Natural Hazards Research最新文献

{"title":"Comparative analysis of drought indices to characterize drought in agro-climatic zones of Assam, northeast region of India","authors":"Vanita Pandey,&nbsp;Salil Kumar Shrivastava","doi":"10.1016/j.nhres.2025.08.008","DOIUrl":"10.1016/j.nhres.2025.08.008","url":null,"abstract":"<div><div>Over the past two decades, Assam, a region prone to flooding, has seen frequent droughts due to reduced rainfall and rising temperatures. The study used different types of drought indices, including the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Groundwater Index (SGI), Vegetation Health Index (VHI), and Vegetation Drought Index (VDI), to evaluate drought conditions at various time scales. Analysis showed a strong correlation between SPI and SPEI (0.75–1.00) at similar lags. Groundwater responses differed by area, with negative correlations in the Hills Region (HR) and positive ones in the North Bengal Plain (NBP). Additionally, the relationship between SPI and VHI became stronger with longer lag times, while the correlation between SPI and VDI decreased as lag increased. Trend analysis reveals a significant decline in SPI and SPEI across all lags for Barak Valley (BV), HR, NBP, and Upper Brahmaputra Valley (UBV), with stronger trends at longer lags. SGI shows a notable decreasing trend at Lower Brahmaputra Valley (LBV), while VHI and VDI exhibit slight, non-significant increases. The study emphasizes the importance of using multiple indices for accurate drought prediction. Total drought months varied by index and region, with SPI showing 21–39 months at LBV, SPEI ranging from 26 to 42 months between BV and LBV, and VDI recording the highest drought duration of 40–65 months at BV, LBV, and NBP. Overall, LBV, Central Brahmaputra Valley (CBV), and BV faced lower drought risk, whereas UBV was the most vulnerable, and NBP and HR had moderate risks.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 251-266"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping and prioritization of adaptation measures for integrated flood and drought risks in the Bangkok Metropolitan Region","authors":"Pyae Mon Naing ,&nbsp;Vilas Nitivattananon ,&nbsp;Mukand S. Babel ,&nbsp;Malay Pramanik ,&nbsp;Simon Guerrero-Cruz","doi":"10.1016/j.nhres.2025.06.003","DOIUrl":"10.1016/j.nhres.2025.06.003","url":null,"abstract":"<div><div>The Bangkok Metropolitan Region (BMR) faces significant flood and drought risks, necessitating effective adaptation measures. Although floods and droughts are two extremes of the hydrological cycle, integrated risk assessments remain limited. Prioritizing effective measures to address both extremes is a critical priority for authorities. Therefore, the study analyzes integrated flood and drought risk, while proposing and prioritizing effective adaptation measures for baseline (2010–2024) and near-future (2025–2050) periods. Integrated flood and drought risks are assessed using the AHP-GIS approach whereas expert interviews are conducted to pre-select adaptation measures which are then combined with vulnerability and exposure levels to propose and prioritize effective measures through spatial analysis. The Bangkok Metropolis has very high flood and drought risk due to urbanized land use, higher precipitation, dense population, and high poverty rate, underscoring the urgency of adaptation. Potential adaptation measures include retrofitting and integrating small-scale green infrastructures which should be prioritized for urbanized areas such as central Bangkok Metropolis whereas large-scale green infrastructures such as floodplain bypass and buffer zones, are suitable for less urbanized upstream areas. Non-structural measures encompass financial support, policies, social protection programs, and community initiatives and should be prioritized in the whole of BMR, particularly in Bangkok Metropolis, Samut Sakhon and Nakhon Pathom. The study's approach and findings can be scaled up and proved instrumental for urban planners and policy makers in decision-making and implementation of adaptation strategies and national action plans, aimed at ensuring effective and sustainable flood and drought risk management.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 92-109"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social vulnerability of Esquipulas Guayabal town in regard to hazards from El Chichón volcano, México","authors":"Mariana Patricia Jácome Paz ,&nbsp;Chris E. Gregg ,&nbsp;Costanza Bonadonna ,&nbsp;Tania Ximena Ruiz Santos","doi":"10.1016/j.nhres.2025.04.002","DOIUrl":"10.1016/j.nhres.2025.04.002","url":null,"abstract":"<div><div>El Chichón, the most active volcano in the Chiapaneco Volcanic Arc of southeastern Mexico, erupted explosively in March–April 1982. The eruption caused extensive environmental destruction, significant loss of life, and profound social upheaval. While much has been documented about the immediate effects of this catastrophic event, the long-term social dynamics and the ongoing consequences in the region remain underexplored. Studying the aftermath of such prolonged crises is essential, as the consequences often extend far beyond the initial shock and continue to shape the future of affected communities.