Environmental Earth Sciences最新文献

{"title":"Spatial analysis of flood susceptibility in Coastal area of Pakistan using machine learning models and SAR imagery","authors":"Muhammad Afaq Hussain,&nbsp;Zhanlong Chen,&nbsp;Yulong Zhou,&nbsp;Hafiz Ullah,&nbsp;Ma Ying","doi":"10.1007/s12665-025-12129-z","DOIUrl":"10.1007/s12665-025-12129-z","url":null,"abstract":"<div><p>Flooding is one of the most important and challenging natural catastrophes to anticipate, and it is getting more intense and frequent. The coastal areas in Pakistan, like Karachi, are highly vulnerable to flooding, especially during the monsoon rains, which cause immense environmental and socioeconomic damage. A massive flood badly destroyed the study area in 2022. We examined the flood susceptibility in the coastal area of Pakistan using various machine learning algorithms such as Extreme Gradient Boosting, Random Forest, and K Nearest Neighbor. Flood points were identified and validated using Landsat data, Google Earth, and news sources to generate a flood inventory map. A total of 262 flood spots were selected and randomly divided into 70% for training and 30% for validation. Susceptibility maps were validated using area under the receiver operating characteristic (ROC) curve and confusion matrix. In this research, remote sensing data was utilized to validate flood-prone areas using the Sentinel Application Platform for remote sensing image evaluation. The RF model achieved outstanding classification accuracy with an area under the curve (AUC) value of 0.983, accuracy of 0.950, kappa value of 0.900, specificity of 0.992, and sensitivity of 0.902. The research is valuable since the suggested models are being evaluated for the first time in the coastal area of Pakistan to measure flood vulnerability. The flood risk map assists coastal area planners and regulatory agencies in managing and mitigating flood events. Despite its simplicity, the approach used in this study exhibits high precision, making it applicable for expert knowledge-based flood mapping in other regions.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geochemistry and machine learning: methods and benchmarking","authors":"N. I. Prasianakis,&nbsp;E. Laloy,&nbsp;D. Jacques,&nbsp;J. C. L. Meeussen,&nbsp;G. D. Miron,&nbsp;D. A. Kulik,&nbsp;A. Idiart,&nbsp;E. Demirer,&nbsp;E. Coene,&nbsp;B. Cochepin,&nbsp;M. Leconte,&nbsp;M. E. Savino,&nbsp;J. Samper-Pilar,&nbsp;M. De Lucia,&nbsp;S. V. Churakov,&nbsp;O. Kolditz,&nbsp;C. Yang,&nbsp;J. Samper,&nbsp;F. Claret","doi":"10.1007/s12665-024-12066-3","DOIUrl":"10.1007/s12665-024-12066-3","url":null,"abstract":"<div><p>Thanks to the recent progress in numerical methods and computer technology, the application fields of artificial intelligence (AI) and machine learning methods (ML) are growing at a very fast pace. The field of geochemistry for nuclear waste management has recently started using ML for the acceleration of numerical simulations of reactive transport processes, for the improvement of multiscale and multiphysics couplings efficiency, and for uncertainty quantification and sensitivity analysis. Several case studies indicate that the use of ML based approaches brings an overall acceleration of geochemical and reactive transport simulations between one and four orders of magnitude. This paper presents a benchmarking exercise that aims at providing a set of reference data and models for developing and applying ML techniques for geochemical and reactive transport simulations. Several well-known geochemical speciation codes are used to generate systematically a consistent set of high-quality chemical equilibrium data, to be used as input for the training of several ML methods. Two benchmarks are formulated, each with multiple levels of gradually increasing degree of complexity. The first benchmark focuses on cement chemistry, while the second one considers uranium sorption on a clay mineral. The performance of different ML techniques is then evaluated in terms of their numerical efficiency and accuracy. A speedup of several orders of magnitude is observed. The benefits and the limitations of different ML based techniques are then analysed and highlighted.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12665-024-12066-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining prospective zones for groundwater recharge using remote sensing, GIS, and AHP modelling techniques: an investigation of the Mandi district in Himachal Pradesh, India","authors":"Aashish Sharma,&nbsp;Kanwarpreet Singh","doi":"10.1007/s12665-025-12137-z","DOIUrl":"10.1007/s12665-025-12137-z","url":null,"abstract":"<div><p>Groundwater resources are used more frequently for industrial, agricultural, and domestic applications due to the quickening rate of population growth and industrialization. The primary goal of this study is to create a groundwater recharge potential zone map of the Mandi district in northern India using Weighted Overlay Index (WOI) and Analytic Hierarchy Process (AHP) techniques based on Geographic Information Systems (GIS). Nine thematic maps were overlaid to create the groundwater potential zone map (GPZs): soil texture, slope, land use/cover, geology, drainage density, rainfall, lineament density, geomorphology, and lithology. The significance of each parameter was evaluated according to its effect on groundwater levels; geomorphology, geology, landuse/cover, and lithology were given the most weight. According to the findings, the groundwater recharge potential was very good in 513.5 km² (13%) of the study area, good in 1027 km² (26%), moderate in 1581 km² (40%), and poor in 829.5 km² (21%). Based on the results of the spatial modelling ROC (Receiver Operating characteristics) Curve Method, the AHP-based GPZs map had an excellent correlation with the locations of the wells (AUC = 80.5%), proving that the AHP-GIS rating approach is accurate. The results can aid in the efficient planning and management of the development of groundwater resources.