Maruffi L, Stucchi L, Casale F, Bocchiola D
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy.
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy.
Sci Total Environ. 2022 Feb 1;806(Pt 2):150651. doi: 10.1016/j.scitotenv.2021.150651. Epub 2021 Sep 28.
Erosion is a main form of soil degradation, with severe consequences on slope stability and productivity, and erosion studies are required to predict possible variations of such phenomena, also under climate change scenarios. Here we estimated distributed soil erosion within Valchiavenna valley in the Rhaetian Alps, drained by Mera river, and covering Italy, and Switzerland. We used a Dynamic-RUSLE (D-RUSLE) model, which provides spatially distributed estimates of soil erosion explicitly considering snow dynamic (accumulation/melting) and snow cover, and vegetation seasonality. The model was tuned here during 2010-2019, and validation was pursued using river turbidity data, used to assess riverine sediment transport. The model parameter R-factor for rainfall erosivity was estimated using a hydrological model Poli-Hydro, properly set up in the study area. C-factor for land cover was assessed against land cover maps, with seasonally variable Normalized Difference Vegetation Index from satellite images, to account for variable vegetation stage, and large leaf cover in summer. The K-factor related to erosion susceptibility was evaluated through soil texture and organic content. LS-factor depending on slope was assessed using a DTM. Poli-Hydro and D-RUSLE models were then used to project forward potential soil erosion under climate change scenarios until 2100. Climate series (temperature, precipitation) were generated using 4 shared socio-economic pathways (SSPs) of the Sixth Assessment Report of the IPCC, with 3 global circulation models, properly downscaled locally. We analysed expected soil erosion during 2051-2060, and 2091-2100. We found increase of potential soil erosion, with exception of the EC-Earth model for the SSP2.6. Erosion would especially increase in winter, in response to smaller snow accumulation, and larger liquid rainfall share thereby, and decrease in summer, as due to decreased precipitation. Our results suggest the need for adaptation strategies to counteract increasing soil loss in the future, and may highlight most critical areas of intervention.
侵蚀是土壤退化的主要形式，对边坡稳定性和生产力会产生严重影响，因此需要开展侵蚀研究来预测此类现象在气候变化情景下可能发生的变化。在此，我们估算了雷蒂亚阿尔卑斯山脉瓦尔基亚文纳山谷内的分布式土壤侵蚀情况，该山谷由梅拉河排水，涵盖意大利和瑞士。我们使用了动态通用土壤流失方程（D-RUSLE）模型，该模型明确考虑了积雪动态（积累/融化）和积雪覆盖以及植被季节性，从而提供土壤侵蚀的空间分布式估算。该模型于2010 - 2019年在此进行了校准，并使用河流浊度数据进行验证，以评估河流泥沙输移情况。降雨侵蚀力的模型参数R因子是使用在研究区域正确设置的水文模型Poli-Hydro估算得出的。土地覆盖的C因子是根据土地覆盖图以及卫星图像中季节性变化的归一化植被指数进行评估的，以考虑植被阶段的变化以及夏季的大叶覆盖情况。与侵蚀敏感性相关的K因子是通过土壤质地和有机含量来评估的。取决于坡度的LS因子是使用数字地形模型进行评估的。然后，Poli-Hydro和D-RUSLE模型被用于预测到2100年气候变化情景下的潜在土壤侵蚀情况。气候序列（温度、降水）是使用政府间气候变化专门委员会第六次评估报告的4种共享社会经济路径（SSP）生成的，并结合了3个全球环流模型，在当地进行了适当的降尺度处理。我们分析了2051 - 2060年以及2091 - 2100年期间预期的土壤侵蚀情况。我们发现，除了SSP2.6情景下的EC-Earth模型外，潜在土壤侵蚀有所增加。侵蚀在冬季尤其会增加，这是由于积雪减少，从而液态降雨份额增大；而在夏季会减少，这是由于降水减少。我们的研究结果表明，需要采取适应策略来应对未来不断增加的土壤流失情况，并且可能会突出最关键的干预区域。
