Abstract
In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models.
Highlights
Hydrological models are the most important and efficient tools developed over the past decades to quantify each terrestrial and meteorological component of the water cycle, for past, present, and future conditions [1,2,3]
Nikolopoulos et al [53] analyzed a number of basin scales ranging between 100 and 1200 km2, by using a distributed hydrological model, and found that the use of satellite precipitation products for flood simulation strongly depended on the catchment area and on the product resolution
Hydrological models are designed to deal with different hydrological and environmental issues, such as flood prediction, draught evaluation, and water resources projection [55,56], leading to great distinction in model structures, which subsequently influences the performance of satellite-derived rainfall estimates (SDFE) or reanalysis precipitation (RA) precipitation datasets in streamflow simulation, some researchers would use one certain precipitation product to study, compare, and improve the hydrological models [19,57], and to try to extend the possible application fields of one product
Summary
Hydrological models are the most important and efficient tools developed over the past decades to quantify each terrestrial and meteorological component of the water cycle, for past, present, and future conditions [1,2,3]. Streamflow simulation using the limited gauge-based precipitation information might not be reliable due to the input uncertainties, with such a poor spatial resolution In this context, researchers are considering the use of satellite-derived rainfall estimates (SDFE) or reanalysis precipitation (RA) products as an alternative [5,6]. Researchers are considering the use of satellite-derived rainfall estimates (SDFE) or reanalysis precipitation (RA) products as an alternative [5,6] These estimates have suitable spatio-temporal resolution (e.g., 0.25◦ and 24 h), released uninterrupted and in near real-time, publicly available and accessible, and hold great potential as forcing data for streamflow modelling, and projection in data-sparse and ungauged basins [7]
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