OVERDUE FIN_E$: 25¢ perm per ital: fu‘w * mumps LIBRARY MATERIALS: 7 9“ ' g 3",,” Place in book return to remove ”' 4 'chargq from circulation records { NUTRIENT EXPORT COEFFICIENTS: AN EXAMINATION OF SAMPLING DESIGN AND NATURAL VARIABILITY WITHIN DIFFERING LAND USES By Michael N. Beaulac A THESIS Submitted to Michigan State University in partia] fu1fi11ment of the requirements for the degree of MASTER OF SCIENCE Department of Resource Deve10pment 1980 dictat Off, r Nita: varie': error! ABSTRACT NUTRIENT EXPORT COEFFICIENTS: AN EXAMINATION OF SAMPLING DESIGN AND NATURAL VARIABILITY WITHIN DIFFERING LAND USES By Michael N. Beaulac Lake management strategies and recent environmental legislation dictate that non point nutrient sources, associated with stormwater run- off, must be assessed. Estimation of nutrient flux is highly com— plicated by watershed and climatic factors which contribute to natural variability. Sampling design concepts, required to l) reduce sampling error, and 2) adequately account for natural variability, are examined. Nutrient flux is assessed through 1) an extensive literature review of nutrient export studies, 2) an examination and screening of nutrient export coefficients according to sampling design criteria, and 3) compilation of these coefficients according to land use. The ecological mechanisms within each land use influencing the magnitude of nutrient flux are discussed. The cross sectional and longitudinal variability of the compiled coefficients are examined through applica- tion to a hypothetical watershed. 3ceam': finds 91 newt Ste ACKNOWLEDGMENTS This research was supported by a grant from the National Oceanic and Atmospheric Administration, grant #03-78-BOl-109, and by funds provided by the Michigan State University Agricultural Experi- ment Station. I would like to acknowledge the contributions made by Janine Niemer, Sue Watt and Barb Visser for typing this document and to Paul Schneider for graphics. Special thanks is also extended to my friends Jonathan T. Simpson, V. David Lee, Ralph Ancil and Peter Paluch for their personal concern, criticisms, editorial advice and proofreading. I hope that our camaraderie continues beyond the academic setting. I expressly thank my committee members Dr. Kenneth H. Reckhow, Dr. Thomas Burton, and Dr. Eckhart Dersch for their contributions, comments and criticisms regarding this thesis. Special appreciation is extended to Dr. Thomas Burton, Dr. Darrell King and to my major professor Dr. Kenneth H. Reckhow. Dr. Burton contributed invaluable technical expertise and guidance during this research. Dr. King provided continued encouragement and ecological insights which formed the basis of sections of this study. Dr. Reckhow's knowledge, energy and high academic standards have contributed immeasurably to my research ability and professional competence. Additionally, our close working relationship has progressed to include close personal friendship. ii Fina! close com; nas been a cate this Finally, I wish to gratefully acknowledge the assistance of my close companion, Linda Wennerberg. Her love and untiring patience has been a major source of moral support. It is to her that I dedi- cate this thesis. 9—4 %: a TABLE OF CONTENTS Page LIST OF TABLES ......................... vi LIST OF FIGURES ........................ viii CHAPTER I. INTRODUCTION ..................... l The Problem ..................... l The Problem Solution ................ 5 II. NON POINT SOURCE SAMPLING DESIGN ........... 7 Introduction .................... 7 Diffuse Source Monitoring Deficiencies ....... 8 Systematic Quantification - Sampling Design ..... l0 III. FOREST LAND USE .................... 42 Introduction .................... 42 Geology ....................... 45 Vegetation, Soil Type and Climate .......... 47 Ecological Succession ................ 54 Disturbed Forested Systems ............. 56 IV. AGRICULTURAL LAND USE ................. 68 Introduction .................... 68 Soil Influences ................... 69 Fertilizer Effects - Commercial and Manure ..... 75 Tillage Practices .................. 78 Crop Types ..................... 80 Pasture and Range Land ............... 90 Feedlot and Manure Storage ............. 96 Watershed Size and Proximity to Lakes and Streams . .lO4 V. URBAN LAND USE .................... 113 Introduction .................... ll3 Hydraulic Characteristics .............. 115 Land Use/Cover Activities - Nutrient Sources . . . .ll7 Non-Event, Storm Sewer Contaminants ......... l20 Data Presentation .................. lZl iv PA 8’?! ”PM Wu GA." ‘Hn- ,5 £491.. T .l'bll CHAPTER VI. COMPARISON OF NUTRIENT EXPORT FROM VARIOUS LAND USES .................... The Phosphorus and Nitrogen Export Coefficients . . . Nutrient Export Variability: Cross Sectional versus Longitudinal .............. VII. AN APPLICATION OF NUTRIENT EXPORT COEFFICIENTS Introduction .................. Application Lake Watershed ........... VIII. SUMMARY AND CONCLUSIONS .............. Sampling Design ................. Comparison of Nutrient Export Coefficients from Differing Land Uses .............. Application of Nutrient Export Coefficients . . . BIBLIOGRAPHY APPENDIX Page 127 127 136 , 140 . . 140 . . 142 . . 158 . . 158 . . 162 . . 167 '- NL TABLE (.0 03014:- 10. ll. 12. 13a. 13b. 14a. 14b. 15. LIST OF TABLES Nutrient export from forested watersheds ....... Trophic classification scheme for soils ....... Predicted total phosphorus unit area loads for rural land, forested land and wetlands ....... Nutrient export from row crops ............ Nutrient export from non row cr0ps .......... Nutrient export from grazed and pastured watersheds Daily production and composition of livestock manure - feces and urine .............. Nutrient export from animal feedlots and manure storage ...................... Nutrient export from mixed agricultural watersheds . . . Nutrient export from urban watersheds ........ Land use areas in the Beau Lac watershed ....... Beau Lac summary statistics ............. Phosphorus export coefficients for high and low precipitation years ................ Nitrogen export coefficients for high and low precipitation years ................ Total phosphorus export for high and low precipita- tion years ..................... Total nitrogen export for high and low precipitation years ....................... Total phosphorus mass loading from nonpoint sources (NPS), sewage treatment plant (STP) and with 90% phosphorus removal ................. vi Page 63 74 81 86 92 97 107 122 144 147 149 150 151 152 155 71315 Alan-Phosp Abs-Nitro AZas-Phosp ASL-Nitrc F3a.--Phos; Bis-Him Baa-Phos NM-Nltr 1.56."th St UA-a‘htr SI APPENDIX TABLES TABLE Page A1a.-—Phosphorus export from forested watersheds ........ 191 A1b.--Nitrogen export from forested watersheds ......... 196 A2a.--Phosphorus export from row crops ............. 201 A2b.--Nitrogen export from row crops .............. 207 A3a.--Phosphorus export from non row crops ........... 212 A3b.-—Nitrogen export from non row crops ............ 215 A4a.--Phosphorus export from grazed and pastured watersheds . . 218 A4b.--Nitrogen export from grazed and pastured watersheds . . . 221 A5a.--Ph05phorus export from animal feedlots and manure storage ........................ 223 A5b.--Nitrogen export from animal feedlots and manure storage ........................ 225 .A6a.--Phosphorus export from mixed agricultural watersheds . . .227 l\6b.--Nitrogen export from mixed agricultural watersheds . . . . 232 l\7a.--Phosphorus export from urban watersheds ......... 237 I\7b.--Nitrogen export from urban watersheds .......... 241 vii "my ‘ w Turf I‘JUL 1. l. «Disso' SiO‘ 2.--Hydro to 3.--Haj0r for Tan-Relat sta ibs-ReTat su: 5a.--Hist< wa‘ Sb'T'Histi 5i.--Hist. wa' Etc-Hist wa 73-"Hist we 7b.-~Hist we LIST OF FIGURES FIGURE 1. 4a. 4b 5a. 5b. 6a. 6b. 7a. 7b. 8a. 8b. 9a. 9b --Dissolved and particulate nutrient response to the storm hydrograph .................. . --Hydrographic response of varying land uses to a storm event ..................... . --Major sites of accumulation and exchange within the forested ecosystem ................. —-Re1ationship of nutrient output to the successional status of ecosystems ................ .--Re1ationship of nutrient accumulation to ecological succession ..................... --Histogram of phosphorus export from forested watersheds ..................... --Histogram of nitrogen export from forested watersheds . . --Histogram of phosphorus export from row cropped watersheds ..................... --Histogram of nitrogen export from row cropped watersheds ..................... --Histogram of phosphorus export from non row cropped watersheds ..................... —-Histogram of nitrogen export from non row cropped watersheds ..................... --Histogram of phosphorus export from grazed and pastured watersheds ................. --Histogram of nitrogen export from grazed and pastured watersheds ................. --Histogram of phosphorus export from animal feedlots and manure storage ................. .--Histogram of nitrogen export from animal feedlots and manure storage ................. Page 20 23 44 57 57 66 67 84 85 88 89 94 95 101 102 '1 “1'3: rAJlJHb 13a.--H55 w 13b.--His W l!a.--His Ilb.--His 12. --The 4.--T0‘ 16- '-Lar 17' --L01 FIGURE Page lOa.--Histogram of phosphorus export from mixed agricultural watersheds ...................... 111 10b.—-Histogram of nitrogen export from mixed agricultural watersheds ...................... 112 11a.--Histogram of phosphorus export from urban watersheds . . 125 11b.--Histogram of nitrogen export from urban watersheds . . . 126 12. --The basic configuration of a box plot and comparison of two plots possessing significantly different medians ....................... 130 l3a.--Box plots of phosphorus export coefficients from various land uses .................. 132 13b.--Box plots of nitrogen export coefficients from various land uses .................. 133 14. --Total phOSphorus export from two corn cropped water- sheds illustrating variability over time ....... 137 15. --Surface water runoff from two corn cropped watersheds illustrating variability over time .......... 138 16. --Land use practices for the Beau Lac watershed ..... 143 17. --Low, most likely and high ph05phorus loading estimates for Beau Lac ..................... 157 ix War of mm rate. Th. iake wate ties with continued the Sum sources, and quam tern has WY ‘In 1 DOMULTO! R€!Chani5; law of EXIStS D The prob \ CHAPTER I INTRODUCTION Inland lakes are being used as water supply reservoirs, sources of recreation and other human related activities at an increasing rate. The extent and number of water uses is strongly dependent on lake water quality which is, itself, influenced by land based activi- ties within the drainage basin. To insure high water quality and continued multiple water use necessitates the proper management of the surrounding watershed and the control of point and non point sources. Because point sources are amenable to direct measurement and quantification, and thus to successful abatement programs, con- cern has shifted to the role diffuse (or non point) pollution sources play in water quality. The main focus of this thesis is non point pollution from quickflow (stormwater runoff)1 and the ecological Inechanisms within the watershed which control its magnitude. Since Inany of these mechanisms and watershed perturbations are land use specific, a hypothesis central to this thesis is that a relationship exists between land use and nutrient flux. The Problem Lakes have a variety of linkages for energy and nutrient exchange with surrounding terrestrial ecosystems. The vectors transporting —¥ 1Quickflow consists of storm induced overland runoff, interflow and baseflow. energy and r hr biologic other chem is the main one of the this regarc ifcance as water syst 39.74). Voile others has {and recy< Sive nutr with Wate Particula 3'4 as Te Nutr non DOTnt ”On DOIAI ”eaSUrab‘; cheque! 1. energy and materials may be categorized as meteorologic, geologic, or biologic. The geologic output of water, dissolved nutrients, and other chemicals and particulate matter from the terrestrial ecosystem, is the main geologic input to most of these aquatic ecosystems, and one of the most important land-water linkages in the biosphere. In this regard, rivers, streams and overland runoff take on special signi- ficance as the primary connection between terrestrial and standing water systems (Bormann and Likens, 1967; Likens and Bormann, 1973, 1974). Vollenweider (1968, 1975, 1976), Reckhow (1979) and numerous others have demonstrated the empirical relationships between the input (and recycling) of nutrients and lake nutrient concentrations. Exces- sive nutrient inputs from cultural sources are commonly associated with water quality problems and cultural eutrophication of lakes. In particular, two nutrients, nitrogen and phosphorus, have been singled out as leading causes of accelerated lake eutrophication. Nutrient flux originates from the two aforementioned point and Inon point sources. Because of the greater emphasis on point sources, '10" point sources have historically been considered natural, un- Ineasurable, and generally uncontrollable. Vollenweider (1968) characterized these sources as: 1. natural sources such as eolian loading and eroded material from virgin lands, mountains, and forests, and L 2. artificial or semi-artificial sources which are directly related to human activities, such as fertilizers, eroded soil, materials from agricul- tural and urban areas, and wastes from intensive animal rearing operations. While natural sources seldom contribute to water quality deteri- oration, man's activities in the watershed tend to alter, remove or overwhelm the homeostatic capabilities of natural terrestrial eco- systems. Although the quantity of nutrient export varies widely, the greater the extent of human utilization and land disturbance, the greater the amount of nutrient export from the watershed. As a result, the increased nutrient load may accelerate the rate of eutro- phication in aquatic systems. The importance of non point sources in relation to water quality is reflected in the Water Pollution Control Act of 1972 (Public Law 92-500) and the 1977 Amendments. Section 208 outlines a cooperative local/state/federal mechanism for areawide water quality planning including the identification of non point sources as well as procedures and methods "...to control to the extent feasible such sources." According to Pavoni (1977), this areawide approach implies that plan- ning for water quality also requires planning for land use since: 1. many water pollution sources are land use — specific, particularly non point sources, and 2. land use controls may be the most cost-effective method for controlling one or more pollution sources. With this increased emphasis placed on non point sources and land use controls, there is a clear and pressing need to develop tested A 7‘ J ry‘if 3‘ a -0' ‘e'it bl tr 9. fr 3'19 ai‘ er 5 t1 =ipr ) vv wit ‘ture. t at a] of 399*: be r procedures and collect reliable data on nutrient flux from various land uses. Current Research Practices Although direct in situ measurements provide more reliable estimates, the time, expense and effort needed to derive annual nu- trient loading coefficients for individual lakes, have prompted many water quality investigators to rely on values reported in the liter- ature. Many of these early literature values have been included in comprehensive surveys relating specific land uses to the nutrient mass transported to surface waters (Lin, 1972; Loehr, 1974; Uttormark et al., 1974). Out of convenience these nutrient coefficients have been frequently cited in nutrient budgeting studies for lakes, and have become an integral part of water quality models. Despite their wide acceptance, nutrient loading estimates in the literature still exhibit considerable uncertainty (O'Hayre and Dowd, 1978; Reckhow et a1., 1980). Closer inspection of many of these studies reveals that errors often result from a lack of under- standing of the factors involved in prOper sampling design. According to Hines et a1. (1977), the two prominent shortcomings of hydrologi- cally related sampling programs are: l. the arbitrarily derived, fixed temporal and spatial design of sampling programs from which quality data are derived, and 2. a failure to account adequately for the seasonal and reach-to-reach variability of water quality 5 rd- SC. 731‘ EX :1 JR vari ah. AH» HT..