Abstract
Satellite products can provide spatiotemporal data on precipitation in ungauged basins. It is essential and meaningful to assess and correct these products. In this study, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) product was evaluated and corrected using the successive correction method. A simple hydrological model was driven by the corrected PERSIANN-CDR data. The results showed that the accuracy of the original PERSIANN-CDR data was low on a daily scale, and the accuracy decreased gradually from the east to the west of the basin. With one correction step, the accuracy of the corrected PERSIANN-CDR data was significantly higher than that of the initial data. The correlation coefficient increased from 0.58 to 0.73, and the probability of detection (POD) value of the corrected product was 18.2% higher than the original product. The temporal-spatial resolution influenced the performance of the satellite product. As the resolution became coarser, the correlation coefficient between the corrected PERSIANN-CDR data and the gauged data gradually became lower. The Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration, and Streamflow (IHACRES) model could be satisfactorily applied in the Lhasa River basin with corrected PERSIANN-CDR data. The successive correction method was an effective way to correct the bias of the PERSIANN-CDR product.
Highlights
Precipitation plays a significant role in hydrological and material cycles and is an important input variable in hydrologic models [1,2]
The deviation of the PERSIANN-Climate Data Record (CDR) data from the gauged data in the Taylor chart is assessed as follows: The closer the Satellite-based precipitation estimates (SPEs) simulation value is to the gauged position on the x axis, the larger the correlation coefficient between the SPE data and the gauged data
Because of local topography and climatic effects, there are uncertainties associated with the SPE data for the Qinghai-Tibet Plateau
Summary
Precipitation plays a significant role in hydrological and material cycles and is an important input variable in hydrologic models [1,2]. On the Qinghai–Tibet Plateau, the meteorological stations are located in areas of lower altitude and are scarce, with a highly heterogeneous spatial distribution. Satellite, and reanalysis products provide important supplementary data. Satellite-based precipitation estimates (SPEs) can provide in situ data without geographical constraints and compensate for insufficient precipitation data from surface meteorological stations. The process of remote sensing image acquisition and data processing has led to many shortcomings in SPE products. These shortcomings include gaps in the revisit times, precipitation data captured by satellites disagreeing with the real precipitation data, and the complex underlying surface interfering with the remote sensing signals [7]. To improve the reliability of SPE products in water resource management and allocation, it is necessary to evaluate and correct the accuracy of SPE products to reduce system errors [8,9,10,11,12]
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