Pub Date : 2025-08-01Epub Date: 2025-07-05DOI: 10.1016/j.cliser.2025.100593
Stephen Snow , Aysha Fleming , Yuwan Malakar , Emma Jakku , Simon Fielke , Rebecca Darbyshire , Graham Bonnett
Future climate projections are incorporated into a growing number of interactive online platforms, changing the way users interact with climate information. Motivated by user-centred research, this paper bridges the micro-level considerations of interface design, usability, comprehension and interpretation with more macro-level interaction design concerns, including adoption. We examine users’ understanding and navigation of short-term weather websites relative to longer-term climate projections. Focusing on farmers’ first impressions of a multi-decadal climate service, called My Climate View, we detail how they used and interpreted the interface and highlight where misunderstandings occurred. Our findings show how: (a) Users’ experience of climate projections are shaped by past experiences, including local knowledge and weather knowledge. (b) Misunderstandings of data, although uncommon, can severely undermine perceived usefulness and trust and can occur despite users reporting satisfactory interface usability. These findings underscore how usability and comprehension research can extend broader social science work on technology acceptance, behaviour and social connections. We provide suggestions for the design of online multi-decadal climate services that seek to maximise usefulness and usability and minimise misunderstandings of the information they provide.
{"title":"Tackling misunderstandings: A farmer-led approach to improve the usability of multi-decadal climate services","authors":"Stephen Snow , Aysha Fleming , Yuwan Malakar , Emma Jakku , Simon Fielke , Rebecca Darbyshire , Graham Bonnett","doi":"10.1016/j.cliser.2025.100593","DOIUrl":"10.1016/j.cliser.2025.100593","url":null,"abstract":"<div><div>Future climate projections are incorporated into a growing number of interactive online platforms, changing the way users interact with climate information. Motivated by user-centred research, this paper bridges the micro-level considerations of interface design, usability, comprehension and interpretation with more macro-level interaction design concerns, including adoption. We examine users’ understanding and navigation of short-term <em>weather</em> websites relative to longer-term <em>climate</em> projections. Focusing on farmers’ first impressions of a multi-decadal climate service, called My Climate View, we detail how they used and interpreted the interface and highlight where misunderstandings occurred. Our findings show how: (a) Users’ experience of climate projections are shaped by past experiences, including local knowledge and weather knowledge. (b) Misunderstandings of data, although uncommon, can severely undermine perceived usefulness and trust and can occur despite users reporting satisfactory interface usability. These findings underscore how usability and comprehension research can extend broader social science work on technology acceptance, behaviour and social connections. We provide suggestions for the design of online multi-decadal climate services that seek to maximise usefulness and usability and minimise misunderstandings of the information they provide.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100593"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-06-13DOI: 10.1016/j.cliser.2025.100585
Katrin Ziegler , Daniel Abel , Lorenz König , Torsten Weber , Insa Otte , Mike Teucher , Christopher Conrad , Michael Thiel , Imoleayo Ezekiel Gbode , Vincent Olanrewaju Ajayi , Amadou Coulibaly , Seydou B. Traoré , Benewinde Jean-Bosco Zoungrana , Heiko Paeth
This paper presents a Spatial Decision Support System (SDSS) designed to assist stakeholders in West Africa in analysing critical climate and land use indicators for risk management in agriculture and further sectors being affected by extreme precipitation and temperature events. Developed as part of the WASCAL WRAP 2.0 project LANDSURF, the SDSS makes scientific data accessible and comprehensible to non-scientific audiences, facilitating informed decision-making among communities affected by climate change. From the beginning of the development process, the web portal was co-designed with relevant West African stakeholders. Due to the challenging conditions during the COVID-19 pandemic, alternative online communication tools, e.g. ZOOM, online surveys and email, successfully were utilized to interact with stakeholders instead of on-site activities. The co-design process carried out with stakeholders includes several steps such as stakeholder analysis, identification of their information needs using specific climate, crop and remote sensing indicators, and the evaluation of the SDSS in a dedicated workshop. In total, the co-design process involved nine different steps, recorded and described in a stakeholder interaction protocol.
The SDSS integrates observational data, including CHIRPS and ERA5-Land datasets, and state-of-the-art high-resolution climate model outputs under two greenhouse gas concentration scenarios (RCP2.6 and RCP8.5) and remote sensing data. It enables the comparison of model outputs with observations and facilitates the assessment of regional climate variability and trends. Two concept studies illustrate the SDSS’s functionality: one focusing on a farmer in Burkina Faso assessing irrigation needs for millet cultivation, and another involving a regional planner analysing drought and heat wave impacts in coastal West Africa. These examples highlight the SDSS’s usability in supporting adaptive strategies and enhancing resilience to climate-related challenges, underscoring the importance of integrating local knowledge with scientific data for effective climate adaptation and mitigation.
