Xie Yun;Yin Shuiqing;Liu Baoyuan;Nearing Marka.;Zhao Ying;

[Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Zhao, Ying] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China.

[Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Zhao, Ying] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China.

[Nearing, Mark A.] USDA ARS, Southwest Watershed Res Ctr, Tucson, AZ 85719 USA.

ABSTRACT:  The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha(-1) h(-1) y(-1). A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPE(sym) ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency for daily erosivity index prediction of 0.93. Thus daily rainfall data was generally sufficient for estimating annual average, yearly, and half-monthly time scales, while sub daily data was needed when estimating daily erosivity values. (C) 2016 Elsevier B.V. All rights reserved.

Published in JOURNAL OF HYDROLOGY.2016,535:547-558

DOI: 10.1016/j.jhydro1.2016.02.020