| 暴雨致洪预报系统及其评估 |
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| 引用本文： | 甘衍军,徐晶,赵平,洪阳,谌芸,郝莹,包红军,曾子悦,徐辉,狄靖月. 暴雨致洪预报系统及其评估[J]. 应用气象学报, 2017, 28(4): 385-398. DOI: 10.11898/1001-7313.20170401 |
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| 作者姓名： | 甘衍军 徐晶 赵平 洪阳 谌芸 郝莹 包红军 曾子悦 徐辉 狄靖月 |
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| 作者单位： | 1.中国气象科学研究院灾害天气国家重点实验室, 北京 100081 |
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| 基金项目： | 国家自然科学基金项目（41505092）,国家重点研究发展计划（2017YFC1404000,2016YFC0402702）,灾害天气国家重点实验室开放课题（2015LASW-A05）,中国气象科学研究院基本科研业务费专项资金（2016Y010） |
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| 摘 要： | 该文研发了基于CREST V2.1分布式水文模型的暴雨致洪预报系统,应用中国气象局降水业务产品,开展全国0.125°×0.125°的逐日洪水预报和区域30"×30"的逐时洪水预报。其中,全国洪水预报以松花江、辽河、海河、黄河、淮河、长江、东南诸河、珠江、西南诸河和西北诸河十大水资源分区的典型流域为研究对象,区域洪水预报以淮河流域为研究对象。以模拟和观测流量之间的效率系数为目标函数,采用SCE-UA方法分别对全国和区域洪水预报模型的参数分流域进行率定。评估参数率定前后模型对效率系数、相关系数和相对偏差的改进程度,并对参数率定后的模型进行检验。结果表明:率定后的模型能够重现控制水文站的实测洪水过程,与率定前相比,效率系数和相对偏差有显著改进,相关系数有较大改进。系统符合业务需求,具有较好的预报精度和时效性,具备业务应用能力。
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| 关 键 词： | 暴雨致洪 水文预报 河流洪水 山洪灾害 CREST模型 |
| 收稿时间： | 2016-12-08 |
| 修稿时间： | 2017-05-18 |
Introduction and Evaluation of a Rainstorm-caused Flood Forecasting System |
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| Gan Yanjun,Xu Jing,Zhao Ping,Hong Yang,Chen Yun,Hao Ying,Bao Hongjun,Zeng Ziyue,Xu Hui and Di Jingyue. Introduction and Evaluation of a Rainstorm-caused Flood Forecasting System[J]. Journal of Applied Meteorological Science, 2017, 28(4): 385-398. DOI: 10.11898/1001-7313.20170401 |
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| Authors: | Gan Yanjun Xu Jing Zhao Ping Hong Yang Chen Yun Hao Ying Bao Hongjun Zeng Ziyue Xu Hui Di Jingyue |
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| Affiliation: | 1.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 1000812.School of Civil Engineering, Tsinghua University, Beijing 1000843.National Meteorological Center, Beijing 1000814.Anhui Meteorological Observatory, Hefei 230031 |
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| Abstract: | Flood disaster is one of the important factors that restrict the sustainable development of the economy and society of China. The development of a well-performing rainstorm-flood forecasting system is an important non-engineering flood prevention measure that would mitigate the loss of imminent flood disasters. A rainstorm-caused flood forecasting system, which is based on the distributed hydrological model CREST V2.1, is developed to provide refined streamflow, evapotranspiration, soil moisture, and other forecast products. By utilizing operational precipitation data from China Meteorological Administration (CMA) to serve as input for this system, nationwide flood forecasting is carried out by 0.125°×0.125° daily, and regional forecast is done by 30"×30" hourly. For the former, one typical watershed is selected for each of ten river basins as Songhua, Liao, Hai, Yellow, Huai, Yangtze, Southeast, Pearl, Southwest and Northwest River Basins, while for the latter just the Huai River Basin is taken as focus. The SCE-UA optimization algorithm is adopted to search the optimal parameter sets that maximize the Nash-Sutcliffe efficiency (E) between the observed and the simulated streamflow discharges for gauging stations of typical watersheds. E, correlation coefficient (C), and relative bias (B) are used to evaluate model performances before and after the calibration of model parameters. Validation tests are conducted by transferring calibrated parameter values to another flood event of the same watershed. Results show that the calibrated model can reproduce the observed flood processes and provide accurate hydrological forecasting service. Compared to the non-calibrated model, the calibrated one significantly improves E and B, and moderately improves C. It has good applicability in watersheds with different hydroclimatic, geological and geomorphological conditions, but has relatively weak forecasting ability for frequently fluctuating low-flow flood. For the model parameters, their values not only depend on the hydroclimatic, soil and vegetation conditions of the watersheds, but are also influenced by interactions among physical processes of the model. Besides, some empirical parameters need to be calibrated according to different levels of the flood events for the same watershed. Generally, this flood forecasting system show good forecasting accuracy and timeliness, which meets operational needs. However, further work is still needed to improve the prediction accuracy of the model. For example, the snowmelt module could be implemented into the CREST model to improve the prediction accuracy for flood disasters caused by snowmelt in the Northwest, Northeast, and Qinghai-Tibet Plateau regions. In addition, more observed streamflow discharge data should be collected to help calibrating model parameters for more watersheds. Furthermore, uncertainty quantification methods should be adopted to understand parameter behaviors, quantify and reduce parametric uncertainties. |
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| Keywords: | rainstorm-caused flood hydrological forecasting river flood mountain torrent disaster CREST model |
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