{"title":"A Spatial Decision Support System for climate-adapted agriculture designed with and for stakeholders in West Africa","authors":"Katrin Ziegler , Daniel Abel , Lorenz König , Torsten Weber , Insa Otte , Mike Teucher , Christopher Conrad , Michael Thiel , Imoleayo Ezekiel Gbode , Vincent Olanrewaju Ajayi , Amadou Coulibaly , Seydou B. Traoré , Benewinde Jean-Bosco Zoungrana , Heiko Paeth","doi":"10.1016/j.cliser.2025.100585","DOIUrl":"10.1016/j.cliser.2025.100585","url":null,"abstract":"<div><div>This paper presents a Spatial Decision Support System (SDSS) designed to assist stakeholders in West Africa in analysing critical climate and land use indicators for risk management in agriculture and further sectors being affected by extreme precipitation and temperature events. Developed as part of the WASCAL WRAP 2.0 project LANDSURF, the SDSS makes scientific data accessible and comprehensible to non-scientific audiences, facilitating informed decision-making among communities affected by climate change. From the beginning of the development process, the web portal was co-designed with relevant West African stakeholders. Due to the challenging conditions during the COVID-19 pandemic, alternative online communication tools, e.g. ZOOM, online surveys and email, successfully were utilized to interact with stakeholders instead of on-site activities. The co-design process carried out with stakeholders includes several steps such as stakeholder analysis, identification of their information needs using specific climate, crop and remote sensing indicators, and the evaluation of the SDSS in a dedicated workshop. In total, the co-design process involved nine different steps, recorded and described in a stakeholder interaction protocol.</div><div>The SDSS integrates observational data, including CHIRPS and ERA5-Land datasets, and state-of-the-art high-resolution climate model outputs under two greenhouse gas concentration scenarios (RCP2.6 and RCP8.5) and remote sensing data. It enables the comparison of model outputs with observations and facilitates the assessment of regional climate variability and trends. Two concept studies illustrate the SDSS’s functionality: one focusing on a farmer in Burkina Faso assessing irrigation needs for millet cultivation, and another involving a regional planner analysing drought and heat wave impacts in coastal West Africa. These examples highlight the SDSS’s usability in supporting adaptive strategies and enhancing resilience to climate-related challenges, underscoring the importance of integrating local knowledge with scientific data for effective climate adaptation and mitigation.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100585"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-07-17DOI: 10.1016/j.cliser.2025.100597
Israel Gebresilasie Kimo , Tewodros Addisu Yate , Bisrat Elias Cholo , Thomas Torora Minda , Esatu Bekele Bayde
Ethiopia relies heavily on rain-fed agriculture, making its agricultural sector highly vulnerable to climate variability and change. Adaptation strategies informed by projections of future climate conditions can help mitigate these impacts effectively. This study demonstrates how a science-based approach, specifically the climate analogues methodology, can be used to envision site-specific future agricultural conditions and identify potential adaptation strategies. While such approaches have been increasingly applied elsewhere, their use in Ethiopia remains limited. Therefore, this study applies the analogues tool developed by the Climate Change, Agriculture and Food Security (CCAFS) program to identify a site whose current climate is analogues to the future climate of the study areas—Arba Minch Zuria and Bonke Districts, aiming to highlight potential adaptation pathways. The result shows that Key-Afer and Boreda in Ethiopia, as well as Migori and Narok counties in Kenya, are analogue sites to Arba Minch Zuria. Furthermore, Dita Zada and Chencha in Ethiopia are analogue sites to Bonke. Farmers in Key-Afer analogue site engage in various climate response strategies, including planting different crop varieties (90.7 %), changing planting dates (51.8 %), and soil and water conservation measures (46.3 %). Moreover, farmers in Migori County, Kenya, analogue site apply mixed farming (96.5 %), non-intensive dairying (95.1 %), and establish their own feed (92.4 %). Understanding current climatic conditions and agricultural practices in these analogue areas can inform adaptation planning, as they provide a glimpse into the future conditions of the study area. The findings of this research offer valuable insights for policymakers and the scientific community aiming to design effective climate change adaptation strategies.
