Characterization and Risk Analysis of Extreme Precipitation in Yangtze River Midstream Urban Agglomerations Based on Multi-Source Rainfall Data

doi:10.11988/ckyyb.20231189

Abstract

Abstract:

The urban agglomeration in the middle reaches of the Yangtze River is a mega city cluster located in the central part of China, playing a crucial role in economic and social development. To explore precipitation characteristics and risks in this urban agglomeration in changing environment, we evaluated the applicability of three precipitation datasets: CMFD, MSWEP, and CN05.1, and selected the CMFD (China Meteorological Forcing Dataset) for analysis. We analyzed the changes in rainstorm hazard risks before and after the rapid urbanization by considering factors such as rainstorm amount, rainstorm duration, elevation, slope, population, and land use. Findings reveal that highly urbanized areas, particularly provincial capital cities, have experienced more frequent occurrences and higher intensities of short-duration rainstorms over the past four decades. The amount and duration of rainstorms in the southern part of the urban agglomeration increased significantly, affecting cities like Xinyu, Fuzhou, Nanchang, and Jiujiang in Jiangxi Province. With the progression of urbanization, populations have concentrated in central cities. Consequently, medium- and high-risk areas for rainstorms have increased, especially in the Poyang Lake Metropolitan Area. To effectively manage and mitigate the risks associated with regional rainstorms, we recommend the following measures: integrate multidisciplinary and multidepartment urban monitoring data to gain a comprehensive understanding of precipitation patterns and their impacts; promote research, development, and application of high-precision multiscale monitoring-forecasting-warning systems to enhance early warning capabilities; strengthen research on the mechanisms of urbanization affecting local climate and extreme rainfall events; coordinate grey (infrastructure), green (natural systems), and blue (water bodies) measures to enhance the climate adaptation and resilience of cities.

Key words: urbanization, extreme rainfall characteristics, risk analysis, urban agglomeration, CMFD

CLC Number:

TV125降水

GONG Li, ZHANG Xiang, LUO Wei, TAO Shi-yong. Characterization and Risk Analysis of Extreme Precipitation in Yangtze River Midstream Urban Agglomerations Based on Multi-Source Rainfall Data[J]. Journal of Changjiang River Scientific Research Institute, 2025, 42(2): 83-90.

Figures/Tables 11

Fig.1 Distribution of administrative divisions, water resource zones, and hydro-meteorological stations in urban agglomeration in the midstream of the Yangtze River

Table 1 Spatial and temporal resolution of rainfall data

数据	时段	时间分辨率	空间分辨率/(°)
国家站点	1961—2019 年	d
CMFD	1979—2018年	3 h	0.1
MSWEP	1979—2020 年	3 h	0.1
CN05.1	1961—2021 年	d	0.25

Table 2 Results of accuracy assessment of three precipitation products

指标计算公式	CMFD	MSWEP	CN05.1
$\begin{array}{l} r = \\ \frac{\overset{n}{\sum_{i = 1}} M_{i} O_{i} - \overset{n}{\sum_{i = 1}} M_{i} \overset{n}{\sum_{i = 1}} O_{i}}{\sqrt{\overset{n}{\sum_{i = 1}} M_{i}^{2} - {(\overset{n}{\sum_{i = 1}} M_{i})}^{2}} \sqrt{\overset{n}{\sum_{i = 1}} O_{i}^{2} - {(\overset{n}{\sum_{i = 1}} O_{i})}^{2}}} \end{array}$	0.72	0.57	0.84
$N M S E = \frac{\sqrt{\frac{1}{n} \overset{n}{\sum_{i = 1}} (M_{i} - O_{i})^{2}}}{\sqrt{\frac{1}{n - 1} \overset{n}{\sum_{i = 1}} {(O_{i} - {\bar{O}}_{i})}^{2}}}$	0.69	0.84	0.64
$P O D = \frac{H}{H + M}$	0.98	0.93	0.98

