Pub Date : 2025-10-10DOI: 10.1016/j.envsoft.2025.106740
Jiajia Huang , Wenyan Wu , Holger R. Maier , Justin Hughes , Quan J. Wang , Yuan Cao
Reservoir systems play a crucial role in providing essential services such as water supply, flood protection, and energy generation. However, reservoir management is highly complex due to (i) multiple conflicting management goals, (ii) long-term changes in water availability and demand over the long life span of these systems, and (iii) deep uncertainty. While some of these challenges have been addressed in previous studies, there is a lack of a comprehensive framework that can maximize the co-benefits of addressing these challenges in an integrated manner. Such an optimization framework has been developed in this study. By incorporating deep uncertainty, the causal relationships between decisions, system performance, and robustness can be explored. By adapting both operation policy and infrastructure upgrade decisions to long-term changes, infrastructure investments can be reduced without compromising system performance. By explicitly accounting for multiple conflicting objectives, the framework also provides a platform for negotiation during the decision-making process.
{"title":"Comprehensive framework for long-term reservoir management under deep uncertainty","authors":"Jiajia Huang , Wenyan Wu , Holger R. Maier , Justin Hughes , Quan J. Wang , Yuan Cao","doi":"10.1016/j.envsoft.2025.106740","DOIUrl":"10.1016/j.envsoft.2025.106740","url":null,"abstract":"<div><div>Reservoir systems play a crucial role in providing essential services such as water supply, flood protection, and energy generation. However, reservoir management is highly complex due to (i) multiple conflicting management goals, (ii) long-term changes in water availability and demand over the long life span of these systems, and (iii) deep uncertainty. While some of these challenges have been addressed in previous studies, there is a lack of a comprehensive framework that can maximize the co-benefits of addressing these challenges in an integrated manner. Such an optimization framework has been developed in this study. By incorporating deep uncertainty, the causal relationships between decisions, system performance, and robustness can be explored. By adapting both operation policy and infrastructure upgrade decisions to long-term changes, infrastructure investments can be reduced without compromising system performance. By explicitly accounting for multiple conflicting objectives, the framework also provides a platform for negotiation during the decision-making process.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106740"},"PeriodicalIF":4.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262075","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 : 2025-10-10DOI: 10.1016/j.envsoft.2025.106716
Giang V. Nguyen , Chien Pham Van , Vinh Ngoc Tran , Linh Nguyen Van , Giha Lee
Timely flood prediction is critical for mitigating risks under the growing impacts of climate change. Traditional physics-based hydrodynamic models, while effective at capturing flood dynamics, are limited by high computational demands, restricting real-time applicability. This study presents a hybrid framework that integrates machine learning (ML) with physics-based modeling to enable efficient real-time flood forecasting. Physics-based simulations provide detailed inundation information, while ML models serve as fast surrogate predictors. Applied to the Cambodia floodplain — a region highly prone to seasonal flooding — the surrogate models were trained on outputs from TELEMAC simulations. Explainable AI was employed to interpret model decision-making. Results show that the hybrid approach achieves substantial computational efficiency while preserving accuracy. The best surrogate attained R 0.97 and KGE 0.91, reducing simulation time by over 70-fold compared with TELEMAC. Incorporating geographic features such as latitude and longitude further enhanced predictive skill, particularly in flat floodplain settings.