{"title":"Exploring climate change adaptation pathways for the agricultural sector in Arba Minch Zuria and Bonke districts: Based on CCAFS climate analogue tool","authors":"Israel Gebresilasie Kimo , Tewodros Addisu Yate , Bisrat Elias Cholo , Thomas Torora Minda , Esatu Bekele Bayde","doi":"10.1016/j.cliser.2025.100597","DOIUrl":"10.1016/j.cliser.2025.100597","url":null,"abstract":"<div><div>Ethiopia relies heavily on rain-fed agriculture, making its agricultural sector highly vulnerable to climate variability and change. Adaptation strategies informed by projections of future climate conditions can help mitigate these impacts effectively. This study demonstrates how a science-based approach, specifically the climate analogues methodology, can be used to envision site-specific future agricultural conditions and identify potential adaptation strategies. While such approaches have been increasingly applied elsewhere, their use in Ethiopia remains limited. Therefore, this study applies the analogues tool developed by the Climate Change, Agriculture and Food Security (CCAFS) program to identify a site whose current climate is analogues to the future climate of the study areas—Arba Minch Zuria and Bonke Districts, aiming to highlight potential adaptation pathways. The result shows that Key-Afer and Boreda in Ethiopia, as well as<!--> <!-->Migori and Narok counties in Kenya, are analogue sites to Arba Minch Zuria. Furthermore, Dita Zada and Chencha in Ethiopia are analogue sites to Bonke. Farmers in Key-Afer analogue site engage in various climate response strategies, including planting different crop varieties (90.7 %), changing planting dates (51.8 %), and soil and water conservation measures (46.3 %). Moreover, farmers in Migori County, Kenya, analogue site apply mixed farming (96.5 %), non-intensive dairying (95.1 %), and establish their own feed (92.4 %). Understanding current climatic conditions and agricultural practices in these analogue areas can inform adaptation planning, as they provide a glimpse into the future conditions of the study area. The findings of this research offer valuable insights for policymakers and the scientific community aiming to design effective climate change adaptation strategies.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100597"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-08-04DOI: 10.1016/j.cliser.2025.100600
Ponsian T. Sewando
In Tanzania, agro-pastoral practices have evolved as a response to climate variability. However, the economic evaluation of adaptation strategies in these systems is limited. This case study explores the cost-effectiveness of various climate adaptation strategies adopted by agro-pastoralists in the semi-arid regions of northern and central Tanzania. Using primary data from 411 households, the study applied cost-benefit analysis (CBA) tools including net present value (NPV), benefit-cost ratio (BCR), and internal rate of return (IRR) to assess planned adaptation versus business-as-usual (BAU) scenarios. Results highlight that crop diversification, drought-tolerant crops, micro-catchment rainwater harvesting (MCRWH), drip irrigation, and livestock diversification are economically viable strategies under changing climatic conditions. This study provides practical insights into how agro-pastoralists can improve climate resilience through locally adapted strategies.
{"title":"Climate-proofing agriculture: economic feasibility of adaptation strategies for agro-pastoral farmers in Tanzania","authors":"Ponsian T. Sewando","doi":"10.1016/j.cliser.2025.100600","DOIUrl":"10.1016/j.cliser.2025.100600","url":null,"abstract":"<div><div>In Tanzania, agro-pastoral practices have evolved as a response to climate variability. However, the economic evaluation of adaptation strategies in these systems is limited. This case study explores the cost-effectiveness of various climate adaptation strategies adopted by agro-pastoralists in the semi-arid regions of northern and central Tanzania. Using primary data from 411 households, the study applied cost-benefit analysis (CBA) tools including net present value (NPV), benefit-cost ratio (BCR), and internal rate of return (IRR) to assess planned adaptation versus business-as-usual (BAU) scenarios. Results highlight that crop diversification, drought-tolerant crops, micro-catchment rainwater harvesting (MCRWH), drip irrigation, and livestock diversification are economically viable strategies under changing climatic conditions. This study provides practical insights into how agro-pastoralists can improve climate resilience through locally adapted strategies.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100600"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-07-18DOI: 10.1016/j.cliser.2025.100598
Reshma Akter, Mohummed Shofi Ullah Mazumder
Agriculture in Bangladesh’s coastal region is persistently affected by waterlogging, yet limited knowledge exists about the specific adaptation strategies farmers employ to mitigate its effects. This study aims to identify waterlogging-specific adaptation strategies and the factors influencing their adoption among coastal farmers. A cross-sectional survey of 412 farmers in Zianagar and Morrelganj sub-districts in Pirojpur district was conducted, employing descriptive statistics, multivariate logistic regression, and structural equation modeling (SEM) for data analysis. Common strategies included early planting (74.8 %), elevated trellis platform (69.9 %), duck rearing (60.9 %), short-duration water-tolerant crops (57.8 %), and raised bed farming (55.8 %). Around 16.3 % of farmers adopted four strategies, while 16 % adopted five. SEM results show training significantly promotes early planting (β = 0.02), floating agriculture (β = 0.07), salt-tolerant crops (β = 0.04), raised bed farming (β = 0.03), short-duration water-tolerant crops (β = 0.02), high bed–low ditch system (β = 0.02), crop zoning (β = 0.05), and community group participation (β = 0.02). Farming experience positively influences the adoption of short-duration water-tolerant crops (β = 0.01). Larger farm size is positively associated with elevated trellis platform (β = 0.11), high bed–low ditch system (β = 0.14), embankments (β = 0.1), and fish farming (β = 0.12), while negatively associated with duck rearing (β = -0.31). Agricultural extension contact enhances community group participation (β = 0.02). The study highlights that training, income, farm size, farming experience, and agricultural extension contact are key drivers of diverse waterlogging adaptation strategies among coastal farmers. These findings provide actionable insights for designing targeted agricultural training, resource allocation, and policy interventions to strengthen climate resilience in coastal farming communities.
