"Algal blooms are new emerging hazards that have had important social impacts in recent years. However, it was not very clear whether future climate change causing warming waters and stronger storm events would exacerbate the algal bloom problem. The goal of this dissertation was to evaluate the sensitivity of algal biomass to climate change in the continental United States. Long-term large-scale observations of algal biomass in inland lakes are challenging, but are necessary to relate climate change to algal blooms. To get observations at this scale, this dissertation applied machine-learning algorithms including boosted regression trees (BRT) in remote sensing of chlorophyll-a with Landsat TM/ETM+. The results show that the BRT algorithm improved model accuracy by 15%, compared to traditional linear regression. The remote sensing model explained 46% of the total variance of the ground-measured chlorophyll-a in the first National Lake Assessment conducted by the US Environmental Protection Agency. That accuracy was ecologically meaningful to study climate change impacts on algal blooms. Moreover, the BRT algorithm for chlorophyll-a would not have systematic bias that is introduced by sediments and colored dissolved organic matter, both of which might change concurrently with climate change and algal blooms. This dissertation shows that the existing atmospheric corrections for Landsat TM/ETM+ imagery might not be good enough to improve the remote sensing of chlorophyll-a in inland lakes. After deriving long-term algal biomass estimates from Landsat TM/ETM+, time series analysis was used to study the relations of climate change and algal biomass in four Missouri reservoirs. The results show that neither temperature nor precipitation was the only factor that controlled temporal variation of algal biomass. Different reservoirs, even different zones within the same reservoir, responded differently to temperature and precipitation changes. These findings were further tested in 1157 lakes across the continental United States. The results show that mean annual algal biomass generally increased with annual temperature. Greater increase was found in lakes with more nutrients. Mean annual algal biomass generally decreased with annual total precipitation. In both the "low" and the "high" greenhouse-gas emission scenarios, mean annual algal biomass in lakes generally increased with climate change, and greater increases are predicted from the high emission scenario."--Pages ii-iii.
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