Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100758
Ahmedbahaaaldin Ibrahem Ahmed Osman , Nouar AlDahoul , Kai Lun Chong , Yuk Feng Huang , Jing Lin Ng , Ahmed Elshafie , Mohsen Sherif , Ali Najah Ahmed
Drought, driven by shifting climate patterns, increasingly threatens hydropower, agriculture, and water supply systems, necessitating robust early detection and forecasting frameworks. This review critically examines recent machine learning (ML) approaches to drought prediction, focusing on meteorological, hydrological, and agricultural drought types across diverse temporal and spatial scales. We analyze the influence of input variables, such as precipitation, streamflow, climate indices, and remote sensing data, on model performance, and identify key trends including the rise of hybrid and deep learning models for capturing nonlinear dependencies and long-term patterns. Unique contributions include a comparative evaluation of model interpretability, scalability, and data requirements, as well as the identification of persistent gaps such as limited regional transferability and underrepresentation of socio-environmental factors. By proposing a framework for selecting optimal models based on data availability, complexity, and operational constraints, this review offers actionable insights for researchers and policymakers seeking to develop adaptive, context-aware drought mitigation strategies that ensure long-term water sustainability.
{"title":"A review on machine learning models for drought monitoring and forecasting","authors":"Ahmedbahaaaldin Ibrahem Ahmed Osman , Nouar AlDahoul , Kai Lun Chong , Yuk Feng Huang , Jing Lin Ng , Ahmed Elshafie , Mohsen Sherif , Ali Najah Ahmed","doi":"10.1016/j.crm.2025.100758","DOIUrl":"10.1016/j.crm.2025.100758","url":null,"abstract":"<div><div>Drought, driven by shifting climate patterns, increasingly threatens hydropower, agriculture, and water supply systems, necessitating robust early detection and forecasting frameworks. This review critically examines recent machine learning (ML) approaches to drought prediction, focusing on meteorological, hydrological, and agricultural drought types across diverse temporal and spatial scales. We analyze the influence of input variables, such as precipitation, streamflow, climate indices, and remote sensing data, on model performance, and identify key trends including the rise of hybrid and deep learning models for capturing nonlinear dependencies and long-term patterns. Unique contributions include a comparative evaluation of model interpretability, scalability, and data requirements, as well as the identification of persistent gaps such as limited regional transferability and underrepresentation of socio-environmental factors. By proposing a framework for selecting optimal models based on data availability, complexity, and operational constraints, this review offers actionable insights for researchers and policymakers seeking to develop adaptive, context-aware drought mitigation strategies that ensure long-term water sustainability.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"50 ","pages":"Article 100758"},"PeriodicalIF":5.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100705
Wei Liu , Pengfei Qi , Jie Xu , Marcus Feldman , Dingde Xu
Farmers in flood-risk areas are exposed to disruptions in their daily production and livelihood operations. However, disaster preparedness can reduce risk and minimize household losses, thereby increasing livelihood sustainability for farming families. Although sustainable livelihoods and disaster preparedness of farmers have been categorized, few studies have explored the correlation between livelihood risk and disaster preparedness. This study examines survey data from 540 farming family households in the three counties Gaoxian, Jiajiang, and Yuechi, which are affected by floods. We consider four distinct types of livelihood risks faced by farmers and three categories of disaster preparedness in the study area and construct a Tobit regression model to test the correlation between livelihood risk and disaster preparedness. The results show (1) a significant correlation between livelihood risk and disaster preparedness among farmers; (2) health risk is positively correlated with farmers’ physical preparedness; (3) social risk is negatively correlated with farmers’ physical, knowledge and skills, and overall disaster preparedness; and (4) financial risk is negatively correlated with farmers’ overall disaster preparedness. Our findings may assist in disaster preparedness and in policy formulation pertaining to flood risk management.
