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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    • Received : 2016-12-08
    • Accepted : 2017-05-18
    • Published : 2017-07-31
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