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Flow Forecasting using Deterministic Updating of Water Levels in Distributed Hydrodynamic Urban Drainage Models

机译：分布式水力城市排水模型中使用确定性水位更新的流量预测

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There is a growing requirement to generate more precise model simulations and forecasts of flows in urban drainage systems in both offline and online situations. Data assimilation tools are hence needed to make it possible to include system measurements in distributed, physically-based urban drainage models and reduce a number of unavoidable discrepancies between the model and reality. The latter can be achieved partly by inserting measured water levels from the sewer system into the model. This article describes how deterministic updating of model states in this manner affects a simulation, and then evaluates and documents the performance of this particular updating procedure for flow forecasting. A hypothetical case study and synthetic observations are used to illustrate how the Update method works and affects downstream nodes. A real case study in a 544 ha urban catchment furthermore shows that it is possible to improve the 20-min forecast of water levels in an updated node and the three-hour forecast of flow through a downstream node, compared to simulations without updating. Deterministic water level updating produces better forecasts when implemented in large networks with slow flow dynamics and with measurements from upstream basins that contribute significantly to the flow at the forecast location.
机译：越来越需要在离线和在线情况下生成更精确的模型模拟和城市排水系统流量预测。因此，需要数据同化工具，以使其能够将系统测量结果包括在基于物理的分布式城市排水模型中，并减少模型与实际情况之间不可避免的差异。后者可以通过将来自下水道系统的测得的水位插入模型中来部分实现。本文介绍了以这种方式对模型状态进行确定性更新如何影响模拟，然后评估并记录了此特定更新过程的流量预测性能。假设的案例研究和综合观察结果用于说明Update方法如何工作并影响下游节点。在544公顷的城市集水区进行的实际案例研究还表明，与没有更新的模拟相比，可以改善更新节点的20分钟水位预测和流经下游节点的三小时流量预测。当在流量缓慢的大型网络中实施上游水域的测量结果时，确定性的水位更新将产生更好的预测，而上游流域的测量结果对预测位置的流量有重大贡献。

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