Environmental Modelling & Software最新文献_第4页

Data-driven approach to robust spatio-temporal assessment of carbon fluxes using Earth observation and ground-based data 利用地球观测和地面数据对碳通量进行稳健时空评估的数据驱动方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.envsoft.2026.106881

Artem Gorbarenko , Mikhail Gasanov , Elizaveta Gorbarenko , Polina Tregubova , Anna Petrovskaia , Usman Tasuev , Svetlana Illarionova , Dmitrii Shardrin , Evgeny Burnaev

{"title":"Data-driven approach to robust spatio-temporal assessment of carbon fluxes using Earth observation and ground-based data","authors":"Artem Gorbarenko , Mikhail Gasanov , Elizaveta Gorbarenko , Polina Tregubova , Anna Petrovskaia , Usman Tasuev , Svetlana Illarionova , Dmitrii Shardrin , Evgeny Burnaev","doi":"10.1016/j.envsoft.2026.106881","DOIUrl":"10.1016/j.envsoft.2026.106881","url":null,"abstract":"<div><div>Effective spatial monitoring of carbon fluxes is crucial for implementing climate change mitigation and adaptation measures. This study develops an advanced machine learning (ML) pipeline to assess integral carbon fluxes at regional scales using Earth observation data and ground-based measurements. We aimed to address main limitations of spatial ML assessments associated with ignorance of environmental processes’ physical nature. We propose a training pipeline ensuring prediction robustness and model generalization, introducing influential features and ground truth data selection strategy. This results in a robust mapping tool with uncertainty estimations, supported by Shapley values-based feature importance analysis for interpretability and physical meaning. Our approach utilizes data from 168 FLUXNET stations, NASA POWER meteorological reanalysis, and MODIS satellite observations to train a CatBoost gradient boosting model. The model achieves <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.76 predicting monthly NEE values with high spatial–temporal coherence, opening possibilities for comprehensive terrestrial ecosystem carbon dynamics assessments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106881"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014637","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}

引用次数: 0

THU-Wildfire: A multitemporal, multimodal observation dataset for wildfire behavior dynamics THU-Wildfire：野火行为动态的多时相、多模态观测数据集

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.envsoft.2026.106872

Jiahao Zhou , Sen He , Shanjunxia Wu , Jia Zhang , Qiuhua Wang , Fei Wang

引用次数: 0

Computer vision in flash flood forecasting: A narrative review of applications, integration pathways, and future directions 计算机视觉在山洪预报中的应用综述、集成途径和未来发展方向

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.envsoft.2026.106910

Hasitha Adikari , Christian O’Leary , Joe Harrington , Conor Lynch

{"title":"Computer vision in flash flood forecasting: A narrative review of applications, integration pathways, and future directions","authors":"Hasitha Adikari , Christian O’Leary , Joe Harrington , Conor Lynch","doi":"10.1016/j.envsoft.2026.106910","DOIUrl":"10.1016/j.envsoft.2026.106910","url":null,"abstract":"<div><div>Flash floods are among the most destructive hydrometeorological hazards, requiring forecasting approaches that balance accuracy, timeliness and scalability. Computer vision (CV) has become a key enabler, providing near-real-time information from unmanned aerial vehicles (UAVs), satellites and ground-based imagery. This structured narrative review synthesises advances across six domains relevant to forecasting workflows: flood extent mapping, debris and water-level detection, land use and land cover (LULC) classification, change detection, impact assessment, and image compression. Integration pathways between CV outputs and hydrological or hydraulic models are examined, revealing that such coupling remains limited in current literature. It also highlights the future areas of research in this domain. The systematic assessment shows that convolutional neural network (CNN)-based segmentation remains the most practical approach for extracting real-time information from image data, while transformers and lightweight models show promise for real-time use. Persistent challenges include the scarcity of UAV benchmarks, reproducibility gaps and weak operational integration.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106910"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152966","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}

引用次数: 0

Parallelization of the estuarine saltwater intrusion numerical forecast model UFDECOM-i using Fortran DO CONCURRENT 用Fortran DO CONCURRENT并行化河口盐水入侵数值预报模型ufdecomi

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.envsoft.2026.106911

Hongyuan Guo , Bingrui Chen , Rui Ma , Yihe Wang , Jianrong Zhu

{"title":"Parallelization of the estuarine saltwater intrusion numerical forecast model UFDECOM-i using Fortran DO CONCURRENT","authors":"Hongyuan Guo , Bingrui Chen , Rui Ma , Yihe Wang , Jianrong Zhu","doi":"10.1016/j.envsoft.2026.106911","DOIUrl":"10.1016/j.envsoft.2026.106911","url":null,"abstract":"<div><div>High-resolution simulations of estuarine saltwater intrusion are computationally demanding and require efficient execution on heterogeneous computing platforms. In this study, the use of standard Fortran parallelization—DO CONCURRENT—to accelerate the unstructured quadrilateral grid finite-differencing estuarine and coastal ocean model (UFDECOM-i) within a unified codebase for both multicore CPUs and GPUs was investigated. Using the NVFORTRAN compiler, three versions were implemented: MC-UFDECOM-i on multicore CPUs, GPU-UFDECOM-i using automatic data migration, and GPUA-UFDECOM-i using lightweight OpenACC directives for explicit data management. The results show that DO CONCURRENT enables scalable shared-memory parallelism on CPUs, with speedups of up to 16.32 × , and provides functional portability to GPUs without code modification. However, optimal GPU performance requires explicit data management, with GPUA-UFDECOM-i reaching a maximum speedup of 21.48 × . These results demonstrate that DO CONCURRENT ensures portability and maintainability, whereas explicit data control remains essential for high GPU efficiency.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106911"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152960","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}