- that results from hydrologic phenomena. Subsequent use of these improperly derived coefficients in water quality management can potentially bias resulting policy decisions. In recognition of nutrient loading uncertainty, a number of investi— gators stress the need for either 1) additional data produced by skilled specialists using sound sampling methods, or 2) careful scrutiny of the nutrient export literature, to provide reliability for models used in large-scale lake management schemes (Thomann, 1977; Wanielista et a1., 1977; Schindler, 1978; Dawdy, 1979). The Problem Solution For water quality planning to be effective, decisions must be based upon reliable and more realistic information. To satisfy this requirement, water quality data must be systematically quantified. According to Reckhow (1978), the design of a systematic sampling program is fundamentally a statistical problem with increasing knowledge or the reduction of uncertainty as the primary objective. Because the desired degree of precision is the function of parameter variability, sampling programs must account for these irregularities. While it is beyond the scope of this research to conduct in situ measurements, this thesis will provide, 1) a careful review of litera- ture studies which focus on non point (quickflow) nutrient flux, and 2) a selection and compilation of nutrient loading estimates derived from an adequate sampling design for each land use. To acquaint the reader with the thought processes involved in the selection criteria, a discussion of sampling design will form the s‘SCJss tics ar sash re Cilia g ‘J‘il E 353113;“ If“; -~w.PVi i! 159 sa- C1 C33f€j( basis of Chapter II. In particular, the components of the sampling design best describing both temporal and spatial variability will be examined. These will include the 1) parameters to be sampled, 2) sampling frequency, 3) methods, 4) duration, and 5) location. Chapters III through V focus on forest, agricultural and urban land uses, respectively. Each chapter will include an in-depth discussion of factors and activities which influence the "characteris- tics and comparative magnitudes" of nutrient cycling and export from each respective land use. For forest watersheds, these factors in- clude geologic type, biome type, and ecological succession. Agricul- tural activities include crop type (row versus non row crops), pasture/grazing land, and feedlot/manure storage facilities. Percent impervious surfaces and other factors which influence nutrient export are discussed in the urban land use chapter. In addition to general discussions, the compiled nutrient export coefficients are presented both in tables and histograms for each land use, in accordance with the sampling/screening criteria described in Chapter II. Chapter VI presents concluding comments on the compiled nutrient coefficients. To demonstrate to the reader and analyst the subjec- tivity involved in application and the resulting nutrient export variability, selected nutrient coefficients are applied to a hypo- thetical, mixed land use watershed for a two year period (reflecting high and low rainfall). Chapter VII summarizes the results of this research with notes on use of the compiled nutrient coefficients. CHAPTER II NON POINT SOURCE SAMPLING DESIGN Introduction The bulk of non point source water quality studies have focused on either surface runoff alone or runoff combined with groundwater flow. Runoff (and the interstitial subsurface flow) flushes not only soluble and suspended matter deposited on the watershed but also impurities contributed by precipitation. Total storm induced water flux from a watershed is often called quickflow and consists of overland runoff, baseflow and interflow. It is the combination of these three frac- tions which poses a most serious threat to our lakes and streams. While exceptions (i.e., floods) have been noted, the pollutants flushed from a particular land use during one storm event often are not significant. The cumulative effect of many such storms, however, are not only considerable enough to seriously degrade water quality, but often negate the positive effects of local point source pollution abatement programs. In spite of the number of water quality runoff studies currently available in the literature, proper assessment of diffuse pollution loads is fraught with a high degree of uncertainty and variability. This variability is the result of essentially two factors; physio- graphic and climatic characteristics. Physiographic characteristics include those conditions within the watershed, such as geology, soil type, land use and other variables imposed by human intervention, which alter biogeochemical processes and pathway conditions of overland runoff. Climatic conditions influence the hydrologic cycle. However, in spite of many thousands of man-years spend in the pursuit of hydrologic knowledge, quantification of any hydrologic resource or process can be performed only with limited accuracy (Moss, 1979; Dawdy, 1979). Diffuse Source Monitoring Deficiencies In order to properly characterize the variable nature of diffuse runoff, a monitoring program must be utilized which accounts for this variability. Monitoring of annual nutrient flux is a statistical prob- lem and this problem may be defined as "the minimization of uncertainty in the annual nutrient flux estimate subject to a budget (cost) con- straint," or conversely, "the minimization of sampling cost subject to a desired precision level" (Reckhow, 1978). As a result, sampling problems are placed in an economic and decision making framework, thus introducing a need for the measure of worth of data (Dawdy, 1979). Accordingly, the problem is reduced to two of the basic variables of sampling design, precision (uncertainty) and cost. Unfortunately, close inspection of a number of stormwater studies in the literature has demonstrated that these factors were not always fully considered. Some of the undesirable characteristics in these studies included: 1. Point sources of contamination were not adequately accounted for. n .058 "ES Storm events were disregarded in favor of more easily obtained baseflow measurements. Sediment or particulate matter was not adequately sampled. Measurements taken during one season only, such as the dry summer period, were extrapolated to give yearly loading rates. Sampling location often did not account for the horizontal and/or vertical variability within the monitored tributary. The monitored watershed comprised a number of land uses thus making results difficult to interpret. (i.e., What are the sources of the pollutants?) Consequently, many of the published sampling results are not truly re- presentative of the actual conditions at the particular time and place under study or are not useful beyond the watershed of study. This inadequacy is often because of one or both of the following two scenarios: 1. Available time, money and personnel constraints often create many compromises which serve to undermine any con- clusive information generated by the study. Often little foresight is given toward the ultimate use (objective) of the generated data. This results in little thought invested in the representativeness of the water samples or types of data analysis to be used. to prO‘ FEISUY‘ .,. ' ig‘,T.1P ‘11 'v 10 Systematic Quantification - Sampling Design To make sound water quality management decisions, the required data must be available, unbiased and exhibit low variability. These needs are facilitated through acknowledgement and application of a systematic monitoring program (or network). Development of a monitor- ing program is dependent on the objective, and a basic objective is to provide the optimal level of information subject to cost. Identification of the network objectives (and criteria for measuring achievement) is perhaps the most important (if not most difficult) step in network design. Acknowledgement of these objectives, however, should provide a more systematic basis to the network development. To account for these objectives, Sanders and Ward (1978) suggest that the entire monitoring network must be examined and designed simultaneously if a balanced (collection versus use) monitoring system is to be developed. They categorize this system approach to monitoring into five major functions: 1. sample collection, 2. laboratory analysis, 3 data handling, 4. data analysis, and 5. information utilization. In the context of water quality, these functions serve as a feed- back loop from in situ water quality conditions to water quality management decision making. The information utilization function (Step 5) not only is dependent on the previous four steps but also establishes their objective or purpose. In particular, the sample 11 collection process (Step 1) is crucial since 1) the data collected are commonly used to quantify processes that vary in one temporal and three spatial dimensions, and 2) it is usually desirable to use the data collected to determine the character of process changes in space and time (Lettenmaier, 1976). Accordingly, particular attention must be paid to the design of the sample collection stage in order to refine and strengthen the remaining functions (and objectives) of the monitoring system. The sample (collection) design explicitly details what, how and where samples are to be collected and is summa— rized by Sherwani and Moreau (1975) as consisting of the: 1. parameters to be sampled, sampling frequencies, 3. sample collection methods, 4. design period determination, and 5. sampling locations. The components of the sampling design should be incorporated into all water quality monitoring programs. Not only should these concepts be applied by the field researcher, but the water quality manager utilizing the reported data should also screen and disregard those reports which do not adequately conform to satisfactory design concepts (and objectives). Such a screening process can occur from an examination of the methods sections of the individual reports. During this investigator's literature survey of nutrient export coefficients, considerable variability was found in the sampling designs. The lack of well-founded methods (or design objectives) unfortunately resulted in the rejection of some reported values. Since the sampling d951gs of the COTEJW some 0 fin + A w‘ uh: CENSUf lsowt! +. £1005 MitreI «3'11 0 12 design components are of such importance to the accuracy and precision of the reported data, the remainder of this chapter will focus on each component individually. It is hoped that researchers will acknowledge some of these standard procedures so that their results may be added to the literature on nutrient export coefficients in the future. Parameters to be Sampled eutrophication and the limiting nutrient concept The problems of eutrophication are very well known and widespread, and the definitions are numerous. Among limnologists, the general con- census is that eutrophic conditions are synonymous with the increased growth rate of lake biota. Although there are many complex interac- tions connected with this process, the most conspicuous measure of increased productivity is the excessive growth of algae, aquatic plants and oxygen depletion (King, 1979). Under severe conditions, this can result in a general reduction of lake recreational value and aesthetics. If the lake is used as a water supply, clogged screens and higher chemical requirements for purification can increase water treatment costs (Borchardt, 1970). To produce aquatic plant growth and reproduction, a large number of chemical elements are needed. Essential macronutrients include carbon, oxygen, nitrogen, sulfur, phosphorus, potassium, magnesium and calcium. Essential micronutrients include iron, boron, zinc, copper, molybdenum, manganese, cobalt and sometimes sodium, chlorine and vanadium (Simpson, 1979). While many are required only in trace amounts, certain elements, especially carbon, nitrogen, Dig/:13”, because , .+’ QUE? 6'1 rutri en ’l'illlfll 13 oxygen, hydrogen, sulfur and phOSphorus, are needed in large quantities because they are the basic building blocks for organic matter (Fruh, 1967; Thomas et a1., 1979). Nutrient utilization is a function of the plant's needs and plant growth is dependent on the presence of a sufficient quantity of nutrients in the water column. According to Liebig's "law of the minimun," the growth of a plant will be limited by those elements available to it in the minimum quantity relative to its stoichiometric requirements. If one compares the nutritional demands of algae to the amounts of nutrients likely to occur in aquatic systems, the limiting nutrients most often would be nitrogen and phosphorus. While these two nutrients are generally accepted as the most limiting, it must be noted that various other elements have at times been suggested as affecting or limiting the eutrophication process. These include iron, molybdenum, sulfate, vitamins, carbon and silicon (Goldman, 1960; Menzel and Tyther, 1961; Goldman and Wetzel, 1963; Goldman, 1964; Lange, 1967; Kuentzel, 1969; Provasoli, 1969; Kerr et a1., 1970; King, 1970; Schelske and Stoermer, 1972; Vallentyne, 1974; Rast and Lee, 1975). The relationship of nitrogen and phosphorus to eutrophication has been well documented (Sawyer, 1947; Sakamoto, 1966; Vollenweider, 1968; Shannon and Brezonik, 1971; Edmonson, 1972; Schindler and Fee, 1974; Vallentyne, 1974; Jones and Backmann, 1975; Rast and Lee, 1978). Of these two nutrients, phosphorus is generally the most common limiting factor, although, under certain conditions, nitrogen may become limiting, especially when man's activities add large amounts of phosphorus to the lake. OCCU‘ and f U nitn to ca 0d0r 011 Di ”lira atllo: to a- EVQn fore: and 1 is "E 14 Effluents from sewage treatment plants and agricultural and urban runoff often contain more phosphorus than nitrogen, relative to plant requirements. Thus, in many culturally-impacted lakes, nitro- gen appears at times to be the factor limiting growth of many algal types (Dobsen et a1., 1974; Miller, 1974; Stadelmann et a1., 1974; Rast and Lee, 1978; Thomas et a1., 1979). Nitrogen limitation also results from various nitrogen stripping mechanisms within the lake. As organic decomposition and oxygen depletion begins, denitrification occurs. If oxygen depletion is severe enough, nitrogen gas is formed and subsequently lost to the atmosphere (Vollenweider, 1975; King, 1978, 1979). A major consequence of nitrogen