{"title":"Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models","authors":"Giang V. Nguyen , Chien Pham Van , Vinh Ngoc Tran , Linh Nguyen Van , Giha Lee","doi":"10.1016/j.envsoft.2025.106716","DOIUrl":"10.1016/j.envsoft.2025.106716","url":null,"abstract":"<div><div>Timely flood prediction is critical for mitigating risks under the growing impacts of climate change. Traditional physics-based hydrodynamic models, while effective at capturing flood dynamics, are limited by high computational demands, restricting real-time applicability. This study presents a hybrid framework that integrates machine learning (ML) with physics-based modeling to enable efficient real-time flood forecasting. Physics-based simulations provide detailed inundation information, while ML models serve as fast surrogate predictors. Applied to the Cambodia floodplain — a region highly prone to seasonal flooding — the surrogate models were trained on outputs from TELEMAC simulations. Explainable AI was employed to interpret model decision-making. Results show that the hybrid approach achieves substantial computational efficiency while preserving accuracy. The best surrogate attained R <span><math><mo>=</mo></math></span> 0.97 and KGE <span><math><mo>=</mo></math></span> 0.91, reducing simulation time by over 70-fold compared with TELEMAC. Incorporating geographic features such as latitude and longitude further enhanced predictive skill, particularly in flat floodplain settings.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106716"},"PeriodicalIF":4.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314964","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 : 2025-10-10DOI: 10.1016/j.envsoft.2025.106738
Hongfei Li , Jun Yang , Jiaxing Xin , Wenbo Yu , Jiayi Ren , Huisheng Yu , Xiangming Xiao , Jianhong (Cecilia) Xia
The urban thermal environment is becoming increasingly severe. In this study, we integrated eXtreme Gradient Boosting with the SHapley Additive exPlanations method to investigate the effects of various urban factor indexes (UFIs) on land surface temperature (LST) at both block and grid scales. Additionally, we examined the differences in LST and its driving factors across local climate zones (LCZs) at the grid scale. The results show that LST is higher in central areas than in peripheral ones during summer and autumn, but this pattern is reversed in spring and winter. LST varies significantly across LCZs, with the normalized difference built-up index, normalized difference vegetation index (NDVI), and Shannon's diversity index (SHDI) identified as the main contributors. The sky view factor inhibits LST at the block scale but promotes it at the grid scale. The impacts of UFIs follow the seasonal trend: summer > spring > autumn > winter. LST responses to UFIs exhibit similar trends across scales, showing specific warming or cooling thresholds—for example, a cooling effect when SHDI exceeds 0.65, and a warming effect when building density exceeds 20 % (summer and autumn) or 40 % (spring and winter). Significant cooling occurs only when NDVI exceeds 0.4; however, NDVI generally remains low in all seasons except summer. High-contribution UFIs typically exhibit the strongest interaction effects with artificial factor indicators.
{"title":"Investigating the effect of urban form on land surface temperature at block and grid scales based on XGBoost-SHAP","authors":"Hongfei Li , Jun Yang , Jiaxing Xin , Wenbo Yu , Jiayi Ren , Huisheng Yu , Xiangming Xiao , Jianhong (Cecilia) Xia","doi":"10.1016/j.envsoft.2025.106738","DOIUrl":"10.1016/j.envsoft.2025.106738","url":null,"abstract":"<div><div>The urban thermal environment is becoming increasingly severe. In this study, we integrated eXtreme Gradient Boosting with the SHapley Additive exPlanations method to investigate the effects of various urban factor indexes (UFIs) on land surface temperature (LST) at both block and grid scales. Additionally, we examined the differences in LST and its driving factors across local climate zones (LCZs) at the grid scale. The results show that LST is higher in central areas than in peripheral ones during summer and autumn, but this pattern is reversed in spring and winter. LST varies significantly across LCZs, with the normalized difference built-up index, normalized difference vegetation index (NDVI), and Shannon's diversity index (SHDI) identified as the main contributors. The sky view factor inhibits LST at the block scale but promotes it at the grid scale. The impacts of UFIs follow the seasonal trend: summer > spring > autumn > winter. LST responses to UFIs exhibit similar trends across scales, showing specific warming or cooling thresholds—for example, a cooling effect when SHDI exceeds 0.65, and a warming effect when building density exceeds 20 % (summer and autumn) or 40 % (spring and winter). Significant cooling occurs only when NDVI exceeds 0.4; however, NDVI generally remains low in all seasons except summer. High-contribution UFIs typically exhibit the strongest interaction effects with artificial factor indicators.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106738"},"PeriodicalIF":4.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314963","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 : 2025-10-10DOI: 10.1016/j.envsoft.2025.106728
Yiteng Zhang , Arjun Pakrashi , Soumyabrata Dev
Accurate CO emission prediction is essential for climate policy, yet existing models often fail to capture regional variability and temporal patterns. This study introduces a hybrid machine learning framework combining Random Forest, XGBoost, and LSTM with temporal feature engineering — lagged features and rolling statistics — to improve emission forecasts across diverse regions (Japan, Brazil, Ireland, Hawaii). Using multi-regional greenhouse gas datasets (CO, CH, NO, CO, SF) from NOAA monitoring stations, the framework leverages inter-gas correlations rather than socio-economic proxies, enhancing predictive accuracy. Results reveal that XGBoost and Random Forest perform best in volatile regions like Brazil (MSE = 1.72), while LSTM excels in trend-driven settings such as Japan, reducing errors by 80% with lagged features. Incorporating a 7-day rolling mean and standard deviation stabilizes performance, lowering short-term forecast uncertainty by 30%–40%. By combining methodological innovation with strong cross-regional generalization, this approach offers policymakers a scalable, adaptable tool for emission monitoring and climate mitigation.
