Pub Date : 2026-02-01Epub Date: 2025-12-05DOI: 10.1016/j.envsoft.2025.106820
Katharina Dost , Kohji Muraoka , Anne-Gaelle Ausseil , Rubianca Benavidez , Brendon Blue , Nic Conland , Chris Daughney , Annette Semadeni-Davies , Linh Hoang , Anna Hooper , Theodore Alfred Kpodonu , Tapuwa Marapara , Richard McDowell , Trung Nguyen , Dang Anh Nguyet , Ned Norton , Deniz Özkundakci , Lisa Pearson , James Rolinson , Ra Smith , Jörg Wicker
Freshwater modeling is vital for addressing environmental and societal challenges. In two workshops preceding this article, we revealed issues in current modeling practices in New Zealand, with a focus on catchment-level water quality modelling. Predominant were low trust in models, lack of transparency, and models unfit for purpose. This article uses a root-cause analysis to explore these issues, identify causes, and propose solutions. We find that current best practices and research are a good foundation but insufficient to fulfill our freshwater research and management needs. We advocate for long-term national strategies with centralized funding, standardized documentation, data, models, evaluation techniques, and communication methods, along with a centralized open-access platform for collaboration. Our vision is to streamline modeling projects, enhance the accessibility and reliability of models, and foster more effective decision-making processes for the sustainable management of freshwater ecosystems.
{"title":"Freshwater modeling in Aotearoa New Zealand: Current practice and future directions","authors":"Katharina Dost , Kohji Muraoka , Anne-Gaelle Ausseil , Rubianca Benavidez , Brendon Blue , Nic Conland , Chris Daughney , Annette Semadeni-Davies , Linh Hoang , Anna Hooper , Theodore Alfred Kpodonu , Tapuwa Marapara , Richard McDowell , Trung Nguyen , Dang Anh Nguyet , Ned Norton , Deniz Özkundakci , Lisa Pearson , James Rolinson , Ra Smith , Jörg Wicker","doi":"10.1016/j.envsoft.2025.106820","DOIUrl":"10.1016/j.envsoft.2025.106820","url":null,"abstract":"<div><div>Freshwater modeling is vital for addressing environmental and societal challenges. In two workshops preceding this article, we revealed issues in current modeling practices in New Zealand, with a focus on catchment-level water quality modelling. Predominant were low trust in models, lack of transparency, and models unfit for purpose. This article uses a root-cause analysis to explore these issues, identify causes, and propose solutions. We find that current best practices and research are a good foundation but insufficient to fulfill our freshwater research and management needs. We advocate for long-term national strategies with centralized funding, standardized documentation, data, models, evaluation techniques, and communication methods, along with a centralized open-access platform for collaboration. Our vision is to streamline modeling projects, enhance the accessibility and reliability of models, and foster more effective decision-making processes for the sustainable management of freshwater ecosystems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106820"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689372","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 : 2026-02-01Epub Date: 2025-12-05DOI: 10.1016/j.envsoft.2025.106818
Xuesong Yang , Bin Xu , Huili Wang , Xinman Qin , Xinrong Wang , Zichen Ren , Yao Yao , Siying Zhou , Yao Liu , Ping Chang
Complex flood control systems which comprise reservoirs, lakes, and external rivers, frequently encounter multifaceted risk sources that are spatiotemporally interconnected, resulting in diverse flood risks. This study developed a comprehensive risk analysis framework integrating stochastic simulation and Bayesian networks to facilitate refined risk prediction and diagnosis. Vine copula and Monte Carlo methods were used for probabilistic modeling and simulation, while Bayesian network was used for bidirectional risk assessment. A case study of Chaohu Lake Basin (China) show that vine copula effectively elucidates both intervariable correlations and single variable characteristics. The lateral inflow volume of lake and the external river water levels are dominant risk sources. When the maximum water level of lake increases from 9.5 m to 11.5 m, the posterior probability of dominant risk sources exceeding the design value at 20 % increases by 46.12 % and 32.22 %. This study represents an innovative approach to risk analysis for complex reservoir-lake systems.