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of aquifer storage and recovery opportunities and challenges in India","authors":"Satiprasad Sahoo,&nbsp;Chiranjit Singha,&nbsp;Ajit Govind,&nbsp;Prabhakar Sharma","doi":"10.1007/s12665-025-12124-4","DOIUrl":"10.1007/s12665-025-12124-4","url":null,"abstract":"<div><p>Managing groundwater is a global challenge as offer rises across agriculture, industry, and energy sectors, while climate change, population explosion, industrialization, and urbanization leads to a decline in surface water resources. Managed aquifer recharge (MAR) is one solution that can enhance long-term water sustainability by increasing the natural replenishment of groundwater supplies through the use of non-traditional water sources. India, as the largest groundwater user, is mitigating over-extraction through MAR initiatives. However, Aquifer Storage and Recovery (ASR) provides a site-specific solution for maintaining a sustainable water supply. This approach targets densely populated regions in the Indian subcontinent, particularly those undergoing agricultural transitions, heavily dependent on groundwater for irrigation and domestic use, and facing water shortages in both ground and surface water supplies. The global land data assimilation systems (GLDAS) of 2003–2023 revealed significant groundwater and total water storage depletion in north-western India, with negative trends between − 27.816 and − 21.186 mm/year. These findings emphasize the urgent need to implement MAR systems in the Western Dry Region, Western Himalayas, and Gangetic Plains to ensure sustainable agricultural planning and management. Thus, the review paper emphasizes the potential of MAR and ASR techniques to meet both current and future demands for high-quality water while addressing the rising need for groundwater. In particular, ASR can tackle issues related to water stress, manage wastewater, alleviate flooding, prevent saltwater intrusion, lessen land subsidence, safeguard crops from damage, avert aquifer depletion, and enhance water quality. The review also discusses the significance of ASR-related groundwater resource projects in India, especially in the context of changing climatic conditions. At last, we explored ASR’s types, challenges, benefits, limitations, and recommendations for sustainable groundwater management. ASR is seen as a viable solution in India to improve water resource policies amid climate change, addressing water rights, public health, and environmental issues. These insights can help identify optimal sites in water-scarce regions of India for the deployment of specific ASR approaches aimed at enhancing water sustainability.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source identification of mine water inrush based on GBDT-RS-SHAP","authors":"Zhenwei Yang,&nbsp;Han Li,&nbsp;Xinyi Wang,&nbsp;Hongwei Meng,&nbsp;Tong Xi,&nbsp;Zhenhuan Hou","doi":"10.1007/s12665-025-12107-5","DOIUrl":"10.1007/s12665-025-12107-5","url":null,"abstract":"<div><p>A novel interpretable intelligent water source identification model, integrating gradient boosting decision trees (GBDT) with SHapley Additive exPlanations (SHAP), has been developed to enhance safety in coal mining operations. To mitigate the impact of outliers on model accuracy during training, box plots and multivariate distribution matrix plots were employed to detect and subsequently remove outlier data from the sample. The processed dataset was subsequently subjected to training via GBDT, culminating in the development of a definitive classification model predicated on the gradient of residuals. The model’s hyperparameters, encompassing the number of trees, tree depth, and learning rate, were meticulously optimized through a random search algorithm to augment the model’s predictive performance. Utilizing the measured data from water samples collected in the Pingdingshan Coalfield, cross-validation was performed, yielding a maximum precision of 0.857 and an average precision of 0.602. Upon the application of the optimized GBDT model to the classification of 24 unknown water samples, the model achieved a high accuracy rate of 95.8%, with a single misclassification, and a minimal root mean square error (RMSE) of 0.183. This demonstrates that stochastic search optimization enhances the model’s stability and robustness, addressing the challenges of inefficiency and inaccuracy in coal mine water source identification, and significantly contributes to the advancement of water hazard prevention and control measures in coal mining. To make the output of the model transparent, this study employs SHAP for the elucidation of the model’s output. SHAP is a Python-based “Model Interpretation” package designed to elucidate the predictions of machine learning models. The findings reveal that fluctuations in Ca²⁺ concentration exert a substantial impact on the discrimination outcomes, whereas the characteristic contribution of SO₄²⁻ is negligible and can be disregarded. This offers a foundational and referential framework for the study of water sources for mine water emergencies.