Quantitative risk evaluation of agricultural drought in China using the improved catastrophe progression method

doi:10.1016/j.regsus.2026.100387

Abstract

Abstract:

The identification of factors contributing to agricultural drought and regions with high drought risk is important to reduce grain losses and ensure food security. However, agricultural drought is a complex phenomenon and governed by factors such as soil, crop, atmosphere, and human activity. Due to the complexity and uncertainty, there is currently a lack of effective quantitative risk assessment method that can reflect the process of risk formation. In this study, the hazard-exposure-vulnerability-resilience framework was applied to establish the risk evaluation index system of agricultural drought. This study used the improved catastrophe progression method (CPM) to diagnose and evaluate the agricultural drought risks in 31 provinces, municipalities, and autonomous regions of China during 2001-2020 based on the index data from China Statistical Yearbook and China Water Resources Bulletin. Results showed that 41.94% of provinces, municipalities, and autonomous regions had a risk above the national average, among which Henan Province had the highest agricultural drought risk. Moreover, there were distinct spatial characteristics in which risk decreased from western and central China (high risk) to northeastern and eastern China (low risk), and the top five high risk regions (Henan Province, Xinjiang Uygur Autonomous Region, Ningxia Hui Autonomous Region, Shandong Province, and Hebei Province) are all located north of the Yangtze River. At the criterion layer, the regional distribution characteristics of risks also exhibited significant variations. In summary, the approach is effective and can be applied to evaluate regional natural disaster risk. The results can facilitate the development of policies for regional agricultural drought prevention and mitigation.

Key words: Agricultural drought risk, Hazard-exposure-vulnerability-resilience framework, Catastrophe progression method (CPM), Comprehensive weighted scoring method (CWSM), Risk evaluation index system China

DU Xiaoyan, FANG Di, WANG Chengqin, TANG Gang, WANG Qian, HAN Dongjun. Quantitative risk evaluation of agricultural drought in China using the improved catastrophe progression method[J]. Regional Sustainability, 2026, 7(5): 100387.

Figures/Tables 12

Table 1

Fig. 1.

Table 2

Fig. 2.

Table 3

Fig. 3.

Fig. 4.

Table 4

Agricultural drought risk values in 31 provinces, municipalities, and autonomous regions of China."

Province, municipality, or autonomous region	R'_B₁	R'_B₂	R'_B₃	R'_B₄	R′	Province, municipality, or autonomous region	R'_B₁	R'_B₂	R'_B₃	R'_B₄	R′
Beijing Municipality	0.4970	0.2940	0.3690	0.4271	0.1936	Hubei Province	0.3958	0.7734	0.6119	0.4892	0.3084
Tianjin Municipality	0.6185	0.3213	0.5306	0.5527	0.2680	Hunan Province	0.3611	0.8203	0.5853	0.4955	0.3209
Hebei Province	0.7113	0.8430	0.6437	0.5633	0.4321	Guangdong Province	0.3014	0.7050	0.4372	0.4500	0.2317
Shanxi Province	0.6600	0.6183	0.5927	0.6061	0.3337	Guangxi Zhuang Autonomous Region	0.3510	0.7073	0.6269	0.4981	0.2836
Inner Mongolia Autonomous Region	0.5864	0.6913	0.6412	0.5849	0.3393	Hainan Province	0.3025	0.3645	0.6647	0.4708	0.2329
Liaoning Province	0.5038	0.6586	0.5215	0.4797	0.2683	Chongqing Municipality	0.3934	0.5875	0.5544	0.5465	0.2541
Jilin Province	0.5146	0.7117	0.5908	0.4920	0.3012	Sichuan Province	0.3935	0.8773	0.6276	0.5416	0.3780
Heilongjiang Province	0.5521	0.8686	0.6413	0.5491	0.4042	Guizhou Province	0.3873	0.6579	0.6751	0.5528	0.2970
Shanghai Municipality	0.4148	0.3087	0.4938	0.5235	0.2156	Yunnan Province	0.3776	0.7226	0.6245	0.5297	0.2968
Jiangsu Province	0.4695	0.8179	0.6109	0.5707	0.3553	Xizang Autonomous Region	0.3824	0.2800	0.6953	0.5960	0.2701
Zhejiang Province	0.3218	0.5828	0.4277	0.4120	0.2000	Shaanxi Province	0.5116	0.6351	0.5973	0.5693	0.2963
Anhui Province	0.4487	0.8386	0.6539	0.5518	0.3700	Gansu Province	0.6212	0.6060	0.6615	0.6596	0.3514
Fujian Province	0.3114	0.5418	0.4788	0.4461	0.2059	Qinghai Province	0.4280	0.3191	0.7283	0.6398	0.2983
Jiangxi Province	0.3340	0.7168	0.5545	0.4813	0.2656	Ningxia Hui Autonomous Region	0.9989	0.3897	0.7233	0.6380	0.4928
Shandong Province	0.6123	0.9302	0.6464	0.5773	0.4733	Xinjiang Uygur Autonomous Region	0.5307	0.6096	0.9998	0.7175	0.4737
Henan Province	0.6232	1.0000	0.7078	0.5637	0.5657