{"title":"Multi-regional CO2 emission forecasting using advanced machine learning and temporal feature engineering","authors":"Yiteng Zhang , Arjun Pakrashi , Soumyabrata Dev","doi":"10.1016/j.envsoft.2025.106728","DOIUrl":"10.1016/j.envsoft.2025.106728","url":null,"abstract":"<div><div>Accurate CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission prediction is essential for climate policy, yet existing models often fail to capture regional variability and temporal patterns. This study introduces a hybrid machine learning framework combining Random Forest, XGBoost, and LSTM with temporal feature engineering — lagged features and rolling statistics — to improve emission forecasts across diverse regions (Japan, Brazil, Ireland, Hawaii). Using multi-regional greenhouse gas datasets (CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>, N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O, CO, SF<span><math><msub><mrow></mrow><mrow><mn>6</mn></mrow></msub></math></span>) from NOAA monitoring stations, the framework leverages inter-gas correlations rather than socio-economic proxies, enhancing predictive accuracy. Results reveal that XGBoost and Random Forest perform best in volatile regions like Brazil (MSE = 1.72), while LSTM excels in trend-driven settings such as Japan, reducing errors by 80% with lagged features. Incorporating a 7-day rolling mean and standard deviation stabilizes performance, lowering short-term forecast uncertainty by 30%–40%. By combining methodological innovation with strong cross-regional generalization, this approach offers policymakers a scalable, adaptable tool for emission monitoring and climate mitigation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106728"},"PeriodicalIF":4.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145359269","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 : 2025-10-09DOI: 10.1016/j.envsoft.2025.106717
Adrian Huerta , Stefan Brönnimann , Martín de Luis , Santiago Beguería , Roberto Serrano-Notivoli
Reconstructing high-quality daily precipitation series is essential for climate studies, hydrological modeling, and environmental applications. This work presents a new version of reddPrec, a versatile and flexible R package designed to reconstruct precipitation datasets through standard quality control, gap-filling, and grid creation procedures. The update introduces greater flexibility in spatial modeling, inclusion of dynamic covariates, and new modules for enhanced quality control and homogenization. Daily precipitation can now be predicted using machine learning approaches within a flexible, user-friendly framework, allowing users to select modeling approaches and customize settings. We demonstrate its capabilities through case studies in Switzerland and Spain, evaluating improvements in reconstruction accuracy, quality control, and homogenization. Enhanced quality control and homogenization procedures were specifically validated to ensure reliable adjustment and consistency of precipitation series. Overall, reddPrec provides a comprehensive and reliable tool for reconstructing precipitation series, supporting the creation of high-quality datasets for climate research and related fields.
{"title":"Enhancing daily precipitation reconstruction: An improved version of the reddPrec R package","authors":"Adrian Huerta , Stefan Brönnimann , Martín de Luis , Santiago Beguería , Roberto Serrano-Notivoli","doi":"10.1016/j.envsoft.2025.106717","DOIUrl":"10.1016/j.envsoft.2025.106717","url":null,"abstract":"<div><div>Reconstructing high-quality daily precipitation series is essential for climate studies, hydrological modeling, and environmental applications. This work presents a new version of reddPrec, a versatile and flexible R package designed to reconstruct precipitation datasets through standard quality control, gap-filling, and grid creation procedures. The update introduces greater flexibility in spatial modeling, inclusion of dynamic covariates, and new modules for enhanced quality control and homogenization. Daily precipitation can now be predicted using machine learning approaches within a flexible, user-friendly framework, allowing users to select modeling approaches and customize settings. We demonstrate its capabilities through case studies in Switzerland and Spain, evaluating improvements in reconstruction accuracy, quality control, and homogenization. Enhanced quality control and homogenization procedures were specifically validated to ensure reliable adjustment and consistency of precipitation series. Overall, reddPrec provides a comprehensive and reliable tool for reconstructing precipitation series, supporting the creation of high-quality datasets for climate research and related fields.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106717"},"PeriodicalIF":4.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314966","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 : 2025-10-09DOI: 10.1016/j.envsoft.2025.106730
Yusheng Qin , Xin Han , Hanwen Shi , Xiangxian Li , Jingjing Tong , Minguang Gao , Yujun Zhang
Road traffic pollution greatly affects urban air quality, making accurate prediction of roadside pollutant concentrations essential for effective environmental management. This study presents a novel DSTMA-BLSTM algorithm, which combines Dynamic Shared and Task-specific Multi-head Attention (DSTMA) with Bidirectional Long Short-Term Memory (BLSTM) networks, to forecast temporal changes in roadside pollutants and analyze their sensitivity. Using real monitoring data, the study identifies wind speed and the counts of gasoline and diesel vehicles as critical factors influencing roadside pollutant levels. The model achieved outstanding predictive performance for NO, NO2, and CO2, with R2 values of 0.959, 0.944, and 0.949, respectively, demonstrating its exceptional ability to capture the dynamics of traffic-related pollutants. This work not only establishes the DSTMA-BLSTM model as a powerful tool for multi-pollutant forecasting but also proposes a fresh perspective for jointly predicting traffic and non-traffic-related pollutants in future research.
