Document Type : Original Article
Authors
1
Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran
2
Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
Abstract
Extended Abstract
Introduction: Floods are among the natural and human-induced disasters that have consistently caused various forms of human casualties, financial losses, and environmental damage in the watersheds of the country. Accordingly, managing this phenomenon and addressing different aspects of resilience against it are of particular importance. Resilience has diverse dimensions, and this research focuses on its socio-cultural and economic aspects. The process of socio-cultural resilience connects a network of adaptive capacities to post-disruption recovery. Economic resilience has two dynamic and static dimensions: dynamic economic resilience refers to the speed at which an institution or system recovers from a severe shock and returns to its desired state, while static economic resilience is defined as the ability of an institution or system to maintain its functionality when experiencing a severe shock. In this study, we measured the socio-cultural and economic resilience of local communities against floods in various hydrological and non-hydrological units of the Sange Sefid region in Ilam Province and classified their flood resilience potential. This understanding will play a significant role in future planning aimed at enhancing the socio-cultural and economic resilience potential of rural environments.
Materials and Methods: In this research, first, the socio-cultural and economic resilience in the Sange Sefid watershed in Ilam Province and Chardavol County was assessed. To evaluate resilience in different sub-watersheds, the indicators for each component were determined based on a literature review, library studies, expert interviews, and field visits. Then, a survey of watershed residents was conducted to measure the intensity or magnitude of the considered indicators as resilience measurement items using a five-point Likert scale questionnaire, after assessing the validity and reliability of the questionnaire. The questionnaire's validity was confirmed by experts. Additionally, Cronbach's alpha method was used to calculate the reliability or trustworthiness of the measurement tool. Furthermore, the sampling unit was rural households, and Cochran's formula, based on the rural household population in each sub-watershed, was used to calculate the sample size. The questionnaire results were then entered into SPSS software, and one-way analysis of variance (ANOVA) was used to examine and analyze the data. Subsequently, the Tukey test was employed to prioritize sub-watersheds and compare means in terms of socio-cultural and economic resilience.
Results and Discussion: Eleven socio-cultural indicators and nine economic indicators were used to measure the socio-cultural and economic resilience of local communities against floods in different hydrological and non-hydrological units. The results showed that Cronbach's alpha values for socio-cultural and economic resilience questionnaires were 0.832 and 0.815, respectively, indicating good reliability. The ANOVA results assessing socio-cultural and economic resilience against floods showed a significant difference between the units. Accordingly, flood resilience grouping of different hydrological and non-hydrological units was performed based on the Tukey test. The prioritization of socio-cultural resilience potential against floods, based on groups' calculated mean values in order from high to low, was: S-int2, S-int3, S8-int, S11, S-int5, S8-2, S-int4, S10, S12, S1, S-int1, and S9. The prioritization of economic resilience potential against floods in order from high to low was: S-int3, S-int2, S-int5, S10, S1, S-int1, S-int4, S8-int, S11, S12, S8-2, and S9. From the local community's perspective, units S-int2 (score 30.88) and S9 (score 47.07) had the minimum and maximum socio-cultural flood resilience, respectively, while units S-int3 (score 11.40) and S9 (score 35.13) had the minimum and maximum economic flood resilience, respectively.
Conclusion: Overall, the results indicate the presence of units with different potentials for socio-cultural and economic resilience against floods in the study area. The grouping of socio-cultural and economic flood resilience potential showed classification into two groups with minimum and maximum resilience potential and three intermediate groups for socio-cultural resilience. For economic resilience, units were grouped into four distinct categories and one intermediate category. Accordingly, strategic planning to enhance the socio-cultural and economic resilience potential of rural environments—especially through applying problem-structuring methods and considering the flood resilience measurement indicators identified in this study—is strongly recommended.
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