limitation is the production of nitrogen-fixing blue-green algae. These forms are especially prone to cause water quality deterioration because they produce taste and odor problems and because of their ability to float and accumulate on beaches. As the gaseous nitrogen in the water is used up by the nitrogen-fixing algae, it is readily replaced from the inexhaustible atmospheric sources. Thus, it is impractical and very often futile to attempt to control eutrophication by restricting inputs of nitrogen even in areas where it is currently limiting the growth of most algal forms. To rehabilitate such areas, phosphorus inputs must be lowered to the point where phosphorus replaces nitrogen as the limiting factor, and then further reduced so that growth and yield of all algal forms is reduced (Thomas et a1., 1979). This reliance on phosphorus control (over nitrogen) for lake r, '1." VVJA 15 management and rehabilitation is based on two reasons (Reckhow et. a1., 1980): 1. Phosphorus is often the major nutrient in shortest supply relative to the nutritional needs of algae and aquatic plants. This means that the concentration of phosphorus is frequently a prime determinant of the total biomass in a lake. 2. Of the major nutrients, phosphorus is the most effectively controlled using existing engineering tech— nology and land use development. Because of these relationships, it is easy to visualize the role phosphorus must play in any management plan to control cultural lake eutrophication. Therefore, phosphorus is the parameter to be sampled for non point source lake nutrient budget estimates. While the major emphasis is on phosphorus and its management, where appli- cable, information on nitrogen relationships and interactions, as they relate to the components of sampling design and diffuse runoff, will be presented for comparison purposes. bioavailability Phosphorus is collectable in basically two forms; particulate and solution. The particulate form consists of total particulate and sorbed or labile phosphorus. The solution form consists of total soluble, molybdate reactive and soluble unreactive phosphorus (Porter, 1975). Until recently, eutrophication control programs have been based S’JC of fla‘ ln1 that and than 3 Va! aP+:|i V .r' i“ 16 largely on the regulation of any fraction of phosphorus that was amenable to management, irrespective of whether the phosphorus was in an available form which could support algae growth. More specifi- cally, availability is defined as that nutrient fraction available for biological uptake and algal growth within one growing season. This has raised some serious questions concerning what fractions should be collected and/or measured. It is generally agreed that the soluble inorganic forms of phosphorus are readily available biologically. This includes forms such as the soluble orthophosphates and condensed phosphates. There is a high degree of uncertainty, however, concerning what fractions of particulate inorganic and organic forms are available. Complicating matters is the presence of dynamic and complex sets of physical, chemical and biological processes which determine this availability in the aquatic system. For example, sediment-attached phosphorus that is not available under certain chemical conditions at one point in time, may become available under the same or different chemical conditions at another point in time. This is in sharp contrast to the static and controlled nature of the laboratory conditions where a variety of techniques are used to correlate algal uptake with actual and highly variable in situ conditions. Consequently, any estimates of bioavailability must be viewed with a high degree of uncertainty and as only "ball park" approximations. One of the more comprehensive studies concerned with assessing algal-available phosphorus was conducted by Cowen and Lee (1976a, b) and Cowen (1974). From both urban runoff samples collected in Madison, Addit SUits Sea: 688;], 17 Wisconsin and agricultural runoff samples obtained in New York State, these investigators determined that in the absence of site-specific data, an upper bound estimate could be made of the available phosphorus in tributary waters: available P = SRP + 0.2PPT (l) where: SRP = soluble reactive phosphorus PPT = total particulate phosphorus Lee et a1. (1979) later made the following recommendation for the available phosphorus load from urban stormwater drainage and normal- tillage agricultural runoff. If the runoff enters a lake directly, or encounters a limited distance of tributary travel between source and lake, then the available phosphorus loading may be estimated as: available P = SP0 + 0.2 PPT (2) where: SP0 = soluble orthophosphorus Additional studies have demonstrated comparable, albeit variable, re- sults. Based on independent, but limited, studies of rivers in the Great Lakes Basin, 40% or less of the suspended sediment phosphorus is estimated to be in a biologically available form. Overall, probably flit! thL zati 18 no more than about 50-60% of the tributary total phosphorus (including soluble P) is likely to be biologically available (Logan et a1., 1979; Armstrong et a1., 1979; Songzoni and Chapra, 1980; Thomas et a1., 1979). In contrast to phosphorus, the fraction of total nitrogen available for plant utilization can be higher since nitrogen is more soluble and, therefore, more easily transportable by water. The concentrations of nitrogen compounds in overland runoff are often many times higher than the critical level of 0.3 mg N/l of inorganic N, which was suggested by Sawyer (1947) for algal growth problems in lakes. Inor- ganic nitrogen forms such as ammonia, nitrite and nitrate are readily available for algal growth. However, the availability of the total nitrogen will depend on the relative amounts of the organic and par- ticulate fractions in the runoff and their equilibrium and minerali- zation rates. From studies of urban runoff in Madison, Wisconsin, Cowen et al, (1976), determined that 70% of total N was biologically available (with a range of 57-82%) as a result of nitrogen mineralization. Similar results were also found in earlier studies with river waters tributary to Lake Ontario (Cowen and Lee, 1976b). It was previously mentioned that availability applies to the nutrient fraction that is utilized within one growing season. However, depending upon the conditions, there is a potential for at least some (if not all) of the remaining fractions of particulate/organic phosphorus or nitrogen to be utilized at a later date (due to sudden equilibrium changes). This remaining fraction can represent a sig- nificant, if unmeasurable, nutrient reservoir. In addition to this, the t is a: in pa sanp‘ 19 the collection of total (soluble and particulate) nutrient fractions is advised since total availability is unpredictable and depends, in part, on the ratio of particulate to soluble nutrient forms in the sample. This is especially important since particulate phosphorus or nitrogen can be an order of magnitude greater in quantity than the reported dissolved fraction. In this situation, failure to adequately assess the particulate forms can result in substantial underestimation of the total available fraction. Sampling Frequency Variation in nutrient flux through time is intimately linked to changes in flow. Both dissolved and particulate fractions respond to these flow changes differently. Proper assessment of the particulate fraction requires a greater emphasis on sampling during storm events since the bulk of this fraction is carried with stormwater runoff. Although variation exists, the response of both dissolved and parti- culate fractions relative to the storm hydrograph can be discussed in somewhat general terms (see Figure l). The initial storm induced increase in streamflow is often associ— ated with a decrease in the dissolved nutrient fraction. This decrease is attributed to the dilution effect of the greater runoff volume as well as the resulting greater contribution from overland flow and reduced contribution from soil water which comprises baseflow. This results in the lowest dissolved concentration at the peak of the hydrograph. As flow rates decrease, the dissolved component tends to gradually increase to concentrations approaching that of the pre-storm 20 .cawgmoguz: Ecoum one ow mmcoammm ucmwgpzz mumpsuwugma ucm um>~ommmo “F «game; all 332: m2: — d 1 N a a T u n J J A a . 3 1. Amy I I. I I I i N §Q§Q \ I I . l 3 . I 3 «ERNQSK / x \ m N / / z \ 1|.nH. , x . :l V , x l. . x . O a x .MN _ . - _ East \ _ ../. + bméemme _ e . . _ m . e 4 11 S ’ x mm niv S/ V 1 9 n4. COY l0 21 baseflow conditions. This is because of the greater soil-water contact time associated with increasing contributions of soil water to baseflow. For the particulate (or sediment) fraction, a different response is evident. During the initial rapid rise of the hydrograph, the particulate component increases dramatically - often reaching a maximum concentration preceding peak flow. This phenomenon, often referred to as "first flush,” is the result of the dislodging of particulate matter from the land surface during the initial stages of runoff, leaving little material for transport at later periods. Regardless of where the particulates "peak out" relative to the hydro— graph peak, a decrease in flow is accompanied or preceded by a decrease in particulate concentration. To adequately account for these variabilities, and to reduce the amount of uncertainty in the phosphorus loading estimate, the sampling frequency should be dictated by the hydrologic response. Many previous sampling studies have failed to address this issue but have instead made broad but untested assumptions concerning watershed hydrology and loading responses. Sampling intervals have ranged from once per week to irregular periods during the year, resulting in many of the more sporadic storm events being missed. Hydrologic response (and sampling frequency) differs according to drainage basin characteristics. As land use progresses toward urbani- zation, channels are straightened or paved, small tributaries are filled and the watershed surface generally becomes smoother and more conducive to sheet runoff. Therefore, as land use is intensified 23 .pcw>m Egoum m on mom: can; mcmagm> we mmcoammm omsamcmogu%: fl Ea: m 2: “N mgzmwu 1 d - 336k \ REE {Est 3kg. \ \Estsutuw T 33$ (9/2 “1) 398VH33|G <—-—-— :cndi eiiec JUCBF This! 7. 8. 24 half-life and response time of constituents, seasonal fluctuations and random effects, representativeness under different flow conditions, short term pollution events, magnitude of response, and variability of the inputs. Simply stated, there is no single best sampling frequency for all conditions. However, sampling frequency should be a function of the effect on the precision of the nutrient budget estimate (i.e., is uncertainty minimized?), and the associated cost. For many sampling programs, the actual design is often based on random sampling. Under random sampling, all elements of the population have an equal likelihood of being selected for the sample. Cochran (1963) presents the following equation for the number of samples necessary to achieve a desired level of precision: n = (3) where: n = number of samples = student's t s = population variance estimate d = desired precision This, in turn, may be related to cost through the common cost function: sa‘ fr VG 25 C(n) C0 + Ci” (4) where: C(n) = total cost of sampling program co = initial, fixed cost c = cost per sample Thus, random sampling design is specified by the variance estimate, the precision, and the sampling program cost (or number of samples). For a single population with constant variance, sampling frequency can be evaluated on the basis of a trade-off between cost versus precision, or uncertainty (Reckhow, 1978). However, in order to effectively apply Equation 3 for random sampling design, an estimate of the population variance is needed. According to Reckhow (1978), this implies specification of the population frequency distribution, and given the limited available data on nutrient mass flux to lakes, this is a difficult task. The sampling collection process can often be made reasonably more efficient (further reducing loading uncertainty and increasing accuracy and precision) if a stratified random sampling program is employed (Reckhow, 1979). Under this sampling scheme, the population is divided into homogeneous sub-populations (strata) that are separately sampled according to the degree of variability which they exhibit (Snedecor and Cochran, 1973). The underlying assumption is that the population can be more accurately represented as the sum of sub-popu- lations, therefore reducing the sample variance. r\\ freqae by hig and t1 accur a sur is al 26 In the context of hydrologic data collection, two temporal strata are evident: 1. high flow events produced by rainfall runoff and snowmelt, and 2. baseflow produced by groundwater flux. To expect a gain in precision over simple random sampling, more frequent measurements should be applied to the stratum represented by high flow events. If the sample size is increased in this stratum and the final concentration properly weighted, a more precise and accurate estimate of the population average will be obtained. From a survey on sampling design, Reckhow (1979), states, that "sampling is allocated in stratified random sampling design according to: "i - "i (c.v.)xi (xi) (5) 3"" Xwi (c.v.)xi (xi) where: n = total number of samples n. = number of samples in stratum i x- = magnitude (mean) of characteristic x in stratum i w. = a weight reflecting the size (number of units, for example) of stratum l (c.v.)x. = coefficient of variation (standard de— viation divided by the mean) of char- acteristic x in stratum i If: Sir; 27 If sampling cost may be estimated by: C = c0 + icini (6) then wi (c.v.)x. (xi)/VE;' = 1 -..... 2w. (c.v.) . (x{)/VC; (7) 1 In order to apply Equation 5 or 7, a relationship is needed for the total number of samples n. Two equations are available, depending upon whether precision or cost is fixed beforehand. If precision is fixed (at d), and cost may be estimated according to Equation 6, then (Cochran, 1963):" [zwi(c.v.)xi(xi)‘VEEJZwi(c.v.)xi(xi)/‘VE;- n = (8) dzltz If cost is fixed, then (Cochran, 1963): (c - co)z(wi(c.v.)xi(xi)/'VE;) (9) n = 2(wi(c.v.)x.(xi)V_c:) 1 In summary, Reckhow (1978) concludes that the composition of the stratified random sampling design equations leads to the following UEOEFI shoul widel for c drawn metho 28 general statements concerning stratified sampling. A larger sample should be taken in a stratum if the stratum is: l. more variable (c.v.) 2. larger (w, x) 3. less costly to sample (c) Sample Collection Methods Hydrographic response and the associated pollutant loads vary widely between event/non-event periods thus increasing the potential for considerable estimation error. Since conclusions are naturally drawn from the nutrient estimates, it is imperative that both 1) methods of acquisition of concentration samples and flow values, and 2) flux estimation techniques, do not introduce unacceptable bias. Concentration samples are determined by a variety of field collec— tion techniques. One common method is manual grab sampling which usu- ally involves the collection of samples at a predetermined rate, such as once per week, month, etc. However, grab sampling conducted on a uniform basis usually provides an adequate description of baseflow conditions only, since periodic storm events are often missed. In this respect, manual collection methods are often inadequate for storm event monitoring since: 1. Storms are highly random with respect to their timing, location, intensity and duration. 