Environmental Modelling & Software最新文献

{"title":"WRF simulation knowledge sharing based on the MC-GAT model and multi-head attention mechanism: A case study in China","authors":"Yazhou Qi ,&nbsp;Chunxiao Zhang ,&nbsp;Hanguang Yu ,&nbsp;Aijia Wang ,&nbsp;Xiaoyang Hao ,&nbsp;Chen Yang ,&nbsp;Rongrong Li","doi":"10.1016/j.envsoft.2025.106712","DOIUrl":"10.1016/j.envsoft.2025.106712","url":null,"abstract":"<div><div>Meteorological processes are a critical component of environmental systems. The management and sharing of meteorological simulation knowledge are essential for the scientific modeling of these processes. Numerous meteorological simulation studies have provided detailed descriptions of regional configurations and parameterization schemes. However, effectively managing and sharing WRF simulation knowledge remains a significant challenge. This study proposes a knowledge prediction framework based on a Multi-Condition Graph Attention Network (MC-GAT), which employs a multi-head attention mechanism to aggregate multi-condition feature information for parameter prediction. The framework focuses on provincial regions in China, using 10 provinces as examples, each serving as the innermost nested region for WRF parameter prediction. The results indicate that as the number of input conditions increases, the model's prediction accuracy improves significantly. Furthermore, a preliminary WRF knowledge prediction platform for Chinese regions has been developed to enable users to quickly obtain WRF parameters, thereby enhancing the efficiency of parameter selection.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106712"},"PeriodicalIF":4.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229129","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":"Advancing urban expansion modeling with a hybrid TRANSGAN deep learning approach","authors":"Farasath Hasan,&nbsp;Xintao Liu","doi":"10.1016/j.envsoft.2025.106693","DOIUrl":"10.1016/j.envsoft.2025.106693","url":null,"abstract":"<div><div>Urban expansion modeling is pivotal for sustainable urban planning, yet conventional approaches often fail to capture intricate spatial and temporal dynamics. In this study, we present TRANSGAN, the first framework combining Transformer networks and Generative Adversarial Networks (GANs) for urban expansion simulation. By harnessing the spatial learning strengths of Transformers alongside the generative capabilities of GANs, TRANSGAN significantly outperforms traditional models, as evidenced by enhanced predictive accuracy and spatial consistency. Trained on historical land use data in Hong Kong and incorporating key drivers, such as proximity to CBDs, road networks, and elevation, the model delivers highly realistic urban expansion forecasts for 2035 and 2045. Comparative analyses with Transformer, GAN, U-Net, and Random Forest models demonstrate that TRANSGAN achieves the highest F1 Score (0.9496), Precision (0.9396), FOM (0.8889), and Recall (0.9428). This robust, interpretable, and scalable approach not only advances urban expansion modeling but also provides critical insights for urban planners and policymakers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106693"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227283","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 unsteady analytical solution for a layered Tóthian basin subjected to time-dependent water table boundary","authors":"Manisha Das,&nbsp;Ratan Sarmah","doi":"10.1016/j.envsoft.2025.106694","DOIUrl":"10.1016/j.envsoft.2025.106694","url":null,"abstract":"<div><div>An analytical solution is developed for predicting hydraulic head variation in a layered Tóthian basin driven by an unsteady water table boundary at the surface. The separation of variable method in conjecture with quasi-orthogonal Fourier series is used to arrive at the analytical solution. The correctness of the proposed solution is confirmed by comparing the hydraulic head and recharge/discharge variation with an existing analytical model for simplified flow geometry. The proposed model evaluates the recharge/discharge rate, pathline, distribution of stagnation points, and residence time distribution. Further, a sensitivity study is performed to measure the importance of different flow and aquifer parameters. The study highlights that the pathline followed by the water particle in a Tothian basin with an unsteady water table boundary doesn't follow a typical smooth path; instead, it takes a curly path. Further, the residence time distribution is found to follow a heavy-tailed distribution.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106694"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181249","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":"CVIc: A web platform for automated Coastal Vulnerability Index-based assessment","authors":"Alexandros Liaskos ,&nbsp;George P. Petropoulos ,&nbsp;Niki Evelpidou ,&nbsp;Spyridon E. Detsikas","doi":"10.1016/j.envsoft.2025.106705","DOIUrl":"10.1016/j.envsoft.2025.106705","url":null,"abstract":"<div><div>Intensifying climate change impacts, such as Sea-Level Rise (SLR), floods, extreme weather events and coastal erosion, threaten ecosystems, infrastructure, and human communities at a global scale, making vulnerability assessments a crucial prerequisite for identifying areas necessitating urgent and effective actions. The Coastal Vulnerability Index (CVI) is a widely used index-based methodology for such assessments; yet its implementation often relies on complex, manual workflows across multiple proprietary desktop Geographic Information Systems (GIS) software. Existing approaches limit accessibility, lack transparency, hinder reproducibility, and are frequently time-consuming. To address these challenges, CVIc (Coastal Vulnerability Index Compiler) is presented herein as a novel, open-source, and open-access geoprocessing web application for the computation of the CVI. CVIc provides an end-to-end dynamic workflow, guiding users from shoreline data import to the application of various standardized indices (CVI, ICVI). CVIc is deployed as a website (<span><span>https://alexandrosliaskos.github.io/CVIc/</span><svg><path></path></svg></span>) and features interactive tools for shoreline digitization, segmentation, parameter value assignment, and visualization and export of results. The only input requirements are a shoreline Shapefile input or a GeoTIFF image for digitization, and the knowledge of the spatial distribution of the parameter values for the area under study. By leveraging IndexedDB for browser-based data storage, CVIc operates without server-side dependencies, ensuring data privacy, protection and large-scale dataset processing. To our knowledge, this consists the first web solution of its kind, as its streamlined approach into a unified and user-friendly platform makes this type of analysis more feasible to researchers and coastal practitioners, while providing policymakers with more accessible and robust data for decision-making. Its open-source nature enables community-driven advancements, and the simple User Interface (UI) and map components mark it as appropriate for educational purposes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106705"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227288","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":"Parallel algorithm of watershed delineation by considering the topological relationship into information organisation","authors":"Fei Zhao ,&nbsp;Liyang Xiong ,&nbsp;Hongen Wang ,&nbsp;Lanhua Luo ,&nbsp;Jiaming Na ,&nbsp;Sijin Li ,&nbsp;Guoan Tang","doi":"10.1016/j.envsoft.2025.106706","DOIUrl":"10.1016/j.envsoft.2025.106706","url":null,"abstract":"<div><div>Watershed boundaries are vital in ecology, hydrology, and geomorphology. With advances in Earth observation, larger and more accurate datasets demand efficient parallel methods for watershed delineation that minimize memory usage and boost efficiency. Existing algorithms often suffer from high memory consumption or low performance. Currently, the Sub-First-Round (SFR) watershed is an essential component of data-partitioning-based parallel watershed delineation. Our approach revises SFR encoding rules within tiles and adopts a new data organization strategy, reducing memory usage while improving efficiency. Compared to traditional methods (WDG, MESHED, Hiep-Thuan), our algorithm saves 6.13 %–59.68 % in memory usage. Though slower than WDG and MESHED, this trade-off is necessary due to data partitioning. The significant reduction in memory usage makes it feasible to perform watershed delineation on much larger datasets. Finally, our proposed algorithm outperforms the Hiep-Thuan algorithm by 11.84 % in efficiency.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106706"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227277","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":"A ten-year meteorological simulation and optimization in China based on traditional data assimilation and machine learning methods","authors":"Meiqi Wu ,&nbsp;Qian Shu","doi":"10.1016/j.envsoft.2025.106707","DOIUrl":"10.1016/j.envsoft.2025.106707","url":null,"abstract":"<div><div>Meteorological conditions are key inputs for chemical transport models and directly impact simulation accuracy. thus, reducing their uncertainties is crucial. To generate long-term meteorological input datasets, we utilizes the WRF model to simulate atmospheric conditions across China over a ten-year period (2014–2023) at a spatial resolution of 27 km. While WRF shows relatively good performance in simulating wind speed, its accuracy in temperature and wind direction remains limited. To further improve the simulation accuracy, the Yangtze River Delta region is selected for a 2023 case study, applying both the traditional 3DVAR data assimilation method and machine learning approaches to optimize these three key variables. The results demonstrate that for non-compliant stations, 3DVAR achieves better optimization in temperature simulation compared to RF and XGBoost, whereas RF and XGBoost outperform 3DVAR in wind field simulation. Among all the methods evaluated, XGBoost delivered the most effective optimization performance.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106707"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227278","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":"A controlled model experiment for the global hydrological model WaterGAP3: Understanding recent and new advances in the model structure","authors":"Jenny Kupzig,&nbsp;Martina Flörke","doi":"10.1016/j.envsoft.2025.106703","DOIUrl":"10.1016/j.envsoft.2025.106703","url":null,"abstract":"<div><div>The global hydrological model WaterGAP3 has a long tradition that involves increased complexity. For the first time, this study conducts a model experiment to test whether this increased complexity leads to better process representation in three individual model parts. First, the findings of our large-sample analysis reveal that the recently implemented volume-dependent river routing does not produce better process representation in about 2/3 of the basins, lacking justification. Second, the reservoir algorithms demonstrate the benefit of using reservoir-specific data. However, the approaches show individual weaknesses, indicating the need for future development. Third, implementing snow processes on wetlands is promising. However, future work is needed to enable sublimation and to address overestimated discharges during winter periods. Overall, this study demonstrates that revisiting past decisions is essential for scientific progress and shows the efficacy of model falsification through large-sample analysis.