Department of Hydraulics and Hydrology, Czech Technical University in Prague, Thákurova 7, 166 29, Prague 6, Czech Republic.
Department of Urban Water Management, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, CH-8600, Dübendorf, Switzerland.
J Environ Manage. 2019 Dec 1;251:109522. doi: 10.1016/j.jenvman.2019.109522. Epub 2019 Sep 18.
Commercial microwave links (CMLs), radio connections widely used in telecommunication networks, can provide path-integrated quantitative precipitation estimates (QPEs) which could complement traditional precipitation observations. This paper assesses the ability of individual CMLs to provide relevant QPEs for urban rainfall-runoff simulations and specifically investigates the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for urban rainfall-runoff predictions and subsequent modelling performance is assessed by comparing simulated runoffs with measured stormwater discharges. The results show that model performance is related to both the sensitivity of CML to rainfall and CML position. The bias propagated into the runoff predictions is inversely proportional to CML path length. The effect of CML position is especially pronounced during heavy rainfalls, when QPEs from shorter CMLs, located within or close to catchment boundaries, better reproduce runoff dynamics than QPEs from longer CMLs extending far beyond the catchment boundaries. Interestingly, QPEs averaged from all available CMLs best reproduce the runoff temporal dynamics. Adjusting CML QPEs to three rain gauges located 2-3 km outside of the catchment substantially reduces the bias in CML QPEs. Unfortunately, this compromises the ability of the CML QPEs to reproduce runoff dynamics during heavy rainfalls. More experimental case studies are necessary to provide specific recommendations on CML preprocessing methods tailored to different water management tasks, catchments and CML networks.
商业微波链路 (CML) 是电信网络中广泛使用的无线电连接，可以提供路径积分的定量降水估计 (QPE)，可以补充传统降水观测。本文评估了单个 CML 为城市降雨径流模拟提供相关 QPE 的能力，并特别研究了 CML 特征和位置对预测径流的影响。该分析基于捷克布拉格一个小 (1.3km) 城市流域的 3 年实验数据集。将来自真实 CML 的 QPE 用作城市降雨径流预测的输入，然后通过将模拟径流与实测雨水排放进行比较来评估后续建模性能。结果表明，模型性能与 CML 对降雨的灵敏度和 CML 位置都有关。传播到径流预测中的偏差与 CML 路径长度成反比。CML 位置的影响在暴雨期间尤为明显，此时位于或靠近流域边界的较短 CML 的 QPE 比延伸到流域边界之外的较长 CML 的 QPE 更好地再现径流动态。有趣的是，从所有可用 CML 平均得出的 QPE 最佳地再现了径流的时间动态。将 CML QPE 调整到位于流域外 2-3km 的三个雨量计可以大大减少 CML QPE 的偏差。不幸的是，这会降低 CML QPE 在暴雨期间再现径流动态的能力。需要更多的实验案例研究来提供针对不同水资源管理任务、流域和 CML 网络的特定 CML 预处理方法的具体建议。