2. For large watersheds, travel time between stations is often great. 3. For small watersheds, runoff duration and associated 29 lag time is relatively short (Nelson et a1., 1978) with sharper peaks and higher storm levels (Likens et a1., 1977). 4. The time between the period when the probability of the storm event is high to the time when it occurs is usually very small (i.e., warning time is short). Because of these factors, the ability to mobilize field personnel and equipment is severely limited especially when one considers the probability of an event occurring during regular working hours. The cost of maintaining competent field crews on a round-the-clock basis for an extended time period would be prohibitive (Colston, 1974; Sherwani and Moreau, 1975). To reduce the inefficiencies (and bias) of manual collection, many water quality studies have relied on automatic collection methods. Probably the simplest of these methods is the batch holding tank, which collects runoff, diverted by gutters or flumes, at the base of the watershed (or runoff plot). Under some conditions, however, this collection method is inadequate. With large watersheds or storms of long duration, holding capacity can be exceeded and a portion of the total load will be undetected. For some storms, the greater nutrient concentration associated with the first flush can be obscured by additional, less concentrated runoff. More adequate, automated devices can collect individual samples during a storm event. The collection process can occur at either equal time intervals or on a flow-weighted basis. Sampling at equal time intervals, however, is often less desirable. Since each aliquot is; wi ti cal 30 is given equal weight, the higher nutrient concentrations associated with first flush will be underrepresented. If the formula for the calculation of average concentration is examined: a=' ' (10) it should be apparent that, as a consequence of this underrepresentation, the "average" computed concentration will be biased too low (McElroy et a1., 1976; Grizzard et a1., 1977; Huber et a1., 1979). Flow- weighted sampling is a more precise concentration estimate since sample volume is accounted for and sample concentration appropriately weighted according to the following equation (Huber et a1., 1979): ll _ :§ 00 c =L;r__1___]_ (1") 123101 where: Qi = flow volume Accordingly, high concentrations associated with first flush are more equitably represented than concentration estimates calculated from equation 10. This results in a lower variance and greater accuracy of the reported data. In addition to the method and frequency of collection, Colston (1974) notes many special requirements of automatic samples. These include the following: 31 l. The sampler should not interfere with water flow and must be immune to damage from larger objects and debris wash- ing downstream during storms. 2. Water velocity through the system must be sufficient to keep all material in suspension to obtain representative samples and minimize system clogging. 3. The sampling mechanism must automatically activate during each runoff event. 4. The sampling system must be able to take discrete samples at predetermined intervals with a known time for the first and last sample. 5. The sampler intake must represent an average of the verti- cle profile of contaminant concentration with respect to depth. While concentration samples should be collected during major storm events (or at least a representative number of storm events), the effectiveness of the "appropriate" number of samples to be taken during the temporal extent of sampling should also be considered. Reckhow (1978) reported on three papers, previously surveyed by Allum et a1. (1977), discussing intensive sampling of tributary phosphorus. In all three studies, the sampling was quite frequent (twice weekly or daily). However, at a concentration sampling interval of between 14 and 28 days, the standard error of the annual phosphorus flux varied between 10% and 20% of the "true" flux. Reckhow (1978) further comments that: 1. More frequent sampling will still reduce uncertainty 32 in the phosphorus concentration, but at a reduced efficiency. 2. Less frequent sampling can still be used to estimate phosphorus concentration, but at a greater risk of significant error. Flow estimation is determined by three methods. The most pre- ferred method is continuous flow measurement, and many sampling studies take advantage of USGS gauging stations. Without these facilities, it is costly and often not feasible. Another alternative is to measure instantaneous flow at the time of concentration sampling. However, Reckhow (1978) argues that this method must be considered unacceptable because it does not yield an estimate of precision. A more acceptable third alternative is an annual flow regression equation developed by the USGS (for most states) which provides an estimate of annual flow and the estimated standard error (Reckhow, 1978). To estimate flux, Reckhow (1978) surveyed a number of approaches described in the literature, each of which could be appropriate under certain conditions. These include techniques dependent upon a: l. regression of mass flux versus watershed characteristics, 2. flow-weighted concentration, 3. regression of concentration versus flow, and 4. regression of flux versus flow. He concludes that the estimation technique used should probably depend upon the: 1. intended use (A regression on watershed characteristics and land uses may be useful for future predictions.), 2. fit of the data to the equations, and 510 ion Die 33 3. simplicity of the mathematics. The studies selected for inclusion in the nutrient export coeffi- cient tables employ a wide variety of sampling techniques, but nearly all are based upon complete flow records and adequate baseflow concen- tration samples. While storm runoff was not sampled at every event, it was felt that a sufficient number of events were examined to allow for realistic estimates of the total load for a particular land use. Temporal Extent of Sampling Climate determines local weather conditions which in turn influence the quantity and duration of baseflow and the number and periodicity of storm events. While some areas of the country exhibit relatively uni- form climates (e.g., pacific northwest) evenly distributed periods of precipitation are usually not the norm. Winter thaws and spring or summer rains often create seasonal cycles of high and low runoff. Intimately associated with climatic periodicity is the modifying impact land use has on hydrologic response. The relatively uniform annual flow patterns of many undistrubed forests is in sharp contrast to the highly variable flows emanating from urbanized and agricultural] basins. As vegetative cover is artificially reduced and the basin is increasingly developed, groundwater recharge and flux are reduced. Baseflow and nutrient export are often either inconsequential or absent during dry summer or winter periods. Consequently, a greater percentage of nutrient export occurs during wet periods of the year for disturbed watersheds than for undisturbed watersheds. As a result of this seasonal variability, high runoff seasons more towe accc ii ZE 5:03? F951 have Accc Slvg live 34 exhibit greater variance in nutrient concentrations and total nutrient loads than do low runoff or baseflow periods. For a given confidence level (precision) and a margin of error (accuracy), the temporal extent of sampling must include these high and low runoff periods (especially for the more disturbed watersheds). If sampling duration focuses exclusively on one season (e.g., spring), the nutrient flux estimate may sufficiently describe that time period but may not be indicative of other unsampled periods. For this reason, the analyst is warned against extrapolating seasonally reported results toward more extended time frames. This will bias the nutrient flux estimate toward whatever season in which the sampling was performed. To better account for this seasonal variability and to allow for a more standar- dized unit of measure for comparison purposes, a more informative approach is to sample and report the data in yearly increments. While the bulk of studies included in the export tables are the result of intensive sampling and annual flow data, many investigators have refined the sampling period within the wateruyear time frame. According to Likens et a1. (1977), the ideal water—year is that succes- sive twelve—month period that most consistently, year after year, gives the highest correlation between precipitation and streamflow. Examination of precipitation-streamflow data at Hubbard Brook resulted in a water-year beginning June 1 and ending May 31. Since the beginning of this water-year corresponds with the appearance of foliage, it allows for a separation of the vegetation growth and dormancy periods. This concept has been effectively applied by other investi- gators working with agricultural land uses (Alberts et a1., 1978; 35 Burwell et a1., 1975). Watershed Designs and Locations Of the criteria necessary for a non point source monitoring program, the sampling location, or more importantly, the watershed design, is crucial for accurate estimation of nutrient yields.’ To facilitate the sampling site/design selection process, two key interrelated factors are involved: the specific objective of the network design and the representativeness of the sample to be collected. To accomo- date these factors, two basic approaches to diffuse load assessment are, in turn, available. The first approach involves sample collection from relatively large streams draining large watersheds. If storm and seasonal hydrologic response are routinely sampled throughout the year, an accurate representation of total annual nutrient flux from particular drainage basins can be obtained. This approach has been extensively used to obtain estimates of Great Lakes tributary loads by the Pollution from Land Use Activities Reference Group (PLUARG) associated with the International Joint Commission. A number of disadvantages to this approach have been noted (Whipple et a1., 1978). First, many large streams, particularly in urban areas, include inputs from industrial and municipal point sources, so that total loading does not relate directly to pollution from storm water runoff. Second, subtraction of known point loads from total yield can result in a biased diffuse load estimate. This occurs be- cause the magnitude of reactions such as sediment attenuation, nutrient upta stea PIG; diff buti we te def -' the fore 36 uptake and degradation by bioseston are not accurately accounted for at the downstream sampling site. Since point sources, determined at their end-of pipe source, do not undergo these transformation processes, their subtraction from total loads may result in an underestimation of diffuse source contributions. (Alternatively, if there is no net accumulation of material in the stream, over a sufficiently long time period all phosphorus discharged will reach the lake. In the steady state, this suggests no bias from point source subtraction.) Third, the land use of large watersheds is very often mixed, in proportions which vary from one tributary to the next. This makes it difficult if not impossible to determine the percent loading contri- bution from each land use, and application of the results to other watersheds for prediction purposes remains questionable. If the objective of the sampling design is to describe runoff loads from specific perturbations, representativeness will depend on a comprehensive approach. This second approach is more specific and is based on the examination of drainage from catchment basins which define a particular land use. In order to maintain homogeneity, the monitored watersheds are relatively small (except for some forested systems). The advantages to this approach are essentially two-fold. First, land use - water quality relationships are more carefully defined allowing for contrasts between natural and manipulated ecosystems. By comparison this can provide information about the functional efficiency and "health" of a particular land use. For instance, is a particular land use conservative of nutrient inputs (forests) or is Ch. c171, "Edi de- 37 the assimilation capacity limited (pasture) or exceeded (feedlots)? Second, the results can be used in conjunction with other similar studies to predict future water quality changes corresponding to projected land alterations. Because of the identified advantages, a large percentage of nonpoint source water quality investigations have utilized this latter approach with forest, agricultural and urban activities as the major land use categories studied. The remainder of this subsection con- tains a discussion of how diffuse runoff is monitored from each of these land use types. forest land use In order to provide hydrologic and nutrient flux information from natural (undisturbed) ecosystems, a number of experimental forested watersheds have been established across a wide range of climates, geology and biological structure. Some of the well—known watersheds are Hubbard Brook Experimental Forest in New Hampshire, Walker Branch Watershed in Tennessee, H.J. Andrews Experimental Forest in Oregon and Coweeta Hydrologic Laboratory in North Carolina. Although biological (species type and age) and geological characteristics (bedrock and soil) are often substantially different among watersheds, the watershed designs are usually quite similar. Each drainage basin has to some degree vertical and horizontal bor- ders, demarcated by ridges and functionally defined by biological activity and the drainage of water (Bormann and Likens, 1967). Accurate monitoring of total hydrologic flux can pose problems. assc of s bedr exp-c thE 38 Since forest cover and litter layer dissipate much of the energy from precipitation events, infiltration is high and the opportunity for overland flow is slight. The runoff that does occur is usually associated with snowmelt events. To register the greater percentage of subsurface slow, v-notch weirs or flumes are often anchored to the bedrock at the base of each watershed. As the size of the forested area increases, flow measurement methods change. Drainage basins covering hundreds or even thousands of hectares use gauging staffs or other flow measuring devices to determine the proportionately greater flow volumes. While automatic sampling devices facilitate collection in the smaller basins, manual methods often still persist in the larger watersheds because of the relative uniformity of forest flow and chemical concentration. agricultural land use Water quality monitoring in agricultural settings is often con— ducted in a manner similar to that for forested systems. Areas of agrarian activity are defined and the resulting runoff is examined separate from the influence of other land activities. Numerous studies are available which give representative loading estimates from general agricultural land use (Avadhanula, 1979; Campbell, 1978; Burton et a1., 1977; Lake and Morrison, 1977; Grizzard et a1., 1977; Nelson et a1., 1978; Burwell et a1., 1974; Taylor et a1., 1971). In contrast to nutrient export from forested systems nutrient export from agricultural areas demonstrates wide variability. Prac— tices are highly diversified and an agricultural basin can consist of fill.