Assessing Climate Impacts on Regional Water Resources in the Midwestern US
Links to Fileshttps://userpages.umbc.edu/~gobbert/papers/REU2015Team2.pdf
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Type of Work14 pages
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SubjectsHigh Performance Computing Facility (HPCF)
decadal climate variability (DCV)
water resources in the Missouri River Basin (MRB)
Climate Model Inter-comparison Project (CMIP5)
Global Climate Models (GCM)
Graphical User Interface (GUI)
Soil and Water Assessment Tool (SWAT)
It is well documented that decadal climate variability (DCV) has a signi cant impact on water resources in the Missouri River Basin (MRB). This project aims to utilize multi-decadal simulations of Global Climate Models (GCM) from the Climate Model Inter-comparison Project (CMIP5) to assess the DCV impact on water yield and stream ow over the MRB using a widely utilized hydrology and crop model known as the Soil and Water Assessment Tool (SWAT). We use low-resolution ( 100km x 100km) data from MIROC5 and HadCM3 GCMs with 57 years of climate simulations at approximately 30,000 locations. The weather parameters included in the GCMs are monthly precipitation, maximum/minimum temperatures, sea-level pressure, relative humidity, and surface wind speed. We downscale all the parameters to match high resolution (12km x 12km) observed data using a two-step procedure. First, GCM-simulated weather parameters are spatially interpolated to the resolution of the observed data, and then multiple linear regression (MLR) is used to capture features of the observed data. The coe cients from regression are combined with hindcast data from the two GCMs to compute monthly predictions of maximum/minimum temperatures, and pre- cipitation to input into SWAT. A Weather Generator tool in SWAT is used to generate the daily values necessary to input into SWAT using the monthly predictions and observed weather statistics. We modi ed a previously developed Graphical User Interface (GUI) in R to streamline the process and include more options for users. We explore if the use of different GCMs and the additional weather parameters in the regression models improve the accuracy of predicting the above-mentioned variables in the MRB. The procedures and GUI developed in this project will allow the client to conduct numerous studies with improved effciency to assess sensitivity of water resources within the MRB resulting from climate variability and change scenarios.