Environmental Modelling & Software最新文献

{"title":"The New South Wales nearshore wave tool – an interactive platform integrating high-resolution wave data for enhanced coastal science and management","authors":"Thomas B. Doyle ,&nbsp;Andrew Bradford ,&nbsp;Sean Garber ,&nbsp;Raimundo Ibaceta ,&nbsp;Bradley D. Morris ,&nbsp;Michael A. Kinsela ,&nbsp;Timothy C. Ingleton ,&nbsp;Iman Jizan ,&nbsp;David Taylor ,&nbsp;David J. Hanslow ,&nbsp;Kym Bilham","doi":"10.1016/j.envsoft.2025.106686","DOIUrl":"10.1016/j.envsoft.2025.106686","url":null,"abstract":"<div><div>The <em>New South Wales Nearshore Wave Tool</em> is a freely available toolbox (<span><span>https://nearshore.waves.nsw.gov.au</span><svg><path></path></svg></span>) that accurately describes how waves transform from offshore to nearshore waters, along the coastline of New South Wales (NSW), in southeast Australia. The <em>Tool</em> utilises a computationally efficient wave transformation matrix, hosted via a web-based interface (i.e. Amazon Web Services) with over 10,000 output locations to rapidly transform offshore wave conditions to 30 and 10 m water depths at an alongshore spacing of 250 m. The wave transformation functions comprise pre-modelled conditions covering the full range of deep-water wave heights, periods and directions occurring in the region and were developed using the spectral wave model WAVEWATCH-III® Version 6.07. The <em>Tool</em> features two distinct nearshore wave transformation methods (i.e., parametric and spectral), each offering unique advantages depending on data availability and computational resources, and provides users the ability to transfer either historical offshore Waverider buoy data or modelled deep-water wave hindcast data (up to 63 years from the ERA5 reanalysis dataset, 1959 to on-going, updated quarterly) to the 30 m and 10 m bathymetry contours. The <em>Tool</em> also features a 10-day wave forecast to all nearshore output locations. Users can display nearshore wave conditions in spatial maps, timeseries plots, directional spectra plots, and download time series data for offline use. Additional features include the ability to perform Extreme Value Analyses on the transformed nearshore wave conditions to investigate probability-magnitude relationships. The purpose of the <em>NSW Nearshore Wave Tool</em> is to provide rigorous and accessible wave data for coastal managers, engineers, scientists, and researchers to: investigate nearshore wave climates, assess coastal hazards (including coastal erosion and inundation), assist in the design of coastal management interventions and coastal structures, and support emergency planning and responses.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106686"},"PeriodicalIF":4.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061138","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 3D discrepancy modeling framework for urban pollution prediction in accelerated time","authors":"Mouhcine Mendil ,&nbsp;Sylvain Leirens ,&nbsp;Paul Novello ,&nbsp;Christophe Duchenne ,&nbsp;Patrick Armand","doi":"10.1016/j.envsoft.2025.106662","DOIUrl":"10.1016/j.envsoft.2025.106662","url":null,"abstract":"<div><div>Computational Fluid Dynamics provides reliable high-resolution simulations of atmospheric transport and dispersion. However, its high computational cost limits applicability in time-sensitive scenarios such as emergency response to toxic releases in urban areas. As a faster alternative for decision-making, we previously proposed MCxM, a cost-effective surrogate learning framework for pollutant exposure prediction. A key limitation was its restriction to a single horizontal layer near ground level, suitable for efficiency, but insufficient to capture the full 3D behavior of pollutant dispersion. In this work, we extend the discrepancy modeling to 3D with MCxM-3D, which refines a 3D simplified physical prior using neural operators. The model is trained on realistic pollutant distributions in built-up areas, generated by a Lagrangian particle dispersion model under varying meteorological conditions. Evaluation on unseen urban configurations shows an average 20% (up to 51%) reduction in prediction error over the 2D approach, with millisecond-scale inference enabling real-time deployment.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106662"},"PeriodicalIF":4.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046613","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 nudging-based data assimilation method coupled with bidirectional gated neural networks for error correction","authors":"Qinghe Yu ,&nbsp;Yulong Bai ,&nbsp;Manhong Fan ,&nbsp;Chunlin Huang ,&nbsp;Xiaoxing Yue ,&nbsp;Kuojian Yang","doi":"10.1016/j.envsoft.2025.106670","DOIUrl":"10.1016/j.envsoft.2025.106670","url":null,"abstract":"<div><div>Machine learning (ML) methods are increasingly integral to data assimilation (DA), and their ability to capture temporal dependencies and incorporate historical states into future state predictions makes the nudging-based approach of employing recurrent neural network forecasts as an error constraint particularly attractive. This study presents a data assimilation method that integrates bidirectional gated recurrent units (BiGRU) with the ensemble Kalman filter (EnKF). This method integrates the concept of nudging-based data assimilation and leverages the strengths of machine learning in nonlinear error prediction and correction, aiming to significantly enhance the accuracy and stability of the data assimilation process. Numerical experiments are conducted using the Lorenz-96 nonlinear system to compare data assimilation performance under different sensitivity parameters. The results demonstrate that the novel approach of coupled BiGRU shows enhanced resilience to noise interference in data assimilation and exhibits greater robustness in generating assimilation outcomes from sparse observations.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106670"},"PeriodicalIF":4.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093820","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 hybrid convolutional neural network for multi-station water level prediction: Enhancing navigation safety through spatial-temporal modelling","authors":"Zhi Yuan ,&nbsp;Wenhai Li ,&nbsp;Qian Zhang ,&nbsp;Xiaoxiao Liu ,&nbsp;Yi Liu ,&nbsp;Jingxian Liu","doi":"10.1016/j.envsoft.2025.106671","DOIUrl":"10.1016/j.envsoft.2025.106671","url":null,"abstract":"<div><div>Accurate prediction of multi-station water levels is crucial for mitigating flood risks and optimizing navigation management in complex riverine environments. Existing approaches often fail to capture the dynamic spatiotemporal interdependencies between monitoring stations, limiting their predictive performance and utility in operational decision-making. To address this challenge, we propose MSWLCN (Multi-Station Water Level Convolutional Network) model, a novel deep spatio-temporal convolution framework tailored for simultaneous and accurate prediction of daily water levels over consecutive days at multiple stations. This architecture integrates multi-layer Convolutional Long Short-Term Memory (ConvLSTM) and three-dimensional convolutional (Conv3D) networks with strong adaptations. The model explicitly extracts temporal dependencies and spatial correlations across stations through the spatio-temporal modelling architecture, enabling simultaneous prediction of multi-station water levels with complex changing characteristics. And we validate the framework using a comprehensive dataset spanning 1826 consecutive days from 19 hydrological stations along the Yangtze River, a globally significant navigational corridor. Experimental results demonstrate that the proposed MSWLCN outperforms conventional modelling methods in terms of prediction accuracy and computational efficiency. This research advances environmental modelling practices by offering a scalable solution for multi-station water level forecasting, with direct applications in water resource management and navigation safety assurance.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106671"},"PeriodicalIF":4.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044852","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 computationally efficient local-inertial model for simulating storm surges and coastal inundation","authors":"B. Sridharan ,&nbsp;Vikram Pratap Singh ,&nbsp;Paul D. Bates ,&nbsp;Soumendra Nath Kuiry","doi":"10.1016/j.envsoft.2025.106680","DOIUrl":"10.1016/j.envsoft.2025.106680","url":null,"abstract":"<div><div>Tropical cyclones pose significant risks to low-lying coastal regions through storm surges and inundation, making numerical modelling crucial for disaster planning and evacuation. This study introduces IROMS-iS2D (Integrated River Ocean Modelling System-Inertial Surge 2D), a novel local-inertial 2D model that simulates storm surge and inundation within a single framework on unstructured grids. Unlike existing local-inertial models that rely on external surge inputs or constrained by structured grids, IROMS-iS2D dynamically simulates storm surges and subsequent inundation. It achieves a 10–15 times speedup over 2D shallow water models while supporting high-resolution grids (30–200 m). Cyclone forcing is derived from JTWC/IMD best-tracks using the Holland model, ensuring realistic surge inputs. IROMS-iS2D was validated for Bay of Bengal cyclones using INCOIS data, showing good agreement (R² &gt; 0.9, RMSE ≈ 0.2 m, NSE &gt;0.9). For Cyclone Mocha, Sentinel-1 validation showed 4359 km² inundation with 79.6 % accuracy and an F1-score of 61.7 %, demonstrating potential for real-time flood forecasting.