Cyanobacteria blooms are an increasing concern in U.S. freshwaters. Such blooms can produce nuisance conditions, deplete oxygen, and alter the food chain, and in some cases they may produce potent toxins, although many factors may modulate the relationships between biomass and toxin production. Cyanobacterial blooms are in turn associated with nutrient enrichment and warm water temperatures. Climate change is expected to increase water temperatures and, in many areas, surface runoff that can transport nutrient loads to lakes. While some progress has been made in short-term prediction of cyanobacterial bloom and toxin risk, the long-term projections of which lakes will become more vulnerable to such events as a result of climate change is less clear because of the complex interaction of multiple factors that affect bloom probability. We address this question by reviewing the literature to identify risk factors that increase lake vulnerability to cyanobacterial blooms and evaluating how climate change may alter these factors across the sample of conterminous U.S. lakes contained in the 2007 National Lakes Assessment. Results provide a national-scale assessment of where and in which types of lakes climate change will likely increase the overall risk of cyanobacterial blooms, rather than finer-scale prediction of expected cyanobacterial and toxin levels in individual lakes. This information can be used to guide climate change adaptation planning, including monitoring and management efforts to minimize the effects of increased cyanobacterial prevalence.

The mosquito virus vector () exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH'EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH'EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where is highly abundant, to ~2100 m, where is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH'EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH'EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH'EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH'EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of and may be useful for informing weather-driven early warning systems for viruses transmitted by .

Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output to develop finer-resolution information for use in water resources impacts assessments. Downscaling, however, can also introduce its own uncertainties into water resources impacts assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). We also assess the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and non-downscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impacts assessments for water - the selection of downscaled climate change information.

While estimates of the impact of climate change on health are necessary for health care planners and climate change policy makers, models to produce quantitative estimates remain scarce. We describe a freely available dynamic simulation model parameterized for three West Nile virus vectors, which provides an effective tool for studying vector-borne disease risk due to climate change. The Dynamic Mosquito Simulation Model is parameterized with species specific temperature-dependent development and mortality rates. Using downscaled daily weather data, we estimate mosquito population dynamics under current and projected future climate scenarios for multiple locations across the country. Trends in mosquito abundance were variable by location, however, an extension of the vector activity periods, and by extension disease risk, was almost uniformly observed. Importantly, mid-summer decreases in abundance may be off-set by shorter extrinsic incubation periods resulting in a greater proportion of infective mosquitoes. Quantitative descriptions of the effect of temperature on the virus and mosquito are critical to developing models of future disease risk.

Climate change is likely to alter the quantity and quality of urban stormwater, presenting a risk to water quality and public health. How might stormwater management practices need to change to address future climate? Answering requires understanding how management practices respond to climate forcing. Traditional "gray" stormwater design employs engineered structures, sized based on assumptions about future rainfall, which have limited flexibility once built. Green infrastructure (GI) uses vegetation, soil, and distributed structures to manage rainwater where it falls and may provide greater flexibility for adaptation. There is, however, uncertainty about how a warmer climate may affect performance of different types of GI. This study uses the hydrologic and biogeochemical watershed model, Regional Hydro-Ecologic Simulation System (RHESSys), to investigate sensitivity of different GI practices to climate. Simulations examine 36 urban "archetypes" representing different development patterns (at the city block scale) of typical U.S. cities, eleven regional climatic settings, and a range of mid-21st century scenarios based on downscaled climate model output. Results suggest regionally variable effects of climate change on the performance of GI practices for water quantity, water quality, and carbon sequestration. GI is able to mitigate most projected future increases in surface runoff, while bioretention can mitigate increased nitrogen yield at nine of eleven sites. Simulated changes in carbon balance are small, while local evaporative cooling can be substantial. Given uncertainty in the local expression of future climate, infrastructure design should emphasize flexibility and robustness to a range of future conditions.