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A warming climate increases thermal inputs to lakes with potential implications for water quality and aquatic ecosystems. In a previous study, we used a dynamic water column temperature and mixing simulation model to simulate chronic (7-day average) maximum temperatures under a range of potential future climate projections at selected sites representative of different U.S. regions. Here, to extend results to lakes where dynamic models have not been developed, we apply a novel machine learning approach that uses Gaussian Process regression to describe the model response surface as a function of simplified lake characteristics (depth, surface area, water clarity) and climate forcing (winter and summer air temperatures and potential evapotranspiration). We use this approach to extrapolate predictions from the simulation model to the statistical sample of U.S. lakes in the National Lakes Assessment (NLA) database. Results provide a national-scale scoping assessment of the potential thermal risk to lake water quality and ecosystems across the U.S. We suggest a small fraction of lakes will experience less risk of summer thermal stress events due to changes in stratification and mixing dynamics, but most will experience increases. The percentage of lakes in the NLA with simulated 7-day average maximum water temperatures in excess of 30°C is projected to increase from less than 2% to approximately 22% by the end of the 21st century, which could significantly reduce the number of lakes that can support cold water fisheries. Site-specific analysis of the full range of factors that influence thermal profiles in individual lakes is needed to develop appropriate adaptation strategies.

Abstract.Agricultural best management practices (BMPs) reduce nonpoint-source pollution from cropland. Goals for BMP adoption and expected pollutant load reductions are often specified in water quality management plans to protect and restore waterbodies; however, estimates of the needed load reductions and pollutant removal performance of BMPs are generally based on historic climate. Increasing air temperatures and changes in precipitation patterns and intensity are anticipated throughout the U.S. over the 21st century. The effects of such changes on agricultural pollutant loads have been addressed by several studies, but how these changes will affect the performance of widely promoted BMPs has received limited attention. We used the Soil and Water Assessment Tool (SWAT) to investigate potential changes in the effectiveness of conservation tillage, no-till, vegetated filter strips, grassed waterways, nutrient management, winter cover crops, and drainage water management practices under potential future temperature and precipitation patterns. We simulated two agricultural watersheds in the Minnesota Corn Belt and the Georgia Coastal Plain with different hydroclimatic settings under recent conditions (1950-2005) and multiple potential future mid-century (2030-2059) and late-century (2070-2099) climate scenarios. Results suggest future increases in agricultural source loads of sediment, nitrogen, and phosphorous. Most BMPs continue to reduce loads, but removal efficiencies generally decline due to more intense runoff events, biological responses to changes in soil moisture and temperature, and exacerbated upland loading. The coupled effects of higher upland loading and reduced BMP efficiencies suggest that wider adoption, resizing, and/or combining practices may be needed in the future to meet water quality goals for agricultural lands. Keywords: Agricultural management, Best management practices, Climate change, Conservation tillage, Cover crops, Drainage water management, Hydrologic and water quality modeling, Nutrient management, Pollutant removal efficiency, Soil conservation, Vegetated buffer