Risk assessment of rainstorm disasters in the Guangdong–Hong Kong–Macao greater Bay area of China during 1990–2018

Figures & data

Figure 1. Terrains, river system, cities and meteorological stations in the GBA.

Figure 2. Monthly mean temperature (a) and precipitation (b) in the GBA from 1981 to 2020.

Figure 3. Schematic diagram of components for assessing the risks of rainstorm disasters.

Table 1. Terrain factor assignment.

Terrain elevation (m)	Elevation standard deviation (m)
	≤1	1–10	≥10
≤100	0.9	0.8	0.7
100–300	0.8	0.7	0.6
300–700	0.7	0.6	0.5
≥700	0.6	0.5	0.4

Table 2. Definition and level intervals for the risks of rainstorm disasters.

Risk level	Index interval	Definition
Level 1	≤0.57	The risk level is low resulting in relatively low socio-economic loss.
Level 2	(0.57, 0.64)	The risk level is medium resulting in moderate socio-economic loss.
Level 3	(0.64, 0.70)	The risk level is high resulting in significant socio-economic loss.
Level 4	>0.70	The risk level is very high resulting in severe socio-economic loss.

Figure 4. Changes in the frequency (a) and mean intensity (b) of rainstorm processes in the GBA from 1990 to 2018.

Figure 5. Annual average frequency of rainstorm processes (a) and tendency rate of the frequency (b), as well as average rainstorm intensity (c) and tendency rate of the intensity (d).

Figure 6. The distribution of hazard index of 20-year-return-period in the GBA. Inverse distance weighted (IDW) interpolation technique in ArcGIS was used to create the figure.

Table 3. Averages of hazard, exposure, vulnerability and risk indexes of cities in the GBA.

City	Hazard	Exposure	Vulnerability	Risk	Risk level
Dongguan	0.72	0.53	0.75	0.70	3
Zhuhai	0.91	0.51	0.70	0.75	4
Shenzhen	0.78	0.55	0.67	0.68	3
Zhongshan	0.78	0.52	0.78	0.73	4
Huizhou	0.75	0.50	0.63	0.64	2
Jiangmen	0.80	0.51	0.69	0.67	3
Foshan	0.61	0.53	0.79	0.64	2
Guangzhou	0.67	0.58	0.69	0.63	2
Zhaoqing	0.55	0.50	0.63	0.55	1

Figure 7. The spatial distribution of GDP (a), POP (b) and exposure index (c) in the GBA. Inverse distance weighted (IDW) interpolation technique in ArcGIS was used to create the figure.

Table 4. Changes in urban land use and socio-economic indicators in cities of the GBA (Didan 2015; http://www.resdc.cn/Default.aspx.).

City	Built-up area (km^2)		Vegetation area (km^2)		Population density (people/km^2)		GDP density (1 million RMB/km^2)
	1990	2015	1990	2015	1990	2018	1990	2018
Guangzhou	623	1435	6129	5267	824	1286	0.4	317
Shenzhen	339	834	1417	999	864	6705	8.8	1247
Zhuhai	83	349	1195	922	406	1197	2.6	204
Foshan	353	1112	2514	1774	736	2083	3.3	262
Jiangmen	467	723	8214	7801	373	491	1.1	31
Zhaoqing	250	415	14155	13894	215	279	0.4	15
Huizhou	401	740	10553	10202	204	427	0.4	36
Dongguan	362	1268	1800	938	717	3426	3.3	340
Zhongshan	120	493	1205	888	716	1898	2.9	175

Figure 8. The distribution of terrain (a), river network (b), vegetation coverage (c) and vulnerability (d) indexes in the GBA. Inverse distance weighted (IDW) interpolation technique in ArcGIS was used to create the figure.

Figure 9. Risk levels of rainstorm disasters in the GBA from 1990 to 2018.

Table 5. Area percentages (%) of rainstorm disaster risk levels in the GBA.

City	Level 1	Level 2	Level 3	Level 4
Dongguan	0.0	12.0	29.1	58.9
Zhuhai	0.0	0.0	12.3	87.7
Shenzhen	0.0	16.7	47.6	35.6
Zhongshan	0.0	1.4	16.3	82.3
Huizhou	1.2	52.1	42.1	4.6
Jiangmen	0.4	18.9	55.2	25.5
Foshan	1.7	38.1	53.8	6.4
Guangzhou	4.4	54.4	29.2	11.9
Zhaoqing	77.5	19.7	2.7	0.1

Figure 10. Distributions for the numbers of rainstorm disasters (a), urban waterlogging disasters (b), floods (c) and geological disasters (d) in the GBA from 1990 to 2018.

Figure 11. Changes of the disaster-induced rainstorms in the GBA from 1990 to 2018.

Didan K. 2015. MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1km SIN Grid V006, distributed by NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13A3.006

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Data availability statement (DAS)

The data that support the findings of this study are openly available on websites (www.resdc.cn and www.data.cma.cn).