基于贝叶斯模型平均的全球洪水中长期预报评估研究

doi:10.13249/j.cnki.sgs.2022.09.014

地理科学 ›› 2022, Vol. 42 ›› Issue (9): 1646-1653.doi: 10.13249/j.cnki.sgs.2022.09.014

基于贝叶斯模型平均的全球洪水中长期预报评估研究

俞明哲(), 刘剑宇(), 游元媛, 刘翠艳

中国地质大学地理与信息工程学院地理科学系（武汉），湖北武汉 430074

收稿日期:2021-09-01 修回日期:2021-12-03 出版日期:2022-09-10 发布日期:2022-11-14
通讯作者: 刘剑宇 E-mail:mingzhe@cug.edu.cn;liujy@cug.edu.cn
作者简介:俞明哲（2000-）男，湖北武汉人，硕士研究生，研究方向为洪水模拟与预测研究。E-mail: mingzhe@cug.edu.cn
基金资助:
国家自然科学基金项目(42001042);省级大学生创新创业训练计划(S202110491188);省级大学生创新创业训练计划(S202110491028)

Mid-long Term Global Flood Prediction Based on Bayesian Model Averaging

Yu Mingzhe(), Liu Jianyu(), You Yuanyuan, Liu Cuiyan

School of Geography and Information Engineering, China University of GeosciencesGeographical Science Major, Wuhan 430074, Hubei, China

Received:2021-09-01 Revised:2021-12-03 Online:2022-09-10 Published:2022-11-14
Contact: Liu Jianyu E-mail:mingzhe@cug.edu.cn;liujy@cug.edu.cn
Supported by:
National Natural Science Foundation of China(42001042);Provincial College Students Innovation and Entrepreneurship Training Program(S202110491188);Provincial College Students Innovation and Entrepreneurship Training Program(S202110491028)

摘要/Abstract

摘要：

筛选全球5839个水文站逐日径流数据，采用超阈值采样法提取洪水发生频率及时间，将各季节最大日流量作为季节洪水量级，以优选的多个大尺度气候因子的最佳前置月份序列作为潜在预报因子，基于贝叶斯模型平均法构建全球尺度洪水中长期预报模型，并利用均方误差技术指数（MSESS）评价模型的预报效果。结果表明：全球范围内，洪水量级和频率模拟预报效果合格（0.6>MSESS>0.2）的水文站点占比分别为48%和28%；利用前置季节气候因子数据，驱动所构建的洪水中长期预报模型，有效预报了2020年鄱阳湖流域洪水量级将异常偏高。

关键词: 洪水, 中长期预报, 贝叶斯模型平均法, 大尺度气候因子

Abstract:

Flooding has taken a devastating societal and economic losses around the world, leading to numerous fatalities. Limted to the numbers and spatial distribution of hydrological stations, the flood prediction across the globe remains to be explored. Therefore, this study attributed seasonal flood magnitude/frequency to large-scale climate indices,including Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic (EA), East Atlantic/West Russia (EAWR), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Niño 3.4 SST and Niño 4 SST, by using hydrological data from 5839 gauging stations. The block maxima approach (BMA) and the peaks-over-threshold sampling technique (POT) were used to obtain flood magnitude and frequency based on these hydrological stations. Global assessmment indicated that flood magnitude and frequency are significantly correlated with large-scale climate indices at most stations (75%), and the influence of climate indices on flood generally has considerable hysteresis quality, with two-quarter lag for 31% of global stations. The climate indices showing statistically significantly correlated with floods are subsequently applied as predictors to build the global-scale season-ahead prediction models by Bayesian model averaging (BMA), with prediction performance evaluated by the mean squared error skill score (MSESS). The performance of simulation was qualified (0.6>MSESS>0.2) for 48% and 28% of the global hydrological stations for flood magnitude and frequency, respectively. Using the pre-season climate index, the flood forecasting models developed in this study perform well in forecast the flood anomalies over South China in 2020. The prediction based on Bayesian model averaging provides a new perspective for global flood prediction by identifying relevant climate indices, with valuable insights for flood risk management and mitigating flood hazards.

Key words: flood, mid-long term prediction, Bayesian model averaging method, large-scale climate index

中图分类号:

K903

俞明哲, 刘剑宇, 游元媛, 刘翠艳. 基于贝叶斯模型平均的全球洪水中长期预报评估研究[J]. 地理科学, 2022, 42(9): 1646-1653.

Yu Mingzhe, Liu Jianyu, You Yuanyuan, Liu Cuiyan. Mid-long Term Global Flood Prediction Based on Bayesian Model Averaging[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(9): 1646-1653.

图/表 5

表1

图1

表2

图2

图3

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前置季节	AMO	AO	EA	EAWR	NAO	Niño 3.4	Niño 4	PDO
注：各气候因子含义见表1。
一	27%/28%	22%/20%	25%/22%	19%/19%	19%/17%	23%/20%	17%/18%	25%/20%
二	20%/24%	28%/31%	39%/35%	40%/35%	26%/30%	30%/30%	35%/30%	33%/35%
三	17%/18%	22%/23%	21%/24%	24%/27%	28%/27%	25%/22%	27%/27%	27%/25%
四	36%/30%	28%/26%	15%/19%	17%/19%	27%/24%	22%/28%	21%/25%	15%/20%

基于贝叶斯模型平均的全球洪水中长期预报评估研究

Mid-long Term Global Flood Prediction Based on Bayesian Model Averaging

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气候因子	简介
AMO	大西洋多年代际振荡，由北大西洋海温变化的模式定义
AO	北极涛动，以北极为中心，位于20°N以北的海平面气压异常
EA	东大西洋低频率变率的第二大显著模态
EAWR	东大西洋/西俄罗斯涛动指数，全年影响欧亚大陆的3个突出的遥相关模式之一
NAO	北大西洋涛动，是北大西洋海平面气压差（SLP）在冰岛低压和亚速尔高压之间波动的天气现象
Niño 3.4 SST	国际日期变更线到南美洲海岸横跨太平洋的平均赤道海温异常
Niño 4 SST	赤道中部太平洋的海温异常
PDO	太平洋年代际振荡，以中纬度太平洋海盆为中心的强周期性海洋-大气气候变化模式

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