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"InSAR-based deformation analysis around the Qiaoqi reservoir, Baoxing, Southwest China","authors":"Muhammad Kamran,&nbsp;Xiewen Hu,&nbsp;Ushna Bint E Ishfaq,&nbsp;Kun He,&nbsp;Randa Ali,&nbsp;Muhammad Sanaullah,&nbsp;Muhammad Awais Hussain,&nbsp;Usman Ali","doi":"10.1007/s12665-025-12135-1","DOIUrl":"10.1007/s12665-025-12135-1","url":null,"abstract":"<div><p>After the initial impoundment of the Qiaoqi reservoir in 2007, numerous landslides were triggered, severely endangering the reservoir’s safety, people’s lives, and settlements. For efficient risk evaluation and mitigation approaches, it is essential to comprehend the extent and behaviour of these landslides. Synthetic aperture radar (SAR) data, especially with the Interferometric SAR (InSAR) techniques, provides continuous surface deformation with mm precision over substantial ranges. A stack of 31 Sentinel-1 images in ascending direction (2014/12-2017/07) is used for interferometric analysis using the Persistent Scatterers Interferometric (PS-InSAR) technique to identify deformation hot spots, determine displacement rates, and characterize deformation behaviour around the reservoir by taking Kadui-2 landslide as a case study. The mean deformation in the line of sight ranged from − 60 to + 60 mm/year around the reservoir, and from − 100 to + 100 mm/yr on the Kadui-2 landslide. Additionally, seven deformation hotspots were identified, with the NW and SW sections exhibiting larger deformation compared to the other regions along the reservoir. InSAR analysis reveals that the deformation rate of the Kadui-2 landslide is not associated with seasonal hydrological accelerations. The findings of this study help to understand the deformation behaviour and associated settlements around the Qiaoqi reservoir.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental consequences of Caspian Sea desalination and water transfer to the central plateau of Iran","authors":"Hanif Reza Golzar,&nbsp;Sina Mallah,&nbsp;Mina Esteghamat,&nbsp;Matthias Prange","doi":"10.1007/s12665-025-12110-w","DOIUrl":"10.1007/s12665-025-12110-w","url":null,"abstract":"<div><p>The Caspian Sea (CS), the largest lake in the world, has reportedly experienced a significant water level decline over the past decades. As an ancient endorheic lake with no surface outflow, it is particularly vulnerable to climate change. The proposed desalination and water transfer project from the CS to Iran’s arid central plateau seeks to address water demands but could carry significant environmental risks. The project envisions transferring over 220 Million Cubic Meters (MCM) of desalinated water annually from an elevation of -27.5 m to 2312 m above sea level via eight pumping stations and two pipelines spanning 465 km. This process is estimated to require 448 megawatts (MW) of electricity and may discharge 2.6 million tons of salt into the CS annually. Brine discharge may increase local lake water salinity by up to 400% above standard limits within a 300-meter radius, posing risks to aquatic species. The annual release of 3.2 million tons of carbon dioxide (CO₂) and the loss of forests (70–100 ha), rangeland (738 ha), agricultural lands (161 ha), and high-quality soils (15 MCM) highlight possible ecological costs of the project. Additionally, outdated water transmission systems and water-intensive crop cultivation in receiving province (Semnan) could exacerbate inefficiencies in resource use. We suggest prohibiting the cultivation of non-strategic crops in the destination area to mitigate water resource depletion as a sustainable alternative. Without such measures, the environmental, economic, and social consequences of this project might be severe, with potentially far-reaching regional and global impacts.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating remote sensing and geospatial AI-enhanced ISAC models for advanced localization and environmental monitoring","authors":"Himanshi Babbar,&nbsp;Shalli Rani,&nbsp;Mukesh Soni,&nbsp;Ismail Keshta,&nbsp;K. D. V. Prasad,&nbsp;Mohammad Shabaz","doi":"10.1007/s12665-025-12121-7","DOIUrl":"10.1007/s12665-025-12121-7","url":null,"abstract":"<div><p>Remote sensing data is inherently complex, frequently consisting of substantial amounts of multi-dimensional data with time-series components and several spectral bands. Geographic information systems and image processing tools have historically handled the labor-intensive and computationally complex task of processing this data to extract usable information. Another major obstacle to interpretation may be the complexity of the data. This paper presents a novel approach to intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) systems, with a focus on precision agriculture applications. By leveraging IRS technology, the proposed method enhances both sensing and communication capabilities, providing reliable data collection and transfer in challenging rural environments. The study introduces a theoretical model and validates its performance through extensive simulations, focusing on achievable rate and localization accuracy. Recognizing the limitations of an ideal line-of-sight channel assumption, we propose incorporating more complex channel models to account for real-world multipath effects. Additionally, we expand the evaluation metrics to include energy consumption, computational complexity, and latency, essential for practical applications. Our comparative analysis with advanced IRS-assisted ISAC schemes demonstrates the system’s robustness and efficiency. To further substantiate our findings, we include a small-scale prototype system test, offering empirical data that strengthens the theoretical insights and simulations. This multi-dimensional evaluation confirms the system’s suitability for deployment in real-world precision agriculture.