Climate History Controls Future Landslide Hazard

与大风暴相关的强降水可能引发数千次山体滑坡和泥石流，危及生命，并对基础设施造成重大破坏。风暴频率和/或强度的变化是人为驱动的气候变化的预测结果（Rosenzweig et al., 2007），因此滑坡的预测模型对于减轻这些影响至关重要。在土壤中引发的浅层滑坡尤其具有破坏性，因为它们经常引发快速移动的泥石流。基于物理的浅层滑坡灾害模型通常将滑坡估计为现代水文、生态和土壤力学特性的函数（Montgomery and Dietrich, 1994; Pack et al., 2001）。这种方法的缺陷在于，它没有考虑到集水区以前滑坡的“记忆”，因为在短时间内（<1000年），滑坡不太可能在同一地点发生两次。当考虑到滑坡“记忆”时，我们假设对未来滑坡的两种可能影响：(1)极端降雨产生大型滑坡事件的可能性取决于最近在集水区发生的大风暴的数量；(2)引发1000次滑坡的风暴可能在景观中产生共振，导致滑坡及时聚集。当我们开始预测与气候变化相关的灾害时，考虑降水和景观共振的综合作用是当务之急。拟议的研究将通过收集一个新颖的、大型的、千年尺度的滑坡频率数据集，量化滑坡是否及时聚集。我们将利用在75个凹陷底部发现的放射性碳来分析滑坡频率（当地沉积中心位于河道顶部10米以上），这些凹陷是浅层滑坡的发源地。这些数据与高分辨率激光雷达地形数据相结合，将推动创建一个独特的、概率的滑坡灾害模型，该模型基于最近的降水和以前风暴带来的潜在共振来估计滑坡灾害。我们的新滑坡数据集和滑坡灾害模型将显著提高我们在当前和未来气候情景下预测滑坡风险的能力。

The intense precipitation associated with large storms can initiate thousands of landslides and debris flows, endangering lives and cause significant damage to infrastructure. Changes to the frequency and/or intensity of storms is a predicted consequence of anthropogenically-driven climate change (Rosenzweig et al., 2007), thus predictive models of landsliding are essential for mitigating these effects. Shallow landslides that initiate in soil are particularly destructive as they often initiate rapidly moving debris flows. Physically-based shallow landslide hazard models usually estimate landsliding a function of modern hydrologic, ecologic, and soil mechanical properties (Montgomery and Dietrich, 1994; Pack et al., 2001). The flaw in this approach is that it does not account for the "memory" of previous landslides in a catchment, where landslides are unlikely to occur twice in the same location within the short window of time (<1000 years). When landslide "memory" is considered, we hypothesise two possible effects on future landsliding: (1) the likelihood that extreme rainfall will create a large landslide event is dependent on the number of large storms that have recently occurred in a catchment, and (2) storms that initiate a 1000's of landslides may have a resonance within a landscape that causes landslides to cluster in time. Accounting for the combined role of precipitation and landscape resonance is of immediate concern as we begin to make predict hazards associated with climate change. The proposed research will quantify whether landslides are clustered in time, through the collection of a novel, large, millennial-scale dataset of landslide frequency. We will analyse landslide frequency using radiocarbon found at the base of 75 hollows (local depocentres located 10's of metres above channel heads) where shallow landslides initiate. These data, in conjunction with high resolution LiDAR topographic data, will drive the creation of a unique, probabilistic, landslide hazard model that estimates landslide hazard based on both recent precipitation and the potential resonance imparted by previous storms. Our novel landslide dataset and landslide hazard model will significantly improve our ability to predict the risks posed by landslides in current and future climate scenarios.