A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique
A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique
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DOI：
10.1088/1748-9326/aae014
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发表时间：
2018-10-01
影响因子：
6.7
通讯作者：
Winsemius, Hessel C.
中科院分区：
文献类型：
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作者：
Bernhofen, Mark V.;Whyman, Charlie;Winsemius, Hessel C.
全球洪水模型(GFMS)对于国际灾害风险管理正变得越来越重要。然而，这些模型几乎没有针对观测到的洪水事件进行验证，这使得比较模型的性能变得困难。在本文中，我们介绍了针对相同事件的多个GFMS的首次集体验证，并分析了不同的模型结构对性能的影响。我们确定了最近发生大规模洪水事件的非洲三个水力不同的地区：尼日利亚的洛科亚、尼日利亚的伊达和莫桑比克的开姆巴。然后，我们根据这些地区历史洪水范围的卫星观测，评估了六个GFMS提供的洪水范围输出。三个区域的单个模型的临界成功指数在0.45到0.7之间，洪水捕获百分比在52%到97%之间。场地的具体条件会影响性能，因为模型在洛科亚受限的泛滥平原上得分较高，但在伊达平坦而广阔的泛滥平原上得分较低。结果表明，二维水动力模型具有较好的模拟效果。与气候再分析数据强迫的模型相比，由测量流量数据强制的模型显示出更高水平的重现期精度。利用我们的分析结果，我们创建并验证了一个三模型集合，以调查集合模型在洪水风险背景下的有用性。我们发现集成模型的性能与最好的个体模型和聚合模型相似。在三个研究区域中，我们发现模型的性能和空间分辨率之间没有相关性。对于这些大河，最好的单人模型显示出可接受的性能水平。
Global flood models (GFMs) are becoming increasingly important for disaster risk management internationally. However, these models have had little validation against observed flood events, making it difficult to compare model performance. In this paper, we introduce the first collective validation of multiple GFMs against the same events and we analyse how different model structures influence performance. We identify three hydraulically diverse regions in Africa with recent large scale flood events: Lokoja, Nigeria; Idah, Nigeria; and Chemba, Mozambique. We then evaluate the flood extent output provided by six GFMs against satellite observations of historical flood extents in these regions. The critical success index of individual models across the three regions ranges from 0.45 to 0.7 and the percentage of flood captured ranges from 52% to 97%. Site specific conditions influence performance as the models score better in the confined floodplain of Lokoja but score poorly in Idah's flat extensive floodplain. 2D hydrodynamic models are shown to perform favourably. The models forced by gauged flow data show a greater level of return period accuracy compared to those forced by climate reanalysis data. Using the results of our analysis, we create and validate a three-model ensemble to investigate the usefulness of ensemble modelling in a flood hazard context. We find the ensemble model performs similarly to the best individual and aggregated models. In the three study regions, we found no correlation between performance and the spatial resolution of the models. The best individual models show an acceptable level of performance for these large rivers.