Rainfall erosivity is one of the most important erosion factors in tropical humid areas including Rwanda. Its current application in erosion modelling is often restricted to the use of externally validated erosivity indices based on annual or monthly data, even though daily rainfall data have become more readily available. The present study aimed to calibrate and validate six different models predicting monthly and annual rainfall erosivity from daily, monthly and annual precipitation records. The reference dataset constituted mid-term average (1980–1989) monthly R_EI30 erosivities reported by Ryumugabe and Berding (1992) at five stations from mid and high altitude zones of Rwanda. The analysis is partly supported by data collected during a short-term field experiment with bare erosion plots at Tangata, located in the northern highlands of Rwanda. Rwanda’s mid altitude zone exhibits a bimodal rainfall erosivity, reflecting both short and long rains. Analysis of this reference dataset revealed moderate to strong linear relations of monthly rainfall erosivity to monthly rainfall amounts. The erosivity density, retrieved from the R_EI30 data, proved strongly variable by season, month and station. The field experiment showed a stronger correlation of event-based soil losses to rainfall amount times intensity than to rainfall amount. Since no unique rainfall amount threshold value for erosive rainfall events could be retrieved, total daily rainfall amount values were used for developing the erosivity models (estimators).

Both regionally (all stations) and locally (one station) calibrated models were tested. At regional scale, estimators with a daily rainfall support performed best. The daily power function adopting monthly-calibrated coefficients (Model 1 by Richardson et al., 1983) proved most suited to capture the irregular patterns in erosivity densities, reported at most stations. In case only monthly rainfall data are available, the power function at monthly temporal resolution (model 4 by Wu, 1994) is to be recommended. Variable performances between stations are explained by the diversity in rainfall generators active at short distances in Rwanda west of Kigali. A local calibration of the selected models confirms the improved performance obtained with daily input data, even when monthly varying model coefficients are replaced by a seasonal function (Yu and Rosewell, 1996), except for Kigali. At this latter station, the power (Wu, 1994) and linear (model 3 by Moore, 1979 or Zhou et al., 1995) functions with monthly support perform best. At Gisenyi, a linear model 5 (Angulo-Martínez and Beguería, 2009) using rainfall amount and number of wet days in the month performs well. Differences in performance reflect the variable capacity of the different models to deal with either seasonal or irregular fluctuations in erosivity density, as well either outstanding or limited differences in erosivity between both rainfall seasons within Rwanda. This frequently leads to over- or underestimations of rainfall erosivity in one of the seasons. The models however perform very well when used to estimate annual erosivity. The monthly power model 4 distinctly outperformed all other models, even those at daily rainfall support, with a prediction quality that is slightly better than a linear regression based on the modified Fournier index (MFI), using all stations except for Kigali. This confirms the power of the MFI as a rainfall erosivity index, but also highlights the importance of this regional calibration within Rwanda and the need for at least two different regression equations.

在包括卢旺达在内的热带湿润地区，降雨侵蚀力是最重要的侵蚀因素之一。它的当前在侵蚀模型中的应用通常仅限于使用基于年度或月度数据的外部验证的侵蚀度指数，即使日降水量数据变得更容易获得。本研究旨在校准和验证从日，月和年降水记录中预测月和年降雨侵蚀力的六个不同模型。参考数据集构成中期平均值（1980-1989），每月R _EI30Ryumugabe和Berding（1992）在卢旺达中高海拔地区的五个站点报告了侵蚀力。卢旺达北部高地坦塔塔（Tangata）的裸露侵蚀地块的短期田间试验所收集的数据部分支持了该分析。卢旺达的中海拔地区表现出双峰降雨侵蚀力，反映了短时降雨和长时降雨。该参考数据集的分析表明，每月降雨侵蚀力与每月降雨量之间存在中等至强的线性关系。腐蚀密度，从R _EI30获得数据，证明其随季节，月份和气象站而变化很大。野外试验表明，基于事件的土壤流失与降雨量乘以强度的相关性强于与降雨量的相关性。由于无法获取侵蚀性降雨事件的唯一降雨量阈值，因此将每日总降雨量值用于开发侵蚀力模型（估算器）。

测试了区域（所有站点）和本地（一个站点）的校准模型。在区域范围内，每天有降雨支持的估算者表现最好。每日功率函数采用每月校准的系数（Richardson等人的Model 1，1983年）被证明最适合捕获侵蚀性密度的不规则模式，这在大多数观测站都得到了报道。如果只有每月的降雨数据，建议使用每月时间分辨率的幂函数（Wu，1994年的模型4）。在基加利以西的卢旺达，短距离内活动的降雨产生器的多样性解释了气象站之间的可变性能。所选模型的本地校准可以确认通过每日输入数据获得的改进性能，即使基加利（Kigali）以外，即使每月变化的模型系数都被季节函数代替（Yu和Rosewell，1996）。在后一个站点，具有月度支持的幂函数（Wu，1994）和线性函数（Moore，1979年的模型3或Zhou等，1995）表现最佳。在吉塞尼（Gisenyi），使用降雨量和当月潮湿天数的线性模型5（Angulo-MartínezandBeguería，2009）表现良好。性能差异反映了不同模型应对侵蚀性密度的季节性或不规则波动的可变能力，以及卢旺达两个降雨季节之间侵蚀性的显着或有限差异。在一个季节中，这经常导致降雨侵蚀力的高估或低估。但是，当用于估算年度侵蚀能力时，这些模型的性能很好。使用除基加利以外的所有观测站，月度功率模型4明显优于所有其他模型，即使是在每日降雨支持下的模型，其预测质量也略好于基于修改后的Fournier指数（MFI）的线性回归。这证实了MFI作为降雨侵蚀力指数的作用，但也强调了卢旺达地区校准的重要性以及至少需要两个不同的回归方程。