A flood-crest forecast prototype for river floods using only in-stream measurements
A flood-crest forecast prototype for river floods using only in-stream measurements
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DOI：
10.1038/s43247-022-00402-z
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发表时间：
2022-04-01
影响因子：
7.9
通讯作者：
Kim, Kyungdong
中科院分区：
文献类型：
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作者：
Muste, Marian;Kim, Dongsu;Kim, Kyungdong
流量预测通常依赖于耦合的降雨 - 径流 - 演算模型，这些模型通过监测协议所估计的数据进行校准和运行，而监测协议无法完全捕捉非恒定流的动态。这限制了准确预测洪峰和发布灾害预警的能力。在此，我们利用为流量估算而获取的直接测量数据集来开发一种数据驱动的预测算法，该算法不需要传统的基于物理的建模。我们使用2014年至2019年间在美国伊利诺伊河的一个指标流速测量站所获取的测量数据来测试我们算法的潜力。我们发现该预测协议能够对洪峰大小和到达时间进行短期预测。该算法在较大事件中吻合度更好，并且对于单峰暴雨可能更可靠，这可能是由于此类事件中滞后行为较为显著。我们得出结论，仅使用直接测量的指标流速和水位就可以预测洪水灾害。
Streamflow forecasting generally relies on coupled rainfall-runoff-routing models calibrated and executed with data estimated by monitoring protocols that do not fully capture the dynamics of unsteady flows. This limits the ability to accurately forecast flood crests and issue hazard warnings. Here we utilize directly measured datasets acquired for streamflow estimation to develop a data-driven forecasting algorithm that does not require conventional physically-based modeling. We test the potential of our algorithm using measurements acquired at an index-velocity gaging station on the Illinois River, USA, between 2014 and 2019. We find that the forecasting protocol is able to deliver short-term predictions of flood crest magnitude and arrival time. The algorithm produces better agreement with larger events and is more reliable for single-peak storms possibly due to the prominence of hysteretic behavior in such events. We conclude that flood hazard can be forecast using directly measured index-velocity and stage alone.