Stransky David, Fencl Martin, Bares Vojtech
Faculty of Civil Engineering, Department of Sanitary and Ecological Engineering, Czech Technical University in Prague, Thakurova 7, 166 29 Prague 6, Czech Republic E-mail:
Faculty of Civil Engineering, Department of Hydraulics and Hydrology, Czech Technical University in Prague, Thakurova 7, 166 29 Prague 6, Czech Republic.
Water Sci Technol. 2018 May;2017(2):351-359. doi: 10.2166/wst.2018.149.
Rainfall spatio-temporal distribution is of great concern for rainfall-runoff modellers. Standard rainfall observations are, however, often scarce and/or expensive to obtain. Thus, rainfall observations from non-traditional sensors such as commercial microwave links (CMLs) represent a promising alternative. In this paper, rainfall observations from a municipal rain gauge (RG) monitoring network were complemented by CMLs and used as an input to a standard urban drainage model operated by the water utility of the Tabor agglomeration (CZ). Two rainfall datasets were used for runoff predictions: (i) the municipal RG network, i.e. the observation layout used by the water utility, and (ii) CMLs adjusted by the municipal RGs. The performance was evaluated in terms of runoff volumes and hydrograph shapes. The use of CMLs did not lead to distinctively better predictions in terms of runoff volumes; however, CMLs outperformed RGs used alone when reproducing a hydrograph's dynamics (peak discharges, Nash-Sutcliffe coefficient and hydrograph's rising limb timing). This finding is promising for number of urban drainage tasks working with dynamics of the flow. Moreover, CML data can be obtained from a telecommunication operator's data cloud at virtually no cost. That makes their use attractive for cities unable to improve their monitoring infrastructure for economic or organizational reasons.
降雨的时空分布是降雨径流模型研究者非常关注的问题。然而，标准的降雨观测数据往往稀缺且获取成本高昂。因此，来自商业微波链路（CML）等非传统传感器的降雨观测数据成为了一种很有前景的替代方案。在本文中，通过CML对城市雨量计（RG）监测网络的降雨观测数据进行补充，并将其作为由塔博尔集聚区（捷克）的水务公司运行的标准城市排水模型的输入。使用了两个降雨数据集进行径流预测：（i）城市RG网络，即水务公司使用的观测布局，以及（ii）由城市RG校准的CML。从径流量和水文过程线形状方面对模型性能进行了评估。就径流量而言，使用CML并没有带来显著更好的预测结果；然而，在再现水文过程线的动态变化（峰值流量、纳什-萨特克利夫系数和水文过程线的上升支时间）时，CML的表现优于单独使用的RG。这一发现对于许多处理水流动态的城市排水任务来说很有前景。此外，CML数据几乎可以免费从电信运营商的数据云中获取。这使得对于因经济或组织原因无法改善其监测基础设施的城市而言，使用CML数据具有吸引力。
