干旱区生态环境知识资源中心(Arid): Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards

Arid

DOI	10.1016/j.scitotenv.2024.172744
	Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards
	Pirasteh, Saied; Fang, Yiming; Mafi-Gholami, Davood; Abulibdeh, Ammar; Nouri-Kamari, Akram; Khonsari, Nasim
通讯作者	Fang, YM
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2024
卷号	930
英文摘要	The evaluation of the vulnerability of coupled socio-ecological systems is critical for addressing and preventing the adverse impacts of various environmental hazards and devising strategies for climate change adaptation. The initial step in vulnerability assessment involves exposure assessment, which entails quantifying and mapping the risks posed by multiple environmental hazards, thereby offering valuable insights for the implementation of vulnerability assessment methodologies. Consequently, this study sought to model the exposure of coupled social -ecological systems in mountainous regions to various environmental hazards. By a set of socio-economic, climatic, geospatial, hydrological, and demographic data, as well as satellite imagery, and examining 11 hazards, including droughts, pests, dust storms, winds, extreme temperatures, evapotranspiration, landslides, floods, wildfires, and social vulnerability, this research employed machine learning (ML) techniques and the fuzzy analytical hierarchy process (FAHP). Expert opinions were utilized to guide hazard weighting and calculate the exposure index (EI). Through the precise spatial mapping of EI variations across the socio-ecological systems in mountainous areas, this investigation provides insights into vulnerability to multiple environmental hazards, thereby laying the groundwork for future endeavors in supporting national -level vulnerability assessments aimed at fostering sustainable environments. The findings reveal that social vulnerability and pests receive the highest weighting, while floods and landslides are ranked lower. All hazards demonstrate significant correlations with the EI, with droughts exhibiting the strongest correlation (r > 0.81). Spatial analysis indicates a north -south gradient in forest exposure, with southern regions showing higher exposure hotspots (EI 29.08) compared to northern areas (EI 10.60). Validation based on Area Under Curve (AUC) and Consistency Rate (CR) in FAHP demonstrates robustness, with AUC values exceeding 0.78 and CR values below 0.1. Considering the anticipated intensification of hazards, management strategies should prioritize reducing social vulnerability, restore degraded areas using drought -resistant species, combat pests, and mitigate desertification. By integrating multidisciplinary data and expert opinions, this research contributes to informed decision -making regarding sustainable forest management and climate resilience in mountain ecosystems.
英文关键词	Google Earth Engine Fuzzy Analytic Hierarchy Process Machine learning Resilience enhancement
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001238943700001
WOS关键词	CLIMATE-CHANGE ; FOREST VEGETATION ; ZAGROS FORESTS ; ECOSYSTEM ; REGION ; ASSOCIATION ; RESILIENCE ; FRAMEWORK ; DROUGHT ; IMPACTS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405497
推荐引用方式 GB/T 7714	Pirasteh, Saied,Fang, Yiming,Mafi-Gholami, Davood,et al. Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards[J],2024,930.
APA	Pirasteh, Saied,Fang, Yiming,Mafi-Gholami, Davood,Abulibdeh, Ammar,Nouri-Kamari, Akram,&Khonsari, Nasim.(2024).Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards.SCIENCE OF THE TOTAL ENVIRONMENT,930.
MLA	Pirasteh, Saied,et al."Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards".SCIENCE OF THE TOTAL ENVIRONMENT 930(2024).