Improving the Computational Effciency of Downscaling GCM data for Use in SWAT

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dc.contributor.authorEvans, Christopher
dc.contributor.authorGartrell, Abigail
dc.contributor.authorGomez, Lauren
dc.contributor.authorMouyebe, Moise
dc.contributor.authorOxley, Darius
dc.contributor.authorPopuri, Sai Kumar
dc.contributor.authorNeerchal, Nagaraj K.
dc.contributor.authorMehta, Amita
dc.date.accessioned2018-09-25T19:43:17Z
dc.date.available2018-09-25T19:43:17Z
dc.date.issued2014
dc.description.abstractThis project creates software tools to streamline the computational procedures to generate high-resolution weather parameters from low resolution Global Climate Models (GCM) to be input into the Soil and Water Assessment Tool (SWAT) and to visualize GCM temperature and precipitation data as well as SWAT outputs of crop data. Data from GCMs have relatively low spatial resolution ( 100km x 100km), which needs to be downscaled to a higher resolution to match the resolution of the observed data so that both the data sets can be input into SWAT. We consider several decades of historical simulations from GCMs, and surface-based observations of historical temperature and precipitation data over the Missouri River Basin (MRB). The downscaling method involves two steps: i) bilinear interpolation to ll in values at higher resolution and ii) Linear or Tobit regression between the GCM and the observed data to accurately capture the features of observed data in the GCM temperature and precipitation at high resolution. This downscaled data is then used to generate forecasts, which are used as inputs for SWAT. Based on these statistical downscaling methods, the SWAT outputs are compared. Since the data is large, we focus on maximizing computational e ciency through parallelization of the data generation, model tting and forecasting. A graphical R interface is developed to facilitate the modeling and visualization components at each step. The interface allows climate models and SWAT outputs to be more easily compared for di erent scenarios, thus help assess climate variability and its impact on crop yields over MRB.en_US
dc.description.sponsorshipThese results were obtained as part of the REU Site: Interdisciplinary Program in High Performance Computing (www.umbc.edu/hpcreu) in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC) in Summer 2014. This program is funded jointly by the National Science Foundation and the National Security Agency (NSF grant no. DMS{ 1156976), with additional support from UMBC, the Department of Mathematics and Statistics, the Center for Interdisciplinary Research and Consulting (CIRC), and the UMBC High Performance Computing Facility (HPCF). HPCF is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS{0821258 and CNS{1228778) and the SCREMS program (grant no. DMS{0821311), with additional substantial support from UMBC. Authors would like to acknowledge Joint Center for Earth Systems Technology(JCET) and the Center for Research in the Changing Earth Systems (CRCES) for their support. In partcular, we would like to thank Katherin Mendoza for help with the SWAT runs. Co-author Darius Oxley was supported, in part, by the UMBC National Security Agency (NSA) Scholars Program through a contract with the NSA. Graduate assistant Sai Kumar Popuri was supported during Summer 2014 by UMBC.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/REU2014Team2.pdfen_US
dc.format.extent9 pagesen_US
dc.genretechnical reporten_US
dc.identifierdoi:10.13016/M2086388C
dc.identifier.urihttp://hdl.handle.net/11603/11386
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Geography and Environmental Systems Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Geography and Environmental Systems Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofseriesHPCF Technical Report;HPCF-2014-12
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectSWATen_US
dc.subjectGUIen_US
dc.subjectTobit Regressionen_US
dc.subjectDownscalingen_US
dc.subjectGCMen_US
dc.subjectMRBen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleImproving the Computational Effciency of Downscaling GCM data for Use in SWATen_US
dc.typeTexten_US

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