40 khﬁl
character*40 hlfthickﬁl
character*40 kvl_27ﬁl
character*40 khkvﬁl
character‘40 parﬁll

c Open parﬁl (input)

print*,'input par ﬁle name?:'
read(5,'(a40)') parﬁll
print*,'input ﬁle name:', parﬁll
open( 1 ,ﬁle=parﬁl l ,status='old')

 

read(1,'(a40)') elevﬁl
read(1,'(a40)') elev2ﬁl
read(1,'(a40)') vcontﬁl
read(1,'(a40)') corc01ﬁl
read( 1,'(a40)') thickﬁl
read(1,'(a40)') khﬁl
read(1,'(a40)') hlfthickﬁl

close( 1 )

c Read data from ASCII ﬁle to ﬁll arrays.
print*,'reading Parﬁl'
open( 1 ,ﬁle=elevﬁl,status='old', form:'formatted')
do k=1,nzusgs

do j=l,ny
do i=1,nx
ijk= i+((k-1)*IIX*ny)+((i-1)*IIX)
read (1,*) e1ev(ijk)
enddo
enddo

enddo
close( 1)

open(2,ﬁle=elev2ﬁl,status='old', form:'formatted')

do k=1,nz
do j=l,ny
do i=1,nx
ijk= i+((k-1)*HX*ny)+(0-1)*IIX)
read (2,*) elev2(ijk)
enddo
enddo
enddo

close(2)

229

open (l7,ﬁle=vcontﬁl,status='old', fonn='formatted')
do k=1,nzusgs
do j=l,ny
do i=1,nx
iJ'k=i+((k-1)"'Im"'ny)+(0-1)"‘rv<)
read( 17,*) vcont(ijk)
enddo
enddo
enddo

close( 17)

open (3,ﬁle=corc01ﬁl,status='old', form:'formatted')
do j=1 ,ny
do i=1,nx
ij=i+((j-1)*nx)
read(3,*) corc01(ij)
enddo
enddo

close(3)

open (4,ﬁle=thickﬁl,status='old', form:'formatted')
do k=1,nzusgs
do j=l,ny
do i=1,nx
ijk=i+((k-1)*nX*n)')+(0-1)‘nX)
read(4,*) thick(ijk)
enddo
enddo
enddo

close(4)

open (5,ﬁle=khﬁl,status=’old’, form=’formatted')
do k=1,nz
do j=l,ny
do i=1,nx
ijk=i+((k-l)*nX*n)')+(0-1)*IIX)
read(5,*) kh(ijk)
enddo
enddo
enddo

close(5)

open (6,ﬁle=hlﬁhickﬁl,status='old', form:'formatted')
do k=1,nzusgs
do j=l,ny
do i=1,nx
ijlr—i+((k-1)*nx*ny)+(d-1rnx)
read(6,*) hlfthick(ijk)
enddo
enddo
enddo

230

close(6)

c Calculate Kv for the 16 layer USGS model from the vcont values.
c iij = cell number located below ijk
print*,'Create KV1_16'
do k=1,nzusgs
do j=l,ny
do i=1,nx
ijk=((k-1)*nX*ny)+i+((i-1)*IIX)
ijk2=(k*nx*ny)+i+((j- l )*nx)

if((k.eq.7.or.k.eq.8).and.(corc0l(ij).eq.1)) then
k8=8
c kcorc=0.0013
ijk8=(k8*nx*ny)+i+((j—l)*nx)
kvl_l 6(ijk)=vcont(ijk8)*hlfthick(ijk8)

c kvl_16(ijk)=kcorc
else
kvl_16(ijk) = vcont(ijk)*(hlfthick(ijk)+hlfthick(ijk2))
endif

open( 15,ﬁle='kvl_16.dat',status='unknown')
write(15,'(E14.5)') kvl_16(ijk)

enddo
enddo
enddo
close( 15)

c Calculate KV1_27 from the vcont values in the original USGS model.
print*,'Create KV1_27 and KH/KV'

do k=1,nz
do j=1 ,ny
do i=1,nx
il=i+((i-1)*nX)
ilk=((k-1)*nX*ny)+i+((i-1)*DX)
ijk2=(k*nx*ny)+i+((j- l)*nx)

c Fill in values for KV1_27 with USGS values for bottom layers 23 through 27 (USGS cells 12 through 16)
c These cells are the same size and thickness as in the original model.