{"title":"Does livelihood risk matter in disaster preparedness? Insights from flood risk areas of rural China","authors":"Wei Liu , Pengfei Qi , Jie Xu , Marcus Feldman , Dingde Xu","doi":"10.1016/j.crm.2025.100705","DOIUrl":"10.1016/j.crm.2025.100705","url":null,"abstract":"<div><div>Farmers in flood-risk areas are exposed to disruptions in their daily production and livelihood operations. However, disaster preparedness can reduce risk and minimize household losses, thereby increasing livelihood sustainability for farming families. Although sustainable livelihoods and disaster preparedness of farmers have been categorized, few studies have explored the correlation between livelihood risk and disaster preparedness. This study examines survey data from 540 farming family households in the three counties Gaoxian, Jiajiang, and Yuechi, which are affected by floods. We consider four distinct types of livelihood risks faced by farmers and three categories of disaster preparedness in the study area and construct a Tobit regression model to test the correlation between livelihood risk and disaster preparedness. The results show (1) a significant correlation between livelihood risk and disaster preparedness among farmers; (2) health risk is positively correlated with farmers’ physical preparedness; (3) social risk is negatively correlated with farmers’ physical, knowledge and skills, and overall disaster preparedness; and (4) financial risk is negatively correlated with farmers’ overall disaster preparedness. Our findings may assist in disaster preparedness and in policy formulation pertaining to flood risk management.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"48 ","pages":"Article 100705"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100696
Yonara Claudia dos Santos , Zoraide Souza Pessoa
The balance between urban growth and global environmental and climate challenges, as well as their local implications, is a fundamental contemporary concern, and often neglected in government agendas at a local scale. The integration of these issues into urban and territorial planning is still incipient in the Brazilian context, especially in smaller cities and in regions such as the Semiarid region of Brazil, where socioeconomic challenges are particularly sensitive to climate impacts. This study diagnoses the adaptive capacity of local management in the Brazilian Semiarid region, revealing high vulnerability and low adaptive capacity that reflect inadequate integration of socio-environmental and climatic issues, as assessed through the Adaptive Capacity Management Index (IGCA). The method used is based on data from the Municipal Basic Information Survey (Munic/IBGE) and operates on a scale from 0 to 1, segmented into five strata corresponding to classification levels ranging from classification levels ranging from “very low” to “very high”. The “very high” stratum indicates a more critical scenario in terms of threats and vulnerabilities, while the “very low” stratum indicates deficiencies in risk management and adaptive capacity. IGCA scores ranged from 0.137 to 0.442, with 76% of municipalities classified as having low adaptive capacity. The operationalization is conducted through weighted variables and the additive approach of the Multi-Criteria Decision Making (MCDM) method using GIS software to map threats, vulnerabilities and adaptive management measures to climate change. The results obtained in 21 municipalities in the Piancó-Piranhas-Açu River basin, located in the state of Rio Grande do Norte, Northeast Brazil, reveal a high exposure to climate threats, particularly in relation to social vulnerability. This vulnerability is evident not only in the studied municipalities but likely throughout the region. Given this scenario of high vulnerability and low adaptive capacity, significant efforts are needed to improve the adaptation and resilience capacity of these regions, including a more integrated approach to climate risk management, strengthening local governance and raising awareness of the importance of integrating climate and environmental issues in government policies.
