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Home > Archive>Volume 50, Issue 4, 2022 >545-554. DOI:10.11908/j.issn.0253-374x.21205
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A Bayesian Runoff Simulation Method Considering the Influence of Flash Flood Disaster
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College of Civil Engineering， Tongji University， Shanghai 200092， China

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TV124

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    Abstract:

    The amplification of discharge caused by flash flood disasters has affected the performance of Bayesian runoff simulation in hilly watersheds. This paper， taking the Baisha River Basin in Dujiangyan with frequent flash flood disasters after the Wenchuan earthquake as the research area， explores the critical rainfall causing flash flood disaster in the basin， introduces the linear amplification factor to correct the amplification effect of flash flood disaster on flood flow， and constructs six Bayesian flood simulation schemes by coupling the first-order autoregressive residual error model with different heteroscedastic transformation functions. The outlet daily runoff process of the basin is then simulated using the GR4J model. The results show that the critical rainfall and linear amplification factor can effectively identify and correct the impact of flash flood disasters on discharge. After considering the influence of flash flood disasters， the accuracy， reliability， and precision metrics of the daily runoff simulation results in the Baisha River Basin are increased by 19%， 32%， and 62%， respectively. This method can be widely used in the basin with frequent flash flood disasters and provide a more accurate scientific basis for runoff forecasting in hilly watersheds.

    Reference
    [1] 刘志雨. 山洪预警预报技术研究与应用[J]. 中国防汛抗旱， 2012， 22（2）：41.
    [2] 赵刚， 庞博， 徐宗学， 等. 中国山洪灾害危险性评价[J]. 水利学报， 2016，47（9）：1133.
    [3] 梁忠民， 戴荣， 李彬权. 基于贝叶斯理论的水文不确定性分析研究进展[J]. 水科学进展， 2010，21（2）：274.
    [4] BEVEN K， FREER J. Equifinality， data assimilation， and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology[J]. Journal of Hydrology， 2001， 249（1）：11.
    [5] YANG J， REICHERT P， ABBASPOUR K C. Bayesian uncertainty analysis in distributed hydrologic modeling： A case study in the Thur River basin （Switzerland）[J]. Water Resources Research， 2007， 43（10）：W10401.
    [6] SCHOUPS G， VRUGT J A. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated， heteroscedastic， and non-Gaussian errors[J]. Water Resources Research， 2010， 46（10）：W10531.
    [7] HAN S， COULIBALY P. Bayesian flood forecasting methods： A review[J]. Journal of Hydrology， 2017， 551：340.
    [8] 刘章君， 郭生练， 李天元， 等. 贝叶斯概率洪水预报模型及其比较应用研究[J]. 水利学报， 2014，45（9）：1019.
    [9] SCHOUPS G， DE GIESEN N C VAN ， SAVENIJE H H G. Model complexity control for hydrologic prediction[J]. Water Resources Research， 2008， 44（12）： W00B03.
    [10] ZHANG Z， WAGENER T， REED P， et al. Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization[J]. Water Resources Research， 2008， 44（12）： W00B04.
    [11] SOROOSHIAN S， DRACUP J A. Stochastic parameter estimation procedures for hydrologie rainfall-runoff models： Correlated and heteroscedastic error cases[J]. Water Resources Research， 1980， 16（2）：430.
    [12] HUNTER J， THYER M， MCINERNEY D， et al. Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions[J]. Journal of Hydrology， 2021， 603：126578.
    [13] EVIN G， KAVETSKI D， THYER M， et al. Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration[J]. Water Resources Research， 2013， 49：4518.
    [14] MCINERNEY D， THYER M， KAVETSKI D， et al. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors[J]. Water Resources Research， 2017， 53（3）：2199.
    [15] LIN K， CHEN X， ZHANG Q， et al. A modified generalized likelihood uncertainty estimation method by using Copula function[C]// Proceedings of the 2008 Symposium on Hydrological Modelling and Integrated Water Resources Management in Ungauged Mountainous Watersheds. Chengdu： IAHS Publication， 2009： 51-56.
    [16] WANI O， SCHEIDEGGER A， Cecinati F， et al. Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology[J]. Journal of Hydrology， 2019， 575：1031.
    [17] EVIN G， THYER M， KAVETSKI D， et al. Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity[J]. Water Resources Research， 2014， 50（3）：2350.
    [18] MCINERNEY D， THYER M， KAVETSKI D， et al. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors[J]. Water Resources Research， 2017， 53（3）：2199.
    [19] SUN R， YUAN H， LIU X. Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation[J]. Journal of Hydrology， 2017， 554：680.
    [20] 崔鹏. 中国山地灾害研究进展与未来应关注的科学问题[J]. 地理科学进展， 2014，33（2）：145.
    [21] KRAUSE P， BOYLE D P， B?SE F. Comparison of different efficiency criteria for hydrological model assessment[J]Advances in Geosciences， 2005， 5：89.
    [22] PERRIN C， MICHEL C， ANDRéASSIAN V. Does a large number of parameters enhance model performance？ Comparative assessment of common catchment model structures on 429 catchments[J]. Journal of Hydrology， 2001， 242：275.
    [23] YANG J， REICHERT P， ABBASPOUR K C， et al. Hydrological modelling of the Chaohe Basin in China： Statistical model formulation and Bayesian inference[J]. Journal of Hydrology， 2007， 340（3）[28：167.
    [24] VRUGT J A. Markov chain Monte Carlo simulation using the DREAM software package： Theory， concepts， and MATLAB implementation[J]. Environmental Modelling & Software， 2016， 75：273.
    [25] SADEGH M， MAJD M S， HERNANDEZ J， et al. The quest for hydrological signatures： Effects of data transformation on Bayesian inference of watershed models[J]. Water Resources Management， 2018， 32（5）：1867.
    [26] SADEGH M， VRUGT J A， XU C， et al. The stationarity paradigm revisited： Hypothesis testing using diagnostics， summary metrics， and DREAM（ABC）[J]. Water Resources Research， 2016，51（11）：9207.
    [27] 田述军， 樊晓一. 基于临界雨量的震后泥石流预测预报研究——以白沙河流域为例[J]. 自然灾害学报， 2015，24（4）：176.
    [28] PERRIN C， MICHEL C， ANDRéASSIAN V. Improvement of a parsimonious model for streamflow simulation[J]. Journal of Hydrology， 2003， 279（1）：275.
    [29] EVIN G， THYER M， KAVETSKI D， et al. Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity[J]. Water Resources Research， 2014， 50（3）：2350.
    [30] THYER M， RENARD B， KAVETSKI D， et al. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling： A case study using Bayesian total error analysis.[J]. Water Resources Research， 2009， 45（12）：1211.
    [31] WANG Q J， SHRESTHA D L， ROBERTSON D E， et al. A log-sinh transformation for data normalization and variance stabilization[J]. Water Resources Research， 2012， 48（5）：W05514.
    [32] KRAUSE P， BOYLE D， B?SE F. Comparison of different efficiency criteria for hydrologic models[J]. Advances in Geosciences， 2005， 5：89.
    [33] 胡凯衡， 崔鹏， 游勇， 等. 汶川灾区泥石流峰值流量的非线性雨洪修正法[J]. 四川大学学报（工程科学版）， 2010， 42（5）：52.
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LIANG Jiyu, LIU Shuguang, ZHOU Zhengzheng, ZHONG Guihui, FANG Qi, ZHEN Yiwei. A Bayesian Runoff Simulation Method Considering the Influence of Flash Flood Disaster[J].同济大学学报(自然科学版),2022,50(4):545~554

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  • Received:May 10,2021
  • Online: May 06,2022
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