Predicting Climate Change impacts on Shallow Landslide Risk at regional scales

1336911（bulluoglu）. 山体滑坡破坏水生栖息地并破坏基础设施（例如，道路、公用设施、水坝）。预计西部的滑坡灾害将随着气候变化而增加，但迄今为止，地质滑坡研究通常独立于水文气候研究。有必要统一这两条研究路线，为资源管理和气候适应战略提供区域规模的滑坡预测。华盛顿喀斯喀特山脉经历了各种气候、植被和地形的滑坡，因此，这里所做的工作与地球仪的山区有关。该项目团队将与州和联邦机构合作伙伴一起开发一个区域规模的分布式数值模型，并结合华盛顿瀑布50多年来的滑坡观测，以回答以下研究问题：1）位置（地质、地形、坡度）与气候（降水、温度、融雪和补给率）对滑坡频率的相对作用是什么？2)一个新的、变革性的模型结合了岩土工程、地质学和水文气候预测的方法，它能在多大程度上再现过去滑坡的时空模式和频率？具体来说，a）概率方法和确定性方法哪个更可靠？以及B）在哪些情况下需要更精细、更特定于地点的数据（例如，山坡上的积雪提供了额外的负载和滑坡的关键触发因素，或改变土壤稳定性的冻融循环），以实现精确的模型性能。3)气候变化将如何影响滑坡的位置和频率？具体而言，a）最大的预期危险是什么（例如，道路冲刷、沉积物通量、涵洞故障）？和B）在这些预测下，复杂地形上景观和河流的可持续管理需要什么决策支持工具？虽然近几十年来利用数值模型广泛研究了气候变化对水资源和河流温度的影响，但只有有限的研究侧重于滑坡沉积物的输送。这些研究要么使用区域尺度的经验降雨阈值和地质敏感性图来识别滑坡，要么专注于几米以上的详细水文。第一种方法排除了基本的物理学，而第二种方法不能在大面积上使用。该项目将整合这两条思路，并可能改变区域滑坡研究的方式。所提出的模型将开创一种创新的数值模型设计，将来自5,500米分辨率陆面模型（维克）的地下水流补给和地表径流与10米分辨率概率边坡稳定性模型（SINMAP）相结合。这项工作的目标是直接影响资源管理，并将纳入K-12，本科和研究生教育。研究计划是与州和联邦机构密切合作制定的，他们将立即将结果用于土地管理。 在项目的第三年将举办一个研讨会，为资源经理提供模型培训，并与他们分享我们最终的建模框架。将与教育专业人员一起为高中生开发一个教育模块。该模块将通过华盛顿大学的“大学课堂项目”在西雅图的选定学校进行介绍，在该项目中，学生将获得高水平工作的大学学分。该项目的研究结果将有助于华盛顿大学的本科生和研究生水平的教学。

1336911 (Istanbulluoglu). Landslides disrupt aquatic habitats and damage infrastructure (e.g., roads, utilities, dams). Landslide hazards in the west are expected to grow with climate change, but to date, geologic landslide research has been typically conducted independently from hydroclimate research. There is need for unifying these two lines of research to provide regional scale landslide prediction for resource management and climate adaptation strategies. Washington Cascade Mountains experience landslides across a wide a range of climates, vegetation, and topography, and thus, work done here is relevant to mountain areas across the globe. Working with state and federal agency partners, this project team will develop a regional-scale distributed numerical model in conjunction with 50+ years of landslide observations across the Washington Cascades to answer the following research questions: 1) What are the relative roles of location (geology, topography, slope) vs. climate (precipitation, temperature, snowmelt, and recharge rates) on landslide frequencies? 2) How well does a new, transformative model combining the methods of geotechnology, geology, and hydroclimate prediction reproduce past spatial-temporal patterns and frequencies of landslides? Specifically, a) Are probabilistic or deterministic methods more reliable? and b) In which cases are finer, more site-specific data needed (e.g., snow pack on mountainsides providing extra load and a key trigger for landslides, or freeze-thaw cycles changing soil stability) for accurate model performance. 3) How will climate change likely impact landslide locations and frequencies? Specifically, a) What are the largest expected hazards (e.g., road wash-out, sediment flux, culvert failure)? and b) Under these predictions, what decision support tools are needed for sustainable management of landscapes and streams over complex terrain? While the effects of climate change on water resources and stream temperatures have been extensively studied using numerical models in recent decades, only limited studies focused on landslide sediment delivery. These studies either use empirical rainfall thresholds and geologic susceptibility maps at regional scales to identify landslides, or focus on detailed hydrology over several meters. The first method excludes essential physics, while the second cannot be used over large areas. This project will integrate these two lines of thought and potentially transform how regional landslide research is done. The proposed model will pioneer an innovative numerical model design by combining subsurface flow recharge and surface runoff from an 5,500-m resolution land surface model (VIC) with a 10-m resolution probabilistic slope stability model (SINMAP). The work is targetedto directly impact resource management and will be incorporated into K-12, undergraduate, and graduate education. The research plan has been developed in close collaboration with state and federal agencies, who will immediately put results to use in land management. A workshop will be conducted in year 3 of the project to give model training to resource managers and share our final modeling framework with them. In conjunction with education professionals, an education module will be developed for high school students. This module will be presented at select schools in Seattle through the University of Washington's "University in the Classroom project," wherein students receive university credit for high-level work. The findings of the project will contribute to undergraduate and graduate level teaching at the University of Washington.