AbstractDetermining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real‐time. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past 7 days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8% to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long‐term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real‐time, providing a flexible, open‐source framework that can be adapted to other spatial and temporal scales based on data availability.

中文翻译：

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基于卫星的降雨引发山体滑坡灾害评估以提高态势感知能力

摘要确定自然灾害影响的时间、地点和严重程度对于制定缓解策略、适当和及时的响应以及稳健的恢复计划至关重要。开发了情境意识滑坡灾害评估 (LHASA) 模型，以近乎实时地指示潜在的滑坡活动。LHASA 将基于卫星的降水估算与根据坡度、地质、道路网络、断层带和森林损失信息得出的山体滑坡敏感性图相结合。全球降水测量 (GPM) 任务的降水数据用于确定过去 7 天的降雨状况。当降雨量被认为是极端的并且敏感性值中等至非常高时，就会发布“临近预报”以指示更有可能发生山体滑坡的时间和地点。当使用全球滑坡目录评估 LHASA 临近预报时，检测概率 (POD) 范围为 8% 至 60%，具体取决于评估周期、使用的降水产品以及每个滑坡点周围考虑的空间和时间窗口的大小。还讨论了 LHASA 系统的应用，包括如何使用 LHASA 来估计几乎全球范围内潜在滑坡活动的长期趋势，以及如何将其用作支持灾害风险评估的工具。LHASA 旨在提供近乎实时的滑坡灾害态势感知，提供灵活的开源框架，可根据数据可用性适应其他空间和时间尺度。