</div><div>This study focuses on the indigenous community of Esquipulas Guayabal, a village that was entirely buried by the eruption. Its primary objectives are to shed light on the complex journey of the affected population and assess the social vulnerability of the town's residents. The findings reveal a high level of vulnerability among people living within 5 ​km of the summit crater. Contributing factors include: (i) the formation of a new multiethnic community, which has led to fragmented local organizations between Tzotzil and Zoque groups, each with differing perceptions of risk and territorial issues; (ii) inadequate basic services, poor communication infrastructure, and insufficient evacuation routes; (iii) a limited understanding of volcanic hazards and a lack of knowledge about the current status of the volcano and how to access relevant information; and (iv) unresolved land tenure conflicts that have persisted since the eruption.</div><div>Addressing these long-term vulnerabilities is vital for overcoming the ongoing challenges faced by communities after volcanic eruptions. This paper also highlights the absence of comprehensive, continuous monitoring of the Chichón volcano. However, it points to the emerging development of shadow network collaboration, which, despite limited political-administrative resources, offers the potential to incorporate local community capacities into medium-term risk management strategies.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 15-33"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spline-based volume estimation of landslides using different DEMs: Case studies of the Kotropi and Prashar Lake landslides, Northwestern Himalaya","authors":"Akshay Raj Manocha ,&nbsp;Neeraj Kumar ,&nbsp;Ajay Kumar","doi":"10.1016/j.nhres.2025.08.005","DOIUrl":"10.1016/j.nhres.2025.08.005","url":null,"abstract":"<div><div>Landslides pose a significant threat to infrastructure, ecosystems, and human settlements in mountainous regions. Accurate estimation of landslide volume is crucial for hazard assessment and mitigation planning, impact hazard assessment, sediment transport, and long-term landscape stability. This study integrates unmanned aerial vehicle (UAV)-based high-resolution mapping with spline-based computational modelling to enhance landslide volume estimation. The research focuses on two active landslides in Himachal Pradesh, India—Kotropi and Prashar Lake landslides. Using UAV-derived Digital Elevation Models (DEMs), detailed 3D terrain models were prepared to analyse failure depth surfaces. A novel MATLAB-based spline interpolation technique was applied to estimate the depth and extent of the failure surfaces, facilitating precise volume calculations. Our results reveal significant variations in total volume estimates and differences in comparison to traditional DEM-based methods, underscoring the effectiveness of UAV-integrated computational modelling. DEMs (SRTM, ALOS), with UAV-derived volumes consistently lower (e.g., 5.7 vs. 6.9 million m³ for Kotropi). This discrepancy stems from UAVs capturing sub-metre topographic variability, reducing errors from coarse resolution and interpolation artifacts inherent in satellite-derived DEMs. Higher confidence in UAV-spline results is justified by (1) field validated dip angles integrated into spline models, (2) centimetre-scale UAV DEM precision resolving complex failure surfaces, and (3) iterative cross-validation of spline curves against terrain geometry. The study demonstrates that UAV-integrated computational modelling offers a scalable, adaptable solution for landslide risk assessment, particularly in data-scarce regions where estimation errors could lead to costly misallocation of mitigation resources.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 267-282"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of long-term spatiotemporal variability of vegetation drought and its link with teleconnection factors in Ethiopia","authors":"Getachew Bayable,&nbsp;Tadele Melese Lebeza","doi":"10.1016/j.nhres.2025.06.001","DOIUrl":"10.1016/j.nhres.2025.06.001","url":null,"abstract":"<div><div>Vegetation drought, a pervasive natural hazard intensified by climate variability, threatens ecosystems and agriculture globally. Information concerning its spatiotemporal characteristics is essential for decision-making in environmental and agricultural applications, particularly in vulnerable regions like Ethiopia. This study investigates the spatiotemporal variability of vegetation drought in Ethiopia from 1982 to 2024 and its relationship with global teleconnection patterns using Global Vegetation Health Index (VHI) data from the National Oceanic and Atmospheric Administration (NOAA) Center for Satellite Applications and Research (STAR), as well as Sea Surface Temperature Anomalies (SSTA) for the Pacific, Indian, and Atlantic Oceans. Trends were assessed using Innovative Trend Analysis, the Modified Mann-Kendall test, and a linear regression model. Regional differences were observed: the Amhara, Oromia, SNNP, and Somali regions exhibited declines in VHI, whereas Gambela showed an increasing trend. Pacific SSTA was associated with drought stress in northern regions such as Amhara and Tigray and in northwestern areas like Benishangul-Gumuz. In contrast, Indian and Atlantic SSTA were linked to drought stress in Oromia and associated with enhanced vegetation growth in Gambela. The improvement in VHI from 2019 to 2024 coincided with Ethiopia's Green Legacy Initiative, suggesting a connection with large-scale afforestation efforts. These findings highlight the importance of integrating teleconnection signals into regional vegetative drought assessments and underscore the potential for informed policy interventions to enhance agricultural resilience and food security in Ethiopia.