c KV can be directly assigned after multiplying by the thickness.

c kusgs = the layer number in the USGS model

c ijkusgs = reading array at the speciﬁed layer in USGS model

c ijkusgsZ = reading array at the layer below ijkusgs

c print*,'Fill in USGS KV1_27 values for bottom layers’
if(k.ge.23) then
kusgs=k~11
ijkusgs=((kusgs-1)*nx*ny)+i+((j-l)*nx)
c ijkusgs2= ((kusgs)*nx*ny)+i+((j-l)*nx)
kvl_27(ijk)=kvl_16(ijkusgs)

else
Calculate elevation of center of cell and ﬁll in correlative USGS value

231

c elevation = elevation of center of cell for rediscretized grid
elevation = ((elev2(ijk)-elev2(ijk2))/2)+elev2(ijk2)

c Find out which USGS cell we are in and assign KV value calculated from vcont
c Layer 8 is the Corcoran clay in the USGS model. KV is calculated differently according to...
c ...'kvc_calib_merten.t‘ from the USGS model.

c print*,'Find out which usgs cell we are in and assign Kvl_27'
do kk:1,12
ijkk=((kk-1)*nX*ny)+i+(0-1)*HX)
iikk2=(kk*nX*n)')+i+((i-1)*IIX)

if(e1evation.le.elev(ijkk).and.e1evation.gt.elev
* (ijkk2)) then
kvl_27(ijk)= kvl_16(ijkk)
endif
enddo

c write(15,'(4i4)') i,j,k,kk
endif

enddo
enddo
enddo

c Calculate Kh/Kv
print*,'Calc Kh/Kv'
do k=1,nz
do j=l,ny
do i=1,nx
ilk=((k-1)*nx*ny)+i+(o-1)*nx)

if(kvl_27(ijk).eq.0) then
khkv(ijk) = kh(ijk)/ 0.0000001
else
khkv(ijk)= kh(ijk)/ kvl_27(ijk)
endif

enddo
enddo
enddo

c writing GMS full grid ﬁle
print“, 'writing GMS full grid ﬁle'
open( 1 8,ﬁle='kv1_27ﬁl.dat',status='unknown')
open(20,ﬁle='khkv.dat',status='unknown')

do k=1,nz
do j=l,ny
do i=1,nx
ijk=i+(o-1)*nx)+«k-1)*nx*ny)
write(18,'(E14.5)') kvl_27(ijk)
write(20,'(E 14.5)') khkv(ijk)

232

enddo
enddo
enddo

print*,'gms ﬁles completed'
print*,’Goodbye!'

close( 1 8)
close( 1 9)
close(20)

print*,'#*#***********##**********Ill*1

print*,'GMS ﬁles written.‘
print*,'***************************I."

print*,'(:'

stop
end

233

Appendix I: General Head Boundary Code

PROGRAM ghb_all

MODIFIED from USGS kvc_calib_merten.f from Steve Phillips July 11, 2005 via e-mail.
Used to remake ghb ﬁles with new discretization for use in lpf model.

Deleted beginning of program-~only need to calc. the ghb ﬁle for model.

"*Lines that begin with asterisks are edits by Amy Lansdale.

