Environmental Modelling & Software最新文献

{"title":"NSVineCopula: R package for modeling non-stationary multivariate dependence","authors":"Q. Zhang ,&nbsp;Y.P. Li ,&nbsp;G.H. Huang ,&nbsp;X.M. Huang ,&nbsp;H. Wang ,&nbsp;Z. Wang ,&nbsp;Z.P. Xu ,&nbsp;Y.Y. Wang ,&nbsp;Z.Y. Shen","doi":"10.1016/j.envsoft.2025.106474","DOIUrl":"10.1016/j.envsoft.2025.106474","url":null,"abstract":"<div><div>A vine copula is a flexible method for multivariate dependence simulations that assumes stationarity. However, only a few studies have focused on non-stationarity and comprehensively developed nonstationary vine copula functions. In this study, a novel R package, NSVineCopula was developed and presented. Canonical-vine and Drawable-vine structure with 36 bivariate copula functions were considered in NSVineCopula. This package is capable of capturing non-stationary multivariate dependence, providing time-varying parameters for each bivariate copula, and quantifying the conditional probability. Notably, NSVineCopula provides a simple way for sampling non-stationary vine copulas. The capability of NSVineCopula was evaluated through two case studies: (1) agricultural drought risk assessment under compound dry-hot extreme conditions and water level prediction. The results demonstrate the advantages of NSVineCopula in non-stationary multivariate dependence analysis, and highlights the potential of NSVineCopula in many fields. Overall, NSVineCopula can provide valuable and robust functionalities for modeling nonstationary multivariate dependence.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106474"},"PeriodicalIF":4.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851400","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":"Automatic detection of in-stream river wood from random forest machine learning and exogenous indices using very high-resolution aerial imagery","authors":"Gauthier Grimmer ,&nbsp;Romain Wenger ,&nbsp;Germain Forestier ,&nbsp;Valentin Chardon","doi":"10.1016/j.envsoft.2025.106460","DOIUrl":"10.1016/j.envsoft.2025.106460","url":null,"abstract":"<div><div>River wood (RW) plays a key role in shaping aquatic and riparian habitats while influencing sediment and water dynamics. This study presents the first automated RW detection model using Random Forest classification and near-infrared aerial imagery on the Meurthe River. By progressively incorporating exogenous indices, the model achieved recall, precision, and F1-scores between 12%–39%, 90%–94%, and 21%–54%, respectively. Validation on the Loire, Doubs, and Buëch rivers confirmed robust detection rates (75.41–86.57%) after filtering. The model also estimated RW characteristics, including length, diameter, area, and volume, with high accuracy post-calibration. These findings demonstrate the potential of remote sensing and AI for RW monitoring, providing an efficient decision-support tool for river management and habitat conservation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106460"},"PeriodicalIF":4.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855797","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 efficient data-driven method for isolating dry-weather flow from total combined sewer flow data","authors":"Katie Straus ,&nbsp;John Barton ,&nbsp;M. Sadegh Riasi ,&nbsp;Lilit Yeghiazarian","doi":"10.1016/j.envsoft.2025.106470","DOIUrl":"10.1016/j.envsoft.2025.106470","url":null,"abstract":"<div><div>Wastewater treatment plants in combined sewer systems are often required to accommodate the widely fluctuating flow due to the dynamic interactions between multiple water flow sources. A major challenge in wastewater management, and particularly in combined sewer overflow (CSO) mitigation, is decoupling the total sewer flow into its components: dry-weather flow (DWF) and rain-derived inflow and infiltration (RDII). While current approaches have been successful for dry climates, their requirement to filter out rainfall-affected data often leads to inaccurate estimates for flow components in wet and semi-wet climates or seasons. The twice-detrended residual method (TDRM) developed in this study is a data-driven model that seeks to alleviate this drawback while utilizing all available data. We implement TDRM with sewer flow data collected from three locations and time periods within the Greater Cincinnati, Ohio Metropolitan Sewer District, and demonstrate that it can successfully decouple rain-inclusive flow datasets into their weekly DWF and RDII components.