PROGRESS IN GEOGRAPHY ›› 2020, Vol. 39 ›› Issue (10): 1747-1757.doi: 10.18306/dlkxjz.2020.10.013

• Reviews • Previous Articles     Next Articles

A review on the stochastic simulation of rainfall process data for soil erosion assessment

YIN Shuiqing(), WANG Wenting   

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2019-09-22 Revised:2020-05-02 Online:2020-10-28 Published:2020-12-28
  • Supported by:
    National Natural Science Foundation of China(41301281)

Abstract:

Soil erosion is one of the most serious environmental problems in China. Soil erosion model is an efficient tool for diagnosing and preventing soil erosion. Stochastic simulation of precipitation can generate synthetic input data for soil erosion models when observation data are absent. This study summarized the main progress of rainfall process stochastic simulation in existing studies. One-minute resolution rainfall data were collected from 18 weather stations distributed in the main water erosion areas to analyze the characteristics of the storm process and calibrate, evaluate, and develop CLImate GENerator (CLIGEN) stochastic models for China. The main results are as follows: 1) Minimum inter-event time (MIT) for separating precipitation events from continuous precipitation recording ranged from 7.6 h to 16.6 h, with an average of 10.7 h. The MIT values calculated from 1-minute data and hourly data were not significantly different. Storm process characteristics such as the amount, duration, average intensity, and peak intensity of precipitation were sensitive to the variation of MIT values when the MIT values were smaller than 6 h, which suggests that the comparison of storm process characteristics for different storms and areas should use the same MIT value. 2) Events with peak intensity occurring in the first half of the duration of an event were dominant, which accounted for more than 65% of the total events and they were characterized by relatively short duration and greater intensity, comparing with events whose peak intensity fell in the second half of the duration of the events. 3) Weather generator CLIGEN in the Water Erosion Prediction Project (WEPP) soil erosion model can satisfactorily simulate the daily precipitation amounts (P), but it underestimated the storm duration (D) and overestimated the maximum 30-minute intensity (I30). The direction and degree of the bias for D and I30 were not consistent for different groups classified by different precipitation amounts. 4) A method was developed to use hourly precipitation data to prepare the TimePk and MX.5P parameters for CLIGEN input files for the generation of storm process data in the absence of high resolution hyetography precipitation data. More efforts are needed to improve the simulation of precipitation extremes and develop multi-site and multi-variable stochastic models conditioned on weather types in the future.

Key words: precipitation, stochastic simulation, weather generator, soil erosion, CLImate GENerator (CLIGEN)