***TO USE: (I) ALWAYS check input horizontal hydraulic conductivity ﬁle #29 on line 61
(2) Change input Kv ﬁle #99 in line 64

0000000000

"*Changed numbers of layers and added some variables.
integer row,col,nsteps,lay,iunit,nghb,ghbloc(200,200),
+ bound(200,200,20),corc01(200,200)
real KH(153,137,27),KV1_27(153,137,27), KH2(153,137,27)
+ ,thick(153,137,27),ghbhead(200,200,80),ghbcond(200,200,80),
+ botelev(153,137,40),top(l53,137,40),head,ghbdist,
+ persat(153,l37,40)

c grid geometry
nrow=153
ncol=137
nlay=27
ny=137
nx=153
nz:27

ncorr=0
print“, 'initialize variables'
initialize variables
5 do 10 lay=1,nlay
do 10 row=1,nrow
do 10 col=l,ncol
ghbhead(row,col,lay)=0.
ghbcond(row,col,lay)=0.
KH2(row,col,lay)=0.
persat(row,col,lay)=0.

0

10 continue

open(95,ﬁle='ghb27ﬁl.ghb')
open(101,ﬁle='KH2ﬁl.dat')

c open ﬁles and read in external data

print“, 'open ghb_locations.dat'
open(94,ﬁle='ghb_locations3.dat')

234

read(94,'()')
do 40 row=1,nrow

read(94,’(200i 1 )‘) (ghbloc(row,col),col=l ,ncol)
40 continue

c *****THIS MUST BE CHANGED FOR EACH GEOLOGIC SCENARIO OF IVF (rbmodL, rbstmodL,
rbmods, rbstmods).******"'*****
print", 'open HK_IVFﬁl, thick, and KV1_27'
c ***This input HK ﬁle should be the HK ﬁle for each of the various IVF scenarios. CHECK before each
run of this code.
open(29,ﬁle=’Khrbstmlless50ﬁnalnodat.dat')
c *** "Thickin" should be recalculated for various discretizations. See 'Thickness.f‘ code to calculate.
open(30,ﬁle='thick27.dat')
open(99,ﬁle='kv l_27rbshnlless$ 0.dat')

do 20 lay=1,nlay
do 20 row=l,nrow

read(29,*) (KH(row,col,lay),col==1,ncol)
read(30,*) (thick(row,col,lay),col=l ,ncol)
read(99,*) (KV1__27(row,col,1ay),col=l ,ncol)

20 continue

c "*Calculate the K vertical for layer 1 to 2 and 2 to 3 (original layer 1 to 2) only (needed below in USGS

code) using the harmonic mean...
c prim”, 'Calc KH2'

do 80 lay=1,nlay
do 90 row=1,nrow
do 100 col=1,ncol
c ...***check to see if KH value is equal to zero to prevent zero divide. Make KH a very small number.
c ...***most of the cells with zeros are inactive.
if (KH(row,col,lay).eq.0) then
KH2(row,col,lay)= 0.000000000000001
elseif (KH(row,col,lay).ne.0) then
KH2(row,col,lay)= KH(row,col,lay)
endif

100 continue

90 continue
80 continue

print“, 'Write KH2'

do 175 lay=1,nlay

235

do 175 row=l,nrow
do 175 col=1,ncol
write(101,'(3i5,g20.12)') lay,row,col, KH2(row,col,lay)

175 continue

close(101 )

print“, 'ghb calc'

c *** generate ghb ﬁle; ghbloc indicates boundary type (4&5 are lateral,
c *** 2 is river)
c
c Vertical gradient of 0.05 speciﬁed along N and S lateral boundaries;
c downward to layer 33 (***was 9 in original model), and upward below 33 (“*was 9 in original
model)
c -- max head below 33 ("*was 9 in original model) = water table
c
c constants:

bedthick=l .0

cellsize=400.

rivwidth=25.

ghbdist=400.

nghb=0

gradient=0.05

vkrivmult=10.

open(93,ﬁle='ghb27inl_ﬁnalEDIT.dat')
open( 105, ﬁle='totthick.dat')
c open(106, ﬁle='totthick2.dat')

