Acta Geophysica最新文献

{"title":"Flood extent mapping using optical remote sensing indices: a case study of the upper Medjerda River basin (Northwestern Tunisia)","authors":"Ahmed Ezzine,&nbsp;Taoufik Hermassi,&nbsp;Emna Kochlef","doi":"10.1007/s11600-026-02036-7","DOIUrl":"10.1007/s11600-026-02036-7","url":null,"abstract":"<div><p>Floods represent one of the most frequent and damaging natural hazards in northwestern Tunisia, particularly along the upper Medjerda River valley, which has experienced several major flood events in recent decades. Accurate and timely mapping of flood extent is therefore essential for effective risk management and decision-making. This study investigates the capability of optical remote sensing data for flood extent mapping using Landsat-8 imagery acquired before and after the February 2015 flood event. Several widely used spectral water indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), Normalized Difference Moisture Index (NDMI), and Water Ratio Index (WRI), were applied and compared to assess their effectiveness in discriminating flooded areas and to identify the most suitable index under semi-arid Mediterranean conditions. The results indicate that indices incorporating the short-wave infrared (SWIR) band, particularly the MNDWI, provide improved delineation of flooded surfaces with sharper boundaries and reduced confusion with irrigated agricultural land and built-up areas. In addition, a rule-based decision-tree approach combining MNDWI, the soil adjusted vegetation Index (SAVI), and the normalized difference built-up index (NDBI) was implemented to enhance flood extent extraction by minimizing misclassification related to vegetation and urban surfaces. To ensure the reliability of the results, Sentinel-1 SAR data were utilized as an independent validation reference. The quantitative analysis demonstrates high accuracy for the proposed methodology, with overall accuracy (OA) values ranging from 73.4% to 81.1% and a Kappa coefficient reaching up to 0.83. The findings demonstrate the relevance of freely available Landsat-8 data and hybrid spectral index-based decision-tree approach for rapid flood mapping in semi-arid environments. This methodology can be transferred to similar flood-prone regions and provides a practical tool for supporting flood risk assessment and management strategies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering rainfall dynamics in Pernambuco, Brazil, through innovative polygon trend analysis","authors":"Antonio Samuel Alves da Silva,&nbsp;Gabriel Graciano de Mendonça,&nbsp;Borko Stosic,&nbsp;Tatijana Stosic","doi":"10.1007/s11600-026-02053-6","DOIUrl":"10.1007/s11600-026-02053-6","url":null,"abstract":"<div><p>This study applied the Innovative Polygon Trend Analysis (IPTA) and the Trend Polygon Star Concept (TPSC) to high-resolution precipitation records (1961–2022) across Pernambuco, Brazil, to assess the spatial variability of monthly rainfall trends and transitions. Monthly precipitation totals derived from 820 BR-DWGD grid cells were analyzed to characterize long-term changes in rainfall patterns across the semiarid Sertão, humid Zona da Mata, and transitional Agreste regions. The results revealed a predominance of negative rainfall trends during March, April, and December, suggesting a reduction in precipitation during both the wet and dry seasons. Distinct regional patterns were observed: The Sertão and Zona da Mata generally exhibited negative trends during the dry season, whereas the Agreste showed predominantly positive trends during the wet season, reflecting its transitional climatic characteristics. Zona da Mata displayed the largest spatial extent of negative rainfall trends across seasons, indicating a greater reduction in precipitation during the second half of the study period relative to the first. The Mann–Kendall (MK) test corroborated the principal spatial patterns identified by IPTA, with significant negative trends occurring mainly in the Sertão and Zona da Mata during March, central Sertão in April, northwestern Sertão during June and July, and Zona da Mata in September. TPSC analysis highlighted temporal transitions in monthly rainfall, identifying increasing transitions during the rainy season in Zona da Mata and during the dry season in the Sertão across both study periods. In contrast, the Agreste exhibited a less consistent seasonal pattern and changes in the direction of monthly transitions between the two periods. These findings improve the understanding of the spatial and temporal variability of rainfall in Pernambuco and provide useful