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Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data; inaccuracies in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin (WLEB) for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO and MACA) were tested for their effectiveness in simulating historic climate (1966-2005) using ground-based station data from the National Climatic Data Center (NCDC). MACA was found to have less bias than GDO and was better in simulating certain climate indices, thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, maximum one-day precipitation could vary between 120 and 650 mm across the basin, while the number of days with no precipitation could reduce by 5-15% under the RCP 4.5 and RCP 8.5. The number of wet sequences could increase up to 9 times and the conditional probability of having a wet day followed by wet day could decrease by 25%. The maximum and minimum daily air temperatures could increase by 2-12% while the annual number of days for optimal corn growth could decrease by 0-10 days. The resulting climate database will be made accessible through an open-access platform. [ABSTRACT FROM AUTHOR]

Models are increasingly being used to quantify the effects of best management practices (BMPs) on water quality. While these models offer the ability to study multiple BMP scenarios, and to analyze impacts of various management decisions on watershed response, associated analyses can be very computationally intensive due to a large number of runs needed to fully capture the various uncertainties in the model outputs. There is, thus, the need to develop suitable and efficient techniques to handle such comprehensive model evaluations. We demonstrate a novel approach to accomplish a large number of model runs with Condor, a distributed high-throughput computing framework for model runs with the Soil and Water Assessment Tool (SWAT) model. This application required more than 43,000 runs of the SWAT model to evaluate the impacts of 172 different watershed management decisions combined with weather uncertainty on water quality. The SWAT model was run in the Condor environment implemented on the TeraGrid. This framework significantly reduced the model run time from 2.5 years to 18 days and enabled us to perform comprehensive BMP analyses that may not have been possible with traditional model runs on a few desktop computers. The Condor system can be used effectively to make Monte Carlo analyses of complex watershed models requiring a large number of computational cycles. [ABSTRACT FROM PUBLISHER]

Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data. Among other inaccuracies, errors in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO: Global Downscaled Climate and Hydrology Projections and MACA: Multivariate Adaptive Constructed Analogs) were tested for their effectiveness in simulating historic climate (1966–2005) using ground-based station data from the National Climatic Data Center. MACA was found to have less bias than GDO and was better at simulating selected climate indices; thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, mean daily precipitation values were expected to remain more or less the same under both RCP (Representative Concentration Pathway) 4.5 and RCP 8.5 scenarios, ranging between 2.4 mm and 3.2 mm, while standard deviations were expected to increase, pointing to a rescaling of the distribution. Maximum one-day precipitation was expected to increase and could vary between 120 and 650 mm across the basin, while the number of wet days could potentially increase under the effects of RCP 4.5 and RCP 8.5. Both mean maximum and mean minimum daily air temperatures were expected to increase by up to 5.0 °C across the basin, while absolute maximum and minimum values could increase by more than 10 °C. The number of days in which precipitation could potentially fall as snow was expected to decrease, as was the annual number of days for optimal corn growth, although an earlier start to the growing season could be expected. Results from this study were very useful in creating a reliable climate database for the entire Western Lake Erie Basin (WLEB), which can be used for hydrologic, water resources, and other applications in the basin. The resulting climate database is published and accessible through the Purdue University Research Repository (Mehan et al., 2019), which is an open-access repository. [ABSTRACT FROM AUTHOR]

Extreme events have the most adverse impacts on society and infrastructure, and present the greatest challenges with respect to impacts. Information on the status and trends of these events is, thus, important for system design, management, and policy decision-making. In this study, variations in daily and seasonal rainfall extremes were explored with a focus on the southwest Florida Gulf coastal zone for the period 1950–2016. Rainfall occurring on very wet days accounted for about 50% of the seasonal rainfall in the area (regardless of the season), while about 25% of the seasonal rainfall came from extremely wet days except in the period between October and December for which this latter value was about 40%. No significant changes were seen in the maximum one-day rainfall at any of the stations regardless of the time scale. However, there was a significant increase in the number of wet days in the rainy season at Myakka River (p = 0.0062) and Naples (p = 0.0027) and during October–December at Myakka River (p = 0.0204). These two stations also experienced significant increases in the number of wet days in a year. Significant increases in the contribution to rainy season rainfall from very wet days (rainfall > 25.4 mm, 1 in) were seen at Arcadia (p = 0.0055). Regional results point to an increasingly wetter climate with increasing contributions from extreme events in some areas, both of which have implications for design and management decision making. [ABSTRACT FROM AUTHOR]