{"title":"DSTMA-BLSTM algorithm for roadside air pollutant time series prediction and sensitivity analysis","authors":"Yusheng Qin , Xin Han , Hanwen Shi , Xiangxian Li , Jingjing Tong , Minguang Gao , Yujun Zhang","doi":"10.1016/j.envsoft.2025.106730","DOIUrl":"10.1016/j.envsoft.2025.106730","url":null,"abstract":"<div><div>Road traffic pollution greatly affects urban air quality, making accurate prediction of roadside pollutant concentrations essential for effective environmental management. This study presents a novel DSTMA-BLSTM algorithm, which combines Dynamic Shared and Task-specific Multi-head Attention (DSTMA) with Bidirectional Long Short-Term Memory (BLSTM) networks, to forecast temporal changes in roadside pollutants and analyze their sensitivity. Using real monitoring data, the study identifies wind speed and the counts of gasoline and diesel vehicles as critical factors influencing roadside pollutant levels. The model achieved outstanding predictive performance for NO, NO<sub>2</sub>, and CO<sub>2</sub>, with R<sup>2</sup> values of 0.959, 0.944, and 0.949, respectively, demonstrating its exceptional ability to capture the dynamics of traffic-related pollutants. This work not only establishes the DSTMA-BLSTM model as a powerful tool for multi-pollutant forecasting but also proposes a fresh perspective for jointly predicting traffic and non-traffic-related pollutants in future research.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106730"},"PeriodicalIF":4.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262079","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 : 2025-10-09DOI: 10.1016/j.envsoft.2025.106714
Jiwei Li , Lingyun Qiu , Zhongjing Wang , Hui Yu
This study extends an established two-dimensional flow measurement approach to three-dimensional scenarios, addressing the growing need for accurate and efficient non-contact measurement techniques in complex hydrodynamic environments. Compared to conventional Acoustic Doppler Current Profilers (ADCPs) and remote sensing-based flow monitoring, the proposed method enables high-resolution, continuous water velocity measurement, making it well-suited for hazardous environments such as floods, strong currents, and sediment-laden rivers. Building upon the original approach, we develop an enhanced model that incorporates multiple emission directions and flexible configurations of receivers. These advancements improve the adaptability and accuracy of the method when applied to three-dimensional flow fields. To evaluate its feasibility, extensive numerical simulations are conducted to mimic real-world hydrodynamic conditions. The results demonstrate that the proposed method effectively handles diverse and complex flow field configurations, highlighting its potential for practical applications in water resource management and hydraulic engineering.
