Document Type : Research Paper
Authors
1 Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran
3 M.Sc. in Watershed Science and Engineering, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran
Abstract
In this study, aimed at assessing managerial-institutional resilience across different sub-watersheds of the Sang Sefid watershed, the indicators for each of the aforementioned components were first identified based on a review of the literature, library studies, interviews with experts, and field observations. In this research, using a multi-response coding method, the questionnaire variables were qualitative ordinal variables, consistent with the Likert scale (Very Low = 1, Low = 2, Moderate = 3, High = 4, and Very High = 5). Following the assessment of the questionnaire's validity and reliability, a survey was conducted among the watershed residents. In this regard, the validity of the measurement instrument was confirmed by a panel of experts, and the sample size was calculated using Cochran's formula. It should be noted that 14 expert specialists were consulted to assess the validity of the questionnaire and the indicators for measuring resilience, and finally, the validity of the measurement instrument was confirmed by them. Regarding reliability, Cronbach's alpha coefficient was employed to calculate the reliability or dependability of the measurement instrument. Furthermore, the managerial-institutional resilience of local communities exposed to flood risk within the hydrological units of the Sang Sefid watershed was assessed using one-way analysis of variance (ANOVA) and the K-means cluster analysis method. The comparison of the two methods—one-way ANOVA and K-means cluster analysis—demonstrated a significant convergence in assessing the managerial-institutional flood resilience potential of the hydrological units. The identification of Units S-int3 and S9 as the least and most resilient units, respectively, in both methods reinforces the internal validity of the findings. Moreover, the similar grouping of the other units into three distinct classes (with the exception of Unit S-int5) reveals a clear pattern of spatial distribution of resilience across the region, which can serve as a basis for prioritizing management interventions.
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