{"title":"Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties","authors":"Xuesong Yang , Bin Xu , Huili Wang , Xinman Qin , Xinrong Wang , Zichen Ren , Yao Yao , Siying Zhou , Yao Liu , Ping Chang","doi":"10.1016/j.envsoft.2025.106818","DOIUrl":"10.1016/j.envsoft.2025.106818","url":null,"abstract":"<div><div>Complex flood control systems which comprise reservoirs, lakes, and external rivers, frequently encounter multifaceted risk sources that are spatiotemporally interconnected, resulting in diverse flood risks. This study developed a comprehensive risk analysis framework integrating stochastic simulation and Bayesian networks to facilitate refined risk prediction and diagnosis. Vine copula and Monte Carlo methods were used for probabilistic modeling and simulation, while Bayesian network was used for bidirectional risk assessment. A case study of Chaohu Lake Basin (China) show that vine copula effectively elucidates both intervariable correlations and single variable characteristics. The lateral inflow volume of lake and the external river water levels are dominant risk sources. When the maximum water level of lake increases from 9.5 m to 11.5 m, the posterior probability of dominant risk sources exceeding the design value at 20 % increases by 46.12 % and 32.22 %. This study represents an innovative approach to risk analysis for complex reservoir-lake systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106818"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689374","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 : 2026-02-01Epub Date: 2025-12-04DOI: 10.1016/j.envsoft.2025.106825
Tommaso Baggio , Maximiliano Costa , Niccolò Marchi , Tommaso Locatelli , Emanuele Lingua
Windstorms are the primary cause of damage to European forests. Although different mechanistic and probabilistic models have been developed to estimate the vulnerability of forests to wind, their practical application remains limited. This study presents a new, semi-automated methodology for deriving tree and forest characteristics over large areas through the analysis of Canopy Height Model (CHM) data. By integrating the semi-mechanistic model ForestGALES, the developed algorithm uses these data to calculate spatially explicit maps of Critical Wind Speed (CWS). The presented methodology is applied to a real case study to calculate the CWS of forests in the Italian Eastern Alps. Results show that adding detailed and spatially distributed forest cover information improves the CWS calculations, thereby enhancing the reliability of models to assess forest wind vulnerability. Forest practitioners can take advantage of this new methodology to enhance the resistance and resilience of their forests through specific management techniques.
{"title":"Improve the estimation of forest wind vulnerability through remote sensed data: a new methodology","authors":"Tommaso Baggio , Maximiliano Costa , Niccolò Marchi , Tommaso Locatelli , Emanuele Lingua","doi":"10.1016/j.envsoft.2025.106825","DOIUrl":"10.1016/j.envsoft.2025.106825","url":null,"abstract":"<div><div>Windstorms are the primary cause of damage to European forests. Although different mechanistic and probabilistic models have been developed to estimate the vulnerability of forests to wind, their practical application remains limited. This study presents a new, semi-automated methodology for deriving tree and forest characteristics over large areas through the analysis of Canopy Height Model (CHM) data. By integrating the semi-mechanistic model ForestGALES, the developed algorithm uses these data to calculate spatially explicit maps of Critical Wind Speed (CWS). The presented methodology is applied to a real case study to calculate the CWS of forests in the Italian Eastern Alps. Results show that adding detailed and spatially distributed forest cover information improves the CWS calculations, thereby enhancing the reliability of models to assess forest wind vulnerability. Forest practitioners can take advantage of this new methodology to enhance the resistance and resilience of their forests through specific management techniques.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106825"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689375","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 : 2026-02-01Epub Date: 2026-01-09DOI: 10.1016/j.envsoft.2026.106870
Tangyao Ai , Liang Gao , Xianfei Yin , Haoxuan Du , Qingbiao Li , Hongcai Zhang
Digital twin enables participatory system assessment and decision-making, establishing bidirectional connections between virtual system and real-world urban operations. Nevertheless, its widespread implementation in the urban flood faces persistent barriers to incorporate physics-guided urban flooding prediction with a scalable visualization platform. This study proposes a high-fidelity hydrodynamic digital twin framework that combines real-time forecasting data visualization platform with a numerical urban flood model by proposing an interactive interface. The framework consists of (1) a data acquisition layer that consolidates various inputs into specialized databases, (2) a modeling layer that employs numerical simulations for high-resolution flood predictions, and (3) a visualization layer that transforms outputs into interpretable web formats. The framework enables users to upload rainfall and storm data through a web interface and initiate urban flooding simulation. It allows real-time prediction of urban floods under a designed storm or a tropical scenario. The feasibility of the framework is tested by applying it to the Macao Peninsula during typhoon Hato (2017). The integration of a numerical model into a digital twin creates an intelligent decision-support framework, enabling real-time hydrodynamic forecasting, and dynamic scenario visualization for urban floods.