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106470"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864134","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":"The response of a northeastern temperate forest to future scenarios of climate change and energy policies through the 21st century","authors":"Linghui Meng ,&nbsp;Afshin Pourmokhtarian ,&nbsp;Pamela H. Templer ,&nbsp;Lucy R. Hutyra ,&nbsp;Charles T. Driscoll","doi":"10.1016/j.envsoft.2025.106473","DOIUrl":"10.1016/j.envsoft.2025.106473","url":null,"abstract":"<div><div>Northeastern temperate forests provide essential ecosystem services that are increasingly threatened by climate change and air pollution. To evaluate integrated ecosystem responses to these changes, we applied the PnET-CN-daily model to project carbon, nitrogen, and water cycling dynamics at Harvard Forest (Petersham, MA, USA) throughout the 21st century. The projections were based on future climate scenarios (RCP4.5, RCP8.5) and different energy policies scenarios (current U.S. policies, decarbonization policies). Simulations suggest that carbon storage in forest ecosystems will continue to increase throughout the 21st century, but the increase will become increasingly limited by nitrogen availability. The energy policy scenarios are projected to continue a decline in atmospheric nitrogen deposition, which will slow carbon accumulation and further accelerate the ongoing nitrogen oligotrophication. Therefore, future management may need to consider the effects of increasing nitrogen limitation on the carbon sequestration potential and the structure and function of northeastern temperate forests.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106473"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877221","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":"Approximate Bayesian inference for calibrating the IPCC tier-2 steady-state soil organic carbon model for Canadian croplands using long-term experimental data","authors":"N. Pelletier ,&nbsp;A. Thiagarajan ,&nbsp;F. Durnin-Vermette ,&nbsp;L. Chang ,&nbsp;D. Choo ,&nbsp;D. Cerkowniak ,&nbsp;A. Elkhoury ,&nbsp;D. MacDonald ,&nbsp;W. Smith ,&nbsp;A.J. VandenBygaart","doi":"10.1016/j.envsoft.2025.106481","DOIUrl":"10.1016/j.envsoft.2025.106481","url":null,"abstract":"<div><div>We conducted a Bayesian calibration of the IPCC tier-2 Steady-State (IPCCT2) model using long-term experimental (LTE) data from Canadian croplands. A global sensitivity analysis identified key parameters influencing the prediction of soil organic carbon (SOC) stocks, including those governing the temperature response curve, optimal decay rate in the passive pool, and stabilization efficiencies for decay products in different pools. We used Sampling-Importance-Resampling to obtain posterior parameter and hyperparameter distributions for the sensitive parameters and the tillage disturbance modifiers.</div><div>The calibration significantly narrowed parameter ranges compared to the original parameter range provided by the IPCC guidelines, reducing relative uncertainty in SOC point estimates from 27-33 % to 3.5–4 % - an 85 % reduction in model uncertainties. However, calibration was much less efficient in reducing model uncertainties if the correlation structure in the posterior samples was unaccounted for. Calibrated parameters effectively minimized Root Mean Squared Error and bias in SOC predictions in a validation dataset. The default IPCC tier-2 steady-state model parameters performed comparably to those obtained from maximum <em>a priori</em> distributions.</div><div>Our findings highlighted the broad nature of original IPCC guideline boundaries, leading to uncertain SOC stock predictions and limiting model informativeness and emphasizing the need for parties to adapt parameters to their country-specific conditions. Simulation results suggested that the calibrated model parameter ranges are essential for accurate predictions. When simulating the impact of reducing tillage or adding inorganic nitrogen to annual crops without manure amendments, model calibration substantially reduced uncertainties in long-term impact predictions—by ∼15 % for tillage and ∼75 % for nitrogen addition.</div><div>This study underscores the accuracy of default IPCCT2 parameters in simulating SOC dynamics in Canadian LTE studies. However, it emphasizes the need for calibrated model parameters in conducting uncertainty analyses. The Bayesian calibration improved uncertainty assessments of cropland management practices leading to reliable carbon accounting. This work supports informed decision-making towards sustainable agriculture, guiding management strategies that optimize carbon storage while aligning with national and international carbon reporting frameworks.