The Path Forward: Landslides in a Future Climate

Abstract

Natural hazards are an ever-present threat to human lives and infrastructure. The need for greater predictive capability has been identified as one of ten Grand Challenges in Earth Sciences (NRC 2008). For example, memories of geo-hazards which have impacted the activities of humans in China extend back several thousand years and represent an important complement to conventional scientific knowledge. While some records have long been captured in written form (or graphs), many in the form of folklore and oral traditions of preliterate cultures that sometimes today remain an important source of information for contemporary disaster risk reduction and climate-change adaptation. The efforts to provide rational explanations of these phenomena and ways of recognizing their precursors are the germane elements in modern efforts of prediction and forecasting based on better physics. In addition, many oral traditions and Chinese sayings incorporate advice about sustaining livelihoods in the face of natural challenges, advice that may be relevant to challenges arising from climate changes. As an effort toward the goal of a reliable landslide mapping and warning system, we present a modeling system that systematically estimates the potential for landslides over a regional area, rather than for a single slope. The process-based modeling system presented here (with SEGMENT-Landslide as a representative) has representation of the relevant physical processes and can be applied in a variety of environments. The promising performance of SEGMENT-Landslide is attributable to the use of a new, fully three-dimensional modeling framework based on a newly proposed granular rheology, and to the use of a land surface scheme that explicitly parameterizes the hydrological characteristics of macro-pores. Quantitative predictions of storm triggered landslides require a numerical modeling system like SEGMENT-Landslide. However, some of the requirements of model, especially the input and verification data, generally are not available even in current geological maps. These parameters include vegetation loading and root distributions in soils and weathered rocks. Applications of SEGMENT-Landslide to other regions are limited primarily by a lack of high-resolution input datasets. The new concepts implemented in SEGMENT-Landslide, if adopted by the relevant community, hopefully will encourage the collection of such vital information in future surveys. Recent advancements in meteorological forecasts have been particularly important in storm-triggered landslides situations. For example, estimates of total fallen precipitation, i.e., Quantitative Precipitation Estimates (QPEs), have improved because of the combination of spatially distributed radar data with accurate point measurements from rain gauges (Seo et al. 1999). In addition, estimates of immediate future precipitation, i.e., Quantitative Precipitation Forecasts (QPFs), have improved (especially at short lead times) because of increased ability of advanced numerical weather prediction models (NWPs) to assimilate remote sensing from radar, satellites, as well as gauged rainfall observations (e.g., Smith and Austin 2000) and to quantify uncertainties through ensemble predictions (e.g., Collier 2007). Improvements in landslide model’s hydrologic submodel component to utilize these new hydrometeorological products are needed to further improve landslide model prediction skill.