Abstract
Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period.
Highlights
Precipitation is one of the primary drivers of the hydrological cycle and, of great importance in hydrological simulation [1], which is a major water resources management tool for forecasting floods and droughts
It should be noted that the comparison method for grid scale used in this paper may introduce errors and uncertainties because the estimate is the average value within a grid while the gauge observation is the value of a point located in the grid
The performance of two reanalysis precipitation datasets (CMADS and NCEP-CFSR) and two satellite-based precipitation datasets (PERSIANN-CDR and 3B42V7) was evaluated at two spatial scales and two timesteps, and the ability of these datasets to simulate streamflow is assessed for both temporal scales
Summary
Precipitation is one of the primary drivers of the hydrological cycle and, of great importance in hydrological simulation [1], which is a major water resources management tool for forecasting floods and droughts. Precipitation data are usually observed and collected using rainfall gauges and meteorological radar networks, but these measurement devices are usually geographically sparse and inadequate to fully capture the spatial and temporal variability of precipitation [3,4]. This situation is serious in China because of the country’s complex topography and relatively unevenly distributed economic resources [5]. Satellite-based and reanalysis precipitation datasets have been effective in complementing traditionally obtained precipitation data as remote sensing and computing technologies have developed [6,7,8]
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