Table 4

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

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Criterion	Index	Description	Unit	Index attribute	Data source
Hazard (B₁)	C₁	Average annual precipitation	mm	Negative	Ministry of Water Resources of the People’s Republic of China (2002-2021)
Hazard (B₁)	C₂	Water resources per unit of cultivated land	m³/hm²	Negative	National Bureau of Statistics of China (2002-2021)
Exposure (B₂)	C₃	Average annual planting area	hm²	Positive
	C₄	Average annual crop (grain, legumes, and root crops) yield	t	Positive
	C₅	Average annual rural population density	persons/hm²	Positive
Vulnerability (B₃)	C₆	Average annual proportion of agricultural production value in the total regional production value	%	Positive
Vulnerability (B₃)	C₇	Average annual proportion of the agricultural population to the total population	%	Positive
Resilience (B₄)	C₈	Average annual forest coverage rate	%	Negative
	C₉	Average annual of the per capita disposable income of rural residents	CNY/capita	Negative
	C₁₀	Average annual ratio of the total reservoir capacity to the total cultivated area	m³/hm²	Negative

Catastrophe model	Dimension of control variable	Potential function	Normalized formula
Fold model	1	$f(x)={x}^{3}+{u}_{1}x$	${x}_{{u}_{1}}$=u₁^1/2
Cusp model	2	$f(x)={x}^{4}+{u}_{1}{x}^{2}+{u}_{2}x$	${x}_{{u}_{1}}$=u₁^1/2 and${x}_{{u}_{1}}$=u₂^12/3
Swallowtail model	3	$f(x)={x}^{5}+{u}_{1}{x}^{3}+{u}_{2}{x}^{2}+{u}_{3}x$	${x}_{{u}_{1}}$=u₁^1/2, ${x}_{{u}_{1}}$=u₂^1/3, and ${x}_{{u}_{1}}$=u₃^123/4
Butterfly model	4	$f(x)={x}^{6}+{u}_{1}{x}^{4}+{u}_{2}{x}^{3}+{u}_{3}{x}^{2}+{u}_{4}x$	${x}_{{u}_{1}}$=u₁^1/2, ${x}_{{u}_{1}}$=u₂^1/3, ${x}_{{u}_{1}}$=u₃^1/4, and ${x}_{{u}_{1}}$=u₄^1234/5

Criterion layer	Objective weight	Subjective weight	Index layer	Objective weight	Subjective weight
B₁	0.0943	0.3084	C₁	0.0703	0.1363
B₁	0.0943	0.3084	C₂	0.0240	0.1721
B₂	0.5661	0.2455	C₃	0.1673	0.0669
			C₄	0.2100	0.0917
			C₅	0.1888	0.0869
B₃	0.1496	0.1349	C₆	0.0910	0.0858
B₃	0.1496	0.1349	C₇	0.0586	0.0491
B₄	0.1818	0.3112	C₈	0.0847	0.0833
			C₉	0.0495	0.0116
			C₁₀	0.0476	0.2163