information for water resource management, agricultural planning, and climate adaptation strategies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-02053-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution seismicity analysis of the Western Branch of the East African Rift System in Uganda using deep learning","authors":"Zamir Khurshid,&nbsp;Hamzeh Mohammadigheymasi,&nbsp;Nasrin Tavakolizadeh,&nbsp;Zhuo Liu,&nbsp;Dawei Gao,&nbsp;S. Mostafa Mousavi","doi":"10.1007/s11600-026-02038-5","DOIUrl":"10.1007/s11600-026-02038-5","url":null,"abstract":"<div><p>The use of advanced signal processing and feature extraction techniques enables the identification of essential information from seismic waveform data. In this context, advanced deep learning (DL) models are especially valuable for analyzing seismic waveforms. In this study, we apply a DL powered earthquake monitoring work-flow to the Uganda Seismic Network dataset, to analyze the seismicity of the region. This approach has resulted in the most high-resolution seismic catalog for the region, documenting 1,731 seismic events recorded between January 2017 and November 2018. The local magnitudes (<span>({M}_{L})</span>) for the detected events range from 0.0 to 5.6. We perform a detailed analysis to identify local seismicity and refine the regional crustal velocity structure. A joint inversion yields an updated 1D crustal velocity model and improved earthquake hypocenter locations. The updated catalog shows that the majority of seismic events are concentrated along the Western Branch of the East African Rift System (EARS), with significant seismic activity also observed in the Aswa Shear Zone (ASZ). The hypocenters are located primarily at depths between 15 and 25 km, confined within the upper crust. Compared to the previous catalogs the updated catalog contains higher level of seismic activity along the Western Branch of Uganda. The updated catalog serves as a valuable foundation for future research, including studies employing techniques such as template matching or transfer learning to better classify event types and understand the range of seismogenic processes operating throughout the region.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution seismicity analysis of the Western Branch of the East African Rift System in Uganda using deep learning","authors":"Zamir Khurshid,&nbsp;Hamzeh Mohammadigheymasi,&nbsp;Nasrin Tavakolizadeh,&nbsp;Zhuo Liu,&nbsp;Dawei Gao,&nbsp;S. Mostafa Mousavi","doi":"10.1007/s11600-026-02038-5","DOIUrl":"10.1007/s11600-026-02038-5","url":null,"abstract":"<div><p>The use of advanced signal processing and feature extraction techniques enables the identification of essential information from seismic waveform data. In this context, advanced deep learning (DL) models are especially valuable for analyzing seismic waveforms. In this study, we apply a DL powered earthquake monitoring work-flow to the Uganda Seismic Network dataset, to analyze the seismicity of the region. This approach has resulted in the most high-resolution seismic catalog for the region, documenting 1,731 seismic events recorded between January 2017 and November 2018. The local magnitudes (<span>({M}_{L})</span>) for the detected events range from 0.0 to 5.6. We perform a detailed analysis to identify local seismicity and refine the regional crustal velocity structure. A joint inversion yields an updated 1D crustal velocity model and improved earthquake hypocenter locations. The updated catalog shows that the majority of seismic events are concentrated along the Western Branch of the East African Rift System (EARS), with significant seismic activity also observed in the Aswa Shear Zone (ASZ). The hypocenters are located primarily at depths between 15 and 25 km, confined within the upper crust. Compared to the previous catalogs the updated catalog contains higher level of seismic activity along the Western Branch of Uganda. The updated catalog serves as a valuable foundation for future research, including studies employing techniques such as template matching or transfer learning to better classify event types and understand the range of seismogenic processes operating throughout the region.