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 81-91"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the socio-economic risks of a potential GLOF from Dudh Pokhari lake in Nepal's Everest Region","authors":"Sujit Acharya ,&nbsp;Nitesh Khadka ,&nbsp;Dibas Shrestha ,&nbsp;Khada Nanda Dulal ,&nbsp;Neeraj Adhikari ,&nbsp;Vishnu Prasad Pandey","doi":"10.1016/j.nhres.2025.08.001","DOIUrl":"10.1016/j.nhres.2025.08.001","url":null,"abstract":"<div><div>Climate change has caused glacial retreat in the Himalaya, resulting in the rapid formation and expansion of glacial lakes and an increased risk of glacial lake outburst floods (GLOFs) that threatened downstream areas. This study aims at assessing the potential socio-economic impacts of GLOFs from the potentially dangerous Dudh Pokhari glacial lake (0.34 ​km²), in the Everest region of Nepal. GLOF was simulated in three breach scenarios using a Simplified Dam Break Model and HEC-RAS for dam breach and inundation analysis, respectively. Results showed varying inundation depth in the downstream with a maximum value reaching up to 25 ​m, leading to an estimated economic loss (2024 estimate) of 2,496,353 US$ in physical infrastructure and 5,696,615 US$ (2022 estimate) in tourism. Furthermore, GLOF will lead to adverse socio-economic and geomorphological effects due to disruption to the local economy and substantial environmental damage transforming forest areas into sediment and boulder deposition zones. This study, by providing valuable insights into downstream flooding, and economic consequences; highlights the need for mitigation initiatives and strategic governmental planning.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 141-153"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling strategies for machine learning-based linear erosion studies: a review approaching contributing area","authors":"Tatiane Ferreira Olivatto ,&nbsp;José Augusto Di Lollo","doi":"10.1016/j.nhres.2025.08.002","DOIUrl":"10.1016/j.nhres.2025.08.002","url":null,"abstract":"<div><div>Linear erosion is a major socio-environmental challenge, influenced by climatic, geomorphological and anthropogenic factors. This study explores how sampling strategies and topographic contributing areas impact machine learning-based applications in linear erosion research. A bibliometric analysis was conducted to assess historical and emerging trends at the intersection of linear erosion and machine learning. Additionally, a systematic extraction of key methodological insights was performed using artificial intelligence tools, followed by an integrative literature review focusing on sampling techniques and drainage influence areas. Results show that spatially stratified sampling, particularly with a 1:1.2 occurrence-to-non-occurrence ratio and non-occurrence points placed outside the hydrological contributing area, improves model generalization and environmental representativeness. However, its effectiveness depends on an adequate understanding of local landscape dynamics. Advanced oversampling techniques further mitigate class imbalance, while spatial cross-validation addresses spatial autocorrelation and enhances robustness. The use of topographic contributing area as a predictive variable shows significant potential due to its role in controlling runoff concentration and sediment flow. Nevertheless, the lack of standardization in how contributing areas are delineated and integrated into models limits broader applicability. Current machine learning approaches often underexplore these spatial components, reducing their physical consistency. This work consolidates methodological advances in data preparation and emphasizes physically informed models that integrate land use and hydrological processes, enabling more robust and interpretable machine learning applications in erosion studies. Future research should focus on refining sampling frameworks and integrating contributing area metrics to enhance the robustness and interpretability of machine learning predictions in erosion studies.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 34-49"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized machine learning based model for the large-scale spatial prediction of landslides at Western Ghats in the State of Karnataka, India","authors":"Babitha Ganesh ,&nbsp;Shweta Vincent ,&nbsp;Sameena Pathan ,&nbsp;Ganesh V. Bhat ,&nbsp;Janardhana Bhat K","doi":"10.1016/j.nhres.2025.08.004","DOIUrl":"10.1016/j.nhres.2025.08.004","url":null,"abstract":"<div><div>The Western Ghats (WG) of India, as per National Disaster Management Authority (NDMA) statistics, is one of the most landslide-prone regions, with frequent monsoon-triggered landslips. Landslide susceptibility Mapping (LSM) is crucial tool for supporting risk mitigation strategies. This study develops large-scale LSM using an optimized Machine Learning (ML) model for the six districts of Karnataka state including Chikmagalur, Uttara Kannada, Dakshina Kannada, Udupi, Shimoga and Kodagu within WG, all with a history of recurrent