Landslide Hazard Modeling in the Skagit Basin

A study of landslide probability in Skagit Basin as a collaboration (MOA) between University of Washington and Seattle City Light (SCL). The project's objective is to better understand landslides in the watersheds containing the electrical transmission lines and facilities of SCL's Skagit Hydroelectric Project. A recently completed landslide model (Strauch et al. 2018) will be run using subsurface flow derived from a basin calibrated hydrologic model (Distributed Hydrology Soil and Vegetation Model - DHSVM) at 150-m grid resolution. The modeling will estimate contemporary and future probability of landslide initiation and create landslide hazard maps at a 30-m resolution. Future hydrology will be generated from running DHSVM with future climatology from two different Global Climate Models (GCMs) with two different representative concentration pathways (RCPs) emission scenarios for two future time periods. The analysis will also evaluate the sensitivity of the landslide model to subsurface flow and reduced cohesion simulating a fire.

本研究为华盛顿大学与西雅图市照明公司（Seattle City Light, SCL）通过备忘录协议（Memorandum of Agreement, MOA）合作开展的斯卡吉特盆地滑坡概率分析研究。本项目旨在深入剖析覆盖西雅图市照明公司斯卡吉特水电项目输电线路及设施的流域内滑坡发育特征。研究将采用Strauch等（2018）提出的已完成滑坡模型，以150米网格分辨率，输入经流域率定的分布式水文土壤植被模型（Distributed Hydrology Soil and Vegetation Model, DHSVM）所生成的地下径流数据开展模拟。本次建模将估算当前与未来的滑坡触发概率，并生成30米网格分辨率的滑坡灾害图。未来水文数据将通过运行DHSVM生成，输入数据包含两套不同全球气候模型（Global Climate Models, GCMs）的未来气候数据集，以及对应两种典型浓度路径（Representative Concentration Pathways, RCPs）排放情景的两个未来时段的气候数据。本次分析还将评估滑坡模型对地下径流以及模拟火灾引发的黏聚力降低的敏感性。