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosing spatial curve number anomalies in SWAT using land–soil interaction: the Mand sub-basin, Mahanadi catchment","authors":"Chitaranjan Dalai,&nbsp;Ramgopal T. Sahu,&nbsp;Avinash V. Navale,&nbsp;Sakthivel Duraisamy,&nbsp;Bhargav P. Majmundar,&nbsp;G. B. Deepak,&nbsp;Saurabh S. Joshi,&nbsp;Kislay Kumar","doi":"10.1007/s11600-026-02045-6","DOIUrl":"10.1007/s11600-026-02045-6","url":null,"abstract":"<div><p>Sub-basin-level heterogeneity frequently produces structural inconsistencies that are ignored in large watershed applications, despite the fact that accurate depiction of runoff generation processes is essential for robust hydrological modelling. This article examines an apparent anomaly in the calibrated Curve Number (CN) for the Mahanadi catchment’s Mand sub-basin, where SWAT reduced CN to 50.92 in comparison to the basin-wide range of 71–79. We evaluate whether this anomaly is a modelling artefact or an intrinsic hydrological response using a spatial diagnostic approach that incorporates land use patterns, soil properties, topographic conditions, and parameter-level modifications influencing surface-subsurface flow partitioning. Despite the significantly lower CN, the model calibration performed well at Kurubhata (NSE = 0.92; <i>R</i>² = 0.92). Sensitivity analysis and calibration revealed a significant downward adjustment of CN2 (− 34%), along with reductions in soil bulk density (~ 5%) and notable shifts in groundwater parameters (GW_DELAY ≈ 25 days; ALPHA_BNK ≈ 0.79), all of which signify increased infiltration and continued baseflow contributions. In contrast to the agricultural and clay-dominated areas downstream, Mand is dominated by forest-shrub vegetation on sandy loam to loam soils, according to land use and soil maps. The water balance’s high baseflow ratio (&gt;  0.84) and restricted surface runoff are strongly aligned with this landscape layout, indicating that the lower CN is hydrologically appropriate rather than a calibration artefact. In SWAT applications for diverse river systems, the results highlight the significance of sub-basin-level diagnostics. Physical realism, parameter equifinality, and the interpretability of hydrological forecasts can all be improved by identifying and verifying such spatial anomalies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering rainfall dynamics in Pernambuco, Brazil, through innovative polygon trend analysis","authors":"Antonio Samuel Alves da Silva,&nbsp;Gabriel Graciano de Mendonça,&nbsp;Borko Stosic,&nbsp;Tatijana Stosic","doi":"10.1007/s11600-026-02053-6","DOIUrl":"10.1007/s11600-026-02053-6","url":null,"abstract":"<div><p>This study applied the Innovative Polygon Trend Analysis (IPTA) and the Trend Polygon Star Concept (TPSC) to high-resolution precipitation records (1961–2022) across Pernambuco, Brazil, to assess the spatial variability of monthly rainfall trends and transitions. Monthly precipitation totals derived from 820 BR-DWGD grid cells were analyzed to characterize long-term changes in rainfall patterns across the semiarid Sertão, humid Zona da Mata, and transitional Agreste regions. The results revealed a predominance of negative rainfall trends during March, April, and December, suggesting a reduction in precipitation during both the wet and dry seasons. Distinct regional patterns were observed: The Sertão and Zona da Mata generally exhibited negative trends during the dry season, whereas the Agreste showed predominantly positive trends during the wet season, reflecting its transitional climatic characteristics. Zona da Mata displayed the largest spatial extent of negative rainfall trends across seasons, indicating a greater reduction in precipitation during the second half of the study period relative to the first. The Mann–Kendall (MK) test corroborated the principal spatial patterns identified by IPTA, with significant negative trends occurring mainly in the Sertão and Zona da Mata during March, central Sertão in April, northwestern Sertão during June and July, and Zona da Mata in September. TPSC analysis highlighted temporal transitions in monthly rainfall, identifying increasing transitions during the rainy season in Zona da Mata and during the dry season in the Sertão across both study periods. In contrast, the Agreste exhibited a less consistent seasonal pattern and changes in the direction of monthly transitions between the two periods. These findings improve the understanding of the spatial and temporal variability of rainfall in Pernambuco and provide useful information for water resource management, agricultural planning, and climate adaptation strategies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-02053-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood extent mapping using optical remote sensing indices: a case study of the upper Medjerda River basin (Northwestern Tunisia)","authors":"Ahmed Ezzine,&nbsp;Taoufik Hermassi,&nbsp;Emna Kochlef","doi":"10.1007/s11600-026-02036-7","DOIUrl":"10.1007/s11600-026-02036-7","url":null,"abstract":"<div><p>Floods represent one of the most frequent and damaging natural hazards in northwestern Tunisia, particularly along the upper Medjerda River valley, which has experienced several major flood events in recent decades. Accurate and timely mapping of flood extent is therefore essential for effective risk management and decision-making. This study investigates the capability of optical remote sensing data for flood extent mapping using Landsat-8 imagery acquired before and after the February 2015 flood event. Several widely used spectral water indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), Normalized Difference Moisture Index (NDMI), and Water Ratio Index (WRI), were applied and compared to assess their effectiveness in discriminating flooded areas and to identify the most suitable index under semi-arid Mediterranean conditions. The results indicate that indices incorporating the short-wave infrared (SWIR) band, particularly the MNDWI, provide improved delineation of flooded surfaces with sharper boundaries and reduced confusion with irrigated agricultural land and built-up areas. In addition, a rule-based decision-tree approach combining MNDWI, the soil adjusted vegetation Index (SAVI), and the normalized difference built-up index (NDBI) was implemented to enhance flood extent extraction by minimizing misclassification related to vegetation and urban surfaces. To ensure the reliability of the results, Sentinel-1 SAR data were utilized as an independent validation reference. The quantitative analysis demonstrates high accuracy for the proposed methodology, with overall accuracy (OA) values ranging from 73.4% to 81.1% and a Kappa coefficient reaching up to 0.83. The findings demonstrate the relevance of freely available Landsat-8 data and hybrid spectral index-based decision-tree approach for rapid flood mapping in semi-arid environments. This methodology can be transferred to similar flood-prone regions and provides a practical tool for supporting flood risk assessment and management strategies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosing spatial curve number anomalies in SWAT using land–soil interaction: the Mand sub-basin, Mahanadi catchment","authors":"Chitaranjan Dalai,&nbsp;Ramgopal T. Sahu,&nbsp;Avinash V. Navale,&nbsp;Sakthivel Duraisamy,&nbsp;Bhargav P. Majmundar,&nbsp;G. B. Deepak,&nbsp;Saurabh S. Joshi,&nbsp;Kislay Kumar","doi":"10.1007/s11600-026-02045-6","DOIUrl":"10.1007/s11600-026-02045-6","url":null,"abstract":"<div><p>Sub-basin-level heterogeneity frequently produces structural inconsistencies that are ignored in large watershed applications, despite the fact that accurate depiction of runoff generation processes is essential for robust hydrological modelling. This article examines an apparent anomaly in the calibrated Curve Number (CN) for the Mahanadi catchment’s Mand sub-basin, where SWAT reduced CN to 50.92 in comparison to the basin-wide range of 71–79. We evaluate whether this anomaly is a modelling artefact or an intrinsic hydrological response using a spatial diagnostic approach that incorporates land use patterns, soil properties, topographic conditions, and parameter-level modifications influencing surface-subsurface flow partitioning. Despite the significantly lower CN, the model calibration performed well at Kurubhata (NSE = 0.92; <i>R</i>² = 0.92). Sensitivity analysis and calibration revealed a significant downward adjustment of CN2 (− 34%), along with reductions in soil bulk density (~ 5%) and notable shifts in groundwater parameters (GW_DELAY ≈ 25 days; ALPHA_BNK ≈ 0.79), all of which signify increased infiltration and continued baseflow contributions. In contrast to the agricultural and clay-dominated areas downstream, Mand is dominated by forest-shrub vegetation on sandy loam to loam soils, according to land use and soil maps. The water balance’s high baseflow ratio (&gt;  0.84) and restricted surface runoff are strongly aligned with this landscape layout, indicating that the lower CN is hydrologically appropriate rather than a calibration artefact. In SWAT applications for diverse river systems, the results highlight the significance of sub-basin-level diagnostics. Physical realism, parameter equifinality, and the interpretability of hydrological forecasts can all be improved by identifying and verifying such spatial anomalies.