Table 2 Results of accuracy assessment of three precipitation products

指标计算公式	CMFD	MSWEP	CN05.1
$\begin{array}{l} r = \\ \frac{\overset{n}{\sum_{i = 1}} M_{i} O_{i} - \overset{n}{\sum_{i = 1}} M_{i} \overset{n}{\sum_{i = 1}} O_{i}}{\sqrt{\overset{n}{\sum_{i = 1}} M_{i}^{2} - {(\overset{n}{\sum_{i = 1}} M_{i})}^{2}} \sqrt{\overset{n}{\sum_{i = 1}} O_{i}^{2} - {(\overset{n}{\sum_{i = 1}} O_{i})}^{2}}} \end{array}$	0.72	0.57	0.84
$N M S E = \frac{\sqrt{\frac{1}{n} \overset{n}{\sum_{i = 1}} (M_{i} - O_{i})^{2}}}{\sqrt{\frac{1}{n - 1} \overset{n}{\sum_{i = 1}} {(O_{i} - {\bar{O}}_{i})}^{2}}}$	0.69	0.84	0.64
$P O D = \frac{H}{H + M}$	0.98	0.93	0.98

Fig.2 Box plots of errors of multi-year average rainfall and maximum 1-day precipitation

Table 3 Sources and processing of urban data

变量		空间分辨率	数据处理
人口	欧盟委员会发布的全球人口栅格数据集GHSL-POP,1075—2020年5 a一期	1 km	统计与降雨数据对应的2 914个0.1°网格内的常住人口数量
土地利用	中国科学院资源环境科学数据中心, 1990—2020年5 a一期	1 km	按照耕地、林地、草地、水域、建设用地和未利用地进行重分类
高程 (DEM)	美国宇航局NASADEM数据	30 m	基于ArcGIS进行坡度计算

Fig.3 Distribution of land use and population in the studied urban agglomeration in 1990 and 2020

Table 4 Extreme rainfall indicators

指标	定义	单位
P_max1d	年最大1 d降雨	mm
P_max3d	年最大连续3 d降雨	mm
R₂₀	日降雨量超过20 mm的天数	d
R₅₀	日降雨量超过50 mm的天数	d
P_F95	超过95%阈值的总降雨量	mm
P_R95	95%阈值对应的日降雨量	mm

Fig.4 Change trends in precipitation characterization indicators

Table 5 Thresholds and ranking of indicators before and after rapid urbanization

时段	等级	危险度指标			易损度指标
时段	等级	强降水量P_F95/mm	暴雨日R₅₀/d	高程/m	坡度/(°)	人口/(万人)	土地利用类型
	1	<368.16	<1.18	≥1 140	<16.39	<2.71	未利用地和林地
	2	[368.16,437.05 )	[1.18 2.00, )	[692,1 140)	[16.39,33.48)	[2.71,7.48)	草地
城市化前	3	[437.05,491.52 )	[2.00,2.81 )	[371,692)	[33.48,51.62)	[7.48,15.63)	水域
	4	[491.52,550.79 )	[2.81,3.72 )	[150,371)	[51.62,68.70)	[15.63,32.96)	耕地
	5	≥ 550.79	≥3.72	<150	≥68.70	≥32.96	建设用地
	1	<411.26	<1.86	≥1 140	[0,16.39)	<4.25	未利用地和林地
	2	[411.26, 521.7 )	[1.86,3.22 )	[692,1 140)	[16.39,33.48)	[4.25,14.44)	草地
城市化后	3	[521.7,610.05 )	[3.22,4.54)	[371,692)	[33.48,51.62)	[14.44,32.28)	水域
	4	[610.05,717.73 )	[4.54,5.97 )	[150,371)	[51.62,68.70)	[32.28,75.60)	耕地
	5	≥717.73	≥5.97	<150	≥68.70	≥75.60	建设用地

Fig.5 Hazard zoning for extreme rainfall indicators before and after rapid urbanization

Fig.6 Risk zoning for rainstorm disaster before and after rapid urbanization

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