“ 01' d. 39 a mosaic of different uses such as pasture, feedlots, row and non row crops. Each type of perturbation creates different hydrologic responses, and depending on the percent composition of the basin, the effect of one activity can influence the final nutrient load. In order to further delineate these effects, individual activities should be, and often are, separately monitored. Separation of the various agrarian activities into discrete hydrologic units is conducted through two basic approaches, and the differences between approaches are based primarily on the size of the basin under study. The first approach relies on relatively large dyrdologic units ranging from 5-500 hectares in size. In spite of these dimensions, the entire catchment basin contains a single activity such as row crop or pasture (Alberts et a1., 1978; Chichester et a1., 1979). The second technique employs several small runoff plots, usually much less than a hectare in area. Separated by raiSed metal, wood or concrete borders, the individual plots are 2-5 meters wide and 10-25 meters long. Runoff studies using these plots may include 1 to 2-individual plots. At the base of each plot is the flow/sampling device often consisting of a collecting tank which generally relies heavily on "batch" collection methods. Because of the low area and labor requirements, this particular design has increased in use by university agricultural experiemnt stations and other research agencies. Small size permits close proximity to research facilities and personnel, which allows for both close monitoring and manipulation of environmental conditions such as .3 dis rat of dog hic 4O soil, slope, fertilizer, tillage methods and crops. urban land uses Sampling site selection for urban runoff monitoring potentially poses some additional problems not encountered in agricultural water- sheds. Since it is not economically feasible to re-create urban settings using small runoff plots, available conditions must be utilized. These conditions simultaneously impose an expanding set of limitations on data transferability. Urban runoff is often channeled into storm sewers which later discharge into nearby tributaries. In order to derive an areal loading rate, however, it is first necessary to ascertain that the network of storm sewers is restricted to the boundaries of the watershed and does not contribute runoff from other basins. Many cities have combined storm and municipal sewers. During high runoff events, domestic sewage often overflows and mixes with effluent within the sewer system. While providing valuable information about a particular site, the results are difficult to apply to other areas because of the inability to separate the proportion of point source contributions from total flow. If the above spatial uncertainties can be accounted for, the "flashy" nature of the individual runoff event must be suitably monitored. To accurately assess these transient events, flow must be continuously monitored. (To reduce monitoring costs, it is often necessary to locate the study site in close proximity to established stream gauges such as those used by USGS.) Similarly, water quality 41 samples are (or should be) collected with automatic samplers. Similar to agricultural lands, urban areas consist of a number of different land activities. These activities include industrial com- plexes, business and commercial districts, parking lots, residential areas, parks and playgrounds. Because of differing surface characteristics, the hydrologic and water quality responses from city parks or even large heavily vegetated residential lots are often quite different from the response from the essentially sealed surface of shopping malls or industrial complexes. Separation of these discrete types of activities into distinct drainage basins is not always pos- sible because of the lack of conformity with topographical boundaries. A study by AVCO (1970) indicates that aside from these problems, the following factors also influence site selection for urban runoff studies. 1. Minimum area requirements for the acquisition of a measurable sample. 2. Security of the sampling equipment from vandalism. 3. Accessibility of the sampling site. in t0 CHAPTER III FOREST LAND USE Introduction The world's increasing population co-exists with a diminishing stock of resource reserves. Because of this dichotomy, man's future welfare may more than ever depend on an accurate knowledge of how the flow of energy and nutrients vital to ecological systems can be maintained. To more effectively facilitate resource decisions in- volving land use, it is imperative that planners/managers have a workable understanding of how undisturbed systems, such as forests, operate. In forested ecosystems, nutrient flux is primarily through meteor- ological, geological and biological transport mechanisms. According to Likens et a1. (1977), 1) biological inputs are assumed to equal outputs (unless a phenomenon such as animal migration imports more than it exports or vice versa), and 2) geological imports will be negligible (if ecosystem boundaries are sufficiently defined). Thus, nutrient inputs into unmanipulated forests are principally from the meteorologic vector (i.e., dust and rainfall) and export from the system is via geologic outputs (i.e., surface and subsurface drainage). Within the ecosystem, forests can be viewed as complex processors in which nutrients are translocated from one portion of the community to the other. This nutrient exchange process involves biological, 42 43 physical and chemical interactions and is often referred to as the biogeochemical cycle. This cycle can be described in very general terms. For example: a percentage of nutrients not retained within the plant's woody tissue is returned to the forest litter where it may be acted upon by microorganisms and subsequently passed on to heterotrophic consumers. Through respiration, organic decomposition and/or leaching from living and dead tissue, bound nutrients once more become soluble and available for plant uptake and recycling. The major sites of accumulation and exchange within the system have been conceptualized as occurring among four basic compartments; 1) atmosphere, 2) living and dead organic matter, 3) available nu- trients, and 4) primary and secondary minerals (Likens and Bormann, 1972; Likens et a1., 1977). This proposed black-box scenario (Figure 3) provides a framework whereby not only structure and func- tion are accounted for but ecosystem development and degredation can also be considered in light of an imbalance in any of these compart- ments. Factors which influence this accumulation and exchange process also have an impact on total nutrient output from the watershed in streamflow. The rates at Which these nutrients leave the watershed are affected by the manner in which these elements are circulated between the forest vegetation and the underlying soil, especially the degree to which these elements are bound into organic matter and held in tight circulation. No matter how tight the circulation, however, some loss of nutrients in water moving under and over the soil surface into the stream is inevitable. 44 .man ..pm pm mcmxwo saga .Ewemzmoum cmummgoe on“ cwcuwz mmcmgoxm vcm covpmpze:oom mo mmumm comm: “m mgzmp , ............... wso>o zmem>mm Figure 4: (a) Relationship of nutrient output rate to the successional status of ecosystems. The V and R curve is from Vitousek and Reiners (1975), and the Odum curve from Odum (1969). The W curve is from Woodmansee (1978). (6) Relationship of nutrient accumulation to ecological succession. [from Woodmansee (1978)] COI We: 58 Timber Harvest Watersheds with ongoing timber harvest tend to have higher nutrient export than do undisturbed systems. This is because deforesta- tion: 1) blocks the nutrient uptake pathway from the available nutrient and organic matter compartments to the vegetation (see Figure 3), 2) increases the nutrient pool by contribution of dead organic material (slash), 3) raises the forest floor temperature through increased exposure to sunlight, 4) increases the frequency of drying and wetting, 5) reduces evapotranspiration rates and interception capacity and 6) increases microbial activity (respiration rate and bacteria numbers) (Birch, 1958; Likens et a1., 1970; Pierce et a1., 1970, 1972; Bormann et a1., 1974; Likens et a1., 1977). Increased biological decomposition is the principal factor responsible for increases in nitrogen export. Nitrifying bacteria of the genera Nitrosomonus and Nitrobacter increased up to 18 and 34-fold, respectively, in the soils of disturbed (clearcut) watershed when compared to populations in undisturbed forest soils (Likens et a1., 1970, 1977). In contrast, phosphorus export is reportedly not as sensitive to harvest operations as is nitrogen. Dissolved phosphorus export tends to remain at either pre-harvest levels or exhibits only slight increases during the first few years (Aubertin and Patric, 1974; Fredriksen et a1., 1975; Likens et a1., 1977; Swank and Douglass, 1977). Bormann et a1. (1974) reported that particulate phosphorus output rose sharply a couple of years after clearcut as biotic control and erodability weakened. 59 The output of nutrients is also dependent on the extent and method of harvest and on the proximity of the harvest operation to tributaries. Many logging operations use undisturbed buffer strips along forest streams to absorb the impact of excess runoff. Humus layer thickness, however, may also be a factor in nutrient yield (Fredriksen et a1., 1975) since the thicker the layer, the higher the organic content and the greater the potential for mineralization and nutrient loss to streamwater. Forest Fire Forest fire has a greater potential for degrading water quality than timber harvest alone. Elimination of the overstory blocks nutrient uptake pathways, and reduces evapotranspiration and rainfall interception capacity. Incineration of litter and humus layers con- verts the forest floor into a readily soluble, nutrient-rich form much faster than do natural decay processes. It also decreases infiltration capacity and water storage, increases soil weathering and enhances nutrient runoff potential (Wright, 1976). The effects of forest fires on the physical, chemical and biological properties of soils and vegetation are variable and directly related to the type and severity of the burn. Materials consumed in a controlled burn are often confined to the understory vegetation and forest floor debris and only a small part of the total may be destroyed. A severe wildfire may destroy a much larger percentage of the standing biomass and organic matter (Wells, 1971). Unfor- tunately, information on the effects of fire on water quality is 6O limited and research results are sometimes conflicting. Many studies have examined the combined effects of forest fire with timber harvest and/or fertilization, making it difficult to determine actual cause- effect relationships. From a study in northeastern Minnesota, Wright (1976) observed that, compared to natural background levels, runoff and phosphorus export increased 60% and 93% respectively, after a fire. In a clear- cut and slash-burned Douglas-fir forest in Oregon, inorganic phos- phorus loads increased four times that of the control area to approximately 0.6 kg/ha/yr. Total loss of nitrogen amounted to 2.2 kg/ha/yr for the first two years after burning before dropping toward control levels of 0.05 kg/ha/yr (Frederiksen, 1970). However, during the two years following a similar treatment in Oregon's coast range, nitrate-nitrogen export increased from 4 to 15 kg/ha/yr (Brown.et a1., 1973). Forest Fertilization With a steadily decreasing production base and an increasing demand for wood products, management practices on forest lands have been intensified. As a consequence, the use of forest fertilization to increase timber growth rates is becoming more widespread. Since nitrogen is the most common growth-limiting element on terrestrial systems, especially in the pacific northwest, fertilization with granular urea (46% N) or ammonium nitrate has been intensified (Fredriksen et a1., 1975). PhOSphorus fertilizers have not been as extensively used and are normally applied to tree plantations near 61 the time of planting. Phosphorus has seen limited use, however, on stagnated stands of slash pines on the phosphorus deficient wet savanna soils of the coastal plain (Pritchett and Smith, 1970, 1974). Application methods include spray irrigation and manual dispersal but most operations require aerial techniques using either helicopters or fixed wing aircraft. Large headwater streams are intentionally avoided but application to the forest floor often includes and impacts upon smaller tributaries. According to Moore (1975), urea application to Douglas-fir stands pose little threat to water quality unless there is direct application to stream channels. This does not rule out the possibility of groundwater contamination and eventual impaction on surface waters further downslope. In this situation, ammonium nitrate has a greater pollution potential than urea because of the mobility of the nitrate ion. While essentially no leaching of phosphorus occurs from most forested systems, exceptions have been noted where soluble phosphates have been applied to acid organic soils or acid quartzitic sands low in iron and aluminum (Hymphrys and Pritchett, 1971). Fertilizer materials undergo a number of transformations when applied to soils and the proportional increase in nutrient flux will depend on 1) the type and form of fertilizer used, 2) rate and time of applications, 3) vegetative type and root uptake efficiency, 4) the soil's physical and chemical properties (i.e., ion exchange capacity), and 5) climate (Hornbeck and Pierce, 1972; Pritchett, 1979). According to available published accounts, stream nutrient levels were only temporarily elevated immediately following fertilization, 62 did not approach toxic levels, revealed no significant impact on aquatic organisms and were usually associated with direct application to surface water. Since only a small fraction of the forested water- shed is fertilized in a given year, the evidence does not implicate forest fertilization in significant eutrophication of lakes or streams (Moore, 1970; Norris and Moore, 1971; Hornbeck and Pierce, 1972; Malueg et a1., 1972; Moore, 1974, 1985; Fredriksen et a1., 1975; Stay et a1., 1978; Tiedemann et a1., 1978). 63 muomm:=_z .umogoa NNm_ Amm. - _.v Ao~.m - No._v Ase.—~ - m~.m—v ADP.Nm . .m.mxv macaw ecu —cu:oe_goaxu Am; we.ov «mote» . .3 u... 298.: comm. «Sim mom.m— «3.3 Eu :52 28.3: :82 - :82 «anon auommccez omcmmgo non .ummcou Am; o—v Lou—m ace Am~_. - c~_.v “Rm.~ - em..v A~.m_ . m.m_v macaw a .oucwepgoaxm moacam goo—a Non m~m_ .xgcm> mmm_. mom.~ oo~.~p xm_o .Enop was p_oucm: :mamm Res Eeo— ouomo:c_x mum, se_u .seo_ .uagmtmsez A»; .o.v ”mated .umam use comcem coo. m.vm o.m~— u~_m .Eeo— execuoccpz ox»; maosu.umc uox_z covumEEou aemucos_cmm mkm— .Locsoc_x Amc_. - Noo.v ocwxpeo>o moccau .ovgouco “macaw use cop—mo asap. m—eom Eco. ccmcuaom mzosu.umu coxez coeuoeLo» maoocmw u*u_:agm m~m~ .goczoc_x Amxo. - mmo.v m:_»—cm>o auacmu .o.couco ummcoe can copp_o useo. m_¢om xucom censusom mao:u_uov vmxpz auacou .o_gouco ouacam xuopc “Km. .=Om_ocu.z ammo. amm._ aeo— sucem amazgueoz - a=_a xoea mvmcau .o_eou:o museum xumpn NR9. .c0m_ogu_z coco. nam.