Vol.28, NO.4, 2017
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Article Navigation >  Journal of Applied Meteorological Science  > 2017  >  28(4): 385-398
Gan Yanjun, Xu Jing, Zhao Ping, et al. Introduction and evaluation of a rainstorm-caused flood forecasting system. J Appl Meteor Sci, 2017, 28(4): 385-398. DOI:  10.11898/1001-7313.20170401.
Citation: Gan Yanjun, Xu Jing, Zhao Ping, et al. Introduction and evaluation of a rainstorm-caused flood forecasting system. J Appl Meteor Sci, 2017, 28(4): 385-398. DOI:  10.11898/1001-7313.20170401.
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Introduction and Evaluation of a Rainstorm-caused Flood Forecasting System

DOI: 10.11898/1001-7313.20170401
  • 1.

    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2.

    School of Civil Engineering, Tsinghua University, Beijing 100084

  • 3.

    National Meteorological Center, Beijing 100081

  • 4.

    Anhui Meteorological Observatory, Hefei 230031

  • Received Date: 2016-12-08
  • Rev Recd Date: 2017-05-18
  • Publish Date: 2017-07-31
    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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    References(32)

  • Fig. 1  Framwork of the rainstorm-caused flood forecasting system

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    Fig. 2  Framwork of the CREST model

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    Fig. 3  Research domain for flood forecasting of China

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    Fig. 4  0.125°×0.125° geographic information data for China domain

    (a)elevation, (b)flow direction, (c)flow accumulation, (d)stream network

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    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

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    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

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    Fig. 7  Research domain for flood forecasting of the Huai River Basin

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    Fig. 8  30″×30″ geographic information data for the Huai River Basin

    (a)elevation, (b)flow direction, (c)flow accumulation, (d)stream network

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    Fig. 9  Hydrographs at Wangjiaba Station of the Huai River Basin for the calibration period of flood events

    (a)20140711, (b)20150616, (c)20160717

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    Fig. 10  Hydrographs at Wangjiaba Station of the Huai River Basin for the validation period of flood events

    (a)20140827, (b)20150601, (c)20160629

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    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

