An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

Che-Hao Chang; Shiang-Jen Wu; Chih-Tsung Hsu; Jhih-Cyuan Shen; Ho-Cheng Lien

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An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

机译：定量降水估算中基于时空变化识别最优雨量计网络的评估框架

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摘要

This study proposes an evaluation framework to identify the optimal raingauge network in a watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and temporal resolution. The proposed evaluation framework is based on comparison of the spatial and temporal variation in rainfall characteristics (i.e. rainfall depth and storm pattern) from the gauged data compared with those from QPE. The proposed framework first utilizes cluster analysis to separate raingauges into various clusters based on the locations and rainfall characteristics. Then, a cross-validation algorithm is used to identify the influential raingauge in each cluster based on evaluating performance of fitting weighted spatiotemporal semivariograms of rainfall characteristics from the gauged rainfall to the QPE data. Thus, the influential raingauges for a specific cluster number form the representative network. The optimal raingauge network is the one corresponding to the best fitness performance among the representative networks considered. The study area and data set are the hourly rainfall from 26 raingauges and 1,336 QPE grids for 10 typhoons in the Wu River watershed located in central Taiwan. The proposed evaluation framework suggests that a 10-gauge network is the optimal and can describe a good spatial and temporal variation in the rain field similar to the grid-based QPE from two additional typhoon events.

机译：本研究提出了一种评估框架，该评估框架使用具有高时空分辨率的基于网格的定量降水估计（QPE）来识别流域中的最佳雨量计网络。拟议的评估框架是基于对实测数据与QPE的降雨特征（即降雨深度和暴风模式）的时空变化进行比较的基础。所提出的框架首先利用聚类分析根据位置和降雨特征将雨量计分为不同的聚类。然后，使用交叉验证算法，基于评估从测量的降雨到QPE数据的降雨特征加权加权时空半变异函数的拟合性能，来识别每个聚类中的影响雨量计。因此，针对特定簇号的有影响的雨量计形成了代表网络。最佳雨量计网络是与所考虑的代表性网络中最佳健身性能相对应的网络。研究区域和数据集是台湾中部吴河流域的10个台风的26个雨量计和1,336个QPE网格的每小时降雨量。拟议的评估框架表明，一个10规的网络是最佳的，并且可以通过两个额外的台风事件来描述雨场中的良好时空变化，类似于基于网格的QPE。

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来源
《Nordic hydrology》 |2017年第2期|77-98|共22页
作者
Che-Hao Chang; Shiang-Jen Wu; Chih-Tsung Hsu; Jhih-Cyuan Shen; Ho-Cheng Lien;
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作者单位

National Center for High-Performance Computing, Hsinchu 30076, Taiwan;

Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;

National Center for High-Performance Computing, Hsinchu 30076, Taiwan;

Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;

Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Akaike information criterion (AIC); quantitative rainfall estimation (QPE); raingauge network; rainfall characteristic; spatiotemporal semivariogram;

机译：赤池信息标准（AIC）;定量降雨估计（QPE）;雨量计网络降雨特征时空半变异函数;

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专利

1. Corrigendum: Chang, C. H., Wu, S. J., Hsu, C. T., Shen, J. C. and Lien, H. C. (2016) An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation, Hydrology Research 48 (1), 77-98. https://doi.org/10.2166h.2016.169 [J] . Che-Hao Chang, Shiang-Jen Wu, Chih-Tsung Hsu, Nordic Hydrology . 2019,第1a2期

机译：更正：Chang，CH，Wu，SJ，Hsu，CT，Shen，JC和Lien，HC（2016）基于时空变化的定量降水估算中识别最优雨量计网络的评估框架，水文学研究48（1），77 -98。 https://doi.org/10.2166h.2016.169
2. Corrigendum: Chang, C. H., Wu, S. J., Hsu, C. T., Shen, J. C. and Lien, H. C. (2016) An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation, Hydrology Research 48 (1), 77-98. https://doi.org/10.2166h.2016.169 [J] . Che-Hao Chang, Shiang-Jen Wu, Chih-Tsung Hsu, Nordic Hydrology . 2019,第1a2期

机译：勘误：张，Ch，吴，SJ，香港，CT，沉，JC和留置权，HC（2016）一种评价框架，用于根据定量降水估算的时空变化，水文研究48（1），77 -98。 https://doi.org/10.2166h.2016.169
3. An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network [J] . Shengchun Wang, Xiaozhong Yu, Lianye Liu, Computers, Materials & Continua . 2020,第1期

机译：基于时空网络的雷达定量降水估计方法
4. Third WMO International Conference On Quantitative Precipitation Estimation And Quantitative Precipitation Forecasting and Hydrology, (Nanjing China, 18-22 October 2010) [C] . Bongkiyung Emmanuel Nyuyki WMO 3rd international conference on quantitative precipitation estimation and quantitative precipitation forecasting and hydrology : extended abstract volume. . 2010

机译：第三届WMO国际定量降水估计和定量降水预报与水文学国际会议（2010年10月18日至22日，中国南京）
5. Continuous lightning monitoring from a long-range receivers network: Applications on satellite precipitation estimation and quantitative precipitation forecasting. [D] . Chronis, Themis G. 2004

机译：从远程接收器网络进行连续的雷电监视：在卫星降水估计和定量降水预测中的应用。
6. An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China [O] . Wenzhong Shi, Chengzhuo Tong, Anshu Zhang, 2021

机译：扩展重量核密度估计模型预测Covid-19发病风险并识别中国锁定效应的时空变化

An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

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