{"title":"An acoustic inversion-based flow measurement model in 3D hydrodynamic systems","authors":"Jiwei Li , Lingyun Qiu , Zhongjing Wang , Hui Yu","doi":"10.1016/j.envsoft.2025.106714","DOIUrl":"10.1016/j.envsoft.2025.106714","url":null,"abstract":"<div><div>This study extends an established two-dimensional flow measurement approach to three-dimensional scenarios, addressing the growing need for accurate and efficient non-contact measurement techniques in complex hydrodynamic environments. Compared to conventional Acoustic Doppler Current Profilers (ADCPs) and remote sensing-based flow monitoring, the proposed method enables high-resolution, continuous water velocity measurement, making it well-suited for hazardous environments such as floods, strong currents, and sediment-laden rivers. Building upon the original approach, we develop an enhanced model that incorporates multiple emission directions and flexible configurations of receivers. These advancements improve the adaptability and accuracy of the method when applied to three-dimensional flow fields. To evaluate its feasibility, extensive numerical simulations are conducted to mimic real-world hydrodynamic conditions. The results demonstrate that the proposed method effectively handles diverse and complex flow field configurations, highlighting its potential for practical applications in water resource management and hydraulic engineering.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106714"},"PeriodicalIF":4.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262081","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 : 2025-10-09DOI: 10.1016/j.envsoft.2025.106729
Miguel M. Lima , Pedro M. Sousa , Tahimy Fuentes-Alvarez , Carlos Ordóñez , Ricardo García-Herrera , David Barriopedro , Pedro M.M. Soares , Ricardo M. Trigo
The SubTropical Atmospheric ridge and BLocking Event (STABLE) algorithm is an open-source Python-based tool for the tracking of high-pressure systems, distinguishing between subtropical ridges and types of atmospheric blockings. The output includes 2-D daily spatial structures allowing the spatio-temporal tracking of high-pressure events, as well as compiled statistics of their characteristics. Building upon state-of-the-art geopotential gradient methodology, STABLE introduces customizable changes to refine the structure identification and classification, improve usability, and extend the algorithm's adaptability. Key inclusions are a zonally varying subtropical boundary, refined criteria for polar blocking, and an advanced classification scheme for hybrid blocking events. Validation with reanalysis data for the 1991–2020 period demonstrates STABLE's ability to capture high-pressure events and improved accuracy while preserving replicability of earlier results. STABLE offers a user-friendly framework for customizable studies focusing on atmospheric dynamics and climate variability, historical trends and future projections or region-specific impact assessments.
{"title":"STABLE: An open-source atmospheric blocking and subtropical ridge detection system","authors":"Miguel M. Lima , Pedro M. Sousa , Tahimy Fuentes-Alvarez , Carlos Ordóñez , Ricardo García-Herrera , David Barriopedro , Pedro M.M. Soares , Ricardo M. Trigo","doi":"10.1016/j.envsoft.2025.106729","DOIUrl":"10.1016/j.envsoft.2025.106729","url":null,"abstract":"<div><div>The <strong>S</strong>ub<strong>T</strong>ropical <strong>A</strong>tmospheric ridge and <strong>BL</strong>ocking <strong>E</strong>vent (STABLE) algorithm is an open-source Python-based tool for the tracking of high-pressure systems, distinguishing between subtropical ridges and types of atmospheric blockings. The output includes 2-D daily spatial structures allowing the spatio-temporal tracking of high-pressure events, as well as compiled statistics of their characteristics. Building upon state-of-the-art geopotential gradient methodology, STABLE introduces customizable changes to refine the structure identification and classification, improve usability, and extend the algorithm's adaptability. Key inclusions are a zonally varying subtropical boundary, refined criteria for polar blocking, and an advanced classification scheme for hybrid blocking events. Validation with reanalysis data for the 1991–2020 period demonstrates STABLE's ability to capture high-pressure events and improved accuracy while preserving replicability of earlier results. STABLE offers a user-friendly framework for customizable studies focusing on atmospheric dynamics and climate variability, historical trends and future projections or region-specific impact assessments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106729"},"PeriodicalIF":4.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262080","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 : 2025-10-09DOI: 10.1016/j.envsoft.2025.106735
T. Lazzarin , L. Xu , S. Yuan , A.J.F. Hoitink , D.P. Viero
At river confluences, transverse density gradients induce secondary currents that interact with those generated by streamline curvature, affecting flow patterns and sediment dynamics. Here, a hydro- and morphodynamic two-dimensional numerical model is enhanced to account for density-driven secondary flows. The model solves the Shallow Water Equations coupled with transport equations for water temperature and streamwise angular momentum, driven by both streamline curvature and spanwise density gradients. A morphodynamic module computes bedload, suspended sediment transport, and the bed evolution. The model is tested against three-dimensional CFD results and applied to the Yangtze River and Poyang Lake confluence in both fixed and mobile bed modes. The results, which favorably compare to measured data, highlight the role of temperature dynamics in the pattern and intensity of secondary currents and their contribution in shaping the riverbed. The model allows for long-term morphodynamic simulations at low computational effort.
