Woldegebrael Surafel M, Romano Nunzio, Pumo Dario, Deidda Roberto, Ippolito Matteo, Cannarozzo Marcella, Langousis Andreas, Serafeim Athanasios V, Manfreda Salvatore, Nasta Paolo
Department of Agricultural Sciences, University of Naples Federico II, Portici, Napoli, Italy.
Department of Engineering, University of Palermo, Palermo, Italy.
Sci Total Environ. 2025 Sep 20;996:180122. doi: 10.1016/j.scitotenv.2025.180122. Epub 2025 Jul 28.
The assessment of rainfall erosivity is often hindered by the limited availability of high-resolution rainfall data. A large dataset, comprising 10-minute rainfall data collected over the last two decades from 335 rain gauges across three regions of southern Italy, was utilized in this study to estimate benchmark values of mean annual rainfall erosivity according to the Revised Universal Soil Loss Equation. A set of ten existing simplified models based on coarser resolution rainfall data (from daily to annual) were compared to two newly developed empirical models based on daily-resolution data. The first proposed model uses two compound meteorological predictors, namely the rainfall episodicity and intensity indices, derived from the mean annual rainfall, the Gini's coefficient, and the mean annual number of rainy days. The second model integrates the previous one with geographic and topographic covariates, including latitude, elevation, and minimum distance to the coastline. We evaluated and compared the performances of all models using various metrics, including the Root Mean Square Error (RMSE), the mean error, the adjusted coefficient of determination, the Kling-Gupta efficiency, and the Akaike information criterion. All the performance indices showed how the newly developed models outperformed the ten existing recalibrated equations, obtaining a reduction in absolute percentage error from 27 to 18 %. Our extensive dataset enabled robust calibration and validation of existing and new simplified models, paving the way for an ensemble modeling approach that enhances model transferability in data-scarce environments under similar precipitation regimes.
降雨侵蚀力的评估常常受到高分辨率降雨数据可用性有限的阻碍。本研究利用了一个大型数据集，该数据集包含过去二十年来从意大利南部三个地区的335个雨量站收集的10分钟降雨数据，以根据修订后的通用土壤流失方程估算年平均降雨侵蚀力的基准值。将一组基于较粗分辨率降雨数据（从每日到每年）的十个现有简化模型与两个基于日分辨率数据新开发的经验模型进行了比较。第一个提出的模型使用两个复合气象预测因子，即降雨间歇性和强度指数，它们由年平均降雨量、基尼系数和年平均雨日数得出。第二个模型将前一个模型与地理和地形协变量相结合，包括纬度、海拔和到海岸线的最小距离。我们使用各种指标评估和比较了所有模型的性能，包括均方根误差（RMSE）、平均误差、调整后的决定系数、克林 - 古普塔效率和赤池信息准则。所有性能指标都表明新开发的模型如何优于十个现有的重新校准方程，绝对百分比误差从27%降至18%。我们广泛的数据集使得对现有和新的简化模型进行稳健的校准和验证成为可能，为在类似降水条件下数据稀缺环境中增强模型可转移性的集成建模方法铺平了道路。
