陆水水库洪水集合概率预报方法与应用研究
Ensemble Probability Flood Forecasting Method and Application in Lushui Reservoir
DOI: 10.12677/JWRR.2020.96061, PDF,  被引量    国家自然科学基金支持
作者: 崔 震, 郭生练*, 王 俊：武汉大学水资源与水电工程科学国家重点实验室，湖北 武汉；巴欢欢：长江勘测规划设计研究，湖北 武汉；张 俊, 陈瑜彬：长江水利委员会水文局，湖北 武汉
关键词: 入库洪水；水文模型；集合概率预报；统计后处理；陆水水库；Reservoir Inflow Flood； Hydrological Model； Ensemble Probability Forecast； Statistical Post Processing； Lushui Reservoir
摘要: 为探讨预见期内模型结构的不确定性对洪水预报精度的影响，分别选用陆水水库流域资料和欧洲中期天气预报中心(ECMWF)的降水预报数据，驱动API、新安江、GR4J三个水文模型。通过对期望值预报精度和概率预报整体性能的分析，比较贝叶斯模型平均(BMA)和改进的BMA (M-BMA)两种统计后处理方法的有效性。结果表明：两种方法的期望值预报在一定程度上提高了原始预报精度；两种方法均能提供可靠的预报区间；通过连续概率排位分数(CRPS)等多个指标分析，M-BMA方法的概率预报性能优于BMA方法。
Abstract: To explore the uncertainty of hydrologic model structure in the leading time flood forecasting accuracy, we selected the dataset of Lushui Reservoir watershed and the precipitation forecast data of the European Center for Medium-Term Weather Forecast (ECMWF) to drive the three hydrological models (API, Xinanjiang and GR4J). Through the analysis of the expected value forecast accuracy and the overall per-formance of the probability forecast, the effectiveness of the two statistical post-processing methods, the Bayesian model averaging (BMA) and the improved BMA (M-BMA) were compared. The results show that the expected value forecast of the two methods improves the original forecast accuracy to a certain extent; both methods can provide reliable forecast intervals; through the analysis of multiple indicators such as Continuous ranked probability score (CRPS), the performance of the M-BMA method is better than that of the BMA method.
文章引用：崔震, 巴欢欢, 郭生练, 王俊, 张俊, 陈瑜彬. 陆水水库洪水集合概率预报方法与应用研究[J]. 水资源研究, 2020, 9(6): 559-570. https://doi.org/10.12677/JWRR.2020.96061

参考文献

[1] 张建云. 中国水文预报技术发展的回顾与思考[J]. 水科学进展, 2010, 21(4): 435-443. ZHANG Jianyun. Review and reflection on China’s hydrological forecasting techniques. Advances in Water Science, 2010, 21(4): 435-443. (in Chinese)
[2] 陆桂华, 吴娟, 吴志勇. 水文集合预报试验及其研究进展[J]. 水科学进展, 2012, 23(5): 728-734. LU Guihua, WU Juan and WU Zhiyong. Advances in hydrologic ensemble prediction experiment. Advances in Water Science, 2012, 23(5): 728-734. (in Chinese)
[3] 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): 1019-1028. LIU Zhangjun, GUO Shenglian, LI Tianyuan, et al. Comparative study of Bayesian probabilistic flood forecasting models. Journal of Hydraulic Engineering, 2014, 45(9): 1019-1028. (in Chinese)
[4] 彭勇, 徐炜, 王萍, 等. 耦合TIGGE降水集合预报的洪水预报[J]. 天津大学学报(自然科学与工程技术版), 2015, 48(2): 177-184. PENG Yong, XV Wei, WANG Ping, et al. Flood forecasting coupled with TIGGE ensemble precipitation forecasts. Journal of Tianjin University (Science and Technology), 2015, 48(2): 177-184. (in Chinese)
[5] LIU, Y. Q., GUPTA, H V. Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resources Research, 2007, 43(7): 1-18. [Google Scholar] [CrossRef] 
[6] MARTY, R., FORTIN, V., KUSWANTO, H., et al. Combining the Bayesian processor of output with Bayesian model averaging for reliable ensemble forecasting. Journal of the Royal Statistical Society: Series C (Appl. Stat.), 2015, 64(1): 75-92. [Google Scholar] [CrossRef] 
[7] DAI, K., ZHU, Y. J. and BI, B. G. The review of statistical post-process technol-ogies for quantitative precipitation forecast of ensemble prediction system. Acta Meteorologica Sinica, 2018, 76(4): 493-510.
