Abstract Non-structural mitigation measures to the globally increasing flood events include forecast based alert generation. However, the extreme rainfall forecasts are associated with low hit rate, high false alarm, and spatiotemporal bias; which makes it difficult to rely on them. Further, the losses due to flood in a region not only depend on rainfall severity but also on topography, socioeconomic conditions and exposure of the region to floods. Here, we introduce a new concept of spatial flood risk mapping and forecasting at weather to medium range based on forecasted hazard, embedded with vulnerability (topographic and socioeconomic) and exposure information. We define hazard as the probability of extreme rainfall event during upcoming days given an available weather forecast for the same days. As hindcast is used for computation of probabilities, hazard contains prior information about the false alarm, hit rate and spatiotemporal bias of the forecast. Vulnerability is calculated by averaging the topographic and socioeconomic indicators, and exposure is calculated using a land use land cover map. Topographic vulnerability is computed with digital elevation model using Height Above the Nearest Drainage method, and Data Envelopment Analysis is performed to derive the socioeconomic vulnerability based on the demographic census data. For a specific region and a specific event, the relative flood risk maps are generated at an administrative level (e.g., district, subdistrict or village level for India) and the high-risk areas can be identified from those maps for mitigation. The methodology is demonstrated for a very recent extremely severe flood event that happened in Kerala, India in August 2018. It is evident from the results that the high-risk areas forecasted well in advance (as high lead time as 15 days) match fairly well with the areas, which suffered maximum losses because of direct flood.

中文翻译：

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结合天气模型、地形、社会经济信息和土地利用暴露的天气到中等范围的洪水风险预测

摘要 针对全球不断增加的洪水事件的非结构性缓解措施包括基于预测的警报生成。然而，极端降雨预报具有低命中率、高误报和时空偏差等特点；这使得很难依赖它们。此外，一个地区洪水造成的损失不仅取决于降雨的严重程度，还取决于该地区的地形、社会经济条件和洪水风险。在这里，我们引入了一个新的概念，即基于预测的灾害、嵌入脆弱性（地形和社会经济）和暴露信息的天气到中等范围的空间洪水风险制图和预测。我们将灾害定义为在未来几天内发生极端降雨事件的概率，前提是同一天的可用天气预报。由于后报用于计算概率，因此风险包含有关预测的误报、命中率和时空偏差的先验信息。脆弱性是通过平均地形和社会经济指标来计算的，而暴露是使用土地利用土地覆盖图来计算的。地形脆弱性通过数字高程模型使用最近排水系统之上的高度方法计算，并进行数据包络分析，以根据人口普查数据得出社会经济脆弱性。对于特定区域和特定事件，在行政级别（例如，印度的区、分区或村级别）生成相对洪水风险图，并且可以从这些图中确定高风险区域以进行缓解。