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variation characteristics and influencing factors of air quality in Sichuan-Chongqing region, China, 2016–2020","authors":"Zheng Zhang,&nbsp;Xiaoai Dai,&nbsp;Zhiqiang Xie,&nbsp;Chen Yu,&nbsp;Ziyin Liao,&nbsp;Jingzhong Li,&nbsp;Fengshan Jiang,&nbsp;Yun Liu,&nbsp;Zheyan Liu,&nbsp;Quan Zhang,&nbsp;Weile Li","doi":"10.1007/s12665-025-12117-3","DOIUrl":"10.1007/s12665-025-12117-3","url":null,"abstract":"<p>With the acceleration of urbanization and rapid economic development in the Sichuan-Chongqing region, air pollution has become an increasingly evident problem. Based on hourly air quality index (AQI) concentration data from 2016 to 2020, this study used statistical and interpolation analysis methods to explore the spatiotemporal variation characteristics of AQI in the Sichuan-Chongqing region at annual, seasonal, and monthly time scales. Moran’s I Index and hot spot analysis characterized the spatial agglomeration of AQI and air pollutants. The main results are as follows: (1) From 2016 to 2020, the annual average AQI value in the Sichuan-Chongqing region decreased, and the air quality improved significantly. The characteristics of seasonal and monthly variation of air quality are obvious. The spatial distribution shows the characteristics of “better in west and north, worse in east and south”, which highly coincides with the difference in terrain characteristics in the region (2) In 2018, AQI, PM_2.5, PM_10, SO₂, NO₂, O₃, and CO showed clear spatial dependence, and each air pollutant showed regional differences in the distribution of hot spots. (3) The air quality in Sichuan-Chongqing region is affected by both natural factors and social and economic factors, but the influence of natural factors is more significant. The research results provide a scientific and theoretical basis for air pollution governance and ecological civilization construction in the Sichuan-Chongqing region.</p>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal evaluation of glacier dynamics and risk analysis using remote sensing techniques in the Buni Zom Valley, Chitral River Basin, Northern Pakistan","authors":"Sidra Bibi,&nbsp;Muhammad Shafique,&nbsp;Neelum Ali,&nbsp;Shahla Nazneen,&nbsp;Rehman Gul,&nbsp;Syed Asif Ali Shah","doi":"10.1007/s12665-025-12123-5","DOIUrl":"10.1007/s12665-025-12123-5","url":null,"abstract":"<div><p>The Hindukush mountain range in northern Pakistan hosts large to medium-sized valley glaciers. These glaciers are continuously retreating due to climate change-induced rising temperatures, leading to erratic discharge and an increasing potential risk to downstream areas. The decadal and annual changes (ablation), snout position, area changes, and associated hazards pose significant risks to downstream communities in the Chitral Valley of the Hindukush Range, northern Pakistan. This study is crucial for understanding the changing dynamics of glaciers and their potential impacts on downstream communities, offering a foundation for developing and implementing evidence-based policies for mitigation and adaptation. This study evaluated glacier risk zones in the Buni Zom valley and the seasonal dynamics of the glacier areas over 28 years (1990–2018) of three glaciers: Khorabhor, Phargam and Gordoghan, using the normalized difference snow index (NDSI) and manual based techniques, utilizing the open-source Landsat images. The Analytical Hierarchy Process (AHP) model was used to identify the risk zones in the study area. The results revealed that the total glacier area is reduced from 16.01% in 1990 to 11.42% in 2018, representing a decline of 4.68%. The highest recession rate was observed between 2015 and 2018, with a 2% loss in the Khorabhor Glacier, 1.5% in the Phargam Glacier, and 0.72% in the Gordoghan Glacier. The glacier snout area receded more at elevations of 3000–4000 m compared to those above 5000 m. The Phargam Glacier, with an area of ≤ 10 km² has receded significantly (4.59%) more than the larger glaciers, such as Gordoghan (≤ 17 km²; 0.09%), and Khorabhor (≤ 15 km²;0.66%). The AHP-based Risk Probability Model showed that the settlements in Phargam Valley, situated 9.7 km from the glacier snout, are at 62.5% risk. The risk arises from the high rate of the glacier recession, resulting in glacial outbursts, water storage in fragile moraine at the terminal area, and increasing the water flow into the Chitral River due to environmental effects. The study is critical to understanding the glacier dynamics in the area, its impacts on the downstream communities and its implications for climate change.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}