{"title":"Adaptation strategies to waterlogging among coastal farmers in Bangladesh: Practices, determinants, and implications for resilient agriculture","authors":"Reshma Akter, Mohummed Shofi Ullah Mazumder","doi":"10.1016/j.cliser.2025.100598","DOIUrl":"10.1016/j.cliser.2025.100598","url":null,"abstract":"<div><div>Agriculture in Bangladesh’s coastal region is persistently affected by waterlogging, yet limited knowledge exists about the specific adaptation strategies farmers employ to mitigate its effects. This study aims to identify waterlogging-specific adaptation strategies and the factors influencing their adoption among coastal farmers. A cross-sectional survey of 412 farmers in Zianagar and Morrelganj sub-districts in Pirojpur district was conducted, employing descriptive statistics, multivariate logistic regression, and structural equation modeling (SEM) for data analysis. Common strategies included early planting (74.8 %), elevated trellis platform (69.9 %), duck rearing (60.9 %), short-duration water-tolerant crops (57.8 %), and raised bed farming (55.8 %). Around 16.3 % of farmers adopted four strategies, while 16 % adopted five. SEM results show training significantly promotes early planting (β = 0.02), floating agriculture (β = 0.07), salt-tolerant crops (β = 0.04), raised bed farming (β = 0.03), short-duration water-tolerant crops (β = 0.02), high bed–low ditch system (β = 0.02), crop zoning (β = 0.05), and community group participation (β = 0.02). Farming experience positively influences the adoption of short-duration water-tolerant crops (β = 0.01). Larger farm size is positively associated with elevated trellis platform (β = 0.11), high bed–low ditch system (β = 0.14), embankments (β = 0.1), and fish farming (β = 0.12), while negatively associated with duck rearing (β = -0.31). Agricultural extension contact enhances community group participation (β = 0.02). The study highlights that training, income, farm size, farming experience, and agricultural extension contact are key drivers of diverse waterlogging adaptation strategies among coastal farmers. These findings provide actionable insights for designing targeted agricultural training, resource allocation, and policy interventions to strengthen climate resilience in coastal farming communities.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100598"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-07-15DOI: 10.1016/j.cliser.2025.100594
Etienne Dunn-Sigouin , Erik W. Kolstad , C. Ole Wulff , Douglas J. Parker , Richard J. Keane
Forecasts are essential for climate adaptation and preparedness, such as in early warning systems and impact models. A key limitation to their practical use is often their coarse spatial grid spacing. However, another less frequently discussed but crucial limitation is that forecasts are often more precise than they are accurate when their grid spacing is finer than the scales they can accurately predict. Here, we adapt the fractions skill score, a metric conventionally used to quantify spatial forecast accuracy by the meteorological community, to help users navigate the trade-off between forecast accuracy versus precision. We demonstrate how this trade-off can be visualized for daily European precipitation, focusing on deterministic predictions of anomalies and probabilistic predictions of extremes, derived from three years of sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Our results show that decreasing precision through spatial aggregation increases forecast accuracy, extends predictable lead times, and enhances the maximum possible accuracy relative to the grid scale, while increased precision diminishes these benefits. Notably, spatial aggregation benefits daily-accumulated forecasts more than weekly-accumulated ones, per unit lead-time. We demonstrate the practical value of our approach in three examples: communicating early warnings, managing hydropower capacity, and commercial aviation planning—each characterized by distinct user constraints on accuracy, spatial scale, or lead-time. The results suggest a different approach for using forecasts; post-processing forecasts to focus on the most accurate scales rather than the default grid scale, thus offering users more actionable information.
