RUSLE tends to overestimate soil erosion in revegetated conditions: Evidence from long-term runoff plots monitoring on China’s Loess Plateau

IF 5.7 1区农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Catena Pub Date : 2025-07-11 DOI:10.1016/j.catena.2025.109285

Jun Liao , Jiaxi Wang , Juying Jiao , Zeng Yan , Jianjun Li , Ziqi Zhang , Mengmeng Li , Qian Xu , Xiaohan Jiang , Wenting Zhao , Qi Ling , Hanyuan Sheng , Yixin Chen , Tong Wu

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Abstract

The Revised Universal Soil Loss Equation (RUSLE), a globally recognized empirical model for soil erosion assessment, has seen extensive efforts to calibrate its erosion factors (R, K, LS, C, P) across diverse regions and scales. However, systematic evaluations of how factor configurations influence model performance, particularly in revegetated ecosystems remain scarce. To address this gap, this study analyzed 8 years of monitoring data (2016∼2023) from 10 runoff plots in a revegetated watershed on China’s Loess Plateau, where vegetation restoration has dramatically altered erosion dynamics. We rigorously tested 144 RUSLE configurations at annual/multi-year scales and 36 configurations at rainfall event scales, evaluating performance through Nash-Sutcliffe Efficiency (NSE) and Mean Absolute Percentage Error (MAPE). Results of the 144 factor configurations revealed systematic bias of soil loss rates across revegetated plots, with NSE values spanning −41.48 to −0.11 and MAPE ranging from 143.22% to 1,540.23%. Performance varied markedly across temporal scales: annual/multi-year simulations (NSE: −4.17∼−0.52; MAPE: 91.96∼554.90%) outperformed event-scale predictions (NSE: −8.04∼−0.63; MAPE: 99.69∼709.25%), aligning with RUSLE’s original design for long-term averages. Rainfall intensity further modulated accuracy, as heavy rainfall events (NSE: −7.61∼−2.39; MAPE: 316.92∼938.50%) exhibited larger errors than non-heavy events (NSE: −1.27∼−0.56; MAPE: 219.17∼515.72%), highlighting the model’s inability to resolve intensity-dependent thresholds. Optimized configurations, such as event-scale R factor refinements (R_W) coupled with vegetation-adaptive C factors (C_L) reduced uncertainties effectively, demonstrating the value of localized factor calibration. However, persistent biases highlighted inherent limitations in RUSLE’s linear empirical framework, which oversimplifies nonlinear interactions between vegetation, soil, and rainfall. These findings emphasize the need for context-driven factor selection to enhance RUSLE’s utility in ecological restoration regions, while advocating for future integration with process-based models to address mechanistic gaps in dynamic, revegetated landscapes.

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RUSLE倾向于高估复植条件下的土壤侵蚀：来自中国黄土高原长期径流样地监测的证据

修订通用土壤流失方程（RUSLE）是全球公认的土壤侵蚀评估经验模型，在不同地区和尺度上对其侵蚀因子（R、K、LS、C、P）进行了广泛的校准。然而，关于因子配置如何影响模型性能的系统评价，特别是在植被恢复的生态系统中，仍然很少。为了解决这一差距，本研究分析了中国黄土高原一个植被恢复的流域的10个径流样地8年（2016 ~ 2023年）的监测数据，植被恢复极大地改变了侵蚀动态。我们在年/多年尺度上严格测试了144种RUSLE配置，在降雨事件尺度上严格测试了36种RUSLE配置，通过纳什-苏特克利夫效率（NSE）和平均绝对百分比误差（MAPE）评估了RUSLE的性能。144个因子配置的结果表明，植被复植样地土壤流失率存在系统偏差，NSE值为- 41.48 ~ - 0.11，MAPE值为143.22% ~ 1,540.23%。在不同的时间尺度上，性能差异显著：年度/多年模拟(NSE:−4.17 ~−0.52；MAPE: 91.96 ~ 554.90%)优于事件尺度预测(NSE:−8.04 ~−0.63；MAPE: 99.69 ~ 709.25%)，与RUSLE长期平均值的原始设计一致。降雨强度进一步调节了精度，如暴雨事件(NSE:−7.61 ~−2.39；MAPE: 316.92 ~ 938.50%)的误差大于非重事件(NSE:−1.27 ~−0.56；MAPE: 219.17 ~ 515.72%)，突出了模型无法解决强度依赖阈值。事件尺度R因子细化（RW）和植被自适应C因子（CL）等优化配置有效降低了不确定性，体现了局域因子定标的价值。然而，持续的偏差突出了RUSLE线性经验框架的固有局限性，该框架过度简化了植被、土壤和降雨之间的非线性相互作用。这些研究结果强调了上下文驱动因子选择的必要性，以增强RUSLE在生态恢复区域的效用，同时提倡未来与基于过程的模型相结合，以解决动态、植被恢复景观中的机制差距。

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.