Can we estimate flood frequency with point-process spatial-temporal rainfall models?
Can we estimate flood frequency with point-process spatial-temporal rainfall models?
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DOI：
10.1016/j.jhydrol.2021.126667
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发表时间：
2021-09
影响因子：
6.4
通讯作者：
Yuting Chen;A. Paschalis;Li-Pen Wang;C. Onof
中科院分区：
文献类型：
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作者：
Yuting Chen;A. Paschalis;Li-Pen Wang;C. Onof
随机降雨模型在实践中通常用于长期洪水风险管理。最广泛使用的模型类型之一是基于点过程的。尽管此类模型得到广泛使用，但它们在描述降雨时空结构方面的已知简化是否会影响洪水估算的准确性尚未得到量化。在本研究中，我们量化了英国东南部两个中型河流流域（717 k m 2 和 844 k m 2）的降雨模型限制对洪水估计造成的偏差。为了实现这一目标，我们使用了九年的每小时雷达降雨数据、每小时雨量计的密集网络、基于点过程的时空降雨随机模型以及完全分布式水文模型。我们使用观测和模拟的每小时降雨量对相应的流域水动态进行建模，然后评估随机模型引入的误差是否会在河流流量动态中传播。我们的结果表明，随机降雨模型正确捕获了点尺度降雨统计数据，包括极值点和跨站点空间相关性。然而，该模型导致面积统计的极端值出现偏差，包括高估面积折减系数、极端面积平均降水量和降雨面积比例（湿面积比）。以此作为连续水文模拟的输入，我们发现流量持续时间曲线保存完好，特别是在丰水季节（相对偏差小于 7%）。该模型还很好地再现了每日尺度的洪水频率曲线，10年回报水平的平均相对偏差为0.36-16.9%，证实了其推断中型流域长期洪水风险的能力。然而，夏季小时高峰流量被严重高估，在相同回水水平下相对偏差超过 163.5%。夏季每小时峰值流量的高估是由于夏季对流系统占主导地位以及模型中错误表述的空间结构造成的。
Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely used model types is based on point processes. Despite the widespread use of such models, whether their known simplifications in describing the space–time structure of rainfall will affect the accuracy of flood estimation has not been quantified. In this study, we quantify the biases introduced by the rainfall model limitations to flood estimates in two medium-sized river catchments (717 k m 2 and 844 k m 2) in the South East of the UK. To achieve this, we used nine years of hourly radar rainfall data, a dense network of hourly rain gauges, a spatial–temporal rainfall stochastic model based on point processes, and a fully distributed hydrological model. We modelled the corresponding catchment water dynamics using observed and simulated hourly rainfall and then assessed whether the errors introduced by the stochastic model will propagate in the river flow dynamics. Our results show that the stochastic rainfall model properly captures the point-scale rainfall statistics, including point extremes and the cross-site spatial correlations. However, the model results in a bias on extremes of areal statistics, including an overestimation of the areal reduction factor, extreme areal mean precipitation, and the areal fraction of rain (wet area ratio). Using this as input for continuous hydrological simulations, we find that the flow duration curves are well preserved, particularly in the high flow seasons (relative bias is less than 7%). The model also reproduces well the flood frequency curves at a daily scale with an averaged relative bias of 0.36–16.9% at 10-year return levels, confirming its ability to infer the long-term flood risk for medium-sized catchments. However, the summer-season hourly peak discharge is highly overestimated with a relative bias of over 163.5% at the same return level. The overestimation in summer hourly peak discharge is explained by the dominating convective systems in summer and the misrepresented spatial structure in the model.