200 read(93,*,eud=290) lay,row,col,head

c print", 'downstream river segments and western boundary'
c downstream river segments and western boundary
if((ghbloc(row,col).eq.2.and.lay.eq.1).or.ghbloc(row,col).eq.5)
& then
nghb=nghb+l
ghbhead(row,col,lay)=head

c ****Changed the original:

c ghbcond(row,col,lay)=vcont(row,col,lay)*((thick(row,col,lay)/
c 2.)+(thick(row,col,lay+1)/2.))*((cellsize*rivwidth)/bedthick)
c *vkrivmult to...see below...

if(ghbloc(row,col).eq.2.and.lay.eq. 1) then
ghbcond(row,col,lay)= KVl_27(row,col,lay)*((cellsize*rivwidth)
& /bedthick)*vkrivmult

236

 

c ***Layer in following if statement (now layer 16) was 7 (lay above C.C) in original code...

c ..."REMOVE" is the line that was used to calc K. Not needed here because all values in...

c... KH ﬁle are actual conductivities, not K and T values as was true..

c...in original code (lay 8-16 were T values). K values were calc by dividing by thickness in GMS.

else
ghbcond(row,col,lay)=KH2(row,col,lay)*thick(row,
& col,lay)*cellsize/ghbdist
endif
c ***REMOVED KH(row,col,lay)=KH(row,col,lay)/thick(row,col,lay)

c north and south lateral boundary
c print“, 'north and south lateral boundary'

else if(ghbloc(row,col).eq.4) then

nghb=nghb+1
c conductance ﬁrst
c *"layer was 7, change to layer 16 which is equivalent to the layer above CC
c print*,'conductance ﬁrst'

c ***below conditional statement separates the unconﬁned portions of the aquifer (above Corcran clay)
from the conﬁned...
c... portions of the model (Corcran clay and below). Because ALL of my KH values are K values...
c... not K and T values, I maintianed the USGS code format but multiply by thickness in both portions of
the statement.
if(lay.1e.16) then
ghbcond(row,col,lay)=KH2(row,col,lay)*thick(row,col,lay)*
& cellsize/ghbdist
else
ghbcond(row,col,lay)=KH2(row,col,lay)*thick(row,col,lay)*cellsize/
& ghbdist
endif

c print“, 'now head'

cc now head
totthick=0
c ****Edit the application of a gradient to the head from the methods used in the USGS.
c ****The gradient is subtracted (to generate downward ﬂow) and applied from layer 1 to
c ****...17 (the Corcoran Clay). Then the layer below the Corcoran clay (where the most
c ****...pumping occurs), is set equal to the head in the Corcoran Clay.
if(lay.eq. 1) then
ghbhead(row,col,lay)=head
210 continue
else if(lay.1e.17) then
do 220 k=l,lay
if(k.eq.1) then
thickness=thick(row,col,k)/2.
else if(k.eq.lay) then
thiclmess=thick(row,col,k)/2.
else

237

thickness=thick(row,col,k)
endif
totthick=totthick+thickness

220 continue
ghbhead(row,col,lay)=ghbhead(row,col, 1 )-(totthick* gradient)
write (105,*) lay,row,col,totthick
c totthick=0

c ***"‘The original USGS gradient had the head in the Corcoran clay higher than...
c ****...the head in the cells above it. Added this lF/THEN statement to rectify that.
else if(lay.eq. 1 8) then
ghbhead(row,col,lay)=ghbhead(row,col,1 7)
write (105,*) lay,row,col

else

c *"Change to layer 19. Below layer 18, the gradient is positive (generates an upward ﬂow).
do 240 k=19,lay
if(k.eq.l9) then
thickness=thick(row,col,k)/2.
else if(k.eq.lay) then
thickness=thick(row,col,k)I2.
else
thickness=thick(row,col,k)
endif
totthick=totthick+thickness

write (106,*) lay,row,col,totthick

240 continue
ghbhead(row,col,lay)=ghbhead(row,col, l 8)+(totthick* gradient)
write (105,*) lay,row,col,totthick
c totthick=0

if( ghbhead(row,col,lay). gt. ghbhead(row,col, l ))
& ghbhead(row,col,lay)=ghbhead(row,col,1)
endif
endif

write (1053') lay,row,col,totthick

goto 200
c output ghb ﬁle, rivers ﬁrst
c
290 k=l

c **** Change the iunit to 40 (was 2 in the old code). This is where ﬂow budget will be recorded.
c **** When the ﬁle is opened by GMS and resaved, it will rewrite 40.

iunit=40

np=0

write(95,'(2i5)') nghb,iunit

238

write(95,'(2i5)') nghb,np
do 300 i=1,nrow
do 300 j=l,ncol
if(ghbloc(ij).eq.2.) write(95,'(3i5,2g20.12)') k,i,j,
& ghbhead(i,j,k),ghbcond(i,j,k)
300 continue
do 350 k=1,nlay
do 350 i=1,nrow
do 350j=1,ncol
if(ghbloc(ij).eq.4.or.ghbloc(i,j).eq.5) write(95,'(315,2g20
& .12)') k,i,j,ghbhead(i,j,k),ghbcond(i,j,k)
350 continue
print*,'****************************'
print*,'Files written.‘
print*"************************#***'
stop
end

239

Appendix J: Well Code

program wel_calib_all
c *"Lines that begin with astericks are edits by Amy Lansdale from the original code by Steve Phillips.
c *****TO USE: Change ﬁle #34 in line 36 to input horizontal hydraulic conductivity.
c

c
c SPP 12/04 -- modiﬁed to distribute pumpage to adjacent layers for

c wells pumping > spec rate in cells with < spec % of coarse materials
c SPP 02/03 -- now called by runss_calib.sh

c SPP 12/02 -- creates well ﬁle for MERSTAN SS model

c

c***Changed the array sizes to accomodate the 40 layer model. (Number of layers was set to 20 for the 16
layer model.)
integer row,col,lay,ibound(200,200),icorc(200,200)
real pumpage,tperf,bperf,x,y,bot(200,200,40),
& HK(200,200,40),tequiv(40),pumplay(40)
character wellid*8,runtyp* l,on1ycorc*1

nrow=153

ncol= l 37

nlay=27
xorigin=-83795.7
yorigin=1572727.3
nwells=0
totpump=0.
onlycorc='y'

read if single or multiple run, passed from script
read(*,*) runtyp
mntyp=lsi

00000

open(30,ﬁle='wel.in')
open(3 1,ﬁle='elevbot1_27nodat.dat')
open(35,ﬁle='elevtopl_27nodat.dat')
open(32,ﬁle='ibound.40')
open(33,ﬁle=‘corc01.32‘)
c “*"THIS FILE MUST BE CHANGED FOR EACH GEOLOGIC SCENARIO OF IVF (rbmodL,
rbstmodL, rbmods, rbstmods).*"'*******"*
open(34,ﬁle='KHrbstmlplusSOﬁnalnodat.dat')
c output ﬁles
open(40,ﬁle='wel.tmp')
open(4l,ﬁle='we127.wel')

c *****K and T input ﬁles--REMOVED ALL 07/21/05

c read ﬁrst array for "bot", which is top layer 1, ibound, icorc
read(32,'( )')
do 100 i=1,nrow
read(35,‘) (bot(i,j, l),j=1,ncol)
read(32,*) (ibound(i,j),j=1,ncol)
read(33,‘) (icorc(i,j),j=1,ncol)
100 continue

240

c read rest of bottoms, and k
do 1501=l,nlay

do 150 i=1,nrow
read(31,*) (bot(i,j,l+1),j=1,ncol)

c iunit=50+l

c read(iunit,*) (HK(i,j,l),j=l,ncol)
150 continue

c

c read texture
do 160 lay=1,nlay

do 160 row=l,nrow
read(34,‘) (HK(row,col,lay),col= 1 ,ncol)
160 continue

read through list of wells one by one, determine equivalent transmissivities
to distribute by layer, and build well ﬁle
*“ again, note that "bot" array starts with top of layer 1 ***