{"title":"A numerical modelling-supported digital twin for urban floods monitoring in typhoon or storm scenario","authors":"Tangyao Ai , Liang Gao , Xianfei Yin , Haoxuan Du , Qingbiao Li , Hongcai Zhang","doi":"10.1016/j.envsoft.2026.106870","DOIUrl":"10.1016/j.envsoft.2026.106870","url":null,"abstract":"<div><div>Digital twin enables participatory system assessment and decision-making, establishing bidirectional connections between virtual system and real-world urban operations. Nevertheless, its widespread implementation in the urban flood faces persistent barriers to incorporate physics-guided urban flooding prediction with a scalable visualization platform. This study proposes a high-fidelity hydrodynamic digital twin framework that combines real-time forecasting data visualization platform with a numerical urban flood model by proposing an interactive interface. The framework consists of (1) a data acquisition layer that consolidates various inputs into specialized databases, (2) a modeling layer that employs numerical simulations for high-resolution flood predictions, and (3) a visualization layer that transforms outputs into interpretable web formats. The framework enables users to upload rainfall and storm data through a web interface and initiate urban flooding simulation. It allows real-time prediction of urban floods under a designed storm or a tropical scenario. The feasibility of the framework is tested by applying it to the Macao Peninsula during typhoon Hato (2017). The integration of a numerical model into a digital twin creates an intelligent decision-support framework, enabling real-time hydrodynamic forecasting, and dynamic scenario visualization for urban floods.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106870"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956762","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 : 2026-02-01Epub Date: 2025-12-12DOI: 10.1016/j.envsoft.2025.106821
Rafaela Martelo , Kimia Ahmadiyehyazdi , Ruo-Qian Wang
Traditional flood risk communication fails to bridge the gap between complex technical data and the needs of the public, hindering effective response. This research addresses this gap by developing and validating a novel AI-powered assistant that uses GPT-4 to democratize flood risk information. Our core methodology includes a Retrieval-Augmented Generation (RAG) framework that synthesizes real-time flood warnings, geospatial data, and social vulnerability indices into clear, conversational responses. To validate its effectiveness, we conducted a mixed-methods evaluation, including a comparison across different GPT models. Key quantitative findings reveal that the assistant achieved high performance scores in general flood knowledge (5/5) and handling flash flood alerts (4.3/5). Response times averaged a rapid 12 s for non-function-calling queries, though more complex data retrieval tasks averaged 36 s, highlighting areas for optimization. Our comparison identified GPT-4o as the optimal model for balancing accuracy with response time. The broader implications of this work demonstrate that large language models can serve as powerful tools to translate complex environmental data for non-experts, paving the way for more equitable, engaging, and effective public participation in disaster risk management.
{"title":"Towards democratized flood risk management: An advanced AI assistant enabled by GPT-4 for enhanced interpretability and public engagement","authors":"Rafaela Martelo , Kimia Ahmadiyehyazdi , Ruo-Qian Wang","doi":"10.1016/j.envsoft.2025.106821","DOIUrl":"10.1016/j.envsoft.2025.106821","url":null,"abstract":"<div><div>Traditional flood risk communication fails to bridge the gap between complex technical data and the needs of the public, hindering effective response. This research addresses this gap by developing and validating a novel AI-powered assistant that uses GPT-4 to democratize flood risk information. Our core methodology includes a Retrieval-Augmented Generation (RAG) framework that synthesizes real-time flood warnings, geospatial data, and social vulnerability indices into clear, conversational responses. To validate its effectiveness, we conducted a mixed-methods evaluation, including a comparison across different GPT models. Key quantitative findings reveal that the assistant achieved high performance scores in general flood knowledge (5/5) and handling flash flood alerts (4.3/5). Response times averaged a rapid 12 s for non-function-calling queries, though more complex data retrieval tasks averaged 36 s, highlighting areas for optimization. Our comparison identified GPT-4o as the optimal model for balancing accuracy with response time. The broader implications of this work demonstrate that large language models can serve as powerful tools to translate complex environmental data for non-experts, paving the way for more equitable, engaging, and effective public participation in disaster risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106821"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732498","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 : 2026-02-01Epub Date: 2025-12-12DOI: 10.1016/j.envsoft.2025.106834
Hoshin V. Gupta
There are several types of generalization ability that we may wish our models to be capable of. All but the most basic of these require the representation to be suitably interpretable so that it can provide meaningful support for scenario analysis, scientific reasoning, and decision making under system non-stationarity and model transfer. However interpretability of a model can only be meaningfully understood in the context of the ‘language’ used for its construction. In this regard it is important to recognize that, while machine-learning-based (MLB) models tend to prioritize accuracy and precision (in service of predictive performance) and physics-based (PB) models tend to emphasize physical/geo-scientific interpretability (in service of understanding), their learned representations are actually based in related but somewhat different languages, levels of linguistic abstraction, and grammatical rules.