landslides. A dataset of 1267 landslides (2014–2020) from the Geological Survey of India (GSI) and twenty landslide conditioning factors (LCF) from diverse sources were used. Feature selection using Pearson correlation, Chi-square identified thirteen key LCFs, with geomorphology, slope, lineament density and rainfall as the most significant. Fourteen ML classifiers including single and ensemble models such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), Gaussian Process (GP), Linear Discriminant Analysis (LDA), Light Gradient Boosting (LGBM), Random Forest (RF), Artificial Neural Networks (ANN), Extreme XGBoost (XGB), ADAboost, Categorical Boosting (CatB), stacking and bagging were trained using LCFs and LIM. XGB, LGBM and CatB classifiers performed well. Further, hyper-parameter tuning techniques including grid search (GS), random search (RS), Bayesian optimization (BO) and nature inspired Particle Swarm Optimization (PSO) were applied. PSO tuned CatB demonstrated superior performance with the values of 0.9854, 0.952, 0.958, 0.937 and 0.948 for AUC-ROC, accuracy, precision, recall and F1-score respectively, making it as the most effective approach. Final LSM maps were classified into five susceptibility levels: very high, high, moderate, low and very low. Results show that Chikmaglur, Kodagu and Uttara Kannada districts have 5.43 %, 4.10 % and 1.21 % of the highly vulnerable areas. Dakshina Kannada, Shimoga and Udupi districts have less portion of vulnerable areas. These maps provide crucial input for the policymakers to differentiate between vulnerable and safe regions, enabling informed development planning and minimizing landslide risk.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 214-236"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do natural hazard events and disasters trigger political and legislative change? A systematic scoping review of the impacts on commodity production","authors":"Nicholas Kirk ,&nbsp;Sandra Ricart ,&nbsp;Jo Fountain ,&nbsp;Christina Griffin ,&nbsp;Nicholas A. Cradock-Henry","doi":"10.1016/j.nhres.2025.06.005","DOIUrl":"10.1016/j.nhres.2025.06.005","url":null,"abstract":"<div><div>Food and fibre commodity production is fundamental to global food security and economic development. However, these commodities are vulnerable to different natural hazards. In this systematic scoping review, we assess the natural hazards literature to determine if and how specific natural hazard events that impact food and fibre commodity production have triggered political or legislative change. Bibliometric and thematic analysis methods were used to identify recurrent patterns and themes in the dataset. Bibliometric analysis confirmed robust international cooperation on hazards and political change, but there were still gaps in cooperation across different hazard types. Thematic analysis revealed limited evidence for political and legislative changes triggered by hazard events. However, typical responses included reviewing policies or restructuring institutional responsibility. Our findings suggest a need for greater collaboration across research topics including climate change impacts, risk assessment, and freshwater management to more accurately identify the causal relationships between hazards and political change.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 110-121"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial analysis of the 2019-20 desert locust damage in northern India using MODIS and VIIRS satellite data","authors":"Bikash Ranjan Parida ,&nbsp;Sandeep Kumar ,&nbsp;Sagar Kumar Swain ,&nbsp;Chandra Shekhar Dwivedi ,&nbsp;Arvind Chandra Pandey ,&nbsp;Navneet Kumar","doi":"10.1016/j.nhres.2025.05.002","DOIUrl":"10.1016/j.nhres.2025.05.002","url":null,"abstract":"<div><div>Worldwide, locust outbreaks frequently impact large areas of land and millions of people. Remote sensing has emerged as one of the most essential data sources for effectively identifying and assessing damaged crops and its yields. This study assesses crop damage caused by the 2019–20 upsurge of the desert locust in several Indian states using multisource satellite data (MODIS, VIIRS, and Sentinel-2). The cropland covers approximately 74 ​% of the study area, with Punjab, Haryana, and Uttar Pradesh having more than 89 ​% of their land dedicated to agriculture. During the locust upsurge from May to July 2020, about 48,669 ​km² of crop area (4.6 ​%) was damaged in India. Maharashtra was the hardest hit, with over 10 ​% of its cropland affected. Madhya Pradesh saw 4–6.75 ​% of damage, while Uttar Pradesh and Gujarat each experienced around 3–5 % crop loss. The analysis found that Leaf Area Index (LAI) data provided more accurate damage estimates than the Fraction of Photosynthetically Active Radiation (FPAR), which often included non-damaged areas. High-risk zones were identified in Eastern Maharashtra, Central Madhya Pradesh, and Central and Southern Uttar Pradesh leading to loss of Gross Primary Productivity (GPP) up to 100 ​g ​C/m² and crop yields by 500 ​kg/ha. These identified areas need targeted locust control measures to protect crop losses, supporting food security and sustainability.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"6 1","pages":"Pages 68-80"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147661665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}