Environmental Modelling & Software最新文献

{"title":"A technique for stream geometry estimation based on watershed morphometric characteristics","authors":"Orlando M. Viloria-Marimón ,&nbsp;Felix L. Santiago-Collazo ,&nbsp;Brian P. Bledsoe ,&nbsp;Walter F. Silva-Araya","doi":"10.1016/j.envsoft.2025.106486","DOIUrl":"10.1016/j.envsoft.2025.106486","url":null,"abstract":"<div><div>This study presents an approach for deriving stream geometry from watershed morphometric characteristics, addressing data scarcity for hydrologic and hydraulic modeling. The approach was tested in Puerto Rico and involves subdividing the study area into homogeneous regions and developing region-specific stream geometry predictive equations using ordinary least squares regression to correlate morphometric characteristics with the stream width and depth for a 2-year return period flow. The resulting equations were verified and validated by applying them to generate cross-sections for eight Storm Water Management Models. Results showed a correlation between drainage area and channel morphology, attributed to the presence of areal/shape characteristics in all the specific-region equations. Verification with an independent watershed sample demonstrated the predictive equations’ robustness and reliability. The study found that subdividing homogeneous regions improved accuracy compared to previous studies. Validation demonstrated up to 45.9 % (∼1.29 m³ s^-1) differences in peak flow modeling, corroborating their applicability for modeling purposes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106486"},"PeriodicalIF":4.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874136","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":"STICr: An open-source package and workflow for stream temperature, intermittency, and conductivity (STIC) data","authors":"Sam Zipper ,&nbsp;Christopher T. Wheeler ,&nbsp;Delaney M. Peterson ,&nbsp;Stephen C. Cook ,&nbsp;Sarah E. Godsey ,&nbsp;Ken Aho","doi":"10.1016/j.envsoft.2025.106484","DOIUrl":"10.1016/j.envsoft.2025.106484","url":null,"abstract":"<div><div>Non-perennial streams constitute over half the world's stream miles but are not commonly included in streamflow monitoring networks. As a result, Stream Temperature, Intermittency, and Conductivity (STIC) loggers are widely used for characterizing flow presence or absence in non-perennial streams. To facilitate ‘FAIR’ (findable, accessible, interoperable, and reusable) stream intermittency science, we present an open-source R package, STICr, for processing STIC logger data. STICr includes functions to tidy data, calibrate sensors, classify data into wet/dry readings, and perform quality checks and validation. We also show a reproducible STICr-based workflow for an interdisciplinary project spanning multiple watersheds, years, and research groups. In South Fork Kings Creek (Konza Prairie, Kansas, USA), we show that stream intermittency is driven by the balance between monthly precipitation inputs, seasonal evapotranspiration fluxes, and underlying geology. Overall, STICr can be used to create FAIR stream intermittency data and enable advances in hydrologic and ecosystem science.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106484"},"PeriodicalIF":4.8,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859978","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":"Enhancing rainfall frequency analysis through bivariate nonstationary modeling in South Korea","authors":"Heejin An ,&nbsp;Hyun-Han Kwon ,&nbsp;Moonyoung Lee ,&nbsp;Inkyung Min ,&nbsp;Kichul Jung ,&nbsp;Daeryong Park","doi":"10.1016/j.envsoft.2025.106471","DOIUrl":"10.1016/j.envsoft.2025.106471","url":null,"abstract":"<div><div>This study conducted a bivariate nonstationary frequency analysis utilizing rainfall events to capture the multidimensional nature of rainfall phenomena and rainfall pattern variability in South Korea. Extreme events were identified by the peaks over threshold (POT) method which enhanced the accuracy of estimation. The nonstationary model, incorporating a nonlinear regression using time as a covariate instead of the scale parameter in the generalized Pareto distribution (GPD), provided a more stable interannual variability of rainfall representation under a dynamic climate compared to stationary models. The ability of the bivariate POT method threshold <span><math><mrow><mo>(</mo><msub><mi>T</mi><mrow><mi>a</mi><mi>n</mi><mi>d</mi></mrow></msub><mo>)</mo></mrow></math></span> to enhance our understanding of climate change by extracting events with high values in both variables was confirmed. Furthermore, bivariate analysis and nonstationarity significantly influenced the estimation of the return period, indicating that the proposed framework facilitates robust adjustment to nonstationary rainfall patterns, ensuring the dependable utilization of current design frequencies in the context of climate change.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106471"},"PeriodicalIF":4.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855798","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":"Recommendations on benchmarks for the DeNitrification–DeComposition model application in China: Insights from literature analysis","authors":"Nanchi Shen ,&nbsp;Jiani Tan ,&nbsp;Qing Mu ,&nbsp;Ling Huang ,&nbsp;Wenbo Xue ,&nbsp;Yangjun Wang ,&nbsp;Maggie Chel Gee Ooi ,&nbsp;Mohd Talib Latif ,&nbsp;Gang Yan ,&nbsp;Lam Yun Fat Nicky ,&nbsp;Li Li","doi":"10.1016/j.envsoft.2025.106485","DOIUrl":"10.1016/j.envsoft.2025.106485","url":null,"abstract":"<div><div>This study addresses the lack of standardized evaluation criteria for the DeNitrification–DeComposition (DNDC) model, widely used to assess greenhouse gas emissions in agricultural systems. Based on a comprehensive analysis of literature data, we propose a set of benchmarks to improve the model's reliability, focusing on crop yield, soil organic carbon (SOC), nitrous oxide (N₂O), and methane (CH₄) emissions within the context of Chinese agriculture. Key performance indicators, including correlation coefficient (R), normalized root mean square error (nRMSE), and index of agreement (IOA), are defined to enhance model calibration and validation. The proposed benchmarks aim to provide a consistent reference for DNDC applications, facilitating accurate assessments of greenhouse gas emissions and supporting sustainable agricultural practices. By synthesizing existing research, this study contributes to improving model accuracy and enhancing agricultural management strategies, with implications for climate change mitigation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106485"},"PeriodicalIF":4.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864133","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":"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":"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":"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":"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}