~ seo_ steam amazgsgoz - mesa Jose «enema Am: m~_v .OFLauco .Aucsou goo—Em; use zuevn cayenne—m: zap—m» gawk .xoo oko— .concozmsa.z vogmemuez to; .gomon .o_aoa ucm copucvgom coo. oo.mc m.o~p «so; compo mvoozucn; xos.pu mco_uuogm xapu oEOm o:_:vmu:oo Am; F.~emv mu_moaou genome nuance .owemuco comma mc__neocu a on.»_eo>o axe; cemzma soc.n sup: .moacam cum. 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Km xc_ao muccmcoz mococmmmm cz\ac\mx cx\mg\ox Lx\Eo cx\so wczuxm»\mazh cowumoog x a 2 mm: can; uconxu “coaxm ucoczm :owumu_a_uwca __om c»\og\mx macozamoza page» coaocawz _muo» gown: co_uauv_qa< cm~_P_ucmg mtmLmeHMZ mezummm Dam UmNme ECLw “Loqu “cmeuaz no QFQMH 93 coca cum» m:_z :o_uos com» Lao; :o_vme Lowx mocgh m:_cam acaococmvca Soc» :o_u:a_;ucoo coho: umc_Eexw x_uco_u_»$=m ac: omega acme_umm m:c_ums com» use; . @DU'UQJ Ac; o.._v Lm>oo Ewamm:_a onm_ aeozo_xo a_oo__ .m:_~acm ._o um mmmc_o om. m~._ m.v m~.m~ Eco. u__m .msmcxu_:u o o c pacovoouom Am; _.__v mm_p_:o w>_uoo cm>oo Emumw=_a 0mm. usage—xo mpuu__ .m:_~oco .Fo um mmmc_o ox. mp.m ~.o. mm.o~ Eco. u_.m .ogmnxo_zu o o o mzoacpucou A»: o.mv cm>ou Seaman—n omm. aeochxo mpou__ .m:_~oco ._m um mmm:_o mo.m -.e m.¢ m~.m~ Eco” u__m .mzmoxoPcu o ok «a pm=o_ucoo¢ Am; m.~V cm>ou Emumo=_a 82 98:25 £3: 5525 ._a 80 mmmc_o oo.¢ ow.m ~.¢_ mm.om Ego, u__m .ozmcxuwgu o NR mm mzo::_u:ou Am; o._.v . co>oo uoom “co>oo aka. A¢¢._ - No.v Am.N - m_.v Am.~_ - mm.v A_.mo_ - «.mmv cease—Jo saunas—n ~_oo__ ._a om .mNsz umm. ew¢._ umm.m emm.mm asao_ o__m .agmmxupcu o o o m=_~acm =o_omoo¢ mocmcwwma cx\o;\mx cx\o;\ox cx\5o cx\Eu ogauxo~\waxh :o_umoo4 x a 2 am: can; “coaxm acoaxm ccoczx =o_ucuwa_omca __om cx\cz\mx macozamoza .cuo» :wmocuwz Pouch goon: cowuou_—aa< co~__,ucou humacwucoov no mpnmh 94 nm E: <1 \ov: mnmozmmozm S. 3 3. on on 2 3 8 I, 0.. m. o [N 3H moazmn ommo am ¢.¢-¢.N m.m-w.N N.opue.m ¢.NF-o.m o.m-o.m _.PF-o.o m. mp m. m muwpom v.m —.m m.NP m.m m.mp N.N m.m m. m Peach colov ovuom NouNm omuom wmupe omuNN N.Nm -- om mm mm am No mm mcacmz anomm :mwmpmo mmmco:_ pmmgm mgp_:oa mcwzm mppumu Noam mNpumu memo Lmummm «.Ausmpmz m>w~ Accumem - mumPLaocaam aoc zgpcm Lo mpampwm>m no: mumu wpmownc? mmcmmo .mczpwcmpwp cw cm>wm mucus mg» pcmmmgamc mmgzmww gmzop ”mmmgm>m m? «czar; coaaav wave: can mmumulmgzcmz xuopmm>v4 mo cowowmogeou new comuuauocm Apron ”N mpnm» 98 livestock producers are faced with the disposal of highly concentrated low volume waste flows in confined areas from either the feedlot or manure storage facility. While various chemical parameters such as the nutrient content of the accumulated manure will vary depending on the age, weight and animal type (see Table 7 for comparison), most studies to date have focused on beef or dairy cattle. Loehr and Agnew (1967) found that waste production averages approximately 6% of the animal's body weight per day. Reddell et al. (l97l) reported that about two tons per year of a semicomposted manure with a 50% moisture content accumulated for each head of cattle in their study feedlot. Much of the total waste generated decomposes on the feedlot surface or is removed by cleaning operations, however, a small proportion (2-l0%) may leave the feedlot in surface runoff (Madden and Dornbush, 1971; McCalla et al., 1972; Loehr, l974; Gilbertson et al., 1975). Under the improper conditions the animal wastes could cause problems com- parable to the discharge of untreated municipal sewage. From an examination of the approved nutrient export data from a number of feedlot/manure storage studies, total nutrient loads were observed to be 2-3 orders of magnitude greater than in runoff draining other agricultural activities (Table 8). Nutrient export variability was also much higher (Figures 9a and 9b). The most dominant influence of runoff water quality and vari- ability has been linked to the intensity, duration, amount and seasonal distribution of rainfall and snowmelt events. Gilbertson et al. (l975) reported that slurries of undecomposed manure flowed from their 99 mNm_ ouaccu .o_cuu:o Na; N_.V oopummc .mgoz new muoou a: dewo mK— cdm «533:: 9; am>ma . .ou ago—Loam: opuuuu boom Na; m~.V o_oaao mNm_ «cacao .o_cao=o com - com .oo_comc .oco: can mooou mme N~.~Nmm ~.mm N.oN wumcoeoo ..oo “cox «guano comm Nag moo. .:ou\m mNm_ Ao.m_m e.¢m_v .~.mo_~ m.wmm_v am.e~v wean mcvxpcaso o.m_ .oo.aa c .._o “a comocma._o neNN gamma ¢¢.¢~ eso— »~.o Nup_m oxmocnoz .uao: «Nuoou coon Nag Nae. :OQNWE . NNm_ Ne.cN¢ o.eN~. No.mme. ¢.em~.v Nm~.m_ mm.o_v neon accx_.m>o 8.x. .uo_ua c .._~ om a_Pmuoz a~.N¢m seem. mo.N. amo— x~.o Nop_m mxmacaoz .uaoz o_uuuu comm Nag Noe. zoo\me NNm. A~.am~_ ~._¢~V A¢.oMmm o.o.o~. NNo.N_ - mo.e_v econ m:.».cm>o aN. o .uo_uo c ..a “a a__auuz a~.mmN a~.m~m~ Nm.m_ soc. 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Nm.N No zuaom oc_oooco o>wou< Nag memo ocaumoo om>oco -E_ ooc mooco Nem._ - on o Aom.o - o_.mo Am._~ - _.N_o Ame. - may ouoco_u mc=o_=u_ca< oNo_ .__mooaco o_.P oMN.¢ cN.oP om.oo ocam ._ocucmo cucoz mm oN_ m>_momu:_ No; a.~oo :coo mcomx NNo_ czu .mcaumoo .._m um .__mzczm NN. _N.ep mo.N_ FN.vo Eco_ u_wm ozoo .Locxch No mom .mcowa owes. mococmomm LxNo:\ox L»\oz\ox cANEo LANEU «czumeNQQAN :o_u~u04 x o 2 mm: coco acooxw acooxm woocam :o_uou_o_owco —_om c»\oz\ox macocomogo _uuoN smoocu_z .cuoh coon: :o_uouF_oa< co~v—*ucmu Aomscwpcoov no «pomp 109 No; mommo nco.uoo3 am.N mcoumoe_P ccoo Hm..— oco mpmcm oomooo .owcaoco pomcwu xo.oN wNm_ .N.omv co>o ao_u ccmgozcm an: ooo .po um ouooo mo. _.__ N.MN oowowuocum .cm>_a Lucas: mcaumoo an._o Na: mNomo ccou RN.o_ mu_eo_oo non—oooz No.N_ cm>o xo_o «cacao .owcuuco _oocwo wo.o_ mNo_ .A.oov omxcozoc ocm ocmguaom .xmmco No; use ._m we ouooo mm.~ m.m_ o.NN m:_cum=uo— opp: xucwzh «caomco “N.eo No: mvomv ooocoou NN.m pp?“ ccoo Nv.o_ maomcou_uo Noomm .mwcmo RN.o_ ocwooom :o mooouo .o_cmu:o x5; woo mNa_ ..A.euo u__m cam occm ecogoaom .xmmco oczumma Nm.¢~ ._c om muooo mo. N.m o.oo axe—ooowz xw__a> coo—82m non—oooz wo.Nm :coo um.N_ _oocmo am.NN moocou .o_cooco coo—oooz NN.mN mNa_ ..N.cmo __ou Eco. ccoguaom Na; use .Pa 90 ouooo op. m.v_ m.No om~wcw_saco .cw>px o:o_u_oz wczumuo no.mm No; OOOMV pmwcmo NN.N_ moacoo .opcmuoo oco_oooz n¢.m_ ogocuoom Nag oco mNo_ .A.omv ___u Nana. .cm>_¢ acoumao no.NN .Po um muooo mm._ _._m m._o_ maomcco_ao moans» «Foo_: ccoo Nm.No Na; oom_o com—ooo: No.o ocou NN.m_ wowocoe coooou .o_couoo .owgmo an.mm mNo_ .A.omv ocaoco ocmzuaom as: com ..a um moooo oo.p m.o~ m.~o No.9 N»..m .co>_¢ ucaco eczemaa "N.Nm muoocmoom LANosNox LANozNox cx\Eu cxxao mczomeNooxh copumuoo x a 2 am: can; ucooxm ugooxm moons: :o_umu_o_uoco pvom LxNoz\ox macozomoso _auop cooocuwz .ouop coon: :o_aau_—oo< sou—poucou “casewocouo ”m m_DMH 110 .m:_moo cm>_m cwmosom oco oooco moo :_ mmHNm omuom_mm mo oopgou_:oe o xmmp omqooo co comma mmooswumm .o cowome Lam» mmcgp .u co_oma com» Lac; .9 some somx orb .o No; cameo ocopooox wv.o P—_u xa—u :coo Rm.o Lm>o xopo noncoo .owcouco ~omcmu NF.N_ mNo_ .A.omv o:_cum:ua~ :cmzusom xo; com _o um muooo _o. ¢.o o.No omxcoxoc cm>wm cmmoamm mcaumoo ao.oo Na; omm_o compo»_zz ooa cmmnxom Rm.N mooumme__ .mmcoo wo.m Lm>o c_a_o _.pu momcco .o_cauco com—oooz vo.o_ mNo_ .A.omo xopu cm>o ocom ocmzuaom :coo No.NN .pa aw muooo Po. N.mN o.NN oc_ocoe xo_pocm .xomco owe—F.z mmpnoumom> No.NN mocwcmoma cch;\ox LANo;\ox LANEU c»\so mczumeNmoxN :o_uoooo x a 2 mm: ocmo ugooxu “coax“ woocom :ovuau_o_omco ._om g>\mz\ox mocozomozo pooch smoocuwz pouch Loom: :o_uoow_oo< cm~_p.ucwu Aomocvpcoov no mNooN 111 E: 5.29: momozqmozm 3. S. 2 9n. 2 3 2 03 m. o o .. _ 1N -m oN mN_m moazmo omN <1 Nov: zmoomtz oo on 0v on ON 0. 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N.oo. moo.>cwne. woN nunoz .sonnno .n.unmo.mmn woo mmuoonom oommwnnm. moo—>cona. amN .w...>xonx soon muonoonnu mmg< .o.unoo.moa mo. oo.. o.o o.mo. o.o.eo.oo o.nn.om m...= no.0ouno.n .on Nov ncncnonw nx\nn\ox nNNonNox cNNEo cNNEo mowum_nouoocono no.uooo. om: one; “conxo uconxo ooonnz no_uou.n.omnn oooocnmN..cm monogamonn .ouo. nooono.z .ooo. Loom: .ow:n.unoov no. o.ooh 125 N Eisteo ES: 8858.... o o a m N _, . _ MN MNnm mnnznw MN.o - 2. moz§ oN.H zo_.<.>mn nnmn nn value _ LLJ _1 ‘1 CI) Statistical Inter- :EE significance quartile CI: of the median range 9:: 75% :=’ value LL. f‘ ‘- CZD J p.. LLJ ‘0 Median (13 value 2:) (I) 2:) Z *— égg 25% [_ a1 e " 0 V “ <=E Minimum -1. value Group A Group 8 Figure 12: The Basic Configuration of a Box Plot and Comparison of Two Plots Possessing Significantly Different Medians 131 3. an indication of skew (from a: comparison of the symmetry above and below the median); and 4. the size of the data set. The box plots of the nutrient export coefficients from different land uses can be compared in Figures 13a and b. Note that the nutrient export medians and associated variability for each land use are readily apparent. The range of nutrient flux values from forested watersheds is quite narrow and the median values for nitrogen and phosphorus are significantly lower than for all other land uses except pasture. A major factor determining the magnitude of phosphorus flux appears to be total annual water flow. Areas of the country with high annual rainfall (and a high percentage of storm events) tend to have high stormwater flow and high phosphorus flux. Variations in the magnitude of nitrogen export from undisturbed forests are more difficult to interpret. Since nitrogen is often the most limiting nutrient (for terrestrial plant growth), it is a more sensitive indicator of biological activity than phosphorus. Because of this sensitivity, readily observable relationships between nitrogen flux and climatic or physiographic factors may be overshadowed by subtle local differences in the supply and demand for nitrogen by growing vegetation. Watersheds dominated by agricultural activities demonstrate both significantly higher median nitrogen and phosphorus export and wider export variability (with the exception of pastureland) than undisturbed forested watersheds. The general trend within agricultural watersheds indicates that as the soil surface is increasingly disturbed 132 m m m 3 m. 2.5% anaboggq 852 . $05 sonzoz ., mTIII 82: 30.. J 795.20 moN\sovmmoz:m mm»<3 mo :3 mammmoxm we”. I v. 0 8 Sara: “.288: \a . m. N M . w w J.o 2.nu (M .25 - N. u. 51 xzkstsw W - m. m N . m. 9 - o._ 1 — C - 1* N CHAPTER VIII SUMMARY AND CONCLUSIONS The major focus of this thesis is non point nutrient flux from quickflow (stormwater flow), and the ecological mechanisms within a watershed which influence nutrient variability. Because many of these mechanisms and watershed perturbations are land use specific, the hypothesis, which is central to this study, is that a relationship exists between land use and nutrient flux. To properly characterize the variable nature of diffuse nutrient export, and test this hypothesis: 1. elements of sampling design theory were described, 2. literature studies conforming to the described sampling design criteria were screened and compiled according to land use, 3. biogeochemical factors influencing nutrient flux within each land use were examined, and 4. compiled nutrient coefficients were applied to a hypothetical watershed and the results interpreted. Sampling Design The major components of sampling design best describing both temporal and spatial variability of quickflow and diffuse nutrient flux include: 158 159 Parameters sampled: Nitrogen and phosphorus are the two nutrients most commonly accepted as affecting the lake eutrophication process. Of these two nutrients, phosphorus is generally the most limiting factor for plant growth, and most effectively con- trolled using existing engineering technology and land use management. Both nitrogen and phosphorus are collectable in basically two forms: particulate and solution. The soluble inorganic forms are generally readily available for plant utilization. However, there is a high degree of uncertainty concerning what (or when) fractions of particulate inorganic and organic forms are biologically available. Because of the unpredicta- bility of bioavailability, the collection of total (soluble and particulate) nutrient fractions is advised. Sampling frequency: The frequency of sampling nutrient flux associated with quickflow is a function of the 1) hydrologic response of the watershed; 2) effect on the precision of the nutrient budget estimate, and 3) associated cost of sampling. Often sampling frequency is based on a random design. Uncertainty can sometimes be reduced and accuracy and precision increased if a stratified random sampling program is employed. The underlying assumption is that the population can be more accurately represented as the sum of sub-populations. The two strata associated with hyrdologic data collection are 160 1) rainfall and snowmelt induced high flow events, and 2) low flow (baseflow) conditions. If sample size is increased in the high flow stratum, a more precise and accurate estimate of the population average can be obtained. According to Reckhow (1978), more samples should be taken in a stratum if the stratum is: l) more variable, 2) larger, and/or 3) less costly to sample. Sample collection and flux estimation methods: a) Concentration samples: Concentration samples are determined by a variety of field collection techniques. Manual (grab) methods are easiest but may not be efficient because storm events which transport a high percentage of the total load are often missed. To correct this problem, automatic samplers should be used. The collection process can be implemented at either equal time intervals or on a flow-weighted basis. Flow-weighted sampling often yields a more precise concentration estimate because high concentrations associated with first flush can be more equitably represented than sampling at equal time intervals. b) Flow estimation: Flow estimation is determined by one of three methods: 1) continuous flow measurement (i.e., USGS stream gauging stations), 2) instantaneous flow measurement at time of concentration sampling, and 3) an annual flow regression equation developed by the USGS. If USGS 161 stations are not available, the third alternative is probably the most precise for a given cost. c) Flux estimation: To estimate flux, a number of mathematical techniques are available. Each is appropriate under certain conditions. The technique chosen depends upon the intended use, fit of the data to the equations, and simplicity of the mathematics. Temporal extent of sampling: The temporal extent of sampling depends on long-range variability. Seasonal periods of high rainfall or snowmelt runoff creates greater variance in nutrient concentrations and loads than do low runoff or baseflow periods. For a given confidence level (precision) and a margin of error (accuracy), the temporal extent of sampling must include these high and low runoff periods. Therefore, a more infor- mative approach is to sample and report data in yearly increments. Sampling location and watershed design: The sampling location is determined by the desired (site- specific) representativeness of the sample and research objective. If the objective is to determine nutrient export from a particular land use, then the watershed under study must be exclusive of other land use types. 162 Comparison of Nutrient Export Coefficients from Differing Land Uses, Local climate and conditions within the watershed contribute to longitudinal and cross sectional variability, and are major influences of the "characteristics and comparative magnitude" of nutrient flux in quickflow and tributary outflow. These influences are analysed and categorized within the context of three land uses: forest, agricultural and urban. 