{"title":"Accounting for density-driven secondary flows at river confluences with a 2-D depth-averaged hydro-morphodynamic model","authors":"T. Lazzarin , L. Xu , S. Yuan , A.J.F. Hoitink , D.P. Viero","doi":"10.1016/j.envsoft.2025.106735","DOIUrl":"10.1016/j.envsoft.2025.106735","url":null,"abstract":"<div><div>At river confluences, transverse density gradients induce secondary currents that interact with those generated by streamline curvature, affecting flow patterns and sediment dynamics. Here, a hydro- and morphodynamic two-dimensional numerical model is enhanced to account for density-driven secondary flows. The model solves the Shallow Water Equations coupled with transport equations for water temperature and streamwise angular momentum, driven by both streamline curvature and spanwise density gradients. A morphodynamic module computes bedload, suspended sediment transport, and the bed evolution. The model is tested against three-dimensional CFD results and applied to the Yangtze River and Poyang Lake confluence in both fixed and mobile bed modes. The results, which favorably compare to measured data, highlight the role of temperature dynamics in the pattern and intensity of secondary currents and their contribution in shaping the riverbed. The model allows for long-term morphodynamic simulations at low computational effort.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106735"},"PeriodicalIF":4.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262078","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 : 2025-10-08DOI: 10.1016/j.envsoft.2025.106739
Weiliang Zhou, Dongmei Zhang, Man Xu
Urban land subsidence is a widespread geological disaster, threatening production and residents' lives. While synthetic aperture radar interferometry enables large-scale monitoring, traditional models often overlook spatial-temporal heterogeneity, reducing accuracy. To address this, we propose SMTG-Net (Spatial Mask Attention and Temporal Granularity Network), designed to extract spatial structural features and temporal dynamic patterns. It captures spatial local changes via a spatial factor mask matrix with a double threshold mechanism and employs dynamic graph convolution with a gated recurrent unit to extract dynamic spatial dependencies. The temporal decomposition mechanism decouples data into trend and periodic components, while the multi-granularity collaborative encoder learns global and local features. Using Sentinel-1A data from Jan 2020 to Aug 2023 in Wuhan, China, experiments show SMTG-Net outperforms GCN, GAT, STGCN, Graph WaveNet, and STTNs in RMSE, MAE, and MAPE. SMTG-Net effectively models spatial-temporal heterogeneity, delivering accurate predictions and offering a novel approach to urban subsidence monitoring.
城市地面沉降是一种广泛存在的地质灾害，严重威胁着城市生产和居民生活。虽然合成孔径雷达干涉测量技术可以实现大规模监测，但传统模型往往忽略了时空异质性，降低了精度。为了解决这个问题，我们提出了SMTG-Net (Spatial Mask Attention and Temporal Granularity Network)，旨在提取空间结构特征和时间动态模式。它通过具有双阈值机制的空间因子掩模矩阵捕获空间局部变化，并采用带门控循环单元的动态图卷积提取动态空间依赖关系。时间分解机制将数据解耦为趋势分量和周期分量，而多粒度协同编码器学习全局和局部特征。利用中国武汉2020年1月至2023年8月的Sentinel-1A数据，实验表明SMTG-Net在RMSE、MAE和MAPE方面优于GCN、GAT、STGCN、Graph WaveNet和STTNs。SMTG-Net有效地模拟了时空异质性，提供了准确的预测，并为城市沉降监测提供了新的方法。
{"title":"SMTG-Net: A spatiotemporal deep learning model for large-scale urban land subsidence prediction with heterogeneity awareness","authors":"Weiliang Zhou, Dongmei Zhang, Man Xu","doi":"10.1016/j.envsoft.2025.106739","DOIUrl":"10.1016/j.envsoft.2025.106739","url":null,"abstract":"<div><div>Urban land subsidence is a widespread geological disaster, threatening production and residents' lives. While synthetic aperture radar interferometry enables large-scale monitoring, traditional models often overlook spatial-temporal heterogeneity, reducing accuracy. To address this, we propose SMTG-Net (Spatial Mask Attention and Temporal Granularity Network), designed to extract spatial structural features and temporal dynamic patterns. It captures spatial local changes via a spatial factor mask matrix with a double threshold mechanism and employs dynamic graph convolution with a gated recurrent unit to extract dynamic spatial dependencies. The temporal decomposition mechanism decouples data into trend and periodic components, while the multi-granularity collaborative encoder learns global and local features. Using Sentinel-1A data from Jan 2020 to Aug 2023 in Wuhan, China, experiments show SMTG-Net outperforms GCN, GAT, STGCN, Graph WaveNet, and STTNs in RMSE, MAE, and MAPE. SMTG-Net effectively models spatial-temporal heterogeneity, delivering accurate predictions and offering a novel approach to urban subsidence monitoring.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106739"},"PeriodicalIF":4.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314967","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}
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