{"title":"Adaptive capacity management in municipalities in the Semiarid region of Brazil: Application of a composite index","authors":"Yonara Claudia dos Santos , Zoraide Souza Pessoa","doi":"10.1016/j.crm.2025.100696","DOIUrl":"10.1016/j.crm.2025.100696","url":null,"abstract":"<div><div>The balance between urban growth and global environmental and climate challenges, as well as their local implications, is a fundamental contemporary concern, and often neglected in government agendas at a local scale. The integration of these issues into urban and territorial planning is still incipient in the Brazilian context, especially in smaller cities and in regions such as the Semiarid region of Brazil, where socioeconomic challenges are particularly sensitive to climate impacts. This study diagnoses the adaptive capacity of local management in the Brazilian Semiarid region, revealing high vulnerability and low adaptive capacity that reflect inadequate integration of socio-environmental and climatic issues, as assessed through the Adaptive Capacity Management Index (IGCA). The method used is based on data from the Municipal Basic Information Survey (Munic/IBGE) and operates on a scale from 0 to 1, segmented into five strata corresponding to classification levels ranging from classification levels ranging from “very low” to “very high”. The “very high” stratum indicates a more critical scenario in terms of threats and vulnerabilities, while the “very low” stratum indicates deficiencies in risk management and adaptive capacity. IGCA scores ranged from 0.137 to 0.442, with 76% of municipalities classified as having low adaptive capacity. The operationalization is conducted through weighted variables and the additive approach of the Multi-Criteria Decision Making (MCDM) method using GIS software to map threats, vulnerabilities and adaptive management measures to climate change. The results obtained in 21 municipalities in the Piancó-Piranhas-Açu River basin, located in the state of Rio Grande do Norte, Northeast Brazil, reveal a high exposure to climate threats, particularly in relation to social vulnerability. This vulnerability is evident not only in the studied municipalities but likely throughout the region. Given this scenario of high vulnerability and low adaptive capacity, significant efforts are needed to improve the adaptation and resilience capacity of these regions, including a more integrated approach to climate risk management, strengthening local governance and raising awareness of the importance of integrating climate and environmental issues in government policies.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"48 ","pages":"Article 100696"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Understanding the health risks associated with indoor overheating and the impacts of cooling energy poverty during summer is becoming increasingly urgent as anthropogenic climate change intensifies heatwave events in many places. We report on results from a cross-sectional postal survey undertaken in Summer 2021/2022, conducted in five regions of New Zealand that typically experience some of the highest temperatures nationally. The study revealed that energy poverty is significant issue during summer, with 43% of the respondents identifying cost as a cooling restriction. Indoor overheating commonly affected the health and wellbeing of participants, with 63% reporting adverse health outcomes. Households citing cost as a cooling restriction were significantly more likely to report adverse health outcomes. Renters and indigenous Māori households were disproportionately affected by indoor overheating and the associated health and energy inequities. These findings highlight the growing health risks from indoor heat exposure in warming climatesparticularly in temperate countries like New Zealand, where inhabitants and infrastructure are not adequately prepared to handle heat-related risks. Relying solely on energy-intensive active cooling exacerbates energy poverty and injustice, increasing residential energy demand. Policy interventions should focus on promoting passive, energy-efficient, and sustainable cooling strategies to protect vulnerable populations from heat-related health disparities.
{"title":"Identifying summer energy poverty and public health risks in a temperate climate","authors":"Zhiting Chen , Kimberley Clare O’Sullivan , Rachel Kowalchuk Dohig , Nevil Pierse , Terence Jiang , Mylène Riva , Runa Das","doi":"10.1016/j.crm.2025.100698","DOIUrl":"10.1016/j.crm.2025.100698","url":null,"abstract":"<div><div>Understanding the health risks associated with indoor overheating and the impacts of cooling energy poverty during summer is becoming increasingly urgent as anthropogenic climate change intensifies heatwave events in many places. We report on results from a cross-sectional postal survey undertaken in Summer 2021/2022, conducted in five regions of New Zealand that typically experience some of the highest temperatures nationally. The study revealed that energy poverty is significant issue during summer, with 43% of the respondents identifying cost as a cooling restriction. Indoor overheating commonly affected the health and wellbeing of participants, with 63% reporting adverse health outcomes. Households citing cost as a cooling restriction were significantly more likely to report adverse health outcomes. Renters and indigenous Māori households were disproportionately affected by indoor overheating and the associated health and energy inequities. These findings highlight the growing health risks from indoor heat exposure in warming climatesparticularly in temperate countries like New Zealand, where inhabitants and infrastructure are not adequately prepared to handle heat-related risks. Relying solely on energy-intensive active cooling exacerbates energy poverty and injustice, increasing residential energy demand. Policy interventions should focus on promoting passive, energy-efficient, and sustainable cooling strategies to protect vulnerable populations from heat-related health disparities.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"48 ","pages":"Article 100698"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100716
Noor ten Harmsen van der Beek , Renske de Winter , Esther van Baaren , Ferdinand Diermanse , Arno Nolte , Marjolijn Haasnoot
Adaptation is needed to keep deltas and coastal zones liveable under changing climatic and socio-economic conditions. To date, adaptation is mostly small scale with incremental adaptation measures, while in some areas more fundamental transformative decisions are required in the future, such as changing objectives and land use. Here, we introduce an adaptation pathways method that uses forward-exploration and backcasting to study the impact of fundamental decisions on the solution space. For this, we extend the Dynamic Adaptive Pathways Planning (DAPP) approach and refer to this as DAPP-Δ (DAPP-delta) with Δ representing the fundamental decisions. Following the traditional DAPP approach, we explore alternative sequences of adaptation measures to continue to achieve objectives under changing conditions. New to the method is the backcasting of critical implementation paths from different envisioned future states, including changes in land use. Additionally, we identify synergies and conflicts between the forward-looking pathways and backcasting implementation paths. We use the southwest of the Netherlands as an illustrative case study. In this region, multiple adaptation tipping points are projected due to sea-level rise and economic changes, while simultaneously large-scale investments in aging infrastructure are expected. The results show that the DAPP-Δ method a) helps to identify pivotal adaptation decisions that goes beyond incremental adaptation and includes transformative decisions; b) effectively reveals the risk of maladaptation; and c) illustrates the necessity to include multiple changes in the analysis, as they together determine investments in the area and with that the solution space.