1. Forest watersheds In forested systems, the median nutrient export values are significantly lower than for all other land uses except pasture. In addition, the nutrient export variability is small, making it difficult to specify any one factor as the determinant of loading in a particular watershed. Much of the variation among coefficients is probably within the range of experimental or sampling error. To determine if cause-effect relationships existed between certain physio- graphic and climatic characteristics, the following factors are examined: a) Geology: While the hypothesis of geologic influences on water quality make theoretical sense (e.g., high phosphorus apatite rocks contribute to high phosphorus loads), little information on specific effects is currently avail- able to verify this phenomenon. b) Vegetation type: Certain vegetation types cause reduced water flow (e.g., pines have high evapotranspiration rates and interception C) d) e) 163 capacity) and increased nutrient concentrations (i.e., nitrogen fixers). Both can reduce or increase nutrient flux. Ecological succession: Three popular hypotheses currently exist linking ecological succession with nutrient accumulation and output. However, the collected data contains a mixture of seral stages and many other causative factors, which complicate any con- clusive argument. Climate: A major factor influencing phosphorus flux appears to be climate. Areas of the country that exhibit warm climates with high rainfall (such as the pacific northwest and the southeastern piedmont regions) are also associated with high productivity, high runoff and high phosphorus export. Disturbed forests: Disturbances within forested watersheds produce increased nutrient flux. Of the three types of disturbances examined, timber harvest operations appear to produce the highest nutrient export. Agricultural watersheds Agricultural watersheds are shown to have both significantly higher median nutrient export and wider export variability (with the exception of pastureland) than undisturbed forested watersheds. In general, as the soil surface is increasingly 164 disturbed and "exposed to the elements," and increasing amounts of fertilizer nutrients are added, the potential for soil erosion and nutrient export increases. Major factors and activities which influence nutrient flux include soil type, management practices, crop type, pasture and grazing operations, animal feedlots and manure storage facilities. a) Soils and management practices 1') ii) iii) Soils Because cropland soils are left fallow for long time periods (i.e., late fall through early spring), the potential for erosion and nutrient flux is high. Of the many soil types, clays and organic soils contri- bute significantly to high nutrient yields from quickflow. Fertilizers The type of fertilizer is not as important to nutrient flux as the time of application. If fertilizers are applied during snowmelt or high rainfall/runoff periods, nutrient export can be high. Excessive fertilization (applied above the recommended rate) will cause increases in nutrient flux. Under-fertilization can also cause similar increases (from soil erosion) since the crop canopy is often reduced which exposes the soil surface for longer time periods. Tillage practices Conventional tillage methods, in which the ground b) 165 is left fallow during non-growing periods and crop residues are removed at harvest, cause soil erosion and high nutrient export. Conservation tillage methods, such as no-till, contour planting or terracing, signifi- cantly reduce water, soil and nutrient export. Crop type Nitrogen and phosphorus export from row and non row cropped watersheds is significantly higher than nutrient export from forested watersheds and significantly lower than export from animal feedlot and manure storage facilities. However, the median and range of nutrient export from non row cropped watersheds is lower and narrower than export from row cropped watersheds. Although management practices for the two crop types are often similar, plant density is usually much higher for non row crops. This reduces channelization,water loss, soil erosion and nutrient export. Pasture and grazing land Nutrient output from pastureland is not significantly different than output from undisturbed forests. This is because the vegetative cover retains water, soil and nutrients. 0f the two general management practices-- continuous and rotational—-the former will result in higher nutrient export. This occurs primarily because soil compaction and waste loads are increased and protective vegetation is decreased. Fertilization of 166 pastures can also increase nutrient export. d) Feedlot and manure storage The nutrient export coefficients for feedlot and manure storage facilities are significantly higher and exhibit the greatest variability of all land use activities. While conditions are highly variable, the feedlot or manure storage area is typically devoid of vegetation, the under- lying soil is saturated with nutrients, and the nutrient pool from animal wastes is often inexhaustible. High nutrient export can be expected if the, 1) percentage of paved surfaces is high, 2)roof area/feedlot area ratio is low, 3) animal density is high, and 4) no detention basin is present. e) Mixed agricultural activities This general category includes varying percentages of all agricultural activities including some forest land. As a result, phosphorus export from this mixed land Use is not significantly different from any of the above described agricultural activities (except feedlots). Nitrogen flux, however, is significantly higher than both export from both pasture and non row crops, possibly because of the greater occurance of nitrogen fixing crops in these mixed watersheds. 3. Urban watersheds Nutrient export from urban watersheds is not significantly different than export from most agricultural watersheds. 167 Variations in nutrient export, however, are also large. This results from two basic considerations, a) hydraulic factors which influence runoff volume, and b) land use/ cover activities which influence concentration. a) Hydraulic Factors Major hydraulic factors include the percentage of impervious cover and nature of the drainage system (i.e., slope and detention basins). As the percentage of impervious surfaces increase, infiltration capacity is reduced, runoff and surface scour is increased, and nutrient flux is also increased. Therefore, commercial areas tend to have higher loads than residential areas. b) Land Use/Cover Activities Many local sources or activities increase stormwater nutrient concentrations. These include i) atmospheric emissions, ii) street surface residues (i.e., ice control chemicals, pavement materials, dirt), iii) erosion from construction sites, and iv) non-event, storm sewer contaminants (i.e., industrial spills, illegal discharges of waste waters). Application of Nutrient Export Coefficients The nutrient loading coefficients have meaningful application in the water quality planning arena. Planning implies the prediction of future impacts of land use on water quality and requires the use of mathematical models. Projected or anticipated land use changes cannot 168 be measured. Therefore, the information necessary for model inputs must be extrapolated from other similar watersheds such as the nutrient export coefficients compiled in the previdus tables. Two considerations are necessary for selecting nutrient export coefficients. The first is that the selected coefficients must carefully match those characteristics of the application lake watershed. The second consideration is that the reliability (or uncertainty) of the prediction be estimated. Assignment of "high," "most likely" and "low" export coefficients represents the uncertainty that the analyst has in the nutrient loading estimate. "While modelers and biologists prefer objective measures of uncertainty, both the limited available data, and the unmeasurable nature of future projections necessitates subjective estimates" (ReCkhow et al., 1980). To demonstrate the transferability of the compiled nutrient loading coefficients and subjectivity associated with the application process, a hypothetical lake watershed is constructed with a wide range of land uses and two years of annual rainfall. The resulting lake trophic status and lake rehabilitation strategy success are dependent not only on the selection of "high," "most likely" and "low" annual nutrient flux estimates, but also on the year (wet or dry) the estimates were based on. Considering the uncertainty associated with this example, and the previous record of improper use of literature export coefficients, two important conclusions are apparent: 1. For lake management purposes, the use of nutrient loading estimates for predicting present, and future water quality conditions with changing land use, is highly subjective. 169 To reduce application uncertainty, the user or analyst of these coefficients must be familiar with the biogeochemical processes which influence nutrient flux. Only after care- ful consideration of watershed and climatic conditions should any attempt be made to match these conditions with literature derived export coefficients. As watersheds become increasingly removed from natural undisturbed conditions and undergo increasing human perturbations, the ecological mechanisms controlling nutrient flux become more complex and less understood. Our ability to accurately predict present or future inter— actions within the drainage basin and resulting lake response, likewise becomes less precise and more uncertain. Given these circumstances, there is a need to acknowledge our inability to "solve" all water quality planning problems with "inflated" confidence. A real effort must also be made to acquaint the public with these limitations so as not to jeopardize our future creditability as water quality planners. BIBLIOGRAPHY BIBLIOGRAPHY Alberts, E.E., Schuman, G.E., and Burwell, R.E. 1978. Seasonal Runoff Losses of Nitrogen and PHosphorus from Missouri Valley Loess Watersheds. J. Environ. Qual. 7(2):203-208. Andren, A.W., and Lindberg, S.E. 1977. Atmospheric Input and Origin of Selected Elements in Walker Branch Watershed, Oak Ridge, Tennessee. J. Water, Air, Soil Pollut., 8:199-250. Armstrong, D.E., Perry, J.R., and Flatness, 0., 1979. 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Km.P mo.o N.~m :o_uouoL .muoo Lu; Loo.o m:o_uou__owL x_m «No. .uLoLooLm vo.m om.o o..o om.~w ow___z La; m - co .zo__nL Lmaazm omo_ .ocmm o.o ~.o o.~ om~.~_uLmL ..mL can Lacu_c_ozu_z L.L v.0 o.m¢ new “gun: mcLLam An: m - co :oLHMHOL Low» oz“ mam. .comm . o.o o.o o.o_ .zo__oL Lwesam can use x=LULoLoguLz m.o L.o o.om “mug; o=LLam Am; m - co co_uouoL Low» ozu oko_ .ooox ..o —.o o.~ .opaosum Lasazm coo x:;u_o~o;u_z _._ ~.o m.~m vca anus: m:_Lom q~.o o.o o.- .m.e m.o o.mo Ac; n.n. m:_ o~o. oo.~ o.o ..ML -ucm.o maoacLoccu ...o um _o~:mz mo.o o.m~ o.oo Lawn: oucoLome coooLa.z 2. mac Immune. v n z-—auo» -v -noz Luxuu Luxlo om: toe. . .ouohz.-‘--l;mwmeu_z wage» a o n :u co prz ou>bommpn Lungs: :o—uau.a.uuLo uLc xu cw oLu_z Luau: Aumzcwpcouv ”am< m_nmL 217 o.o ¢.m o.mm ~._m_ ~._ o.¢ o.o_ m.no_ mLm_ .mcoo ~.m o.oN m._¢ o.em_ N._ ..N o.om ~._m_ m. 0., o.o. m.mo_ oLm_ .acoo o._ o.m o.~e n.em_ vLmL ...m um Lm=m=o_x om. mL. L.o_ ..om oucoLmLoz emaOLu.z zu.m~op;zznua¢.;=hzu -. “mp2. mumenL..zn . z-moz LLLLD LL\.o ,pwwmht.uz - coaoLa,2,wcme_ew manpou—uLan cw oLu.z mo>_omm»nall, : LLocaz =o_asu—o.umLo -1 ALxxocwmxm‘uLoaxm cmaoLu_z Loam: Aomocwucouv Lo; «o.o manpoL< ..mom Eco. aucom .:o_oo~__puLmL szc§= x>omz .mmoLo uuaeLaa .oumooo Aug co.v «Eooap< .__om Ema. xucom .co_uo~___uLow mLacoe uzowp mmcLo oozeLmn peummou xLO> zoz .oLOL3< Lu; Nm.o “mag: mm: 7:o_ Hnm< m—nm» 2153 A»; opo muop mpuumu oooLn .mePoo new mamLx mem._ ouo. mo~._ o.Po o.oop Lo» oLaummo Npm. omp. com. ow.m oe.m~ mLoL Loo. L_o. «no. em. om.mL A“; m.~¢o ...m um cesagum Pmm. omo. mop. om.¢ mo.- o=_~mLo copumuom aka. an; .v f? 3 :38;qu mo. o. co. No.~ o.oop BSLo L923 Am; po :o_umuoL oNoF stssm new ..Pm om Lmumosu_;o o.o mp. o.m eo.mp o.oo. om~aLo Lmu:_z Ac; m~.oo eNop .._m um mELm: mm. ee.¢ «.mm mLoumma Np. o._m o.opp Am; on._v pp. o.o. m.mop Lm>ou mmoLo oL. o.ow m.eo_ -mapa .m=_~mLa vxo. ..pm um Lee—fix om. o.ow o.oo_ wamo >>mm1 Np. o.¢~ o.m__ Am; om._ mp. m.~— m.mo_ Lm>ou mmmL o_. m.- m.eop -mapn .ocp~oLo qNop ..Pm um Lmsp_x mp. ~.o~ o.oop ngau muaLmuoz 3:383. 33.3.3.3 33.33.: h :35. Anebnlu L35 LL23 on: 25.. Page», acmerwo oucp=u*uLnnw maLocomomn m¢>_ommmn Laces: :o_uua— aLxxa; xv uLooxm moLozomogo Luau: -o_uuLa momgmmemz omNmLo ocm omLspmmo Eoow “Looxm moLonomocm HM¢< wFomH 219 owmp owm_ wxop 0 .Po om ._o om .Po om ._o um mmmopo mmm:_o mNmF Fm~cmz _m~cmz NNmP ...o om __mLLoo muzwLmme Lo; o.mo Lm>ou Emumw:_o m_LLLL .mcL Atmocmpcoov mo.m oo.o mq.m m.q N.mL -NmLm .mco_gmuom Am: m.~v Lm>ou Emummapo m_uu__ oo.q mo._ Rm.m N.¢_ o.o“ ocw~mLo moo::_u:oo No. No. oo. mm. «.mm Am; o.__v Lm>ou LN. mm. No. L.L ¢.mm coca .LoLOU em. mm. No. o.o M.NN Emummapo m_uu__ ¢¢._ cm._ o_. o.L_ ..ooL mcLNoLm cowumoom om. om. Lo. No.N L.Om La; ..LLV mm___=m oo.. mo. mo. o.L_ N.oo m>_uum .Lm>ou oo.. oo. No. o.m. o.- Emommapa m_uop_ om.m NL.m 6.. «.mm o.mo_ m:_~aLm mao:=_u:oo Lo; Ngmmo :oLuuzu -oLo so; wEOm ocwommm Lmo:_3 xLouomampooom meow goLz o.o m.¢_— ocw~mLo moo::_uooo maLosomogo maLogomoga n PWQOP o-ooo Lxxso L>\su mm: coco pouch “cws_mom\mump:uwuLco maLosomocn,om>—ommwn‘ Lhasa: cowuou— 1- ALa\m:\mxv uLooxm moLonomogo Loam: novumLo ”mv< mpomh 220 , La; o.L_o Lm>ou Ewummapn 23: .9: ooo. .._m um mama—o om. op. No. m.e ~.o~ -~oLo _u=o_uuuom Lu; F.L.o waywpoo m>wuuu Lm>ou smammapo o_uu__ .m=_ oomp ..po um mmm:_o ox. m5. Fo. ~.o. o.on --Lo maoacwucoo muomLmLom msLogomogm maLogomogo n Pouch g-coa Laxao Laxso mm: was; Fouok‘ acma_owM\ounrouwuLmn1‘ moLosomocnrma>pommwo LLoczz oopuau— «ua\mg\oxo uLoomm msLogomoso Luau: -o.uuLo Aumocwpcouv ”mv< m_noF 221 ...c um :o522um ...c om Lmumwcu_co ...n om Lmummcu_;o :2 . .2. am 2:2. mm._ No.~ L.om Lo; _.__o mm.m o.L_ L.om mmLLLsa mLLLum wLm_ m¢.m o.N_ m.LL .Lm>ou smumm=_a «LLL__ ...o um Pm~coz «o.o o.oN o.mo_ .oc_~oLo maooo_ucoo w~.¢ om. mq. om. oo.m oe.mL LLm_ Le. LL. mo. LL. em. om.ML Lag o.L¢o mm.N mm. oo. e... om.e mo.LL m=LLQLo =o_umuoa mLo_ mo._~ o o.o m.o La.m o.mo_ Lo; .o umLQLm Lwcsam La; Lo oLop _o:o_ucu3L ngasm mo.oL mm.o o.L mL.m em.L_ o.oo. can uQLQLa L0L=L3 Nm._ N... oe. ev.v ¢.wm Lo; o~.oo mLsummL oN.o o«.. No. mN.L o._m o.m__ .m.o ow. mL. OL.L o.o. m.mo. Lo; wo._o «LoL .L.N_ ~m.. Lo. aL.o_ o.ow m.vo_ Lm>ou mmome=LQ ...” um Lwe__x mo.mL mm._ oL.o_ o.om o.ooL .o=LLMLm LLLLU L>~mz Lq.m m... LN. v_.~ o.q~ o.m_. Lo; om._o .q.~ mm. o_. oo.. m.N_ m.mo_ Lm>ou mmoLom=_m qu_ mo.m LL. cc. mo.~ m.LN m.¢o_ .m=_~aLm ...u Lo LeE_Lx ¢¢.m Le. La.~ ~.o~ o.oOL LLLmu ouaLouoz mucmeLmz cwaoLLLz - -e -n z-_aLoL - - -c -n LLLLo LLLso aw: =;L_ -zpmwmh::uuos cu oLo_z game. m m a aw La .1 ;111.11. cw oLw_z mm>_omm—n 1|, - mucosa cc.uaa_o.uuLg L mgvmxw “Lomxu cwaoLa.z Luau: momszmpmz vamLo now owLoummo 20L» “Looxm :mooLuLz Hn¢< whack 222 .mccwuuogw w—na—Omlcoc Uta w—nn—Om .302 *0 mumvmcou .6 La; o.__v Le>ou oxm_ =aumao_o mpuu__ .._o oo mmmcpo ML._ mmm._ amp. mom. o.o ~.oL .m:_~oLm pocoFHmz La; L.L.V mmLL_=m m>vuuo .Lm>ou onm_ . ewummapa w_LLL_ ...o um mmo=_o m_.m mmm.e QQL. mvm. ~.o_ m.oL .m=_~aLm m=o=:_u=oo . Am; o.mv Lw>ou onm. emumw:_o w—uo__ ..Lc um mmwc_o NL.¢ c_v.