Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project - ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.