00000

read(30,'( )')
200 read(30,‘,end=99) wellid,x,y,tperf,bperf,pumpage
c
c transform coordinates, and calculate row & column

deltax=xorigin-x

deltay=y-yorigin

tdeltax=deltax
xdist=((deltay"'.601815023152048)-(deltax*.798635510047293))
ydist=((deltay*.798635510047293)+(tdeltax"'.601815023152048»
col=int(xdist/400.)+l

row=153-int(ydist/400.)

sweep through layers, calculating effective transmissivity based on thickness
of screened interval within each layer and K value for layers 1-7; layers 8-16
"K" values are "T" already

000000

if(ibound(row,col).ne.0) then
sumtequiv=0.
do 500 lay=1,nlay
if((tperf.gt.bot(row,col,lay+1)).and.(bperf.lt.bot(row,col,lay))) then
if(tperf.lt.bot(row,col,lay)) then
zl=tperf
else
2 l =bot(row,col,lay)
endif
if(bperf. gt.bot(row,col,lay+ l )) then
22=bperf
else
22=bot(row,col,lay+1)
endif
c The old way
c tequiv(lay)=K(row,col,lay)
c if(lay.1e.7) tequiv(lay)=tequiv(lay)*(z1-22)

241

c sumtequiv=sumtequiv+tequiv(lay)

c The new way
c****Change old layer 7 in USGS model to layer 16, which is still the layer above the Corcran Clay in the
newly...
c...discretized model.
if(lay.1e.16) then
tequiv(lay)=HK(row,col,lay)*(z1 -22)

else
tequiv(lay)=HK(row,col,lay)*((z1~22)/
& (bot(row,col,lay)-bot(row,col,lay+1)))
endif
sumtequiv=sumtequiv+tequiv(lay)
else
tequiv(lay)=0.
endif

500 continue
now distribute pumpage by layer, write to ﬁle, and read next well
$$$ option 1: correct for wells screened only in corcoran
it(onlycorc.eq.'y') then

ﬁrst check for wells screened only in corcoran (unrealistic) and,
if so, distribute pumpage to layers 7 - 9
c*"***Change 8 (USGS Corcran Clay) to 17, 7 (USGS lay abv Corcran Clay) to 16,...
c ....and 9 (USGS lay blw Corcran Clay) to 18.
if((tequiv(l7).ne.0.).and.(tequiv(16).eq.0.).and.(tequiv( 18).eq.
& 0.)) then
if(icorc(row,col).ne.0) then
thickl 6=bot(row,col, l 6)-bot(row,col, l 8)
tequivl 6=HK(row,col, 16)*thick16
tequivl 7=HK(row,col, l 7)
tequivl 8=HK(row,col, l 8)
sumtequiv=tequiv l 6+tequivl 7+tequivl 8
tequiv( l 6)=l .
tequiv(18)=l.
c Layer 16 (USGS layer 7)
pump=pumpage*(tequiv1 6/sumtequiv)
write(40,'(3i5,g15.6)') l6,row,col,pump
nwells=nwells+l
totpump=totpump+pump
c Layer l7 (USGS layer 8--CC)
pump=pumpage*(tequivl 7/sumtequiv)
write(40,'(3i5,g15.6)') l7,row,col,pump
nwells=nwells+l
totpump=totpump+pump
c Layer 18 (USGS layer 9)
pump=pumpage*(tequiv1 8/sumtequiv)
write(40,'(3i5,g15.6)') 18,row,col,pump
nwells=nwells+l
totpump=totpump+pump
else
pump=pumpage
c Layer l7 (USGS layer 8)

C
C
C
C
C
C
C
C
C

242

write(40,'(3i5,g15.6)') l7,row,col,pump
nwells=nwells+l
totpump=totpump+pump
endif
else

Check for other wells pumping in single layer at > speciﬁed rate and
within cells with < speciﬁed % coarse; if so, distribute pumpage
proportionally to adjacent layers. If adjacent layers already pumping,
activate nearest layers above and below.