{"title":"Balancing accuracy versus precision: Enhancing the usability of sub-seasonal forecasts","authors":"Etienne Dunn-Sigouin , Erik W. Kolstad , C. Ole Wulff , Douglas J. Parker , Richard J. Keane","doi":"10.1016/j.cliser.2025.100594","DOIUrl":"10.1016/j.cliser.2025.100594","url":null,"abstract":"<div><div>Forecasts are essential for climate adaptation and preparedness, such as in early warning systems and impact models. A key limitation to their practical use is often their coarse spatial grid spacing. However, another less frequently discussed but crucial limitation is that forecasts are often more precise than they are accurate when their grid spacing is finer than the scales they can accurately predict. Here, we adapt the fractions skill score, a metric conventionally used to quantify spatial forecast accuracy by the meteorological community, to help users navigate the trade-off between forecast accuracy versus precision. We demonstrate how this trade-off can be visualized for daily European precipitation, focusing on deterministic predictions of anomalies and probabilistic predictions of extremes, derived from three years of sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Our results show that decreasing precision through spatial aggregation increases forecast accuracy, extends predictable lead times, and enhances the maximum possible accuracy relative to the grid scale, while increased precision diminishes these benefits. Notably, spatial aggregation benefits daily-accumulated forecasts more than weekly-accumulated ones, per unit lead-time. We demonstrate the practical value of our approach in three examples: communicating early warnings, managing hydropower capacity, and commercial aviation planning—each characterized by distinct user constraints on accuracy, spatial scale, or lead-time. The results suggest a different approach for using forecasts; post-processing forecasts to focus on the most accurate scales rather than the default grid scale, thus offering users more actionable information.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100594"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-07-09DOI: 10.1016/j.cliser.2025.100589
Timothy J. Krupnik , José Mauricio Cunha Fernandes , Felipe Vargas , Emerson Medeiros Del Ponte , Khaled Hossain , Mustafa Kamal , Mutasim Billah , Md. Harun-Or-Rashid , Sk. Ghulam Hussain , Pawan Kumar Singh , Krishna Kanta Roy , Carlos Augusto Pizolotto , Md. Shah Kamal Khan , Willingthon Pavan , Golam Faruq
We describe the user-centered design (UCD) of a numerical weather-forecast-driven early warning system (EWS) as a climate service for managing wheat blast, a fungal disease capable of causing complete crop yield losses that is strongly dependent on weather conditions. Our mixed-methods process was guided by stakeholder input on the design, testing, and refinement of the EWS from agricultural extension organizations, meteorological departments, and farmers’ groups in Bangladesh and Brazil, where concerns about blast disease risks are high. The UCD process led to a wheat blast disease prediction model, server systems, and user-facing enhancements, including an open-source dashboard (https://beattheblastews.net/) that displays historical, real-time, and forecasted weather data, along with geographically explicit disease predictions, to support informed decision-making on wheat blast management. We describe the back- and front-end design of the dashboard, which supports disease risk forecasting, hindcasting, and the dissemination of early warning advisories co-designed with user organizations. We validated the EWS through comparisons with field observations in both countries. Model results generally agreed with disease incidence records, and model hindcasting confirmed alignment with disease outbreak patterns in Bangladesh and Brazil. Collaboration between agricultural research, meteorological and extension organizations in developing and supplying weather forecasts, disease management advisories, and early warning systems—along with presenting hindcast validation results to stakeholders—led to the formal endorsement of the EWS in both countries. This process also enabled the registration and training of over 14,500 extension officers, lead farmers, and farmers' cooperative members who now receive advisories via email, SMS, agro-meteorological bulletins, smartphone applications, WhatsApp and social media messages. These tools support them in interpreting and sharing wheat blast early warnings with farmers to improve disease preparadness and management actions in both countries.
{"title":"A weather-forecast driven early warning system for wheat blast disease: User-centered design, validation, and scaling in Bangladesh and Brazil","authors":"Timothy J. Krupnik , José Mauricio Cunha Fernandes , Felipe Vargas , Emerson Medeiros Del Ponte , Khaled Hossain , Mustafa Kamal , Mutasim Billah , Md. Harun-Or-Rashid , Sk. Ghulam Hussain , Pawan Kumar Singh , Krishna Kanta Roy , Carlos Augusto Pizolotto , Md. Shah Kamal Khan , Willingthon Pavan , Golam Faruq","doi":"10.1016/j.cliser.2025.100589","DOIUrl":"10.1016/j.cliser.2025.100589","url":null,"abstract":"<div><div>We describe the user-centered design (UCD) of a numerical weather-forecast-driven early warning system (EWS) as a climate service for managing wheat blast, a fungal disease capable of causing complete crop yield losses that is strongly dependent on weather conditions. Our mixed-methods process was guided by stakeholder input on the design, testing, and refinement of the EWS from agricultural extension organizations, meteorological departments, and farmers’ groups in Bangladesh and Brazil, where concerns about blast disease risks are high. The UCD process led to a wheat blast disease prediction model, server systems, and user-facing enhancements, including an open-source dashboard (<span><span>https://beattheblastews.net/</span><svg><path></path></svg></span>) that displays historical, real-time, and forecasted weather data, along with geographically explicit disease predictions, to support informed decision-making on wheat blast management. We describe the back- and front-end design of the dashboard, which supports disease risk forecasting, hindcasting, and the dissemination of early warning advisories co-designed with user organizations. We validated the EWS through comparisons with field observations in both countries. Model results generally agreed with disease incidence records, and model hindcasting confirmed alignment with disease outbreak patterns in Bangladesh and Brazil. Collaboration between agricultural research, meteorological and extension organizations in developing and supplying weather forecasts, disease management advisories, and early warning systems—along with presenting hindcast validation results to stakeholders—led to the formal endorsement of the EWS in both countries. This process also enabled the registration and training of over 14,500 extension officers, lead farmers, and farmers' cooperative members who now receive advisories via email, SMS, agro-meteorological bulletins, smartphone applications, WhatsApp and social media messages. These tools support them in interpreting and sharing wheat blast early warnings with farmers to improve disease preparadness and management actions in both countries.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100589"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-08-06DOI: 10.1016/j.cliser.2025.100599