Importantly, these differences are not fundamentally necessary. It is my opinion that the future of geo-scientific ML need not compromise accuracy and precision to achieve improved understanding. Instead, we must develop “telescopic” hierarchical representations that prioritize “learning from data” at their fundamental levels, while simultaneously enabling “geo-scientific abstraction” so that higher-level interpretable and understandable representations can be extracted by directed compression. Ultimately, the geosciences will benefit from a specific kind of “interpretable generative modeling” that can learn how to construct causal and/or understandable representations of the underlying physical data generating processes from data, and that can facilitate the kind of hierarchical, multi-level abstraction processes alluded to above.
{"title":"On generalization, language, interpretability and the future of geo-scientific machine learning","authors":"Hoshin V. Gupta","doi":"10.1016/j.envsoft.2025.106834","DOIUrl":"10.1016/j.envsoft.2025.106834","url":null,"abstract":"<div><div>There are several types of generalization ability that we may wish our models to be capable of. All but the most basic of these require the representation to be suitably interpretable so that it can provide meaningful support for scenario analysis, scientific reasoning, and decision making under system non-stationarity and model transfer. However interpretability of a model can only be meaningfully understood in the context of the ‘<em>language’</em> used for its construction. In this regard it is important to recognize that, while machine-learning-based (MLB) models tend to prioritize accuracy and precision (in service of predictive performance) and physics-based (PB) models tend to emphasize physical/geo-scientific interpretability (in service of understanding), their learned representations are actually based in related but somewhat different languages, levels of linguistic abstraction, and grammatical rules.</div><div>Importantly, these differences are not fundamentally necessary. It is my opinion that the future of geo-scientific ML need not compromise accuracy and precision to achieve improved understanding. Instead, we must develop “<em>telescopic</em>” hierarchical representations that prioritize “<em>learning from data</em>” at their fundamental levels, while simultaneously enabling “<em>geo-scientific abstraction</em>” so that higher-level interpretable and understandable representations can be extracted by directed compression. Ultimately, the geosciences will benefit from a specific kind of “<em>interpretable generative modeling</em>” that can learn how to construct causal and/or understandable representations of the underlying physical data generating processes from data, and that can facilitate the kind of hierarchical, multi-level abstraction processes alluded to above.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106834"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732499","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 : 2026-02-01Epub Date: 2025-12-11DOI: 10.1016/j.envsoft.2025.106831
Rui Tan , Geng Guo , Kaiwen Huang , Zicheng Liu , Chaorui Wang , Jie Lin , Yizhong Huang
Absence of high-resolution spatial data on Soil and water conservation measures (SWCM) hampers the accuracy of erosion modeling, particularly in regions with complex terrain and frequent land use/cover changes (LUCC). This study integrated multi-source remote sensing (RS), field surveys, and visual interpretation to map SWCM distribution and estimate soil erosion. It further quantified the response of erosion to LUCC. Soil erosion conditions have improved, with an average annual decrease in erosion modulus of 0.51 % and a total reduction of approximately 9.5 × 105 t. LUCC was characterized by cropland reduction, expansion of garden, and increasing landscape fragmentation. Garden development enhances economic returns but may exacerbate erosion when vegetation cover is insufficient. Nonetheless, under similar conservation intensity, slope, and elevation, conversion of cropland or bare land to woodland or garden effectively reduces erosion. The findings provide a new perspective for evaluating soil erosion in fragmented mountainous landscapes with complex management measures.