引用次数: 0

STPredict: A Python package automating spatio-temporal predictions STPredict：一个自动化时空预测的python包

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.envsoft.2026.106901

Arash Mari Oriyad , Arezoo Haratian , Mahdi Naderi , Nasrin Rafiei , Maryam Meghdadi , Zeinab Maleki , Pouria Ramazi

{"title":"STPredict: A Python package automating spatio-temporal predictions","authors":"Arash Mari Oriyad , Arezoo Haratian , Mahdi Naderi , Nasrin Rafiei , Maryam Meghdadi , Zeinab Maleki , Pouria Ramazi","doi":"10.1016/j.envsoft.2026.106901","DOIUrl":"10.1016/j.envsoft.2026.106901","url":null,"abstract":"<div><div>Spatiotemporal prediction of ecological events informs management and mitigates negative impacts. However, the process is complex, requiring extensive data preprocessing, model selection, and evaluation. We introduce an automated spatio-temporal prediction package (STPredict) in Python that receives the raw data in various acceptable formats from the user, performs the preprocessing, including data imputation, selects from the covariates, chooses systematically from several default or user-defined predictive models, evaluates the performance of the final model, and makes a future prediction. As a case study, we demonstrate its use in predicting Mountain Pine Beetle infestations in Cypress Hills Park, Canada. Researchers can use STPredict to apply diverse types of models, including user-defined models, for predicting the time and location of ecological events with minimal effort. This automation not only reduces human error, but also allows ecologists to spend more time on improving, rather than implementing, the existing models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106901"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134550","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}

引用次数: 0

Development of a hybrid Bayesian-Hyperband optimization procedure: GeoAI-driven hyperparameter tuning of AdaBoost for enhancing Mineral Prospectivity Mapping 混合贝叶斯-超带优化程序的开发：geoai驱动的AdaBoost超参数调优，用于增强矿产远景图

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.envsoft.2026.106883

Mahsa Hajihosseinlou , Abbas Maghsoudi , Reza Ghezelbash

{"title":"Development of a hybrid Bayesian-Hyperband optimization procedure: GeoAI-driven hyperparameter tuning of AdaBoost for enhancing Mineral Prospectivity Mapping","authors":"Mahsa Hajihosseinlou , Abbas Maghsoudi , Reza Ghezelbash","doi":"10.1016/j.envsoft.2026.106883","DOIUrl":"10.1016/j.envsoft.2026.106883","url":null,"abstract":"<div><div>Selecting appropriate hyperparameters is essential for achieving stable and reliable results in machine learning–based mineral prospectivity mapping (MPM). In this study, the AdaBoost algorithm was used to predict Pb–Zn mineral potential. AdaBoost was chosen for its ability to integrate multiple weak learners and enhance the recognition of underrepresented mineralization patterns within imbalanced datasets. Nevertheless, its performance may decrease in the presence of spatial heterogeneity and noisy data. To mitigate these issues, a hybrid Bayesian-Hyperband optimization strategy was applied to tune both the AdaBoost and its base learners. Bayesian optimization explores the hyperparameter space using probabilistic modeling, whereas Hyperband improves efficiency by allocating resources to promising configurations. The optimized model, trained on geological, geochemical, tectonic, and remote sensing data, demonstrated high predictive stability and spatial consistency, supporting its applicability in complex mineral systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106883"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134552","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}

引用次数: 0

The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences ODE（概述，数据和执行）协议，用于在环境，社会和相关跨学科科学中标准化使用机器学习。

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1016/j.envsoft.2026.106912

Samuel Seuru , Volker Grimm , Michael Barton , Liliana Perez , Navid Mahdizadeh Gharakhanlou , Raja Sengupta , Alejandro Miguel Dagnino