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual-framework approach for watershed health assessment using VOR and RRV models: a case study from Central Iran","authors":"Zeinab Karimi,&nbsp;Khabat Khosravi,&nbsp;Javad Hatamiafkoueieh","doi":"10.1007/s11600-026-02048-3","DOIUrl":"10.1007/s11600-026-02048-3","url":null,"abstract":"<div><p>This study provides a comprehensive evaluation of watershed health (WH) in the Tashk–Bakhtegan–Maharloo watershed, Iran, by integrating the vigor–organization–resilience (<i>VOR</i>) and reliability–resilience–vulnerability (<i>RRV</i>) frameworks. The <i>VOR</i> model as an ecological assessment framework incorporates landscape, erosion, and runoff indicators, while the <i>RRV</i> captures hydro-climatic stability model and is informed by the Standardized Precipitation Index (SPI). Furthermore, a two-dimensional <i>VOR</i>–<i>RRV</i> matrix was developed to guide prioritization and strategic planning for watershed restoration by enabling multi-dimensional watershed health assessment, linking ecosystem condition with climate-driven hydrological risks. Next, the watershed was subdivided into 10 sub-watersheds, and all management decisions were evaluated individually for each sub-watershed. Land use, normalized difference vegetation index (NDVI), digital elevation model (DEM), <i>R</i> (rainfall erosivity) and <i>K</i> (soil erodibility) factors, and precipitation were considered as an input data. The findings indicated that the Tashk–Bakhtegan–Maharloo watershed is in a relatively unhealthy condition, with an overall watershed health score of 0.28. Among the <i>VOR</i> components, vigor (0.47), organization (0.36), and resilience (0.30) contributed most significantly to the current status. In parallel, the <i>RRV</i> model classified the watershed as being in moderate condition (0.47), with reliability exerting the greatest influence (0.58), followed by vulnerability (0.52) and resilience (0.37). Spatial analysis of restoration priorities revealed that both the upper and lower watershed zones require immediate attention, with upstream and downstream areas designated as protection or protection-priority zones and restoration-priority zones, respectively. Overall, these results provide a valuable basis for targeted management interventions, particularly in regions characterized by elevated vulnerability.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual-framework approach for watershed health assessment using VOR and RRV models: a case study from Central Iran","authors":"Zeinab Karimi,&nbsp;Khabat Khosravi,&nbsp;Javad Hatamiafkoueieh","doi":"10.1007/s11600-026-02048-3","DOIUrl":"10.1007/s11600-026-02048-3","url":null,"abstract":"<div><p>This study provides a comprehensive evaluation of watershed health (WH) in the Tashk–Bakhtegan–Maharloo watershed, Iran, by integrating the vigor–organization–resilience (<i>VOR</i>) and reliability–resilience–vulnerability (<i>RRV</i>) frameworks. The <i>VOR</i> model as an ecological assessment framework incorporates landscape, erosion, and runoff indicators, while the <i>RRV</i> captures hydro-climatic stability model and is informed by the Standardized Precipitation Index (SPI). Furthermore, a two-dimensional <i>VOR</i>–<i>RRV</i> matrix was developed to guide prioritization and strategic planning for watershed restoration by enabling multi-dimensional watershed health assessment, linking ecosystem condition with climate-driven hydrological risks. Next, the watershed was subdivided into 10 sub-watersheds, and all management decisions were evaluated individually for each sub-watershed. Land use, normalized difference vegetation index (NDVI), digital elevation model (DEM), <i>R</i> (rainfall erosivity) and <i>K</i> (soil erodibility) factors, and precipitation were considered as an input data. The findings indicated that the Tashk–Bakhtegan–Maharloo watershed is in a relatively unhealthy condition, with an overall watershed health score of 0.28. Among the <i>VOR</i> components, vigor (0.47), organization (0.36), and resilience (0.30) contributed most significantly to the current status. In parallel, the <i>RRV</i> model classified the watershed as being in moderate condition (0.47), with reliability exerting the greatest influence (0.58), followed by vulnerability (0.52) and resilience (0.37). Spatial analysis of restoration priorities revealed that both the upper and lower watershed zones require immediate attention, with upstream and downstream areas designated as protection or protection-priority zones and restoration-priority zones, respectively. Overall, these results provide a valuable basis for targeted management interventions, particularly in regions characterized by elevated vulnerability.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}