{"title":"Identifying transformative decisions: A dual approach to adaptation pathways design using forward-exploration and backcasting","authors":"Noor ten Harmsen van der Beek , Renske de Winter , Esther van Baaren , Ferdinand Diermanse , Arno Nolte , Marjolijn Haasnoot","doi":"10.1016/j.crm.2025.100716","DOIUrl":"10.1016/j.crm.2025.100716","url":null,"abstract":"<div><div>Adaptation is needed to keep deltas and coastal zones liveable under changing climatic and socio-economic conditions. To date, adaptation is mostly small scale with incremental adaptation measures, while in some areas more fundamental transformative decisions are required in the future, such as changing objectives and land use. Here, we introduce an adaptation pathways method that uses forward-exploration and backcasting to study the impact of fundamental decisions on the solution space. For this, we extend the Dynamic Adaptive Pathways Planning (DAPP) approach and refer to this as DAPP-Δ (DAPP-delta) with Δ representing the fundamental decisions. Following the traditional DAPP approach, we explore alternative sequences of adaptation measures to continue to achieve objectives under changing conditions. New to the method is the backcasting of critical implementation paths from different envisioned future states, including changes in land use. Additionally, we identify synergies and conflicts between the forward-looking pathways and backcasting implementation paths. We use the southwest of the Netherlands as an illustrative case study. In this region, multiple adaptation tipping points are projected due to sea-level rise and economic changes, while simultaneously large-scale investments in aging infrastructure are expected. The results show that the DAPP-Δ method a) helps to identify pivotal adaptation decisions that goes beyond incremental adaptation and includes transformative decisions; b) effectively reveals the risk of maladaptation; and c) illustrates the necessity to include multiple changes in the analysis, as they together determine investments in the area and with that the solution space.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"48 ","pages":"Article 100716"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100718
Douglas C. Jaks , Ashish Shrestha , Christopher M. Chini
The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.
{"title":"Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations","authors":"Douglas C. Jaks , Ashish Shrestha , Christopher M. Chini","doi":"10.1016/j.crm.2025.100718","DOIUrl":"10.1016/j.crm.2025.100718","url":null,"abstract":"<div><div>The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"49 ","pages":"Article 100718"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100751
Cecina Babich Morrow , Laura Dawkins , Francesca Pianosi , Dennis Prangle , Dan Bernie
Climate adaptation decisions are made under great uncertainty, arising from uncertainties about both the level of climate risk and the attributes of decision options. Decision-makers must understand how uncertainties in the input factors of risk assessment and decision models affect the ultimate adaptation decision, and whether the modelling yields a robust decision, i.e. one that is consistently identified as optimal over a range of uncertain input factors. Here, we present a framework for analysing the robustness of climate adaptation decisions. We apply a Bayesian Decision Analysis framework to determine the optimal output decision in a region based on both climate risk and decision-related attributes. Then, we present an approach for performing global uncertainty and sensitivity analysis on the optimal adaptation decision itself to assess robustness and understand which input factors most influence the decision in a particular region. We demonstrate this framework on an idealised example of adaptation decision-making to mitigate the risk of heat-stress on outdoor physical working capacity in the UK. In this application, we find that regions with high uncertainty in climate risk can still exhibit greater robustness in the optimal decision, and the decision is often more sensitive to variations in decision-related attributes rather than risk-related attributes. Previous research often stops short at assessing uncertainty and sensitivity in climate risk alone. These results highlight the necessity of conducting uncertainty and sensitivity analysis on the ultimate decision output itself in order to understand what factors drive decision robustness.
