基于极端降水概率分布的山洪灾害预警指标估算模型研究
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摘要
针对山洪灾害预警指标确定方法中产汇流机制复杂,多参数不确定性影响因素较大的问题,依据极端降水与山洪灾害内在响应联系,提出以GPD分布为基础耦合小流域内自然因素和人为因素,构建基于极端降水概率分布的山洪灾害预警指标估算模型。选取裴河小流域夏湾组为研究对象,采用时段特征雨量检验法和水位流量反推法与模型计算结果进行对比分析。结果表明:该模型简化预警指标确定过程,合理推求山洪灾害预警指标范围,削弱多参数不确定性影响,计算结果安全可靠,适用于山洪灾害预警预报。基于极端降水概率分布的山洪灾害预警指标估算模型完善预警指标计算理论体系,为山洪灾害预警预报提供技术支撑和经验参考。
Aiming at the larger problems from the complicated mechanism of runoff yield and concentration and the influence factors of the uncertainty of multi-parameter during determining the early warning index for mountain torrent disaster, it is proposed herein to build up an extreme precipitation probability distribution-based estimation model of early warning index for mountain torrent disaster by coupling nature factors and human factors within watershed on the basis of GPD distribution in accordance with the inherent response relation of extreme precipitation with mountain torrent disaster. By taking Xiawan Group of Peihe River Watershed as the study object, a comparative analysis is made on the model calculation result with those from the check method of time-interval characteristic rainfall and the inverse method of water level-discharge. The result shows that this model simplifies the determining process of the early warning index, reasonably deduces the early warning range of mountain torrent disaster and reduces the influence from the uncertainty of multi-factor, thus the calculation result is safe and reliable, and then is adaptable for the early warning forecast of mountain torrent disaster. The extreme precipitation probability distribution-based estimation model of early warning index for mountain torrent disaster improves the theoretical system of the early warning index calculation, while provides technical support and experience for the early warning forecast of mountain torrent disaster as well.
Aiming at the larger problems from the complicated mechanism of runoff yield and concentration and the influence factors of the uncertainty of multi-parameter during determining the early warning index for mountain torrent disaster, it is proposed herein to build up an extreme precipitation probability distribution-based estimation model of early warning index for mountain torrent disaster by coupling nature factors and human factors within watershed on the basis of GPD distribution in accordance with the inherent response relation of extreme precipitation with mountain torrent disaster. By taking Xiawan Group of Peihe River Watershed as the study object, a comparative analysis is made on the model calculation result with those from the check method of time-interval characteristic rainfall and the inverse method of water level-discharge. The result shows that this model simplifies the determining process of the early warning index, reasonably deduces the early warning range of mountain torrent disaster and reduces the influence from the uncertainty of multi-factor, thus the calculation result is safe and reliable, and then is adaptable for the early warning forecast of mountain torrent disaster. The extreme precipitation probability distribution-based estimation model of early warning index for mountain torrent disaster improves the theoretical system of the early warning index calculation, while provides technical support and experience for the early warning forecast of mountain torrent disaster as well.
引文
[1] 戚蓝,李楠.基于事故树-层次分析法相耦合的山洪灾害防御系统评价分析[J]. 水利水电技术, 2017, 48(4):141- 145.
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[13] 杜鸿, 夏军, 曾思栋, 等. 淮河流域极端径流的时空变化规律及统计模拟[J]. 地理学报, 2012, 67(3): 398- 409.
[14] 袁嘉晨, 胡庆芳, 陈元芳, 等. 基于极值理论的太湖流域降水极值分析[J]. 水电能源科学, 2017, 35(7): 1- 5.
[15] 苏布达, 姜彤, 董文杰. 长江流域极端强降水分布特征的统计拟合[J]. 气象科学, 2008, 28(6): 625- 629.
[16] 李占玲, 王武, 李占杰. 基于GPD分布的黑河流域极端降水频率特征分析[J]. 地理研究, 2014, 33(11): 2169- 2179.
[17] CUNNANE C. Unbiased plotting position—A review[J]. Journal of Hydrology, 1978,37(3): 205- 222.
