A hydroclimatological approach to predicting regional landslide probability using Landlab

This resource supports the work published in Strauch et al., (2018) "A hydroclimatological approach to predicting regional landslide probability using Landlab", Earth Surf. Dynam., 6, 1-26 . It demonstrates a hydroclimatological approach to modeling of regional shallow landslide initiation based on the infinite slope stability model coupled with a steady-state subsurface flow representation. The model component is available as the LandslideProbability component in Landlab, an open-source, Python-based landscape earth systems modeling environment described in Hobley et al. (2017, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017). The model operates on a digital elevation model (DEM) grid to which local field parameters, such as cohesion and soil depth, are attached. A Monte Carlo approach is used to account for parameter uncertainty and calculate probability of shallow landsliding as well as the probability of soil saturation based on annual maximum recharge. The model is demonstrated in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2.

This resource contains a 1) User Manual that describes the Landlab LandslideProbability Component design, parameters, and step-by-step guidance on using the component in a model, and 2) two Landlab driver codes (notebooks) and customized component code to run Landlab's LandslideProbability component for 2a) synthetic recharge and 2b) modeled recharge published in Strauch et al., (2018). The Jupyter Notebooks use HydroShare code libraries to import data located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/.

The Synthetic Recharge Jupyter Notebook <Synthetic_recharge_LandlabLandslide.ipynb> demonstrates the use of the Landlab LandslideProbability Component on a synthetic grid with synthetic data with four options for parameterizing recharge. This notebook was used to verify and validated the theoretical application and digital representation of Landslide processes.

The Modeled Recharge Jupyter Notebook <NOCA_runPaper_LandlabLandslide.ipynb> models annual landslide probability in the North Cascades National Park Complex, and was used to verify that model results in Strauch et al., (2018) could be reproduced online.

本资源支持 Strauch 等人（2018）发表的论文《基于 Landlab 的无限边坡稳定性模型耦合稳态地下水流表示的区域浅层滑坡概率预测的水文气候学方法》，地球表面动力学，第 6 卷，第 1-26 页的工作。该资源展示了一种基于水文气候学的方法，用于模拟区域浅层滑坡的起始过程，该方法结合了无限边坡稳定性模型与稳态地下水流表示。模型组件作为 Landlab 的一部分提供，Landlab 是一个开源的基于 Python 的景观地球系统建模环境，由 Hobley 等人（2017，地球表面动力学，第 5 卷，第 21-46 页，https://doi.org/10.5194/esurf-5-21-2017）所描述。该模型在数字高程模型（DEM）网格上运行，并将局部场参数，如粘聚力和土壤深度附加到网格上。采用蒙特卡洛方法来考虑参数不确定性，并计算浅层滑坡的概率以及基于年最大再充电的土壤饱和概率。该模型在美国华盛顿州北部陡峭的山地地区进行演示，使用 30 米网格分辨率，覆盖 2700 平方公里。资源包含以下内容：1）用户手册，描述 Landlab 滑坡概率组件的设计、参数以及如何在模型中使用组件的步骤指南；2）两个 Landlab 驱动代码（笔记本）和定制组件代码，用于运行 Landlab 的滑坡概率组件，以进行2a）合成再充电和2b）模型化再充电，这些内容均发表在 Strauch 等人（2018）的论文中。Jupyter 笔记本使用 HydroShare 代码库导入位于以下资源的数据：https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/。合成再充电 Jupyter 笔记本（Synthetic_recharge_LandlabLandslide.ipynb）演示了在合成网格上使用 Landlab 滑坡概率组件，并使用合成数据以及四种参数化再充电的选项。该笔记本用于验证和验证滑坡过程的理论应用和数字表示。模型化再充电 Jupyter 笔记本（NOCA_runPaper_LandlabLandslide.ipynb）对北喀斯喀德国家公园复合体中的年滑坡概率进行建模，并用于验证 Strauch 等人（2018）中模型结果的在线重现。