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106703"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227281","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":"Design and development of a python-based interface for parallel calibration of SWAT+ model at large scale using dynamically dimensioned search algorithm","authors":"Jungang Gao ,&nbsp;Michael J. White ,&nbsp;Natalja Čerkasova ,&nbsp;Jeffrey G. Arnold ,&nbsp;Peter Allen ,&nbsp;Kelly R. Thorp ,&nbsp;Mazdak Arabi ,&nbsp;Joo-Hee Lee ,&nbsp;Sagarika Rath ,&nbsp;Celray J. Chawanda","doi":"10.1016/j.envsoft.2025.106708","DOIUrl":"10.1016/j.envsoft.2025.106708","url":null,"abstract":"<div><div>Efficient calibration of hydrological models such as the Soil and Water Assessment Tool Plus (SWAT+) is crucial for accurate watershed simulations and informed water resource management. Traditional calibration strategies often struggle to handle the computational complexities inherent in exploring high-dimensional parameter spaces. This research introduces an approach that harnesses the power of parallel computing for SWAT + model calibration, employing the Dynamically Dimensioned Search (DDS) algorithm. Parallel computing was utilized to distribute computational tasks across multiple processors or computing nodes, significantly reducing calibration time while maintaining precision. By leveraging parallel computing resources, the DDS algorithm explored more extensive parameter spaces for SWAT + calibration in a reasonable timeframe. In a case study conducted in the Upper Mississippi River Basin (UMRB), the effectiveness of parallel computing-enabled DDS was demonstrated for calibrating the SWAT + model. The algorithm efficiently navigated complex parameter spaces, leading to enhanced model performance in simulating critical hydrological processes such as streamflow dynamics, sediment yield, and nutrient transport. Comparisons of simulated and observed data demonstrated satisfactory performance of the calibrated model. The calibration method offered substantial benefits for large-scale hydrologic modeling, enabling researchers and water resource managers to calibrate more complex or fine-grained watershed models more quickly. The integration of parallel computing with DDS in SWAT + calibration facilitated accelerated model calibration and scenario analysis capabilities, enabling more timely decision-making for sustainable water management projects.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106708"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227284","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":"Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence","authors":"Jie Bao,&nbsp;Yunxiang Chen,&nbsp;Lupita Renteria,&nbsp;Morgan Barnes,&nbsp;Brieanne Forbes,&nbsp;Sophia McKever,&nbsp;Amy Goldman,&nbsp;Timothy Scheibe,&nbsp;James Stegen","doi":"10.1016/j.envsoft.2025.106715","DOIUrl":"10.1016/j.envsoft.2025.106715","url":null,"abstract":"<div><div>Continuous measurement and monitoring of surface water coverage in non-perennial streams are essential for understanding the exchange fluxes between surface and subsurface waters under both inundated and non-inundated conditions. In this study, a wildlife camera photo-based framework was developed to monitor small stream water inundation, depth, discharge, and velocity. Two advanced machine learning models, YOLOv8 and Mask2Former, were utilized to efficiently analyze images captured by wildlife cameras. The accuracy of the framework was validated against on-site depth measurements at six sites in the Yakima River Basin, along with the gage height, discharge, and velocity data from four USGS sites. This approach facilitates long-term, continuous monitoring and quantification of river intermittency and water availability with high precision and low cost, thereby advancing river ecosystem research and management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106715"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227280","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":"ClimateServ: An open source earth observation climate data access tool","authors":"Meryl Kruskopf ,&nbsp;Lance Gilliland ,&nbsp;Brent Roberts ,&nbsp;Amanda Markert ,&nbsp;William Ashmall ,&nbsp;Ashutosh Limaye","doi":"10.1016/j.envsoft.2025.106709","DOIUrl":"10.1016/j.envsoft.2025.106709","url":null,"abstract":"<div><div>SERVIR, a US government program run by NASA, developed ClimateSERV, an application that enables users to visualize, analyze and download climate related datasets that have been curated for water and food security applications. ClimateSERV lowers the barrier for researchers, policymakers, and students to use Earth observation derived climate data to address challenges such as drought, water resources, and crop health. This paper describes the features, architecture, performance, and uses of the application, detailing a case study of ClimateSERV's use in Southeast Asia.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106709"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227287","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}