[8] RAFTERY, A. E., BALABDAOUI, F. and Gneiting, T. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 2005, 133(5): 1155-1174. [Google Scholar] [CrossRef] 
[9] 董磊华, 熊立华, 万民. 基于贝叶斯模型加权平均方法的水文模型不确定性分析[J]. 水利学报, 2011, 42(9): 1065-1074. DONG Leihua, XIONG Lihua and WAN Min. Uncertainty analysis of hydrological modeling using the Bayesian model aver-aging method. Journal of Hydraulic Engineering, 2011, 42(9): 1065-1074. (in Chinese)
[10] 刘章君, 郭生练, 许新发, 等. 贝叶斯概率水文预报研究进展与展望[J]. 水利学报, 2019, 50(12): 1467-1478. LIU Zhangjun, GUO Shenglian, XV Xinfa, et al. Bayesian probabilistic hydrological forecasting: Progress and prospects. Journal of Hydraulic Engineering, 2019, 50(12): 1467-1478. (in Chinese)
[11] SLOUGHTER, J. M., GNEITING, T. and RAFTERY, A. E. Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. Journal of the American Statistical Association, 2010, 105(489): 25-35. [Google Scholar] [CrossRef] 
[12] 巴欢欢, 郭生练, 钟逸轩, 等. 考虑降水预报的三峡入库洪水集合概率预报方法比较[J]. 水科学进展, 2019, 30(2): 186-197. BA Huanhuan, GUO Shenglian, ZHONG Yixuan, et al. Comparative study on probabilistic ensemble flood forecasting consi-dering precipitation forecasts for the Three Gorges Reservoir. Advances in Water Science, 2019, 30(2): 186-197. (in Chi-nese)
[13] DARBANDSARI, P., COULIBALY, P. Inter-Comparison of different Bayesian model averaging modifications in streamflow simulation. Water, 2019, 11(8): 1707. [Google Scholar] [CrossRef] 
[14] THYER, M., RENARD, B. J., KAVETSKI, D., et al. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis. Water Resources Research, 2009, 45(12): 1-22. [Google Scholar] [CrossRef] 
[15] 李明亮, 杨大文, 陈劲松. 基于采样贝叶斯方法的洪水概率预报研究[J]. 水力发电学报, 2011, 30(3): 27-33. LI Mingliang, YANG Dawen and CHEN Jinsong. Probabilistic flood forecasting by a sampling-based Bayesian model. Journal of Hydroelectric Engineering, 2011, 30(3): 27-33. (in Chinese)
[16] ENGELAND, K., STEINSLAND, I. Probabilistic post processing models for flow forecasts for a system of catchments and several lead times. Water Resources Research, 2014, 50(1): 182-197. [Google Scholar] [CrossRef] 
[17] HE, S. K., GUO, S. L., LIU, Z. J., et al. Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method. Hydrology Research, 2018, 49(5): 1636-1651. [Google Scholar] [CrossRef] 
[18] 邓鹏鑫, 王银堂, 胡庆芳, 等. GR4J模型在赣江流域日径流模拟中的应用[J]. 水文, 2014, 34(2): 60-65. DENG Pengxin, WANG Yintang, HU Qingfang, et al. Application of GR4J in daily runoff simulation for Ganjiang river basin. Journal of China Hydrology, 2014, 34(2): 60-65. (in Chinese)
[19] 钟小燕, 文磊, 余钟波. 连续API模型在沂河临沂站径流预报中的应用[J]. 人民长江, 2017, 48(13): 26-30. ZHONG Xiaoyan, WEN Lei and YU Zhongbo. Application of continuous API hydrological model at Linyi Hydrological station of Yihe River. Yangtze River, 2017, 48(13): 26-30. (in Chinese)
[20] ZHONG, Y. X., GUO, S. L., XIONG, F., et al. Probabilistic forecasting based on ensemble forecasts and EMOS method for TGR inflow. Frontiers of Earth Science, 2020, 14(1): 188-200. [Google Scholar] [CrossRef] 
[21] MA, Z. Q., XU, J. T., HE, K., et al. An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China. Journal of Hydrology, 2019, 581: 1-42.[CrossRef]