$$$ Speciﬁed values:

00000000

specrate=-1000.
specpct=0.8007E+01
c*******Change 'specpct=10.' which refers to the corase\ﬁne fraction from the initial USGS input...
c ..... to 'specpct=0.8007E+Ol'. This is the calculated HK of a cell with a coarse\f'me fraction of 10...
c ..... CALCULATE HK from htextr (corase\ﬁne fraction)-->
c ..... htextr=10; fcoarse=10/100=0.1; ffme=l-0.l= 0.9...
c ..... To calc. use arithmetic mean from page 6 in 'kvc_calib-merten.f‘-->
c ..... Ksand and Kclay from kvc_calib_merten_s.dat'
c ....HK = (ffcoarse)(Ksand)+(ffﬁne)(Kclay)= (0.1 *80)+ (0.9*0.008)= 8.0072 = 0.8007E+01

c $$$
580 do 590 lay=1,nlay
pumplay(lay)=0.
590 conﬁnue
do 6001ay=1,nlay

if(tequiv(lay).ne.0.) then
pumplay(lay)=purnpage*(tequiv(lay)/sumtequiv)
if((pumplay(lay).lt.specrate).and.(HK(row,col,lay)
& .lt.specpct)) then

thick-=bot(row,col,lay)-bot(row,col,lay+ 1 )

tequiv(lay)=HK(row,col,lay)

if(lay.1e. 16) tequiv(lay)=tequiv(lay)*(thick)

sumtequiv=tequiv(lay)

c activate closest non-pumping layers, recalculating tequiv
l=lay-1
610 if(tequiv(1).eq.0.) then

thick=bot(row,col,l)-bot(row,col,l+1)
tequiv(l)=HK(row,col,l)
if(l.le. 1 6) tequiv(l)=tequiv(l)*(thick)
sumtequiv=sumtequiv+tequiv(l)

else
thick=bot(row,col,l)-bot(row,col,1+1 )
tequiv(l)=HK(row,col,l)
if(l.le.16) tequiv(l)=tequiv(1)"'(thick)
sumtequiv=sumtequiv+tequiv(l)

l=l-1

goto 610

endif

l=lay+1

620 if(tequiv(l).eq.0.) then

thick=bot(row,col,l)-bot(row,col,l+ l )
tequiv(l)=HK(row,col,l)
if(l.le. l 6) tequiv(l)=tequiv(l)*(thick)

243

sumtequiv=sumtequiv+tequiv(l)
else
thick=bot(row,col,l)-bot(row,col,1+1)
tequiv(l)=I-IK(row,col,l)
if(l.le. 16) tequiv(l)=tequiv(l)*(thick)
sumtequiv=sumtequiv+tequiv(l)
l=l+1
goto 620
endif
endif
endif
600 continue
c
c Write out pumpage
c
do 650 lay=1,nlay
if(tequiv(lay).ne.0.) then
pump=pumpage‘(tequiv(lay)/sumtequiv)
write(40,'(3i5,g15.6)') lay,row,col,pump
nwells=nwells+l
tomump=totpump+pump
endif
650 continue
endif
endif
goto 200

end of input ﬁle has been reached, so write headers to well ﬁle,
and transfer previous output to this ﬁle

0 ()(5 0

99 nunir=2
nparam=0
rewind (40)
write(41,'(2i5)') nwells,nunit
write(4 l ,'(2i5)') nwells,nparam
do 1000 i=1,nwells
read(40,'(3i5,g15.6)') lay,row,col,pump
write(4l,'(3i5,g15.6)') lay,row,col,pump
1000 continue
write(*,'(a,g20.6)') 'total pumpage = ',totpump

prim" "tut*ttttt*ttttttttttttttttttt'
9

print*,'Files written.‘
t'tlt ** t t ** ** ** **W
prh“ , * *tti * t *# it #

stop
end

244

 

 

 

   
  

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