{"title":"Integrating field surveys and visual interpretation to enhance CSLE model of soil erosion response to LUCC in Southwest China","authors":"Rui Tan , Geng Guo , Kaiwen Huang , Zicheng Liu , Chaorui Wang , Jie Lin , Yizhong Huang","doi":"10.1016/j.envsoft.2025.106831","DOIUrl":"10.1016/j.envsoft.2025.106831","url":null,"abstract":"<div><div>Absence of high-resolution spatial data on Soil and water conservation measures (SWCM) hampers the accuracy of erosion modeling, particularly in regions with complex terrain and frequent land use/cover changes (LUCC). This study integrated multi-source remote sensing (RS), field surveys, and visual interpretation to map SWCM distribution and estimate soil erosion. It further quantified the response of erosion to LUCC. Soil erosion conditions have improved, with an average annual decrease in erosion modulus of 0.51 % and a total reduction of approximately 9.5 × 10<sup>5</sup> t. LUCC was characterized by cropland reduction, expansion of garden, and increasing landscape fragmentation. Garden development enhances economic returns but may exacerbate erosion when vegetation cover is insufficient. Nonetheless, under similar conservation intensity, slope, and elevation, conversion of cropland or bare land to woodland or garden effectively reduces erosion. The findings provide a new perspective for evaluating soil erosion in fragmented mountainous landscapes with complex management measures.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106831"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732500","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 : 2026-02-01Epub Date: 2025-12-29DOI: 10.1016/j.envsoft.2025.106813
Samantha Ivings , James A. King , Alexander Roocroft , Patricio Ortiz , Toby Willis , Maria Val Martin , Hadi Arbabi , Giuliano Punzo
Urban air pollution from traffic poses serious public health risks. Pollution exposure can be minimised through traffic routing systems; these currently rely on detailed local environmental information, which is often difficult to collect or generalise within and across cities. Here, we introduce a new data-driven approach for ready application to different urban road networks by directly relating NO2 to traffic density in a time-dependent and weather-corrected manner. We demonstrate this application by comparing pollution-optimal routings, using our novel direct NO2/density approach, to the conventional traffic assignment minimising user travel time, in a case study of Sheffield, UK. There, we find user-optimal traffic flows result in 21% higher total NO2 concentrations than pollution-optimal routings, while saving only 9% in total travel time: an average of 0.3 min per road. Our generalisable framework offers a practical alternative to current emissions-based models for air-quality-aware traffic control and environmental zone planning.
{"title":"Origin–destination specific traffic emissions and data-driven NO2 pollution-optimal routing in urban environments","authors":"Samantha Ivings , James A. King , Alexander Roocroft , Patricio Ortiz , Toby Willis , Maria Val Martin , Hadi Arbabi , Giuliano Punzo","doi":"10.1016/j.envsoft.2025.106813","DOIUrl":"10.1016/j.envsoft.2025.106813","url":null,"abstract":"<div><div>Urban air pollution from traffic poses serious public health risks. Pollution exposure can be minimised through traffic routing systems; these currently rely on detailed local environmental information, which is often difficult to collect or generalise within and across cities. Here, we introduce a new data-driven approach for ready application to different urban road networks by directly relating NO<sub>2</sub> to traffic density in a time-dependent and weather-corrected manner. We demonstrate this application by comparing pollution-optimal routings, using our novel direct NO<sub>2</sub>/density approach, to the conventional traffic assignment minimising user travel time, in a case study of Sheffield, UK. There, we find user-optimal traffic flows result in 21% higher total NO<sub>2</sub> concentrations than pollution-optimal routings, while saving only 9% in total travel time: an average of 0.3 min per road. Our generalisable framework offers a practical alternative to current emissions-based models for air-quality-aware traffic control and environmental zone planning.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106813"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894782","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 : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.envsoft.2026.106859
Na Wen , Junyu Qi , Yue Wang , Gary W. Marek , Srinivasulu Ale , Puyu Feng , De Li Liu , Raghavan Srinivasan , Yong Chen
Elevated CO2 affect crop growth and water dynamics by altering stomatal conductance (gs, m s−1) and leaf area index (LAI). However, the effects on C3 and C4 crops under different water conditions remain unclear. This study employed a modified SWAT model, incorporating a nonlinear gs equation and a LAI function, to evaluate elevated CO2 impacts on actual evapotranspiration (ET), irrigation, and crop yields. Results indicated that solely elevated CO2 reduced ET by 6.8%–20.7% under irrigated conditions, but had no apparent effect on ET under dryland conditions. Elevated CO2 enhanced crop yields, with its effect more pronounced under dryland conditions. Under future climate scenarios (2041–2100), ET increased by 6.7%–9.4% for dryland crops, while irrigated winter wheat ET declined by 0.6%–8.6%. Future crop yields generally increased, except for irrigated sorghum, which declined by up to 11.9% under high emission scenario. C3 crops were more positive response to future climate than C4 crops.