Jesmin Akhter , Muhammad Ramzan Ali , Foezullah Talukder , Sheikh Mohammad Sayem
This paper presents the findings of a systematic literature review on coastal climatic stress, adaptation strategies, and the challenges of adapting to climate variability in coastal Bangladesh. The review aims to summarize the existing research on adaptation actions addressing climate change and to explore thematic studies related to these adaptation efforts with the purpose of providing directions for future research scope in coastal Bangladesh. The review adhered to the systematic methods outlined in the Preferred Items for Systematic Review Recommendations (PRISMA) protocol, facilitating a comprehensive synthesis, evaluation, and tracking of scientific literature on agricultural adaptation strategies in coastal Bangladesh. Peer-reviewed articles and grey literature from the Scopus and Google Scholar databases spanning 2015 to 2023 were considered. Through the rigorous application of the four main stages of a systematic review—identification, screening, eligibility, and inclusion—a total of 60 articles were selected. The adaptation actions highlighted in the review several significant challenges to agricultural adaptation in coastal regions.. This review finds the thematic studies on agricultural adaptation strategies in coastal Bangladesh, focusing on the strategies employed, the challenges, the outcomes, and the key drivers influencing their adoption. It also identifies gaps in the study of gender roles and relations that influence the effectiveness of adaptation strategies and the expected outcomes of these actions’ impact on life and livelihood to coastal farmers. Further studies are needed to explore these aspects to ensure equitable and significant adaptation outcomes.
{"title":"Agricultural adaptation actions to address climate change in the coastal region of Bangladesh: A systematic review","authors":"Jesmin Akhter , Muhammad Ramzan Ali , Foezullah Talukder , Sheikh Mohammad Sayem","doi":"10.1016/j.cliser.2025.100599","DOIUrl":"10.1016/j.cliser.2025.100599","url":null,"abstract":"<div><div>This paper presents the findings of a systematic literature review on coastal climatic stress, adaptation strategies, and the challenges of adapting to climate variability in coastal Bangladesh. The review aims to summarize the existing research on adaptation actions addressing climate change and to explore thematic studies related to these adaptation efforts with the purpose of providing directions for future research scope in coastal Bangladesh. The review adhered to the systematic methods outlined in the Preferred Items for Systematic Review Recommendations (PRISMA) protocol, facilitating a comprehensive synthesis, evaluation, and tracking of scientific literature on agricultural adaptation strategies in coastal Bangladesh. Peer-reviewed articles and grey literature from the Scopus and Google Scholar databases spanning 2015 to 2023 were considered. Through the rigorous application of the four main stages of a systematic review—identification, screening, eligibility, and inclusion—a total of 60 articles were selected. The adaptation actions highlighted in the review several significant challenges to agricultural adaptation in coastal regions.. This review finds the thematic studies on agricultural adaptation strategies in coastal Bangladesh, focusing on the strategies employed, the challenges, the outcomes, and the key drivers influencing their adoption. It also identifies gaps in the study of gender roles and relations that influence the effectiveness of adaptation strategies and the expected outcomes of these actions’ impact on life and livelihood to coastal farmers. Further studies are needed to explore these aspects to ensure equitable and significant adaptation outcomes.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100599"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Predicting variations in weather conditions beyond a few days is of great interest to decision-makers, as this time horizon aligns with the strategic planning needs of stakeholders in climate-vulnerable sectors affected by seasonality. While the effects of climate variability are well understood in sectors such as energy and agriculture, where the potential applications of climate predictions in decision-making are already being explored, in other sectors, the direct impacts of climate variability on operations or on defining seasonal transitions remain unclear. In this context, our paper describes the knowledge exchange and co-development process carried out during the co-production of an operational climate service for a sports retail company. We developed a climate service that combines sub-seasonal and seasonal forecasts to provide tailored and user-friendly climate information for the upcoming weeks and months. The operational system supported decision-making in selected stores over a year, with regular evaluations helping to build trust in the service and informing new developments for an improved version. This study demonstrates that a co-production approach, where interaction between the user and the scientist is established early in the forecast product development, is fundamental to the creation of a successful climate service. Beyond this specific case, the long-term aim of the work is to compile and synthesise the lessons learned in developing this service at sub-seasonal and seasonal timescales, to encourage its adoption in other comparable retail businesses also affected by climate variability (e.g. the fashion industry and food-snack production).
