Spatial scale evaluation of forecast flood inundation maps
Spatial scale evaluation of forecast flood inundation maps
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DOI：
10.1016/j.jhydrol.2022.128170
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发表时间：
2022-07-21
影响因子：
6.4
通讯作者：
Shelton, Kay
中科院分区：
文献类型：
--
作者：
Hooker, Helen;Dance, Sarah L.;Shelton, Kay
洪水淹没预测图为灾害管理团队提供了一个重要工具，以便在洪水事件发生前进行规划和准备，以减轻洪水对社区的影响。评估洪水预报图的准确性对于模型开发和改进未来的洪水预报至关重要。传统的，定量的二进制验证措施通常提供一个域平均得分，在网格级别，预测技能。该分数取决于洪水的量级和洪水图的空间尺度。二进制分数具有有限的物理意义，并不表明特定位置的预测技能，使有针对性的模型改进的变化。这里提出了一种新的，尺度选择性的方法来评估预测洪水淹没图对远程观测的洪水范围。基于分数技能得分的邻域方法被应用于评估预测在捕捉所观察到的洪水时变得熟练的空间尺度。这种巧妙的比例尺随位置而变化，当与应急地图相结合时，可以创建一个新的分类比例尺地图，这是模型评估和开发的一个有价值的视觉工具。模型改进对预测洪水图准确性技能分数的影响通常被大面积正确预测的洪水/未洪水单元所掩盖。为了解决这个问题，洪水边缘位置的准确性进行评估。洪水边缘的位置精度被证明是更敏感的预测技巧和空间尺度的变化相比，整个洪水范围的准确性。此外，由此产生的巧妙规模的洪水边缘提供了一个物理上有意义的验证措施的预测洪水边缘的差异。通过应用于2020年2月怀伊河和卢格河（英国）的案例研究洪水事件（估计重现期为120至550年）来说明这些方法。代表性的错误引入遥感观测捕捉洪水范围在不同的空间分辨率与模型相比。验证熟练的规模的分辨率的观测的灵敏度进行了研究。重新缩放和插值的意见导致一个小的技能分数减少相比，来自模型分辨率的观测洪水图。域平均熟练规模保持不变，在分类规模地图上明显的熟练规模略有位置特定的变化。总的来说，我们对规模的新强调，而不是域平均得分，意味着可以在不同的洪水情景和预测系统之间以及不同空间尺度的预测之间进行比较。
Flood inundation forecast maps provide an essential tool to disaster management teams for planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions. Conventional, quantitative binary verification measures typically provide a domain-averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location-specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented here to evaluate forecast flood inundation maps against remotely observed flood extents. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated. The flood-edge location accuracy proves to be more sensitive to variations in forecast skill and spatial scale compared to the accuracy of the entire flood extent. Additionally, the resulting skilful scale of the flood-edge provides a physically meaningful verification measure of the forecast flood-edge discrepancy. The methods are illustrated by application to a case study flood event (with an estimated return period of 120 to 550 years) of the River Wye and River Lugg (UK) in February 2020. Representation errors are introduced where remote sensing observations capture flood extent at different spatial resolutions in comparison with the model. The sensitivity of the verified skilful scale to the resolution of the observations is investigated. Re-scaling and interpolating observations leads to a small reduction in skill score compared with the observation flood map derived at the model resolution. The domain-averaged skilful scale remains the same with slight location-specific variations in skilful scale evident on the categorical scale map. Overall, our novel emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales.