Environmental Modelling & Software最新文献

{"title":"A feature-level ensemble framework for improving daily PM2.5 estimation across the contiguous United States (2000–2023)","authors":"Yu Ding, Jiaxin Dong, Mengfan Teng, Shiyao Meng, Jie Yang, Siwei Li","doi":"10.1016/j.envsoft.2025.106733","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106733","url":null,"abstract":"Accurate PM<ce:inf loc=\"post\">2.5</ce:inf> surface concentration estimates are vital for air quality management and exposure assessment. This study introduces a novel feature-level ensemble framework to enhance daily PM<ce:inf loc=\"post\">2.5</ce:inf> estimation across the contiguous United States from 2000 to 2023. The framework integrates multiple XGBoost models trained with diverse temporal features, including calendar encodings and physically derived indicators like rolling averages and change rates from reanalysis PM<ce:inf loc=\"post\">2.5</ce:inf>. By capturing complementary pollution dynamics, the ensemble outperforms models using only calendar features. Under spatial cross-validation, R<ce:sup loc=\"post\">2</ce:sup> increases from 0.64 to 0.70 and RMSE drops from 7.87 to 7.31 μg/m<ce:sup loc=\"post\">3</ce:sup>. Temporal extrapolation also improves, with △R<ce:sup loc=\"post\">2</ce:sup> gains of 0.08 (historical backcasting) and 0.07 (future forecasting). These results demonstrate the framework's robustness, generalizability, and value for long-term PM<ce:inf loc=\"post\">2.5</ce:inf> monitoring, epidemiological research, and data-driven air quality policymaking.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"116 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262077","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}

{"title":"Comprehensive framework for long-term reservoir management under deep uncertainty","authors":"Jiajia Huang ,&nbsp;Wenyan Wu ,&nbsp;Holger R. Maier ,&nbsp;Justin Hughes ,&nbsp;Quan J. Wang ,&nbsp;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}

{"title":"DSTMA-BLSTM algorithm for roadside air pollutant time series prediction and sensitivity analysis","authors":"Yusheng Qin ,&nbsp;Xin Han ,&nbsp;Hanwen Shi ,&nbsp;Xiangxian Li ,&nbsp;Jingjing Tong ,&nbsp;Minguang Gao ,&nbsp;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, 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.</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}

{"title":"An acoustic inversion-based flow measurement model in 3D hydrodynamic systems","authors":"Jiwei Li ,&nbsp;Lingyun Qiu ,&nbsp;Zhongjing Wang ,&nbsp;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}

{"title":"STABLE: An open-source atmospheric blocking and subtropical ridge detection system","authors":"Miguel M. Lima ,&nbsp;Pedro M. Sousa ,&nbsp;Tahimy Fuentes-Alvarez ,&nbsp;Carlos Ordóñez ,&nbsp;Ricardo García-Herrera ,&nbsp;David Barriopedro ,&nbsp;Pedro M.M. Soares ,&nbsp;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}

{"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":"https://doi.org/10.1016/j.envsoft.2025.106735","url":null,"abstract":"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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"26 1","pages":""},"PeriodicalIF":4.9,"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}

{"title":"HydroQuantum: A new quantum-driven Python package for hydrological simulation","authors":"Mostafa Saberian ,&nbsp;Nima Zafarmomen ,&nbsp;Adarsha Neupane ,&nbsp;Krishna Panthi ,&nbsp;Vidya Samadi","doi":"10.1016/j.envsoft.2025.106736","DOIUrl":"10.1016/j.envsoft.2025.106736","url":null,"abstract":"<div><div>This research aims to leverage the power of quantum computing for hydrological simulation. A new “HydroQuantum” Python package is created to facilitate this implementation, enabling researchers to explore the potential of quantum algorithms in hydrological simulations. “HydroQuantum” was implemented for daily streamflow and stream water temperature (SWT) simulations across continental US. The package includes Variational Quantum Circuits (VQC), a fully quantum Long Short-Term Memory network (QLSTM), and a hybrid quantum-classical LSTM. All algorithms were benchmarked against classical LSTM and trained and tested during 2000–2014 and 2015–2022 for daily streamflow and SWT simulations, respectively. While QLSTM showed impressive results in capturing temporal dependencies in streamflow data, it consistently underperformed classical LSTM for SWT simulation. Sensitivity analysis further revealed that precipitation and snow-water equivalent were two major contributors to quantum-driven simulation. This research explores the potential of quantum computing in complex time series simulations, leading to breakthroughs in hydrological modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106736"},"PeriodicalIF":4.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262082","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}