[18] 原文林, 高倩雨, 张晓蕾, 等. 基于成灾信息的山洪灾害临界雨量综合检验复核方法[J]. 水电能源科学, 2018, 36(7): 47- 51.
[19] 叶勇, 王振宇, 范波芹. 浙江省小流域山洪灾害临界雨量确定方法分析[J]. 水文, 2008, 28(1): 56- 58.
[20] 河南省水利勘测设计院.河南省中小流域设计暴雨洪水图集[M]. 郑州:黄河水利出版社, 1984.
[2] 邱瑞田, 黄先龙, 褚明华, 等. 2015年全国山洪灾害防治工作进展[J]. 中国防汛抗旱, 2016, 26(1): 41- 44.
[3] HAPUARACHCHI H A P, WANG Q J, PAGANO T C. A review of advances in flash flood forecasting[J]. Hydrological Processes, 2011, 25(18): 2771- 2784.
[4] 刘媛媛, 胡昌伟, 张红萍, 等. 资料匮乏地区山洪灾害临界雨量确定方法分析[J]. 水利水电技术, 2014, 45(8): 15- 17.
[5] YUAN Z, YANG Z, YAN D, et al. Historical changes and future projection of extreme precipitation in China[J]. Theoretical & Applied Climatology, 2017, 127(1):1- 15.
[6] CHEN C S, CHEN Y L, LIU C L, et al. Statistics of heavy rainfall occurrences in Taiwan[J]. Weather & Forecasting, 2006, 22(22):981- 1002.
[7] 李红霞, 覃光华, 王欣, 等. 山洪预报预警技术研究进展[J]. 水文, 2014, 34(5): 12- 16.
[8] 宋晓猛, 张建云, 孔凡哲, 等. 北京地区降水极值时空演变特征[J]. 水科学进展, 2017, 28(2): 161- 173.
[9] 曹永强, 袁立婷, 郑爽, 等. 近50年辽宁省极端气候事件的趋势变化及空间特征[J]. 水利水电技术, 2018, 49(7): 45- 53.
[10] 原文林, 高倩雨, 张晓蕾, 等. 基于Morris-Sobol的临界雨量参数敏感性分析[J]. 人民黄河, 2018, 40(7): 33- 37.
[11] 朱涯, 黄玮, 李蒙, 等. 云南省山洪地质灾害分布特征及致灾因子分析[J]. 灾害学, 2018, 33(3): 96- 100.
[12] 宋晓猛, 张建云, 孔凡哲. 基于极值理论的北京市极端降水概率分布研究[J]. 中国科学(技术科学), 2018, 48(6): 639- 650.
[13] 杜鸿, 夏军, 曾思栋, 等. 淮河流域极端径流的时空变化规律及统计模拟[J]. 地理学报, 2012, 67(3): 398- 409.
[14] 袁嘉晨, 胡庆芳, 陈元芳, 等. 基于极值理论的太湖流域降水极值分析[J]. 水电能源科学, 2017, 35(7): 1- 5.
[15] 苏布达, 姜彤, 董文杰. 长江流域极端强降水分布特征的统计拟合[J]. 气象科学, 2008, 28(6): 625- 629.
[16] 李占玲, 王武, 李占杰. 基于GPD分布的黑河流域极端降水频率特征分析[J]. 地理研究, 2014, 33(11): 2169- 2179.
[17] CUNNANE C. Unbiased plotting position—A review[J]. Journal of Hydrology, 1978,37(3): 205- 222.
[18] 原文林, 高倩雨, 张晓蕾, 等. 基于成灾信息的山洪灾害临界雨量综合检验复核方法[J]. 水电能源科学, 2018, 36(7): 47- 51.
[19] 叶勇, 王振宇, 范波芹. 浙江省小流域山洪灾害临界雨量确定方法分析[J]. 水文, 2008, 28(1): 56- 58.
[20] 河南省水利勘测设计院.河南省中小流域设计暴雨洪水图集[M]. 郑州:黄河水利出版社, 1984.
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