Abstract
Precipitation is an essential driving factor of hydrological models. Its temporal and spatial resolution and reliability directly affect the accuracy of hydrological modeling. Acquiring accurate areal precipitation needs substantial ground rainfall stations in space. In many basins, ground rainfall stations are sparse and uneven, so real-time satellite precipitation products (SPPs) have become an important supplement to ground-gauged precipitation (GGP). A multi-source precipitation fusion method suitable for the Soil and Water Assessment Tool (SWAT) model has been proposed in this paper. First, the multivariate inverse distance similarity method (MIDSM) was proposed to search for the optimal representative precipitation points of GGP and SPPs in sub-basins. Subsequently, the correlation-coefficient-based weighted average method (CCBWA) was presented and applied to calculate the fused multi-source precipitation product (FMSPP), which combined GGP and multiple satellite precipitation products. The effectiveness of the FMSPP was proven over the Tuojiang River Basin. In the case study, three SPPs were chosen as the satellite precipitation sources, namely the Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR). The evaluation indicators illustrated that FMSPP could capture the occurrence of rainfall events very well, with a maximum Probability of Detection (POD) and Critical Success Index (CSI) of 0.92 and 0.83, respectively. Furthermore, its correlation with GGP, changing in the range of 0.84–0.96, was higher in most sub-basins on the monthly scale than the other three SPPs. These results demonstrated that the performance of FMSPP was the best compared with the original SPPs. Finally, FMSPP was applied in the SWAT model and was found to effectively drive the SWAT model in contrast with a single precipitation source. The FMSPP manifested the highest accuracy in hydrological modeling, with the Coefficient of Determination (R2) of 0.84, Nash Sutcliff (NS) of 0.83, and Percent Bias (PBIAS) of only −1.9%.
Highlights
In the case study based on the Tuojiang River Basin (TJRB), the satellite precipitation sources involved in the database included Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and PERSIANN-CDR
The simulated streamflow forced by fused multi-source precipitation product (FMSPP), gauged precipitation (GGP), and each satellite precipitation products (SPPs) was compared with the observations to evaluate their applicability to the hydrological modeling
The correlations and their confidence intervals between different precipitation products were further discussed, as well as the correlation of the water balance components in these precipitation products. This method can enhance the applicability of satellite precipitation products in hydrological models and improve the accuracy of hydrological forecasts by reducing the deviation caused by the uncertainty of precipitation sources
Summary
Accurate runoff prediction is conducive to water management and planning, agricultural irrigation [1], climate and human activity impact study [2,3,4], and mitigating the major disasters and losses caused by floods and droughts. The methods of runoff prediction [5,6,7] mainly include two categories: data-driven models and physical models. Data-driven models are comparatively simple to construct, but they cannot explicitly reveal the internal mechanisms of hydrological processes. Physical models, such as semi-distributed hydrological models TOPMODEL [8], variable infiltration capacity [9,10,11], Remote Sens.
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