¢ oLo.m mFm. m.e ~.oL .m:L~mLm pocopumuoa Ac; m.LV Lm>oU omm. Emumma_a w_uuw_ ...n om mmo:_o o~.m «mm.m am_.c coo. L.c_ o.oL .m:_~oLm mzozcwucoo mL. mm. «.mm Lo; o.__o cm.m ~.L ¢.mm Lm>ou zoom .Lm>ou oLm_ mm. ~.e m.LL Emummapn m_uo__ ...u om _m~:mz ~o.~ o.L— —.mo_ .mc.~oLm =o_uuuo¢ muchmme ommoLu_z zu.-o»1.zfi. «on: z-mbz z-_oocp - - e -ncz Lxxto Laxsu mm: o::_ -a-wmwmhz!-: ...... LmWImmpLz.wmmmL a o a auWoLu .11. co LLL= omLLommLo LLocaz chL~LLLLULLL L as am “Lo xu commLu_z Logo: Lumchucouo nn¢< mLQML 2253 La; m_oo.o BOU\NE mm.m mLm_ N._mm oo.vL LopommL ...m um «LLmouz N.mmm_ Lo.L_ mpoumu meo mLmL .cmuomz L.LG. _L.o mo.mm Lo; c._o ocm gmao:Loo ..mm mm.m ML.mo mcwommL meo m.Lm_ oq.¢_ o_.wq Ln; moo.o mLm_ .cmoocz m.mmm mm.om .o.om uo_ommL use cmzocLoo e.mmom cm.m_ mm.mo omozm use meo Ac; m_.v m._om oL.LN oL.o¢ mLLomu Lo mLmL .cmuucz o.mmm mo.mo Lo.om mam; mo .ucg= can cmzocLoo m._mm o_.mL mm.mo -m:_Lccu LLLmo mLmL .cQUUmz La; mo.L_v uzm cm=DCLoo oo.—N o_.m om.mq oOLUmmL 52m. mLmL .cmoumz o_.om oL.L om.m¢ Lo; mm._Lo new smoocLoo qo.mm om._ o.mm ooLommL can; m.m¢L Lo._L m_.mm La; 0L.¢o mLmL .cwoomz o.ocL mm.o LL.oo Lopovmo use cmsacLoo o.mmm mm.om oo.Lo Locomm>LL meo mucmLmme moLozomogm moLozomogm a pouch gloom Lm\Eu La\su mm: coma _auop Homewoom\mucp:u_uLan mzLoommogn,om>Pommwm‘ LLocox cameo“, «Lm\as\mxv uLooxu maLoommozm Laos: -ovumLo mmmLOHm mLocmz com popommm Fmswc< EOLL uLooxm moLocomozm Hom< mpnm» 224 La; L_.V o_;o NLm_ om.m_ m.LN .m;\mzou oLm ...o um moLmzom P_.¢P m.Pm .LLoooL pochmm LLm_ . La; qu.o LLLLLUQL ...o Hm LLoumcz m.mmm m.mm L.Lm mmcLoom mLacmz La; mo.o mmLc mmmLoom mLmL .mLo: coo moooo o.oLL o.oo m.om Lm.Lo mLacaz oLLOm Ln; mo_.o oopommL oLmL .mLo: can muooo o.oLL o.mL m.LL o.oL o.oucu meo Lu; mqm.v mpuumu ooo-oom .uo_ummL oLmL .mLo: occ muooo o.mmq o.mo_ N.mm L.oL mLLch Loco La; m_oo.o 3oUL a o.o. mLmL ¢.¢m_ om.mm . o.ommL ...m um cemoLmoLLo o.o—m mm.qm m_uumu meo Lo; m_oo.o on\mE e.w~ mLm_ o.omm om.m_ .oo_ommL ..Lm om m__mouz o.oLv mm.o_ «Luumu meo mucmLmem moLonomozm mzLogomozm mthuoh a-¢on mesu Lxxsu mm: coco .mooh acmewwmm\momP:uWume moLoammogn,vm>Pommwp Loccoz coLuaoL I. AL»\a;\mxv «Loomm maLogomosm Loom: -o.umLm Aumzcwpcouv "mm< upon» 225 NLm_ ...o um c__ouuz MLm_ .zmvunz coo :mancLoo mLo— .ovocoz coo smoocLoo MLm_ .cuoouz use smoncLoo mLm_ .omoooz woo cmaooLoo Mmm_ .cwouoz new smoocLoo mmo_ .cmucoz can zmancLoo 1:1. '- 111' 1.011!!!- aucoLoLmz Lo; m_oo.o mmmLopm mLocmz com uopommu Fmewc< EoLm pLooxm :mmoprz o.c_om mo.¢_ zOUL a mL.m e.ommm Lo.L_ .LOLUQoL mLLL u mem coc.mLm _L.m mo.mm Lo; o._o coo.mm mm.m ML.mo mchmeL mem oqo.LmL oq.¢_ o_.o¢ La; moe.o av.mmq mm.om Lo.om Lo_ummL am.mLm e~.m_ mm.mo Loos” wen mem coL.¢m__ m_.LN o_.ov La; m_.o omm._om_ mo.Nm Lo.mm mLLLou Lo tum; we no.mcL o_.m_ mm.Lo .Lcm=m=_L=ou LL_oo Lo; mo.m_o mom.cc o_.m om.m¢ LoLUQLL Demo ooL.mm m_.N om.mc Lo; mm._Lv omv.~m om._ o.mm LoLummL asco om.em__ Lm._~ m_.mm mm.LLm Nm.o LL.oo La; 0L.¢V Lo_uomL no.~mm. mm.o~ oo..o Luaume>__ Loam :wmoLu.z zu-mwoh112nu - -e -n - LAglo Lasso an: 2:1. trumwmhz.z:.-::cmmoLw.z woma— o m a :upuLuL co OLwrzvmm>pOmm_=<1 LLocaz co.uuu_m.umLm L a: gm «Lonny ammoLa.z Loan: Hnm< mpomh A“; 226 ...o om mochow .._o um LLowmmz .mLoz now muooo .mLo: com moooo .mLo: ace muooo ..Lo um cemLLmDLLo ..La am o__couz .m:o_uumLL mum—:u_uLao use um>_omm_v coon Lo mummeoo .m mLm_ «o.o m.LN angzou oLm mc.o m._m LLocsL oo.:Luo LLm_ La; Leo.o LHLLLULL m.mLmL oo.—mom m.mm L.Lm mmaLoum mLaco: La; mo.o wme mmLa mmoLoum Lo._mo_ o.mpp_ o.oLL Lo.~ m.o~ Lm.Lo oLzooE vL_om mLm_ Lo; mo_.o mm.ooo o._cm o.om_ ~¢._ m.L_ o.oL uo_ommL m_uuau meo La; me~.o oLm_ m_uumu ooo-oom LN.~me o.vomm o.~oo L~.o «.mm L.oL .uopommw m_uuuu meo Lu; m_oo.o mLm_ m.mm_~ om.m~ on\me o.o— m.mom_ mm.c~ .uo_ommL m_uu u Loom La; m_oo.o NLm_ o.mm¢_ o~.m_ zouLWE o.o— e.om~_ mm.op uopummw wpuu u meo ouchwme cwmoLo.z zn—oao z-moz thtQ Lxgiu mm: =:e_ _ Smr1;.:. :;m~ 0Lw_z Homepvo a a :0 La 11;: cm OLuLz eo>pcmmpn LLocax co—ueu.o.umLo ~meacvmxw “Loan“ :mmoLu_z Luau: Aumocwpcouv nam< mpomh 227 La; ammo mUmOL Lq m_u_ Lo.m .:_mLm oo.o L.~m .Pmam Rm .mmch oLm_ oo.o o.mm com mmszmm. Loo .mLo: use _:umm oL.o o.mm cLou xmm Lo; m~_v ummLoL LLo. o.~m L.Nm coozULa: Low Loo. m.mm m.oo acaLQoLu . ooo. e.em o.mm =o_umaoL Low .Lm_ ...m um LoLLmL Lmo. m.om L.LL mLaumma Rom Am; ¢LLV :anz oo. moooz No szummo new LLmL o._ mL. mm. o_. _.o_ o.oL cmem __osm mom .com_LLoz new mxmo o.o o.o on. em. o.om o.~__ mqoLu zoL Rom La; quv :ana L moooz No szume can LLm_ _._ om. mo. oo. ¢.N_ o.oL :_mLm meam Now .comLLLoz use mxco o.o om.m cm. «L. L.m~ o.N_L mooLu 2oL Lmo Le; ommvo cana Lm muooz we mLoummo com LLmL L._ N... o.oL CLmLm LL~sm LLM .:omeLoz use mxmo N.m o.mm o.mpp mooLu 30L amm mucmLome maLozomoco maLogomogo n,_auop nueon Lm\su L»\su mm: wow; _muoh‘ acmsrwmm\wua_:uWuLun. moLosomogn oo>pOmmwo LLooaz co.uau. «La\m;\mxv «Lomxu maLozomogm Luau: -omumLm momgmLmumz FmLopL:ULLm< owxwz EoLw “Looxm mzLocomocm "mo< mpnmh 2283 mmLu_>_uuw pr=u_ou_me ou vmuo>mv vmomeuwz mLm_ .wpoowovw>< mm._ mom. Lo woo umwm_ u< LLmL «Loumwo vow ...w um vaNNPLo mow. mo_oooLu m>wuu< Aw; mowo mLoumwo vm>oLoE_ vow mooLo ow. mw. L.LL o.mm LwL=HL=ULme wLmL ...mnoowo ¢M.L LN._ L.LL o.moL m>Lm=wuco Aw; m.~ov oLou memx ozu LLmP vow mLsumwo ..Lw om __m3L:o LN. oo. mp. mo.L_ _L.wo memm mmLoL Aw; m.Lm_o muopvmmw xuoumm>w_ oz» mLoumwo wLmL wow Lw; Low ..Lw om __m3L:m woo. mmm. mpm. «L.o— mL.Lo mooLu 30L woo wuomLmLmo moLooomoom moLooomcoo o.rmaoh o'eco meso L»\Eu mm: vowo Pwuo», «omemvmm\mowp:UwaLwn moLoAmmooo vm>pomm_o Locos: oovuwup ~wawo\mxv uLoomw.moLoommooo Lmuwz -opqum Avmoowuoouv ”wo< mpowh 229 mmmp .._w um muoou wLm— ..Pw um muoou wLmL ...w ow muooo wmmp ..pw um muoou co.— —m. 0N. mm.— mm. mo. pm. La; owwLV vowpvoo3 gm.o oLou RL.o_ Pmemu xm.mm mwo vow o.mm «Laumwo LN.Lm Aw; cameo vowpvooz om.L owmomu_o3 vow owmoxom x_.~_ mwo vow mLaumwo am.L— Pmemu No.om o.om oLou am.pm Aw; «.mLo xwo vow mLaumwo gm oLou N_.o_ ooowoou N~.NN Pmemu No.mm vowpvooz Np.om ng owomv oLou Nmm pmmeU RP .NN owmomuwoz vow m.~L cwmgaom Le.Lm muomLmex moLooomooo Pwaoh. maLooomooo o pwwmw o-ooo L»\Eu aomsfivwmxmuwrvu»uLwn moLocmmomn mm>_ommmn LLoooo ALm\wo\mxo uLoomm1m=Looowooo Lmuwz Laxso oo.uwu. -opuoLm am: vow; Avmoowuooov "wo< mpowh 230 wLmL ..Lw um «Loco mmm— ..Pw um muoou wLmL ..Lw Hm moooo mmmp ..Pw um muoou mm.— mo. op. mm.p —m. mo. mo. .mw. o.mm o.om M.Nw m.~op ng mmomo =Lou LN.wL wcwpwooz w.L_ meLmu L¢.w_ Xwo vow mLoumwo &~.¢o Aw; mwomv ouuwoou xL.m oLou xw.o~ Pmewu NL.o~ xwo vow mLoumwo Rm.om vow_vooz Ro.Lm Aw; NLomv oLou am.m_ Pmemu Nm.- vowpvooz Nw.m~ mwo vow mLoumwo No.mm Lw; OOOMV meLmu NN.~_ vowpv83 Lo.m_ mwo vow mLoumwo om.wm oLou Nm.~o muomLmLmo moLooomooo rwaop, moLooomooo n Pwuop oueoo L»\Eu uomELvmm\muw_:uwuLwn moLooomosowvm>Pommvn LLoooo aLNLwoxmxv uLooxu moLooomooo Lmuw: L»\Eo covawa. -opumLo Avmoowwoouv om: vowo ”wm< mpawh 231 wLmL ..Lw um mpooo wmmp ..Pw um muooo mmm— ..pw um mooou pm. pm. me. mm. mm. om. o.mm o.mm mm m.m~ Lo; womqo vowpvooz No.m oLou om.m pmemu &_.~_ Aw; vow mLoumwo wo.oo La; ommLV owmomuwoz vow owmomom xm.L —memu Nm.m vowpvoo3 wo.op oLou “o.mm _owummm> No.LN Aw; mommy wchwooz om.L :Lou flm. Z. _memu Ro.m~ aw; vow wLoumwo am._¢ muomLmLmz moLooomooo Fwaoh. maLooomooo o1waOP1 o-coo Lm\su uomswvmm\muwp=umuLwn msLocmmvon vw>Pomm—o Locos: ALx\wo\mxv “Looxm moLooomooo Lmuwz Lm\so oo_uwu+ -ovumLo Avmoowpoouv cm: vowo ”wo< mpnwh cwcscsc+n3 LaL2+LszLc< vmxmz Eogk uxcqxw :mDOLpLZ anq mLOML 2232 .moo_uquL muwpau_uLwo vow vm>_0mm_v ouoa Lo mum_mooo .w Aw; «mmo wwwOL we o_w_ Lw.~ ow.w ww.~ ww.m L.Lm =LwLm _Lwam om wLmL om.q~ wo.w_ wm.w m.wm mmem wow mwsammL “we .mLo: wow _=Lwo ow.w_ mm.w wm.L_ o.me cLou me Lo; w~_o om.v o.mm L.Lm umeoL woozwLw; me .w.o_ - m.mm m.ww .w=w_ LLmL ._.m w.wm w.ww -aoLu =o_wwwoL me ...w um LOLLwL Lw._ m.w~ L.LL oLawqu Low . Lo; v_Lo cwnLa LL. mvooz om oLaumwo vow LLm_ o.o _.o_ o.oL =LwLm LLwem Low .=Om_LLoz wow wow; ..qe o.wm o.L__ onLu :oL wmm Aw; memo owoL: wm mvooz so oLaumwo vow LLmL w.o_ e.~_ o.oL =_wLm LLwem LwL .=Om_LLoz wow wxwo L.Lm ..mN o.L__ mQOLu zoL www Aw: ommw. cwaLa on mvoo: No . mLoumwo vow LLm_ w.w L... o.oL =LwLm ..wsm L_m .oOmLLLoz vow mxwo L.x¢ m.LN o.mpp mooLu xoL woo muomLoLmz oomoLu_z -noz Luxlo Lxxlo om: vow. :1waw».. ..... .zmml..w .1m........1LL=.ww»Lwmwop...|...- Loogso =°...._L.uuLL Luuw: mvmomLmuwz PwLoppoqum< vmxwz 50L» pLooxm ommoLuwz ”no< mpnwh 233 oLm_ .._w um wuoco mLm_ .wpzowovw>< LLm_ .._w um va-_Lo wLm_ .__maaswo LLm_ ..—w um __m3L:m wLm_ ..—w um p_m3L:m Aw; owomv =Lou “mm _memu u_.L~ owmomo_oz wuowLome _.o_ o.o L.o_ m.NL vow owoomom nw.Lm mm_uv>_uuw _wL:u_:u.me on kuo>wv vmomLmuwx m.w_ om.o Lo wow umwm. u< mLaumwo vow mm.~ mowoooLu m>_uu< Awo wowv mLaumwo vo>oLoE_ vow o_.~ ~m.~ mo. mo. _.~L o.mm mooLu wL:u_:u_me om.o m.m oo. Lm. m.P~ o.mo_ m>wmowuoL La; m.~wo oLou meom ozu vow «Laumwo __.e_ mo.~ ow. oo.~_ mo.L_ .L.wm mewm meoL Aw; m.Lm_. moopvmmo xuoumm>__ oxu mLzumwo vow xwo mow wo.m oo.L Lm.P m_._ «L.o_ mL.Lo mQOLu xoL woo oamoLu.z - woo cannon z-zu_ z-o:z z-np2 z- woo» - v n Luxuo thto mm: vow. _wuvh1:..--:umwmmwb ca: 0 m w ou»uLwn ow +w_zwmo>—cmmpn mucosa :o—uwa—o_umLo L w: an “Lomxw oomOLu.z Loews Avmoowpoouv nnm< anwH 234 oLm_ .._w om muooo amm— .._w ow moooo awo— .._w om woooo nua— .._w om moooo ouomLoLmz Aw: ooomv waLmu w~.m_ vowpvoo3 Re.m_ xwo vow wLoumwo «o.mm o._o_ oLou nm.~w . Aw; owwLV wwwLwoox Lm.w cLou LL.w_ waLmu fin .mm Aw; vow m.mm «Laumwo fi~.Nm Lu; womwo vow_vooz mm.L owmomu_oz vow owmoxom N_.~_ Xwo vow mLzumwo mm.L_ _mewu xv.om o.ov oLou Hm._m Aw; m_mLo Lw; vow wLoumwo um oLou a_.o_ cuuwoou w~.- _woqu mo.mm vow—vac: w_.ow ow Lu_z.mo>—omm_o ...m ..L L.LL m.om v.m m.v_ m._c L.c q.Lm e.w ~.~ w.v :23: .z 3.?qu 2-55 21.5: =35. ....nn: 11pmww».:-.. .mmmOLL_= LewELw~ML~ow =QLLLQL LLALw; gm “Loaxu oomoLu_z sh\ID Lh\Iu mm: tzo. Loocaz =o_a.w.o.uoLL Loews wachwcooo "new anwL 2135 oLm_ ...w um wuooo wLm_ ...w 30 «Loco oLm_ ...w om moooo amm— ..Lw aw muooo m.m— ~.m m.v— o.~ m.m o.o o.L ..L ..m m.~ m.p_ Lwo mwmmo vow—vac; om.L oLou no... .wwLmu wo.m~ xwo vow L.ML «Laumwo oo.—o Aw: mLoLV oLou w~.o_ vow—vac: wo.L_ .memu ao.o_ Aw; vow m.\.N mgzumua fiN.¢v Aw; mcwmo ouuwoou nL.m waLou «L.o_ oLou Nv.o_ .xwo vow szomwo wm.v~ o.oo vow—vac: ow.Lm Aw: LLemo oLou am.~. .woLou um.- vow—vac: N~.mm xwo vow M.No mLaumwo «o.mm ouomeLom oomoLa.z Lee, znpwuo - a wwwp=u_uLwo ow oLwnz pmmshv aanwonmv “Lomxu owmoLu_z LLL-u LLs-g mm: ===. Loocao co.uwa.o.quL Luaw: Avmoowpoouv ”no< mpowb 236 mLm_ .._w um wuooo oLm_ ...w um muoco ouowLeowz o.o ~.mm ommoLu_z rwaoh1 m.v o._N .umm11znzuh11zn1=21onboP z —wucp. w w a :9 La ELLmowomew “Looxw.mmmmuwflm -om L~_z1mm>—ommwn Aw; womco wowLwooz Le.m oLou om.m .wwqu “L.LL xwo vow o.mm oLaumwa No.0o Aw; omm_o owmowu_ox vow oweoxom om.L .woLmu wm.m vow—vac: oo.o_ oLou ”o.mm o.LL mmpnwuomo> no.LN shslo «m: too. 5508:“ Louw: Lmouu oo.awu_o.umLo mechpcouo ”new mewL 237 Aw; wL.mmV pwpuomvommL LLm_ .oovowo L.~ mL.LL muwmomv zoo Aw; Nw.wev _wwuomv_mmL Lme .oovowo m—.o m_.LL zuvmomv zoo wLmL Lu; L.Lwo .oomx vow ops: mm. LN. o.oL Lwouomvpmmo wLmL Aw; ..wo .ooox vow :02: mL. No. o.oL _w_Lum=vo_ wLmL Aw; m.mv wULmeLw .oomx vow ouoz oo.w om.m o.oL mmmoomon LwLuomo wLm_ Aw; w.m_o .oomx vow 5:: oo. wo . m .2 .wELmEoo oLmL o~.w op.ow LwLULwEEou Now ...w um vaoox oo.L oo.L waLumovow ooL wLmL . Lwo omv .mmo vow Lwommmopx oL.P oo. mo.op mm.mo pwmuomvmmwo muomLmLmo moLooomooo moLooomooo n pquP o-ooo meso meeo mm: vow; quoh, comerva\muwp=u*uLwo moLoommomn mv>pommwn Locos: oc.uwup ALm\wo\mxv uLomxm moLooomooo Lmuw: nowuoLo mvmomLmuwz ownLo 20L» “Looxm moLooomooo qu< mpowh 2238 La; Loo comp . LwELmoooou 2m: ..Lw pm .mowmz . om. m..o~ o.oL vow _w_aomv_mmo Lwo wmmo LwPLamovo_ a. oowowuLoomowLu go FwFULoesou om, Pwoo_uwwLomL om— mLmL ..Lsz.o LmL. . pwpuowv_me Loo Lwo m..ov LLmL .oovowo oo. m..LL LwLuLmoooo Lwo m_.w_o LLm_ .oovowo L.P mP.LL FwPULmoEoo Lao mo._mo . .wLHovame LLmL .oovowo om. mL.LL muLmomv om_: Lo; Lo —w_aowvwme LLmL .oowowo _._ m_.LL LHLWomw omoz wuomLmLmo moLooomooo maLooomooo o Pwuoh n1eoo mesu meso an: vowo 1pwao~ Hows—vomLoowpouwuLwo moLoommooo vm>rvmm_o oooooo oopuwa. ALwao\mxv aLoomw moLooowooo Lmuwz 1oLuoLo Avmoowuoouv "wm< anwh 239 Loo oomLov .ooooowLm oLm_ _ 1mL w_wum mme_ ..Lw um va-oLo pm.F vm~_owoL: wow vmmoo: flop Loooowoooomoo om. pwwLumovo_ vow PwFULmEEou amp ommp .ooumpou mm.m oo.vm pwwuomvwmmL Noe vmmoo: ao— Lwoooooooomoo LN— pwwLumovop vow pw_ULwEEou am. oLoL .ooooo m~.L wL.o_ N.wo_ Loooowwommo Low Loo woo wLmL .oOmomo mo. om. o.o o.mmo ooooooom Loo LLNV wLmL .538 moo Lo. 7: o.mm. 23358 Loo oLoV —w_uomvwmmL wLoL .oomooo LL.o mm.~ o.ow o.omL wow Loooomoooo mm: vow— owoLo o» vmao>mv vwomLmuwz Lo mLm_ .wooowovw>< mo._ LoL. woo pmwmp u< muomLmomm maLooomooo maLooomooo o _wuo» o-oOo Lx\so meso an: vow; pwaow. uooammwmxmuwp39wuLwo maLooommmn vo>pOmmPo1 mucosa oo.uwu— «L»\wo\mxv uLooxu mzLooomooo Lmuw: 1o.umLo Avmoowuoouv nwm< m_owh 240 gswpxo .mmpzh Loo Lowmo 23.3550 an .m Loooomowoo oo.L pace: -oooomoo ow.~_ ome .oo>< mm. op.m_ —o.wm Lwouowvome RL.oL wLmL Loo w.wo .momow: vow oomoopm oo. mm. L_. mm.op oo.m__ Lwouowvomoo Loo NoLo proupouLme om vowpv83 am— LLmL meoLwosou NML ..Lw om oouLoo m~.o mm. op. N._N o.oNL _wouowvome “Lo Loo ~.o_o LLmL pwpuomvome .vooszom vow 2wLuuwz _~.o Nw.m o.m~_ mpoowo mpmo_m moomeowo moLooomooo maLooomooo 1n pwuob o1coo mesu meso mm: vowo pwwOH uowopvmmxmuwpmumuLwo moLosmmomn mm>_Ommwb Looooz oo.uwa. auxwwo\mxv uLooxu moLooomooo Lmuw: 1opumLo Avmoowuoouv “wL< mpow» 241 .omo vow Locomoo_x Loo Lo PwLuomvome LLoL .oooooo o.o ow.~ oo.~ m_.LL Loomoww oo.: Loo ML.wmo —w_uomvpmmL LLmL .oooooo o.o oo.o oo.~ m_.LL Loomoow zoo Loo Lw.ooo —w_uomv+me LLm_ .oowooo mm._ ooL. omw. m_.LL Loomoow zoo wLo_ Loo L.Loo .oomx woo ouoo Lw.m ooo.~ oLL. oo_._ m.wL owoooowomwo wLoL Loo _.wo .oooo wow oooz mm.w ooo.o wwm.~ ooo.~ o.oL .oooomoooo wLm_ , Loo o.mo ouooomoo .oowo wow ouoz Lo.wm o.o.oL owo.o oom.o_ m.wL moooomoo .oLoooo oLm_ Loo w.m_o .ooax oow 5:: $6 ooow mom. oz; m .oL 2:358 «Lo. oo.m om. Lw. mo.o_ mo.oo Loo coo Loooowwommo muouLmowo omuoLe—z o n v n Lasso mesD mm: vooo pwooh om oL~_z vw>—Omm_o Loooox oo.uwa.o.uoLo «Lo xu owmoLu_z Loews mvmommewz owoLo EOLL “Looxu ommoLuwz ”nn< mpowh 242 mLo_ .oLoooowo>< anon-Innnnllln. 53 m . . . . . Awo Nov oLm_ o o om _ wom w__ wLo o m o.mm. owoLooom Loo NLNV oLm_ .838 3.2 w~.m woo. wfiw ..S o.mm. LwELwooooo Loo o_ov .wLHovame oLm_ .oomowo mm.v_ wm.o wmo. woo.m m.oo o.om_ vow LwLLumovo_ om: vow— owaLo cu vouo> -wv vwomeuwx oo.m mo.m oo woo umww. go Loo ..o wom_ EELS-So “om: ...w ow .mo_w3 Lm.m m_.o~ o.oL vow Pwouoovomwo Loo m_.oo :2 £85.. o.o wo; S.LL .wBLwEooo Loo o_.w_o LLm_ .oovowo m.o~ wo.~_ m_.LL .wLULwoooo Loo oo.—No _w_uowvome LLm_ .oovowo m6. w~.~ . 3.: 335v oo.: muooLmLmo ommoLu.z - 1 1. -n z-_wao 1 - .v n Lax-o Lxxso mm: voo_ _wuvh1 ow Lao: “oo2.vmm ouwpou—wme om oprz1wu>ponnpo woooaz oo’awu_o.uoLo LLANwoxmxv “Lona“ oumoLu.z Louwz Avmoowuoouv unm< mpaw» 2113 oLoL .oooo mLm— .wowEw: vow cemoe_m “Lo— ...w um oeuLoo nLo— .vooszom vow :wLuow: mLo— ...w am va-vLo vkmp .oOum—ou o.o cm.mm .mooouquo ouwpou.uLwo vow vw>pomva ouco Lo mum.moou .w o—.m— mo.m— 5.. cm.. N.—N em.e~ mooowLoo ..WLoL Loo LoLLo pamUpr—EOU RM .m ...Lomooo. o..L .wooLooooomo. ao.~. .w.oo .wouoowomwo NL.oL wo.m.. Loo o.oo _o_ooooomoo Loo NoLo —wL:u.ou.me no vow—vac: um— _w_uLoo=au a: m.e~_ .wooomvommL nae Aw; ~.m_v pw.ooov_moL o.mm. Looooo o.ooom Loo oooLoo pw.aomv.moL upwum cme— woooooooo now Aw: vm.~nvv vomao: no— —woovu:u.umo— uwp .prumovo— vow pw_uLusEou um. .ooooowomuL now muouLooom oomoLa.x w o 1 pooch. ow L~_z1uoos_ o Lagos a «Looww ommOLa.z ow oLo.:1oo>—0mm.o v n Lasso mucosa Logo: Avmoowpoouv {\IU mm: to». . oo.“.o.o.uooo Hom< mpowk