{"title":"From climate risk to action: Analysing adaptation decision robustness under uncertainty","authors":"Cecina Babich Morrow , Laura Dawkins , Francesca Pianosi , Dennis Prangle , Dan Bernie","doi":"10.1016/j.crm.2025.100751","DOIUrl":"10.1016/j.crm.2025.100751","url":null,"abstract":"<div><div>Climate adaptation decisions are made under great uncertainty, arising from uncertainties about both the level of climate risk and the attributes of decision options. Decision-makers must understand how uncertainties in the input factors of risk assessment and decision models affect the ultimate adaptation decision, and whether the modelling yields a robust decision, i.e. one that is consistently identified as optimal over a range of uncertain input factors. Here, we present a framework for analysing the robustness of climate adaptation decisions. We apply a Bayesian Decision Analysis framework to determine the optimal output decision in a region based on both climate risk and decision-related attributes. Then, we present an approach for performing global uncertainty and sensitivity analysis on the optimal adaptation decision itself to assess robustness and understand which input factors most influence the decision in a particular region. We demonstrate this framework on an idealised example of adaptation decision-making to mitigate the risk of heat-stress on outdoor physical working capacity in the UK. In this application, we find that regions with high uncertainty in climate risk can still exhibit greater robustness in the optimal decision, and the decision is often more sensitive to variations in decision-related attributes rather than risk-related attributes. Previous research often stops short at assessing uncertainty and sensitivity in climate risk alone. These results highlight the necessity of conducting uncertainty and sensitivity analysis on the ultimate decision output itself in order to understand what factors drive decision robustness.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"50 ","pages":"Article 100751"},"PeriodicalIF":5.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100730
Esmaiel Askari , Moslem Savari , Marzieh Rezaei
Floods rank among the most destructive natural hazards, inflicting extensive damage on rural areas by compromising infrastructure, destroying crops, and undermining livelihoods. Beyond the immediate economic losses, such events often trigger rural outmigration and erode social cohesion. Effective flood prevention strategies are therefore critical and can be advanced through public awareness campaigns, the reinforcement of social capital, the development of flood-resilient infrastructure, and the implementation of integrated crisis management plans. Despite the importance of these measures, previous research has largely concentrated on assessing community vulnerability, with limited attention given to proactive protective factors. Addressing this gap, the present study aims to identify the determinants influencing the adoption of protective measures among rural communities in Iran prior to flood events. The statistical population of this study comprised all rural households in Shushtar County, located in Khuzestan Province, southwest Iran. A sample of 353 rural household heads was selected using the Krejcie and Morgan table, applying a multi-stage stratified sampling method to ensure representation across different villages. Data were collected through a structured questionnaire, the validity of which was confirmed by a panel of subject matter experts, while reliability was assessed using Cronbach’s alpha coefficient. Data analysis was conducted in two phases—descriptive and inferential statistics—using SPSS and SmartPLS software. The findings demonstrated that the variables of psychological distance, social media use, place attachment, flood experience, social capital, and flood risk perception had significant and positive effects on protective behaviors prior to flooding. Collectively, these variables accounted for 76.3% of the variance in protective measures. These findings offer critical insights for policymakers seeking to enhance the resilience and safety of rural communities in flood-prone regions. By proactively addressing flood risks, such measures can contribute to the sustainability of rural livelihoods and the strengthening of local resilience.
{"title":"Factors affecting the application of protective measures before flood occurrence among local communities in Iran","authors":"Esmaiel Askari , Moslem Savari , Marzieh Rezaei","doi":"10.1016/j.crm.2025.100730","DOIUrl":"10.1016/j.crm.2025.100730","url":null,"abstract":"<div><div>Floods rank among the most destructive natural hazards, inflicting extensive damage on rural areas by compromising infrastructure, destroying crops, and undermining livelihoods. Beyond the immediate economic losses, such events often trigger rural outmigration and erode social cohesion. Effective flood prevention strategies are therefore critical and can be advanced through public awareness campaigns, the reinforcement of social capital, the development of flood-resilient infrastructure, and the implementation of integrated crisis management plans. Despite the importance of these measures, previous research has largely concentrated on assessing community vulnerability, with limited attention given to proactive protective factors. Addressing this gap, the present study aims to identify the determinants influencing the adoption of protective measures among rural communities in Iran prior to flood events. The statistical population of this study comprised all rural households in Shushtar County, located in Khuzestan Province, southwest Iran. A sample of 353 rural household heads was selected using the Krejcie and Morgan table, applying a multi-stage stratified sampling method to ensure representation across different villages. Data were collected through a structured questionnaire, the validity of which was confirmed by a panel of subject matter experts, while reliability was assessed using Cronbach’s alpha coefficient. Data analysis was conducted in two phases—descriptive and inferential statistics—using SPSS and SmartPLS software. The findings demonstrated that the variables of psychological distance, social media use, place attachment, flood experience, social capital, and flood risk perception had significant and positive effects on protective behaviors prior to flooding. Collectively, these variables accounted for 76.3% of the variance in protective measures. These findings offer critical insights for policymakers seeking to enhance the resilience and safety of rural communities in flood-prone regions. By proactively addressing flood risks, such measures can contribute to the sustainability of rural livelihoods and the strengthening of local resilience.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"49 ","pages":"Article 100730"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2025.100769
Ikram Ullah , Niraj Prakash Joshi , Luni Piya
This systematic review examines how financial and social remittances contribute to resilience under environmental and socioeconomic stress by synthesizing 79 peer-reviewed studies published between 2002 and 2025. Guided by a Disaster Resilience Integrated Framework for Transformation (DRIFT)-informed, stage-based framework, we analyze resilience before, during, and after disasters, linking each phase to capacities such as consumption smoothing, livelihood diversification, asset-based adaptation, social capital, and institutional empowerment. Remittance-resilience research expanded rapidly after 2020, with most studies focusing on Asia and Africa and fewer in Latin America, Europe, and Pacific Small Island Developing States. Financial remittances primarily support immediate stabilization and asset repair, whereas social remittances strengthen skills, networks, risk awareness, and collective action that underpin longer-term adjustment. Regional patterns differ: Asian cases emphasize consumption smoothing and housing upgrades, while African studies highlight diversification and institutional pathways. Our review contributes by mapping remittance roles across household, community, and system levels; linking micro-level mechanisms to governance and market conditions; and offering a comparative regional synthesis. Key constraints include dependency risks, unequal access by gender and income, and market or institutional volatility. Policy priorities include integrating remittances into national adaptation and disaster risk reduction strategies, reducing transfer costs through digital rails, leveraging diaspora co-financing, and strengthening financial and digital literacy to enhance inclusive, shock-responsive resilience aligned with Sustainable Development Goals 11, 13, and 16.
