Improvement of satellite‐based rainfall product <scp>CHIRPS</scp> in estimating rainfall erosivity on the Loess Plateau

Zeng, Wenying; Ding, Xiaotong; Sun, Wenyi; Mu, Xingmin

doi:10.1002/ldr.4790

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Satellite remote sensing precipitation products have emerged as alternative sources of gauge observations in the past decade and have been widely utilized in mapping rainfall erosivity [26,27,[29][30][31][32]34,36]. Most studies have reported that these satellite products effectively capture the spatial distribution of rainfall erosivity [26,29,31,34], which is consistent with the results achieved in this study.…”

Section: Comparison With Previous Studiessupporting

confidence: 88%

“…Most studies have reported that these satellite products effectively capture the spatial distribution of rainfall erosivity [26,29,31,34], which is consistent with the results achieved in this study. However, the evaluation accuracy of rainfall erosivity magnitude derived from satellite products varies across studies, mainly due to differences in the satellite products used and the reference data employed in previous studies [26,29,31,34,36].…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…Previous studies have widely explored the applicability of remote sensing precipitation in rainfall erosivity estimation, such as the level-3 satellite precipitation product released by the Tropical Rainfall Measuring Mission (TRMM) [29][30][31][32], the IMERG series satellite inversion precipitation products produced by the Global Precipitation Measurement mission (GPM) [26,27,29], the National Oceanic and Atmospheric Administration's Climate Precipitation Center morphing technique (CMORPH) [21,33,34], and Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Wang,

Jiang,

et al. 2024

Remote Sensing

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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.

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