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    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1.00.51.2
    Ksat土壤饱和导水率/(mm·d-1)2.8411000
    WM流域最大蓄水量/mm129.951500
    B下渗曲线指数0.480.051.5
    IM不透水面积比0.0700.2
    KE潜在蒸散发转换系数0.850.11.5
    coeM地面径流流速系数58.891150
    expM地面径流流速指数0.250.12
    coeR地面径流流速转换为河道水流流速的转换因子0.730.23
    coeS地面径流流速转换为壤中流流速的转换因子0.630.0011
    KS地面径流产流参数0.4101
    KI壤中流产流参数0.2201
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  • [1]
    任洪玉, 邹翔, 张平仓.我国山洪灾害成因分析.中国水利, 2007, 1(14):18-20. doi:  10.3969/j.issn.1000-1123.2007.14.010
    [2]
    李中平, 张明波.全国山洪灾害防治规划降雨区划研究.水资源研究, 2005, 26(2):32-34. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGSL200508001016.htm
    [3]
    张庆云, 陶诗言, 彭京备.我国灾害性天气气候事件成因机理的研究进展.大气科学, 2008, 32(4):815-825. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200804009.htm
    [4]
    丁一汇.人类活动与全球气候变化及其对水资源的影响.中国水利, 2008, 2(8):20-27. http://www.cnki.com.cn/Article/CJFDTOTAL-SLZG200802008.htm
    [5]
    王馥棠.近十年来我国气候变暖影响研究的若干进展.应用气象学报, 2002, 13(6):755-766. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020699&flag=1
    [6]
    王玉洁, 周波涛, 任玉玉, 等.全球气候变化对我国气候安全影响的思考.应用气象学报, 2016, 27(6):750-758. doi:  10.11898/1001-7313.20160612
    [7]
    孙建华, 赵思雄, 傅慎明, 等.2012年7月21日北京特大暴雨的多尺度特征.大气科学, 2013, 37(3):705-718. doi:  10.3878/j.issn.1006-9895.2013.12202
    [8]
    谌芸, 孙军, 徐珺, 等.北京721特大暴雨极端性分析及思考(一)观测分析及思考.气象, 2012, 38(10):1255-1266. doi:  10.7519/j.issn.1000-0526.2012.10.012
    [9]
    张顺利, 陶诗言, 张庆云, 等.1998年夏季中国暴雨洪涝灾害的气象水文特征.应用气象学报, 2001, 12(4):442-457. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20010459&flag=1
    [10]
    周自江, 宋连春.1998年长江流域特大洪水的降水分析.应用气象学报, 2000, 11(3):287-296. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20000344&flag=1
    [11]
    薛秋芳, 任传森, 陶诗言.1998年长江流域洪涝的成因分析.应用气象学报, 2001, 12(2):246-250. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20010233&flag=1
    [12]
    国家防汛抗旱总指挥部.中国水旱灾害公报(2012).北京:中国水利水电出版社, 2013.
    [13]
    李大鸣, 陈海舟, 范玉.国内外防洪减灾发展与现状.中国农村水利水电, 2005(9):33-37. http://www.cnki.com.cn/Article/CJFDTOTAL-ZNSD200509009.htm
    [14]
    刘志雨.山洪预警预报技术研究与应用.中国防汛抗旱, 2012, 22(2):41-45. http://www.cnki.com.cn/Article/CJFDTOTAL-FHKH201202016.htm
    [15]
    赵琳娜, 刘莹, 党皓飞, 等.集合数值预报在洪水预报中的应用进展.应用气象学报, 2014, 25(6):641-653. doi:  10.11898/1001-7313.20140601
    [16]
    王莉莉, 陈德辉, 赵琳娜.GRAPES气象-水文模式在一次洪水预报中的应用.应用气象学报, 2012, 23(3):274-284. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120303&flag=1
    [17]
    杨传国, 余钟波, 林朝晖.基于TRMM卫星雷达降雨的流域陆面水文过程.水科学进展, 2009, 20(4):461-466. http://www.cnki.com.cn/Article/CJFDTOTAL-SKXJ200904000.htm
    [18]
    Todini E.Hydrological catchment modelling:past, present and future.Hydrology and Earth System Sciences, 2007, 11(1):468-482, DOI: 10.5194/hess-11-468-2007.
    [19]
    Shen Y, Zhao P, Pan Y, et al.A high spatiotemporal gauge-satellite merged precipitation analysis over China.J Geophys Res, 2014, 119(6):3063-3075, DOI: 10.1002/2013JD020686.
    [20]
    宇婧婧, 沈艳, 潘旸, 等.概率密度匹配法对中国区域卫星降水资料的改进.应用气象学报, 2013, 24(5):544-553. doi:  10.11898/1001-7313.20130504
    [21]
    潘旸, 沈艳, 宇婧婧, 等.基于贝叶斯融合方法的高分辨率地面-卫星-雷达三源降水融合试验.气象学报, 2015, 73(1):177-186. doi:  10.11676/qxxb2015.010
    [22]
    Wang J, Hong Y, Li L, et al.The coupled routing and excess storage (CREST) distributed hydrological model.Hydrological Sciences Journal, 2011, 56(1):84-98, DOI: 10.1080/02626667.2010.543087.
    [23]
    Xue X, Hong Y, Zhang K.CREST-Coupled Routing and Excess Storage User Manual, Version 2.1.Norman, OK:University of Oklahoma, 2015.
    [24]
    Zhao R.The Xinanjiang model applied in China.Journal of Hydrology, 1992, 135(3):371-381, DOI: 10.1016/0022-1694(92)90096-E.
    [25]
    Xue X, Hong Y, Limaye A S, et al.Statistical and hydrological evaluation of TRMM-based multi-satellite precipitation analysis over the Wangchu Basin of Bhutan:Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J Hydrol, 2013, 499(13-14):91-99, DOI: 10.1016/j.jhydrol.2013.06.042.
    [26]
    Nash J E, Sutcliffe J V.River flow forecasting through conceptual models:Part Ⅰ-A discussion of principles.J Hydrol, 1970, 10(3):282-290, DOI: 10.1016/0022-1694(70)90255-6.
    [27]
    Duan Q, Sorooshian S, Gupta V.Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resources Research, 1992, 28(4):1015-1031, DOI: 10.1029/91WR02985.
    [28]
    Taylor R.Interpretation of the correlation coefficient:a basic review.Journal of Diagnostic Medical Sonography, 1990, 6(1):35-39, DOI: 10.1177/875647939000600106.
    [29]
    Rothman A J, Klein W M, Weinstein N D.Absolute and relative biases in estimations of personal risk.Journal of Applied Social Psychology, 1996, 26(14):1213-1236, DOI: 10.1111/j.1559-1816.1996.tb01778.x.
    [30]
    Lehner B, Verdin K, Jarvis A.HydroSHEDS technical documentation, version 1.0.Washington, DC:World Wildlife Fund US, 2006.
    [31]
    Brown M E.Famine Early Warning Systems and Remote Sensing Data.Heidelberg:Springer-Verlag, 2008.
    [32]
    刘昌明, 张丹.中国地表潜在蒸散发敏感性的时空变化特征分析.地理学报, 2011, 66(5):579-588. doi:  10.11821/xb201105001
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    • Received : 2016-12-08
    • Accepted : 2017-05-18
    • Published : 2017-07-31
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