Browsing by Subject "bias correction"
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Item Assessing the Impacts of Climate Change on Cotton Production in the Texas High Plains and Rolling Plains(2014-12-11) Modala, Naga RaghuveerThe Texas Plains, which include the Texas High Plains and Rolling Plains, is one of the largest cotton growing areas in the world. Cotton cultivation in this region is facing severe challenges from rapidly declining groundwater levels and increasing number of droughts. Projected changes in climate are expected to further add to the uncertainty of cotton production in this region. The overall goal of this research was to study the effects of climate change on cotton yield using the CROPGRO-Cotton Cropping System Model (CSM) within the Decision Support System for Agrotechnology Transfer (DSSAT). The future (2041-2070) climate data generated by three Regional Climate Models (RCMs), namely RCM3-GFDL, RCM3-CGCM3 and CRCM-CCSM was obtained from the North American Regional Climate Change Assessment Program (NARCCAP) and was bias corrected using Distribution mapping techniques. The CROPGRO-Cotton model was calibrated, validated and further evaluated using the observed data collected from cotton experiments at Chillicothe in the Texas Rolling Plains during the years 2008 and 2012. A GIS-based distributed modeling approach was used to predict cotton yields across major cotton-growing counties in the Texas Plains under historic and future climate scenarios using the calibrated CROPGRO-Cotton CSM. The RCMs predicted an overall decrease in the average rainfall (30 to 127 mm), increase in the intensity of extreme rainfall events (4% to 14% as per RCM3-GFDL), and increase in both minimum (1.9 to 2.9 ?C) and maximum temperatures (2.0 to 3.2 ?C) (as per three RCMs) in the future. Deficit irrigation simulations indicated that the maximum seed cotton yields under normal and dry weather conditions could be achieved at 100% and 110% ET replacement scenarios, respectively. The cotton yield at Chillicothe was projected to decrease within a range of 2% to 14.9% under the three RCM future climate scenarios. Majority of the counties in the Texas Plains showed a decline in average cotton yield within a range of 2% to 20% under RCM3-GFDL projected future climate scenario, with the counties in the Texas Rolling Plains being the most affected. A combination of early planting and adoption of no-till practices can minimize the climate change-induced yield losses to some extent.Item Evaluation of SWAT model - subdaily runoff prediction in Texas watersheds(Texas A&M University, 2007-09-17) Palanisamy, BakkiyalakshmiSpatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff.