RESUMEN
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.
RESUMEN
This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF's overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.