Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-01, Vol.15 (1), p.213
• Main Authors: Lei, Huajin, Zhao, Hongyu, Ao, Tianqi, Hu, Wanpin
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric models; Calibration; Gauges; Hydrologic models; hydrological utility; Hydrology; Hydrometeorology; multi-source precipitation products; Parameters; Precipitation; precipitation evaluation; Rain gauges; Regions; Reliability analysis; Remote sensing; River basins; River networks; Simulation; Stream discharge; Stream flow; Uncertainty; uncertainty analysis; Variance analysis
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15010213

With the continuous emergence of remote sensing technologies and atmospheric models, multi-source precipitation products (MSPs) are increasingly applied in hydrometeorological research, especially in ungauged or data-scarce regions. This study comprehensively evaluates the reliability of MSPs and quantifies the uncertainty of sources in streamflow simulation. Firstly, the performance of seven state-of-the-art MSPs is assessed using rain gauges and the Block-wise use of the TOPMODEL (BTOP) hydrological model under two calibration schemes over Jialing River Basin, China. Then, a variance decomposition approach (Analysis of variance, ANOVA) is employed to quantify the uncertainty contribution of precipitation products, model parameters, and their interaction in streamflow simulation. The MSPs include five satellite-based (GSMaP, IMERG, PERCDR, CHIRPS, CMORPH), one reanalysis (ERA5L), and one ensembled product (PXGB2). The results of precipitation evaluation show that the MSPs have temporal and spatial variability and PXGB2 has the best performance. The hydrologic utility of MSPs is different under different calibration methods. When using gauge-based calibration parameters, the PXGB2-based simulation performs best, whereas CHIRPS, PERCDR, and ERA5L show relatively poor performance. In comparison, the model recalibrated by individual MSPs significantly improves the simulation accuracy of most MSPs, with GSMaP having the best performance. The ANOVA results reveal that the contribution of precipitation products to the streamflow uncertainty is larger than model parameters and their interaction. The impact of interaction suggests that a better simulation attributes to an optimal combination of precipitation products and model parameters rather than solely relying on the best MSPs. These new findings are valuable for improving the suitability of MSPs in hydrologic applications.