Abstract
The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).
Highlights
The ground rain gauge is a common approach for measuring precipitation at specific locations during a prescribed period, which is of high credibility after calibration
For the daily statistical metrics in the rainy season, compared with those of satellite-based and multiple linear regression (MLR) methods, as well as PERSIANN rainfall data, the performance of nonparametric general regression (NGR) was better in terms of CC, root-mean-square error (RMSE) and NashSutcliffe coefficient of efficiency (NSE), with values of 0.715, 11.54 and 0.51 mm respectively, and marginally larger mean absolute error (MAE) (4.83 versus 4.76 mm from the MLR method)
The gauge-based data originated from the satellite-based datasets, i.e., 3B42V7 and root relative mean squared errors (RRMSE) were at 30 validation sites was used to assess the trained framework
Summary
Accurate precipitation is an essential model input to predict the hydrological responses of the selected watershed and the potential rain-induced hazards [8,9,10,11]. Attention is drawn to estimating the precipitation distribution using different methods. The ground rain gauge is a common approach for measuring precipitation at specific locations during a prescribed period, which is of high credibility after calibration. The sparsely distributed rain gauges could not provide sufficient precipitation data which can represent its spatial variability in detail [1,12]. Remote sensing techniques can supply precipitation data on a global scale [13], which is exempt from the topographic restriction
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