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Introduction and Evaluation of a Rainstorm-caused Flood Forecasting System
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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。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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图 4 全国0.125°×0.125°地理信息数据
(a)高程，(b)流向，(c)汇流累积量，(d)河网
Fig. 4 0.125°×0.125° geographic information data for China domain
(a)elevation, (b)flow direction, (c)flow accumulation, (d)stream network
图 5 水文站率定期水文过程线
(a)江桥，(b)铁岭，(c)滦县，(d)华县，(e)王家坝，(f)寸滩，(g)七里街，(h)南宁，(i)拉萨，(j)莺落峡
Fig. 5 ydrographs for the calibration period at streamflow gauging stations
(a)Jiangqiao, (b)Tieling, (c)Luanxian, (d)Huaxian, (e)Wangjiaba, (f)Cuntan, (g)Qilijie, (h)Nanning, (i)Lhasa, (j)Yingluoxia
图 6 水文站检验期水文过程线
(a)江桥，(b)铁岭，(c)滦县，(d)华县，(e)王家坝，(f)寸滩，(g)七里街，(h)南宁，(i)拉萨，(j)莺落峡
Fig. 6 Hydrographs for the validation period at streamflow gauging stations
(a)Jiangqiao, (b)Tieling, (c)Luanxian, (d)Huaxian, (e)Wangjiaba, (f)Cuntan, (g)Qilijie, (h)Nanning, (i)Lhasa, (j)Yingluoxia
图 8 淮河流域30″×30″地理信息数据
(a)高程，(b)流向，(c)汇流累积量，(d)河网
Fig. 8 30″×30″ geographic information data for the Huai River Basin
(a)elevation, (b)flow direction, (c)flow accumulation, (d)stream network
图 9 淮河流域王家坝站率定期水文过程线
(a)20140711号洪水，(b)20150616号洪水，(c)20160717号洪水
Fig. 9 Hydrographs at Wangjiaba Station of the Huai River Basin for the calibration period of flood events
(a)20140711, (b)20150616, (c)20160717
图 10 淮河流域王家坝站检验期水文过程线
(a)20140827号洪水，(b)20150601号洪水，(c)20160629号洪水
Fig. 10 Hydrographs at Wangjiaba Station of the Huai River Basin for the validation period of flood events
(a)20140827, (b)20150601, (c)20160629
图 11 淮河流域王家坝站2016年7月12—21日未来10 d滚动预报水文过程线
Fig. 11 Hydrographs at Wangjiaba Station of the Huai River Basin for ten-day rolling forecast during the period from 12 Jul to 21 Jul in 2016
表 1 CREST模型参数及其取值范围
Table 1 CREST model parameters and their feasible ranges
参数 意义 默认值 最小值 最大值 RainFact 降水量转换系数 1.0 0.5 1.2 Ksat 土壤饱和导水率/(mm·d-1) 2.84 1 1000 WM 流域最大蓄水量/mm 129.95 1 500 B 下渗曲线指数 0.48 0.05 1.5 IM 不透水面积比 0.07 0 0.2 KE 潜在蒸散发转换系数 0.85 0.1 1.5 coeM 地面径流流速系数 58.89 1 150 expM 地面径流流速指数 0.25 0.1 2 coeR 地面径流流速转换为河道水流流速的转换因子 0.73 0.2 3 coeS 地面径流流速转换为壤中流流速的转换因子 0.63 0.001 1 KS 地面径流产流参数 0.41 0 1 KI 壤中流产流参数 0.22 0 1 
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