{"title":"SMARTscape: An agent-based model to analyse rural landscapes multifunctionality","authors":"Clémence Vannier ,&nbsp;Klervi Choulette ,&nbsp;Dan Richards ,&nbsp;Alan Heays ,&nbsp;Alexander Herzig ,&nbsp;Sandra Lavorel","doi":"10.1016/j.envsoft.2025.106732","DOIUrl":"10.1016/j.envsoft.2025.106732","url":null,"abstract":"<div><div>Land-use changes and behavioural adaptation will be essential in global efforts to address environmental crises. For this, landscape multifunctionality will become critical to support a wide range of ecosystem services (ES), land productivity, and economic profitability. This study aims to explore pathways towards achieving sustainable food production while enabling multifunctionality. We developed SMARTscape, an empirically stylized agent-based model (ABM) developed to simulate interactions between land-use patterns, ES supply, and decision-making across a range of stylized landscapes inspired by New Zealand rural systems. SMARTscape combines three agent behavioural types—business-as-usual (BAU), profit-oriented (PO), and environment-oriented (EO)—with three decision-making rules of increasing social complexity. Agents interact with four collective entities (neighbours, peers, industry bodies, government), allowing simulation of bottom-up and top-down influence mechanisms. We designed a factorial experiment of 60 scenarios, from the combination of five landscape compositions, three decision rules, four agent-population behaviour mixes, and tested cluster size effect (on 10 cluster sizes from a very fragmented landscape to very aggregated) on 15 out of the 60 scenarios. Results show that land-use intensity and fragmentation are primary drivers of multifunctionality. However, profit-oriented behaviours can outperform environment-oriented strategies in intensive contexts, particularly through adoption of perennial systems, and behavioural diversity enhances trade-off management. Combined decision-making consistently led to higher multifunctionality, highlighting the importance of social influence. SMARTscape demonstrates the value of integrating social and spatial dynamics in a flexible modelling platform, offering both theoretical insights and a tool for exploring sustainable land-use transitions in data-poor but policy-relevant contexts.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106732"},"PeriodicalIF":4.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262083","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}

{"title":"Use of regional sensitivity analysis for diagnosing parsimony of models: A water model case study","authors":"Ratnasingham Srikanthan,&nbsp;Quan J. Wang,&nbsp;Yuhang Zhang","doi":"10.1016/j.envsoft.2025.106727","DOIUrl":"10.1016/j.envsoft.2025.106727","url":null,"abstract":"<div><div>Sensitivity analysis is usually carried out to examine how the variation in the output of a model can be attributed to variations of its input variables or model parameters. In this study, classical regional sensitivity analysis (RSA) is used in a novel way to determine parameter dependency in a model and subsequently to make the model more parsimonious. Parameter dependencies are identified through visual inspection of pairwise correlations and quantified using the mutual information index (MII) within the behavioural region of RSA. The dependence information is then used to reduce the number of parameters in the model. This approach was demonstrated through a case study with the WAPABA model, a monthly water balance model with five parameters: <em>α</em>₁ and <em>α</em>₂, which represent catchment consumption and evapotranspiration curve parameters; <em>β</em>, which governs the proportion of catchment yield that becomes groundwater; <em>S</em>_max, the maximum water-holding capacity of the soil store; and <em>K</em>, the groundwater store time constant that controls baseflow recession. Application of this approach resulted in a more parsimonious model with only three parameters (<em>β</em>, <em>S</em>_max, <em>K</em>), while <em>α</em>₁ and <em>α</em>₂ were shared and fixed using predefined values due to their dependency on other parameters. In addition, robustness of the model was investigated by calibrating the model using different lengths of historical data, and it was found that the model can be used reliably even with very short length of historical data. Although illustrated with WAPABA, the proposed approach is general and can be applied to other hydrological and system models where parameter parsimony is desirable.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106727"},"PeriodicalIF":4.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262084","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}

{"title":"Time-series prediction of water quality at multiple points in water sources","authors":"Dongsheng Wang,&nbsp;Congcong Zhang,&nbsp;Fei Wu","doi":"10.1016/j.envsoft.2025.106726","DOIUrl":"10.1016/j.envsoft.2025.106726","url":null,"abstract":"<div><div>Effective monitoring and prediction of water quality is essential for the management and protection of aquatic ecosystems. Although great progress has been made, there still exist numerous challenging cases, such as the lack of consideration for global changes in water quality and comprehensive factors affecting it. To address this limitation, a Multiple-View Online Long Short-Term Memory (MV-Online-LSTM) model was proposed, which integrated multi-view learning with online sequential adaptation. Each monitoring points were treated as a separate view processed by dedicated LSTM sub-networks, which were fused to capture spatial dependencies and temporal dynamics. An online learning strategy enabled real-time model updates, enhancing adaptability to environmental changes. Experiments demonstrated that MV-Online-LSTM achieved <em>R</em>^<em>2</em> values consistently above 0.96 across six key water quality indicators, significantly outperforming baseline models. These findings underscore the effectiveness of the proposed model in dynamic, multivariate water quality forecasting, offering a practical solution for real-time environmental monitoring applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106726"},"PeriodicalIF":4.6,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261956","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}