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106680"},"PeriodicalIF":4.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020189","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":"WATcycle: A software for terrestrial water cycle and budget analysis with case studies on the Amazon and Mississippi Basins","authors":"Roniki Anjaneyulu ,&nbsp;Praveen Kashyap ,&nbsp;Jinghua Xiong ,&nbsp;Abhishek","doi":"10.1016/j.envsoft.2025.106656","DOIUrl":"10.1016/j.envsoft.2025.106656","url":null,"abstract":"<div><div>The complexity, heterogeneity, and uncertainties implicit in multisource data make the terrestrial water cycle and water budget analyses challenging across scales. Further, a dilemma in selecting the objective-specific best dataset makes the process difficult and time-consuming. Here, we introduce <em>“WATcycle”,</em> an open-access generalized UI/UX-based Python software, available on GitHub <span><span>https://github.com/ronikianji/WATcycle</span><svg><path></path></svg></span>, with five main steps: (1) data downloading, (2) data pre-processing, (3) multi-scalar spatiotemporal analysis, (4) validation of 13 precipitation datasets with in-situ records, (5) residual error and physically consistent water budget closure using Proportional Redistribution. Our findings of a case study in the Amazon and Mississippi River Basin using 79 multi-source meteo-hydrological datasets are coherent with the literature, with added insights into the recent variability in the basin's hydrological cycle. The results discern the performance, accuracy, and capability of the newly developed software. It will play a crucial role in skillful inferences for water resource management, risk assessment, and infrastructure planning in the basins globally.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106656"},"PeriodicalIF":4.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046611","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":"High-resolution precipitation downscaling in mainland Southeast Asia: A novel integration of BMA and U-Net CNN","authors":"Teerachai Amnuaylojaroen","doi":"10.1016/j.envsoft.2025.106682","DOIUrl":"10.1016/j.envsoft.2025.106682","url":null,"abstract":"<div><div>This study introduces a hybrid approach that combines Bayesian Model Averaging (BMA) with a U-Net Convolutional Neural Network (CNN) to improve precipitation estimates in mainland Southeast Asia. The method addresses key limitations of Global Climate Models in capturing fine-scale variability, particularly in topographically complex. An ensemble of five GCMs, supplemented by ERA5 reanalysis data, was used to produce high-resolution downscaled precipitation estimates. Compared to the original BMA, the hybrid model significantly improved performance, increasing the Symmetric Concordance Correlation Coefficient from 0.68 to 0.82 and reducing the Root Mean Squared Error from 1.63 to 1.27 (validated against ERA5). Validation using TRMM and IMERG data revealed similar enhancements. Additionally, Wasserstein distance analysis confirmed improved distributional similarity between model outputs and observed data. The most notable improvements occurred in mountainous areas, especially in northern Myanmar. This approach enhances the utility of climate data for water resource management and adaptation planning in Southeast Asia.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106682"},"PeriodicalIF":4.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027471","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":"Subsidence vulnerability assessment due to groundwater over-abstraction using AI-based multiple cluster analysis","authors":"Sina Sadeghfam ,&nbsp;Soroush Mohammadi ,&nbsp;Ata Allah Nadiri ,&nbsp;Ali Ehsanitabar ,&nbsp;Senapathi Venkatramanan ,&nbsp;Abu Reza Md Towfiqul Islam ,&nbsp;Yong Xiao ,&nbsp;Mehdi Rahmati","doi":"10.1016/j.envsoft.2025.106679","DOIUrl":"10.1016/j.envsoft.2025.106679","url":null,"abstract":"<div><div>Land subsidence triggered by excessive groundwater extraction is a topical research activity, and the ALPRIFT framework calculates the Subsidence Vulnerability Index (SVI). In this study, we employed Artificial Intelligence (AI) to reduce the inherent subjectivity in the ALPRIFT framework using Inclusive Multiple Modeling (IMM). IMM incorporates Random Forest (RF) and Support Vector Machine (SVM) to conduct cluster analysis at Level 1 and identify clusters fed into another RF model at Level 2. We applied this formulation to an unconfined aquifer, which was affected by water table decline. The study identified vulnerable areas in the central part of the aquifer, representing a maximum of 14 cm of subsidence detected by InSAR. The ratio of vulnerable areas to total areas are 5.5, 8.8 and 5.4 % for RF, SVM and IMM, respectively. Compared to the basic ALPRIFT framework, the AI models at both levels considerably improved the modeling performance from 0.7 to 96.5.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106679"},"PeriodicalIF":4.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020188","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":"NextGen In A Box (NGIAB): Open-Source containerization of the NextGen framework to enable community-driven hydrology modeling","authors":"Arpita Patel ,&nbsp;James Halgren ,&nbsp;Zach Wills ,&nbsp;Nels Frazier ,&nbsp;Benjamin Lee ,&nbsp;Joshua Cunningham ,&nbsp;Jordan Laser ,&nbsp;Mohammadsepehr Karimiziarani ,&nbsp;Trupesh Patel ,&nbsp;Giovanni Romero ,&nbsp;Matthew Denno ,&nbsp;Sam Lamont ,&nbsp;Iman Maghami ,&nbsp;Hari Teja Jajula ,&nbsp;M. Shahabul Alam ,&nbsp;Sifan A. Koriche ,&nbsp;Manjila Singh ,&nbsp;Nia Minor ,&nbsp;Bhavya Duvvuri ,&nbsp;Savalan Naser Neisary ,&nbsp;Daniel P. Ames","doi":"10.1016/j.envsoft.2025.106666","DOIUrl":"10.1016/j.envsoft.2025.106666","url":null,"abstract":"<div><div>The NextGen Water Resources Modeling Framework (NextGen) greatly increases the flexibility and interoperability of hydrologic modeling workflows. However, deploying NextGen models remains challenging due to a complex installation process. To address this, we developed NextGen In A Box (NGIAB), an all-in-one distribution of the NextGen framework that simplifies deployment across environments using Docker and Singularity containers. NGIAB comes pre-configured with most components expected in version 4.0 of the National Water Model, which will use the NextGen framework. Additionally, NGIAB utilizes a cloud-based continuous integration/continuous deployment pipeline to automate releases. Through its ease of access and robust suite of included utilities, NGIAB promotes community engagement in hydrologic modeling and facilitates research-to-operation (R2O) pathways. In this work, we present the designs and technologies that enable these outcomes, along with studies and performance benchmarks that demonstrate NGIAB's applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106666"},"PeriodicalIF":4.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923034","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":"Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice","authors":"Gustavious Paul Williams","doi":"10.1016/j.envsoft.2025.106665","DOIUrl":"10.1016/j.envsoft.2025.106665","url":null,"abstract":"<div><div>I evaluate the use of Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) for hydrologic model accuracy assessment. Using synthetic data with identical error distributions, (σ = 2), NSE and KGE values vary widely—from −190 to 0.999—due to flow characteristics, not model accuracy. Applying identical noise, (σ = 10), to 6595 U.S. gages, models with identical RMSE (∼10) results in NSE values from −325,272 to 1.0 which corresponds with flow variability rather than model fit. If noise is scaled to 25 % of mean flow, spatial patterns in NSE and KGE persist that reflect flow characteristics rather than accuracy and misrepresent accuracy. NSE and KGE are skill scores and useful for within-site model calibration, not cross-site accuracy comparisons. Metrics such as RMSE, normalized RMSE, or percent bias offer more interpretable, transferable accuracy evaluations. I advocate abandoning NSE and KGE for comparisons of model performance and urge hydrologists to adopt fit-for-purpose metrics. I present this study as a position paper, rather than a research paper, the limitations of NSE and KGE—particularly their dependence on flow variability and unsuitability for cross-site comparisons—are well known and have been addressed extensively in the literature. However, my experience and review of the literature indicate an over-reliance and misuse of these metrics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106665"},"PeriodicalIF":4.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933614","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}