Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Environ Res. 2018 Feb;161:61-75. doi: 10.1016/j.envres.2017.10.038. Epub 2017 Nov 2.
Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.
水文数据，如降水，是规划、设计、开发和管理水资源项目以及水文研究的基础。一个最优的雨量计网络可以更准确地估计流域内任何地点的平均或点降水。过去的一些研究表明，增加雨量计网络密度可以减少估计误差。然而，更多的站点意味着安装和监测的成本更高。本研究提出了一种基于克里金和熵理论的方法，以确定中国上海市的最优网络设计。与过去使用克里金插值和熵理论进行网络设计的研究不同，本研究中开发的方法不仅使用克里金方法作为插值器来确定未测站点的降水数据，还结合了最小克里金标准误差（KSE）和最大净信息（NI）含量。因此，该方法将导致一个最优的网络，并能够减少整个流域的降水估计克里金标准误差，并实现最优的降雨信息。本研究还提出了一个基于 NI-KSE 的准则，该准则依赖于单一目标优化。为了评估最终的最优雨量计网络，估计了面平均降雨量，并将其与现有的雨量计网络获得的结果进行了比较。