{"title":"Sub-seasonal and seasonal climate predictions for a sporting goods retailer company: Co-development of a climate service from scratch","authors":"Albert Soret , Albert Martínez-Botí , Raul Marcos-Matamoros , Nube Gonzalez-Reviriego , Francesc Roura-Adserias , Lluís Palma , Sergio Benito Martín , Sergio González-Ubierna","doi":"10.1016/j.cliser.2025.100583","DOIUrl":"10.1016/j.cliser.2025.100583","url":null,"abstract":"<div><div>Predicting variations in weather conditions beyond a few days is of great interest to decision-makers, as this time horizon aligns with the strategic planning needs of stakeholders in climate-vulnerable sectors affected by seasonality. While the effects of climate variability are well understood in sectors such as energy and agriculture, where the potential applications of climate predictions in decision-making are already being explored, in other sectors, the direct impacts of climate variability on operations or on defining seasonal transitions remain unclear. In this context, our paper describes the knowledge exchange and co-development process carried out during the co-production of an operational climate service for a sports retail company. We developed a climate service that combines sub-seasonal and seasonal forecasts to provide tailored and user-friendly climate information for the upcoming weeks and months. The operational system supported decision-making in selected stores over a year, with regular evaluations helping to build trust in the service and informing new developments for an improved version. This study demonstrates that a co-production approach, where interaction between the user and the scientist is established early in the forecast product development, is fundamental to the creation of a successful climate service. Beyond this specific case, the long-term aim of the work is to compile and synthesise the lessons learned in developing this service at sub-seasonal and seasonal timescales, to encourage its adoption in other comparable retail businesses also affected by climate variability (e.g. the fashion industry and food-snack production).</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100583"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-08-01Epub Date: 2025-06-25DOI: 10.1016/j.cliser.2025.100592
Cecilia Borries-Strigle , Uma S. Bhatt , Peter A. Bieniek , Mitchell Burgard , Eric Stevens , Heidi Strader , Richard L. Thoman , Alison York , Robert H. Ziel
<div><div>As wildland fires in Alaska and its boreal forest become more extreme, preparing for the upcoming wildfire season has become increasingly challenging for fire managers. This study was developed in close collaboration with fire managers to address their need for advanced summer fire outlooks issued in March and May. Three seasonal forecast models are used to create summer fire outlooks: NOAA CFSv2, ECMWF SEAS5, and Météo-France System8. Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. Skill is greatest for the wind (April 1–June 10) and drought (July 21–August 9) subseasons and in the Western Boreal subregion of Alaska. Combining the models into a multimodel ensemble increases forecast skill by an average of 11% (19%) for the March (May) forecast AUROC score and an average of 87% (92%) for the March (May) forecast Heidke skill score. May forecasts typically have equal or greater skill than March forecasts, with the greatest increases in skill seen during the wind subseason. However, instances of higher Heidke and MSE skill scores for March forecasts, especially in later subseasons and during large fires years, could be explained by the seasonally decreased predictability.</div></div><div><h3>Practical Implications</h3><div>Alaska’s wildfire season has changed over the past 30 years. The season has lengthened by about a month, and extreme fire events have become more frequent. Fire managers begin preparing for the upcoming fire season in March, several weeks before the administrative start of the fire season (April 1) and about three months before the typical peak in late June to early July. With the increasing availability of dynamical seasonal forecasts, the Alaska fire management community has expressed growing interest in using these tools for operational planning.</div><div>In this study, we used March-initialized seasonal forecasts to generate early-season outlooks of the Buildup Index (BUI), a key fire weather variable. These outlooks align with the timing of critical early-season decision-making by fire managers, including resource allocation and national coordination. After several years of providing these outlooks, fire managers requested additional outlooks initialized in May to support decisions after the season has begun but before its peak. Although May-initialized forecasts are typically more skillful, our early focus on the more challenging March forecasts reflects our commitment to meeting fire managers’ needs. This long-term collaboration, including presentations at spring meetings and sustained engagement through biweekly calls, has helped refine our scientific focus—e.g., by emphasizing the duff