CO2升高通过改变气孔导度（gs, m s−1）和叶面积指数（LAI）影响作物生长和水分动力学。然而，不同水分条件对C3和C4作物的影响尚不清楚。本研究采用改进的SWAT模型，结合非线性gs方程和LAI函数来评估CO2升高对实际蒸散发（ET）、灌溉和作物产量的影响。结果表明，在灌溉条件下，CO2浓度升高可使土壤蒸散发减少6.8% ~ 20.7%，而在旱地条件下，CO2浓度升高对土壤蒸散发无明显影响。二氧化碳浓度升高可提高作物产量，其影响在旱地条件下更为明显。在未来气候情景下（2041-2100），旱地作物的蒸散发增加6.7% ~ 9.4%，而灌溉冬小麦的蒸散发减少0.6% ~ 8.6%。在高排放情景下，除灌溉高粱产量下降高达11.9%外，未来作物产量普遍增加。C3作物对未来气候的响应比C4作物更积极。
{"title":"Effects of rising CO2 concentrations on water dynamics and yields for C3 and C4 crops under both irrigated and dryland conditions in the Texas High Plains","authors":"Na Wen , Junyu Qi , Yue Wang , Gary W. Marek , Srinivasulu Ale , Puyu Feng , De Li Liu , Raghavan Srinivasan , Yong Chen","doi":"10.1016/j.envsoft.2026.106859","DOIUrl":"10.1016/j.envsoft.2026.106859","url":null,"abstract":"<div><div>Elevated CO<sub>2</sub> affect crop growth and water dynamics by altering stomatal conductance (g<sub>s</sub>, m s<sup>−1</sup>) and leaf area index (LAI). However, the effects on C3 and C4 crops under different water conditions remain unclear. This study employed a modified SWAT model, incorporating a nonlinear g<sub>s</sub> equation and a LAI function, to evaluate elevated CO<sub>2</sub> impacts on actual evapotranspiration (ET), irrigation, and crop yields. Results indicated that solely elevated CO<sub>2</sub> reduced ET by 6.8%–20.7% under irrigated conditions, but had no apparent effect on ET under dryland conditions. Elevated CO<sub>2</sub> enhanced crop yields, with its effect more pronounced under dryland conditions. Under future climate scenarios (2041–2100), ET increased by 6.7%–9.4% for dryland crops, while irrigated winter wheat ET declined by 0.6%–8.6%. Future crop yields generally increased, except for irrigated sorghum, which declined by up to 11.9% under high emission scenario. C3 crops were more positive response to future climate than C4 crops.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106859"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903155","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 : 2026-02-01Epub Date: 2025-12-16DOI: 10.1016/j.envsoft.2025.106835
Haozheng Yin , Yu Cao , Linlin Liu , Dan Chen , Qiong Zhang
Meteorological observation plays a critical role in ensuring safety, promoting agricultural development, optimizing energy management, and achieving sustainable development. Although image recognition methods based on deep learning have made notable progress, existing models still face challenges in complex weather scenarios, such as insufficient feature extraction, inadequate utilization of scale information, and poor robustness to interference. To address these issues, this study proposes a novel deep learning model based on EfficientNetV2-CBAM-PANet. By leveraging transfer learning, the training efficiency and accuracy of the EfficientNetV2 pretrained model are enhanced. The integration of the CBAM attention mechanism improves the perception of meteorological features, while the PANet structure enables multi-level feature fusion. Experimental results demonstrate that the proposed model achieves a recognition accuracy of 97.6% on a self-constructed dataset, indicating strong classification capability across various weather conditions and providing useful insights for future research in weather image classification and forecasting.
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