{"title":"The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences","authors":"Samuel Seuru , Volker Grimm , Michael Barton , Liliana Perez , Navid Mahdizadeh Gharakhanlou , Raja Sengupta , Alejandro Miguel Dagnino","doi":"10.1016/j.envsoft.2026.106912","DOIUrl":"10.1016/j.envsoft.2026.106912","url":null,"abstract":"<div><div>Machine Learning (ML) is increasingly applied across environmental, social and interdisciplinary sciences to analyze complex systems and inform decision-making. Yet, this rapid growth has exposed significant gaps in methodological consistency, documentation, and reproducibility. The lack of standardized frameworks often leads to fragmented workflows and difficulties in interpreting or reproducing results across disciplines. To address these challenges, we introduce the ODE (Overview, Data, and Execution) protocol: a structured, accessible framework to support transparent documentation of ML workflows. Inspired by established standards such as ODD (Overview, Design concepts, Details), ODD + D (adding human Decision-making) for agent-based modeling, ODMAP (Overview, Data, Model, Assessment, Prediction) for species distribution models and FAIR (Findable, Accessible, Interoperable and Reusable) principles, ODE's novelty is to translate ML workflows into a standardized reporting format, specifying what must be described for transparency, reuse, and reproducibility. In practice, ODE is a reporting checklist, typically provided as supplementary material, supporting authors and reviewers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106912"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160925","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}

引用次数: 0

Compact bioretention cell for urban stormwater management: Assessment of hydrologic, hydraulic, and water quality performance via laboratory and SWMM modelling 城市雨水管理的紧凑型生物滞留电池：通过实验室和SWMM模型评估水文、水力和水质性能

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.envsoft.2026.106877

Shaahin Nazarpour Tameh , Jennifer Drake , Anna Palla , Ilaria Gnecco

{"title":"Compact bioretention cell for urban stormwater management: Assessment of hydrologic, hydraulic, and water quality performance via laboratory and SWMM modelling","authors":"Shaahin Nazarpour Tameh , Jennifer Drake , Anna Palla , Ilaria Gnecco","doi":"10.1016/j.envsoft.2026.106877","DOIUrl":"10.1016/j.envsoft.2026.106877","url":null,"abstract":"<div><div>Bioretention cells (BRCs) are widely implemented to restore undeveloped hydrologic cycle; however, conventional BRCs need considerable surface area, limiting their applicability in densely populated areas. Compact BRCs like Filterra® have been designed to provide comparable hydrologic and pollutant removal effectiveness with a smaller footprint. The hydraulic characteristics of Filterra's engineered media were assessed through laboratory testing using KSAT and HYPROP devices and these results were integrated with field monitoring to implement a field-validated storm water management model (SWMM). Laboratory results showed a hydraulic conductivity of 1750 mm/h. The validated SWMM model replicated the outflow dynamics with satisfactory accuracy (KGE >0.35, R<sup>2</sup> > 0.47), and the total suspended solids (TSS) removal was suitably predicted (R<sup>2</sup> = 0.83). Results demonstrate that the field-validated SWMM model can be used to evaluate both hydrologic performance and pollutant TSS removal efficiency of compact BRCs, while noting its limitations in representing complex TSS dynamics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106877"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962602","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}

引用次数: 0

A surrogate-aided approach for accelerated Bayesian calibration of hydrologic models 水文模型加速贝叶斯定标的代理辅助方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.envsoft.2026.106894

Rezgar Arabzadeh, Jonathan Romero-Cuellar, James Craig, Bryan Tolson, Robert Chlumsky

引用次数: 0

Exploring the spatiotemporal evolution and driving mechanism of ecological resilience in main urban area of Xi'an based on the “Resistance-Elasticity-Adaptability” model 基于“阻力-弹性-适应性”模型的西安主城区生态弹性时空演化及驱动机制研究

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.envsoft.2026.106875

Yanan Dong, Fei Wang

{"title":"Exploring the spatiotemporal evolution and driving mechanism of ecological resilience in main urban area of Xi'an based on the “Resistance-Elasticity-Adaptability” model","authors":"Yanan Dong, Fei Wang","doi":"10.1016/j.envsoft.2026.106875","DOIUrl":"10.1016/j.envsoft.2026.106875","url":null,"abstract":"<div><div>Enhancing ecological resilience is essential for sustainable urban development under climate and urbanization pressures. This study focuses on Xi'an's main urban area, where ecological resilience remains relatively weak. Based on the conceptual framework of resistance, elasticity, and adaptability, we construct a comprehensive evaluation model, innovatively incorporating disaster adaptability factors, and assess ecological resilience from 2000 to 2023 using a differentiated weighting method. Land Use Transition Matrices and Geographic Detectors Model (GDM) are applied to analyze spatiotemporal patterns and driving forces. Results show: (1) Ecological resilience first declined and then stabilized, mainly due to large-scale farmland conversion (273.9 km<sup>2</sup>, 32.9 %) and insufficient restoration (only 13.93 km<sup>2</sup> added). A “weak center–strong periphery” spatial pattern emerged. (2) Resistance shifted outward (Baqiao-centered), elasticity declined, and adaptability peaked in 2023 (0.088), reflecting a transition to a society–infrastructure coupling model but constrained by central overload and peripheral deficits. (3) Driving mechanisms evolved from natural dominance to multi-factor synergy, with Landscape Shape Index (LSI)-population interaction (q = 0.22447) becoming key. The framework supports data-driven ecological resilience assessment and spatial planning in China's rapidly urbanizing regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106875"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995199","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}

引用次数: 0