Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, remote sensing. Each has its own sets of uncertainties limitations. Therefore, evaluating these datasets is critical in assessing their applicability various climatic regions. We used ten datasets, (in situ), gauge-based, satellite-based products, to assess relevance for hydrologic modeling at Bosque River Basin North-Central Texas. Evaluated include: (1) situ station from Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet Parameter-elevation Regression on Independent Slope Model (PRISM); (3) Integrated Multi-Satellite Retrievals Precipitation Measurement (IMERG), Early Late, Estimation Remotely Sensed Information Artificial Neural Networks (PERSIANN) PERSIANN-CCS (Cloud Classification System); (4) gauge-corrected IMERG-Final, PERSIANN-CDR (Climate Data Record), CHIRPS Hazards Group Infrared with Station data). Daily (2000–2019) were Soil Water Assessment Tool (SWAT) simulations. was monthly United States Geological Survey (USGS) streamflow three locations watershed model calibration validation. The SUFI-2 (Sequential Uncertainty Fitting) method SWAT-CUP (Calibration Program) quantify compare uncertainty simulations all datasets. study also analyzed SWAT parameter values under different results showed similar or better calibration/validation statistics (Daymet PRISM) (CHIRPS) compared GHCN data. However, such as unveil comparatively inferior capture simulate streamflow. that had comparable even superior performances some metrics