{"title":"The role of remittances in building resilience through adaptive capacities amid environmental and socioeconomic vulnerabilities: A systematic literature review","authors":"Ikram Ullah , Niraj Prakash Joshi , Luni Piya","doi":"10.1016/j.crm.2025.100769","DOIUrl":"10.1016/j.crm.2025.100769","url":null,"abstract":"<div><div>This systematic review examines how financial and social remittances contribute to resilience under environmental and socioeconomic stress by synthesizing 79 peer-reviewed studies published between 2002 and 2025. Guided by a Disaster Resilience Integrated Framework for Transformation (DRIFT)-informed, stage-based framework, we analyze resilience before, during, and after disasters, linking each phase to capacities such as consumption smoothing, livelihood diversification, asset-based adaptation, social capital, and institutional empowerment. Remittance-resilience research expanded rapidly after 2020, with most studies focusing on Asia and Africa and fewer in Latin America, Europe, and Pacific Small Island Developing States. Financial remittances primarily support immediate stabilization and asset repair, whereas social remittances strengthen skills, networks, risk awareness, and collective action that underpin longer-term adjustment. Regional patterns differ: Asian cases emphasize consumption smoothing and housing upgrades, while African studies highlight diversification and institutional pathways. Our review contributes by mapping remittance roles across household, community, and system levels; linking micro-level mechanisms to governance and market conditions; and offering a comparative regional synthesis. Key constraints include dependency risks, unequal access by gender and income, and market or institutional volatility. Policy priorities include integrating remittances into national adaptation and disaster risk reduction strategies, reducing transfer costs through digital rails, leveraging diaspora co-financing, and strengthening financial and digital literacy to enhance inclusive, shock-responsive resilience aligned with Sustainable Development Goals 11, 13, and 16.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"50 ","pages":"Article 100769"},"PeriodicalIF":5.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-01-01DOI: 10.1016/j.crm.2024.100686
Jacob Emanuel Joseph , K.P.C Rao , Elirehema Swai , Anthony M. Whitbread , Reimund P. Rötter
Understanding growing period conditions is crucial for effective climate risk management strategies. Seasonal climate forecasts (SCF) are key in predicting these conditions and guiding risk management in agriculture. However, low SCF adoption rates among smallholder farmers are due to factors like uncertainty and lack of understanding. In this study, we evaluated the benefits of SCF in predicting growing season conditions, and crop performance, and developing climate risk management strategies in Kongwa district, Tanzania. We used sea surface temperature anomalies (SSTa) from the Indian and Pacific Ocean regions to predict seasonal rainfall onset dates using the k-nearest neighbor model. Contrary to traditional approaches, the study established the use of rainfall onset dates as the criterion for predicting and describing growing period conditions. We then evaluated forecast skills and the profitability of using SCF in crop management with the Agricultural Production System sIMulator (APSIM) coupled with a simple bio-economic model. Our findings show that SSTa significantly influences rainfall variability and accurately predicts rainfall onset dates. Onset dates proved more effective than traditional methods in depicting key growing period characteristics, including rainfall variability and distribution. Including SCF in climate risk management proved beneficial for maize and sorghum production both agronomically and economically. Not using SCF posed a higher risk to crop production, with an 80% probability of yield losses, especially in late-onset seasons. We conclude that while SCF has potential benefits, improvements are needed in its generation and dissemination. Enhancing the network of extension agents could facilitate better understanding and adoption by smallholder farmers.
{"title":"How beneficial are seasonal climate forecasts for climate risk management? An appraisal for crop production in Tanzania","authors":"Jacob Emanuel Joseph , K.P.C Rao , Elirehema Swai , Anthony M. Whitbread , Reimund P. Rötter","doi":"10.1016/j.crm.2024.100686","DOIUrl":"10.1016/j.crm.2024.100686","url":null,"abstract":"<div><div>Understanding growing period conditions is crucial for effective climate risk management strategies. Seasonal climate forecasts (SCF) are key in predicting these conditions and guiding risk management in agriculture. However, low SCF adoption rates among smallholder farmers are due to factors like uncertainty and lack of understanding. In this study, we evaluated the benefits of SCF in predicting growing season conditions, and crop performance, and developing climate risk management strategies in Kongwa district, Tanzania. We used sea surface temperature anomalies (SSTa) from the Indian and Pacific Ocean regions to predict seasonal rainfall onset dates using the k-nearest neighbor model. Contrary to traditional approaches, the study established the use of rainfall onset dates as the criterion for predicting and describing growing period conditions. We then evaluated forecast skills and the profitability of using SCF in crop management with the Agricultural Production System sIMulator (APSIM) coupled with a simple bio-economic model. Our findings show that SSTa significantly influences rainfall variability and accurately predicts rainfall onset dates. Onset dates proved more effective than traditional methods in depicting key growing period characteristics, including rainfall variability and distribution. Including SCF in climate risk management proved beneficial for maize and sorghum production both agronomically and economically. Not using SCF posed a higher risk to crop production, with an 80% probability of yield losses, especially in late-onset seasons. We conclude that while SCF has potential benefits, improvements are needed in its generation and dissemination. Enhancing the network of extension agents could facilitate better understanding and adoption by smallholder farmers.</div></div>","PeriodicalId":54226,"journal":{"name":"Climate Risk Management","volume":"47 ","pages":"Article 100686"},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
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