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106481"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837978","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":"RIce-Net: Integrating ground-based cameras and machine learning for automated river ice detection","authors":"Mahmoud Ayyad ,&nbsp;Marouane Temimi ,&nbsp;Mohamed Abdelkader ,&nbsp;Moheb M.R. Henein ,&nbsp;Frank L. Engel ,&nbsp;R. Russell Lotspeich ,&nbsp;Jack R. Eggleston","doi":"10.1016/j.envsoft.2025.106454","DOIUrl":"10.1016/j.envsoft.2025.106454","url":null,"abstract":"<div><div>River ice plays a critical role in controlling streamflow in cold regions. The U.S. Geological Survey (USGS) qualifies affected water-level measurements and inferred streamflow by ice conditions at a date later than the day of the actual measurements. This study introduces a novel computer vision-based framework, River Ice-Network (RIce-Net), that uses the USGS nationwide network of ground-based cameras whose images are published through the National Imagery Management System (NIMS). RIce-Net consists of a binary classifier to identify ice-affected images that are segmented to calculate the fraction of ice coverage, which is used to automatically generate a near real-time ice flag. RIce-Net was trained using images from selected NIMS stations collected in 2023 and tested using images collected in 2024. Also, the framework’s scalability and transferability were tested over another station that was not included in the training process. RIce-Net ice flags are well-aligned with those reported by USGS.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106454"},"PeriodicalIF":4.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843600","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":"Towards good practice In engaging users In evaluation of computer model Software: Introducing the critical appraisal approach (CAA)","authors":"Caroline Rosello ,&nbsp;Joseph H.A. Guillaume ,&nbsp;Peter Taylor ,&nbsp;Susan M. Cuddy ,&nbsp;Carmel A. Pollino ,&nbsp;Anthony J. Jakeman","doi":"10.1016/j.envsoft.2025.106469","DOIUrl":"10.1016/j.envsoft.2025.106469","url":null,"abstract":"<div><div>Good practices in model software development are essential for boosting uptake. While user-centric approaches are much advocated, challenges remain in including users in development due to diverse definitions for ‘users’, their perceived credibility as an information source, and the influence of market-based innovation choices and the anticipation of (future) demands. While enhancing user feedback could help in addressing these challenges, there is a notable lack of guidance for best practices focused on users, as the emphasis has traditionally been on developers.</div><div>To tackle this gap, we propose a Critical Appraisal Approach (CAA) for model software evaluation, informed by the Basin Futures model software and detailed steps in its evaluation, thereby providing guidance to support associated best practices for such software. The paper shows the CAA can assist in 1) enhancing shared understanding between users and developers, 2) coordinating development and evaluation, and 3) aligning development with market dynamics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106469"},"PeriodicalIF":4.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868652","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 data fusion approach to enhancing runoff simulation in a semi-arid river basin","authors":"Afshin Jahanshahi ,&nbsp;Haniyeh Asadi ,&nbsp;Hoshin Gupta","doi":"10.1016/j.envsoft.2025.106468","DOIUrl":"10.1016/j.envsoft.2025.106468","url":null,"abstract":"<div><div>Accurate streamflow modeling is crucial for water resource management in dry and semi-arid regions. This study proposes a novel approach combining machine learning (ML) with conceptual and physically-based models to address of traditional model limitations in Iran's semi-arid Jazmourian River Basin. The HBV and SWAT hydrological models are used for conceptual and physically-based simulations, respectively, while Support vector regression (SVR) and multilayer perceptron (MLP) integrate hydrological model outputs with hydro-meteorological variables. Using hydroclimatic data from two periods-1963-1989 (dry phase) and 1993–2019 (wetter phase)-the study evaluates model performance under contrasting conditions. The proposed \"fusion SVR\" and \"hybrid SVR with whale optimization algorithm\" (SVR-WOA) models demonstrate improved accuracy in simulating runoff peaks. The SVR-WOA model achieves a 26.17 % performance improvement over SWAT for 1993–2019 and 25.36 % for 1963–1989, with RMSE values of 9.90 m³/s and 10.33 m³/s, respectively. This highlights hybrid modeling's potential for diverse hydrological challenges.