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Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment
Environmental Modelling & Software ( IF 4.9 ) Pub Date : 2019-12-18 , DOI: 10.1016/j.envsoft.2019.104607
Sheng Wang , Ke Zhang , Ludovicus P.H. van Beek , Xin Tian , Thom A. Bogaard

Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 km-resolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all <20% and >0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which fine-scale topographical information is incorporated.



中文翻译:

大面积基于物理的滑坡预测:按比例缩放低分辨率水文模型结果以进行高分辨率边坡稳定性评估

降雨引发的浅层滑坡是世界范围内广泛的自然灾害,对人类生命和财产造成了许多损害。在这项研究中,我们着重于通过结合1 km分辨率的水文模型和90 m分辨率的边坡稳定性模型来预测大范围的滑坡,其中应用了通过地形湿度指数对土壤水分进行降尺度的方法。建模的水文过程与观察到的流量总体上显示出良好的一致性:三个验证站的相对偏差和相关系数分别分别小于20%和大于0.60。导出的土壤水分比例定律允许将原始的1 km土壤水分以近乎保守的比例缩小至90 m分辨率,以进行边坡稳定性评估。就滑坡预测而言,整体准确性和真实阳性率分别为97.2%和66.9%,分别。这项研究提供了一种有效且计算效率高的耦合方法,可预测合并了精细地形信息的大区域的滑坡。

更新日期:2019-12-19
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