Enhanced spatiotemporal prediction of rainfall erosivity through flood season data integration

IF 5.7 1区农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Catena Pub Date : 2025-09-03 DOI:10.1016/j.catena.2025.109369

Fei Qi , Yunpeng Wang , Luyue Xu , Xia Liu , Chunqiang Zhang , Zhaofei Fan , Jiayu Guo , Xing Yang

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引用次数: 0

Abstract

Rainfall data collected from the flood-season rainfall stations could be used to improve the prediction accuracy and spatiotemporal variability of the rainfall erosivity (R-factor) in addition to the data from the annual rainfall stations. This study used the 1980–2018 daily rainfall data of 71 annual rainfall stations and 19 flood-season rainfall stations to construct a flood-season model and analyze spatiotemporal patterns of the R-factor in the Yimeng Mountain Area. Results show that the R-factor has a high monthly centrality with a Fournier Index of 399.88 and a Concentration Index of 0.24, mainly concentrated in the flood season (accounting for 85.92 %), during the year with unimodal distribution. A suitable rainfall model for the flood season is constructed (r² = 0.96, Root Mean Square Error (RMSE) = 126.61 MJ·mm·ha⁻¹·h⁻¹·a^-1) based on daily rainfall data, with a similar spatial distribution to the daily rainfall model. Including flood-season stations improved the spatial prediction of the R-factor，reducing the Average Error by 64.76 % and the Root Mean Square Error reduced by 8 %. The average annual R-factor in the study area is 3652 MJ·mm·ha⁻¹·h⁻¹·a^-1, showing a trend of low in the north and west, and high in the south and east. The overall R-factor shows an insignificant upward trend from 1980 to 2018, with a significant upward trend in the northwest and southwest (z > 1.65). Therefore, by developing a rainfall erosivity calculation model based on flood season rainfall data, it is feasible to increase the number of rainfall stations, and enhance the spatial prediction accuracy of the R-factor’s temporal and spatial variations. Concurrently, it’s essential to implement more robust measures to cope with the soil erosion risks caused by the changed R-factor under the climate change.

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基于汛期数据整合的降雨侵蚀力时空预测

汛期雨量站的降雨资料可以在年降雨量资料的基础上提高降雨侵蚀力（r因子）的预测精度和时空变异。利用1980—2018年沂蒙山区71个年雨量站和19个汛期雨量站的日降雨量数据，构建了沂蒙山区汛期模型，并分析了r因子的时空格局。结果表明，r因子具有较高的月中心性，其富尼耶指数为399.88，浓度指数为0.24，主要集中在汛期（占85.92 %），呈单峰分布。基于日降水数据构建了适合汛期的降雨模型（r2 = 0.96，均方根误差（RMSE） = 126.61 MJ·mm·ha−1·h−1·A -1），其空间分布与日降水模型相似。纳入汛期站提高了r因子的空间预测精度，平均误差降低64.76 %，均方根误差降低8 %。研究区年平均r因子为3652 MJ·mm·ha−1·h−1·a-1，呈北、西低、南、东高的趋势。1980 - 2018年总体r因子呈不显著上升趋势，西北和西南呈显著上升趋势（z >； 1.65）。因此，建立基于汛期降雨数据的降雨侵蚀力计算模型，可以增加降雨台站数量，提高r因子时空变化的空间预测精度。同时，应对气候变化下r因子变化带来的水土流失风险，必须采取更加有力的措施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Catena 环境科学-地球科学综合

CiteScore

10.50

自引率

9.70%

发文量

816

审稿时长

54 days

期刊介绍： Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.