随着阿拉斯加及其北方森林的野火变得越来越极端,对火灾管理者来说,为即将到来的野火季节做准备变得越来越具有挑战性。这项研究是与消防管理人员密切合作开展的,以满足他们对3月和5月发布的提前夏季火灾展望的需求。三个季节预报模式用于创建夏季火情前景:NOAA CFSv2、ECMWF SEAS5和msamtsamo - france System8。来自这些预报的变量用于计算累积指数(BUI),这是一种来自加拿大森林火灾危险等级系统的实际使用的火灾天气指数。BUI的前景是基于阿拉斯加野火子季节、BUI品种和预测服务区域子区域,以及ROC曲线下面积(AUROC)、Heidke和均方误差(MSE)技能得分来评估的。技能是最伟大的风(4月1日至6月10日)和干旱(7月21日至8月9日)亚季节和阿拉斯加西部北纬亚地区。将这些模型组合成一个多模型集合,在3月(5月)预测AUROC得分时,预测技能平均提高11%(19%),在3月(5月)预测Heidke技能得分时,预测技能平均提高87%(92%)。5月的预报通常比3月的预报具有相同或更高的预报能力,在风亚季期间预报能力的提高最大。然而,3月份预报的Heidke和MSE技能得分较高的例子,特别是在后来的子季节和大火年,可以用季节性降低的可预测性来解释。在过去的30年里,阿拉斯加的野火季节发生了变化。这个季节已经延长了大约一个月,极端火灾事件变得更加频繁。消防管理人员在3月份开始为即将到来的火灾季节做准备,这比火灾季节的行政开始(4月1日)早几周,比典型的高峰(6月底至7月初)早约三个月。随着动态季节预报的可用性越来越高,阿拉斯加火灾管理界对使用这些工具进行操作规划越来越感兴趣。在这项研究中,我们使用三月初始化的季节预报来生成累积指数(BUI)的季前前景,BUI是一个关键的火灾天气变量。这些展望与火灾管理人员的关键早期决策时间一致,包括资源分配和国家协调。在提供这些展望数年之后,5月份有5位经理要求提供额外的展望,以支持在赛季开始后但在峰值之前做出的决策。虽然5月初始化的预测通常更有技巧,但我们早期对更具挑战性的3月预测的关注反映了我们对满足消防经理需求的承诺。这种长期合作,包括在春季会议上的报告和通过每两周一次的电话会议的持续参与,有助于完善我们的科学重点。通过强调焚烧垃圾的亚季节和季末降雨的时间。在整个工作过程中,我们采取了操作角度,旨在保持方法的计算效率,以适应大数据量和时间敏感的决策。因此,目前的研究使用相对简化的方法建立了预测技能的基线。这一基础使火灾管理界能够探索定制和增强预测产品的方法,例如为高纬度模型应用更先进的偏差校正技术,或按次区域或分季节改进技能评估。它还通过调整模型权重或扩展集成成员,为优化多模型集成方法创建了一个平台。这项工作是改善阿拉斯加季节性火灾天气预报的更广泛努力的一个组成部分。随着合作的继续,这些BUI展望可以与新兴的燃料和闪电的长期预测产品相结合,以建立一个更全面的即将到来的火灾季节的图景。我们仍然积极与消防经理沟通,在春季和秋季的运营会议上分享最新情况,并将他们的反馈纳入正在进行的研究和工具开发中。
{"title":"On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska","authors":"Cecilia Borries-Strigle , Uma S. Bhatt , Peter A. Bieniek , Mitchell Burgard , Eric Stevens , Heidi Strader , Richard L. Thoman , Alison York , Robert H. Ziel","doi":"10.1016/j.cliser.2025.100592","DOIUrl":"10.1016/j.cliser.2025.100592","url":null,"abstract":"<div><div>As wildland fires in Alaska and its boreal forest become more extreme, preparing for the upcoming wildfire season has become increasingly challenging for fire managers. This study was developed in close collaboration with fire managers to address their need for advanced summer fire outlooks issued in March and May. Three seasonal forecast models are used to create summer fire outlooks: NOAA CFSv2, ECMWF SEAS5, and Météo-France System8. Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. Skill is greatest for the wind (April 1–June 10) and drought (July 21–August 9) subseasons and in the Western Boreal subregion of Alaska. Combining the models into a multimodel ensemble increases forecast skill by an average of 11% (19%) for the March (May) forecast AUROC score and an average of 87% (92%) for the March (May) forecast Heidke skill score. May forecasts typically have equal or greater skill than March forecasts, with the greatest increases in skill seen during the wind subseason. However, instances of higher Heidke and MSE skill scores for March forecasts, especially in later subseasons and during large fires years, could be explained by the seasonally decreased predictability.</div></div><div><h3>Practical Implications</h3><div>Alaska’s wildfire season has changed over the past 30 years. The season has lengthened by about a month, and extreme fire events have become more frequent. Fire managers begin preparing for the upcoming fire season in March, several weeks before the administrative start of the fire season (April 1) and about three months before the typical peak in late June to early July. With the increasing availability of dynamical seasonal forecasts, the Alaska fire management community has expressed growing interest in using these tools for operational planning.</div><div>In this study, we used March-initialized seasonal forecasts to generate early-season outlooks of the Buildup Index (BUI), a key fire weather variable. These outlooks align with the timing of critical early-season decision-making by fire managers, including resource allocation and national coordination. After several years of providing these outlooks, fire managers requested additional outlooks initialized in May to support decisions after the season has begun but before its peak. Although May-initialized forecasts are typically more skillful, our early focus on the more challenging March forecasts reflects our commitment to meeting fire managers’ needs. This long-term collaboration, including presentations at spring meetings and sustained engagement through biweekly calls, has helped refine our scientific focus—e.g., by emphasizing the duff","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"39 ","pages":"Article 100592"},"PeriodicalIF":4.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}