A high-resolution global flood hazard model.
A high-resolution global flood hazard model.
复制标题
DOI：
10.1002/2015wr016954
复制
发表时间：
2015-09
影响因子：
5.4
通讯作者：
Freer JE
中科院分区：
文献类型：
--
作者：
Sampson CC;Smith AM;Bates PD;Neal JC;Alfieri L;Freer JE
洪水是一种影响全世界社区的自然灾害，但迄今为止，绝大多数洪水灾害研究和测绘工作都是由富裕的发达国家进行的。随着发展中国家人口和经济的增长，政府、企业和非政府组织对这些数据稀缺地区的洪水灾害数据建模的需求也在增长。我们确定了在开发可在全球应用的洪水灾害模型时面临的六个关键挑战，并提出了一个框架方法，该方法利用最近的跨学科进展来应对每一个挑战。该模型为56°S和60°N之间的整个陆地表面生成了分辨率为1090 m的重现期洪水灾害图，并根据英国和加拿大的高分辨率政府洪水灾害数据集对结果进行了验证。全球模型显示，捕捉三分之二和四分之三的基准数据中确定为处于风险的区域，而不会产生过多的假阳性预测。当聚合到101 km时，淹没部分的平均绝对误差福尔斯下降到105%。完整的复杂性全球模型包含一个自动参数化的次网格通道网络，与简化的二维变体和独立开发的泛欧模型的比较表明，通道的明确纳入是提高模型性能的关键因素。虽然仔细处理现有的全球地形数据集可以在城市地区实现合理的模型性能，但采用即将到来的下一代全球地形数据集将为模型性能的逐步改进提供最佳前景。采用全球可用数据集的高分辨率洪水灾害模型相对于基准本地数据对模型性能进行定量评估性能足以满足数据稀缺地区某些真实的应用
Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data‐scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross‐disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high‐resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2‐D only variant and an independently developed pan‐European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next‐generation global terrain data sets will offer the best prospect for a step‐change improvement in model performance. High‐resolution flood hazard model that employs globally available data sets Quantitative assessment of model performance relative to benchmark local data Performance adequate for certain real world applications in data‐scarce regions