Abstract
The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R2), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R2, NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R2 = 0.54, NSE = 0.49, PBIAS = [−52.70–28.30%]) and CMADS (R2 = 0.44, NSE = 0.42, PBIAS = [−29.30–41.80%]) data perform better than those utilizing PERSIANN (R2 = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R2 = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics.
Highlights
Precipitation is one of the most essential components of the hydrological cycle [1]
The results of this study are satisfactory, given that rainfall data obtained from Tropical Rainfall Measuring Mission (TRMM) and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy
With respect to the probability of detection (POD) values, TRMM has the largest POD with an average of 0.73, which is closest to the perfect score of 1
Summary
Precipitation is one of the most essential components of the hydrological cycle [1]. The quantity and quality of the precipitation data used as the principal input to hydrological models affect theWater 2018, 10, 642; doi:10.3390/w10050642 www.mdpi.com/journal/waterWater 2018, 10, 642 accuracy of the simulation results [2,3]. Rain gauges provide direct precipitation measures; scarcity and irregularity problems with respect to the gauge network considerably influence the data reliability [4,5]. Compared with rain gauges that provide rainfall data by accumulating rainfall over a time interval, weather radar systems provide an instantaneous spatial measure of precipitation and produce rapid climate information [6]. Westrick et al [7] investigated the limitations of the radar network for quantitative precipitation measurement and showed that the radar-derived precipitation estimates could not represent the regional precipitation since radar coverage is limited to lowland areas. The drawbacks of radar-derived data, such as coverage area limitations, costly infrastructure construction, and inaccuracy under complex atmospheric conditions, result in the poor performance of hydrological models [8]. Based on the advancements of these techniques, several satellite-based precipitation products with global high-resolution (up to 0.25◦ ) are available such as those derived from the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed
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