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106468"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837980","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 post-processing machine learning framework for bias-correcting National Water Model outputs by accounting for dominant streamflow drivers","authors":"Savalan Naser Neisary ,&nbsp;Ryan C. Johnson ,&nbsp;Md Shahabul Alam ,&nbsp;Steven J. Burian","doi":"10.1016/j.envsoft.2025.106459","DOIUrl":"10.1016/j.envsoft.2025.106459","url":null,"abstract":"<div><div>While the National Water Model (NWM) provides high-resolution, large-scale streamflow data across the United States, its effectiveness as a key water resources management tool in the drought-prone Western US needs further investigation. Previous studies revealed that the NWM has limitations in controlled basins, impacted by reservoir operations and diversions not explicitly included within the model framework. Responding to the observed reduction in model skill throughout the Western US, we developed a model agnostic post-processing machine learning (PP-ML) framework to account for the impacts of water resources management and regionally dominant hydrological processes on model performance. For our case application of the PP-ML framework, we use daily NWM v2.1 retrospective flow rates as the hydrological model and input upstream reservoir storage, SNOTEL snow water equivalent, and catchment characteristics. Applying the PP-ML framework in the contributing Great Salt Lake watersheds, a key watershed of interest due to its drought-prone nature, we observed a 65%, 335%, and 25% improvement in the median Kling-Gupta Efficiency, Percent Bias, and Root Mean Square Error, respectively, for 30 gauged locations compared to the NWM outputs. Comparing model skills across different flow regimes and station types revealed a substantial (225%) improvement in low-flow estimates at stations with extensive upstream water infrastructure, such as those impacted by reservoir operations, as well as in catchments within negligible water management activities. The research underscores how post-processing hydrological model outputs with ML can account for the effects of water management activities on streamflow estimates, most notably without explicitly incorporating infrastructure rulesets, and demonstrate its capability in bias-correcting streamflow forecasts in response to the regionally dominant streamflow drivers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106459"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837979","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":"Interpolation of missing ozone data using graph machine learning and parameter analysis through eXplainable artificial intelligence comparison","authors":"Seonje Jung ,&nbsp;Junsu Gil ,&nbsp;Meehye Lee ,&nbsp;Clara Betancourt ,&nbsp;Martin Schultz ,&nbsp;Yunsoo Choi ,&nbsp;Taekyu Joo ,&nbsp;Daigon Kim","doi":"10.1016/j.envsoft.2025.106466","DOIUrl":"10.1016/j.envsoft.2025.106466","url":null,"abstract":"<div><div>Ozone (O₃), a short-lived climate pollutant, continues to increase despite policies aimed at suppressing its precursors in South Korea. The government operates approximately 500 observatories to monitor O₃ and trace gases. Researchers use these data to address the ongoing issue of increasing O₃ levels. However, challenges in data retrieval from observatories may introduce biases in O₃ studies. In this study, we developed a graph-based machine learning model to simulate missing O₃ concentrations for mitigate bias. The model incorporates spatiotemporal distribution characteristics using a merged observation dataset from South Korea in 2021. Regardless of region or length of missing data, the model effectively simulates O₃ variations with R² of up to 0.9 and RMSE of 3.6. To determine the influence of input parameters on O₃ interpolation, we used eXplainable AI methods. The results indicated that NO₂ is the most important factor in cities, while photochemical indicators are more influential in provinces.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106466"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851401","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}