Environmental Modelling & Software最新文献

The SW2D software for porosity-based shallow water simulations 基于孔隙度的浅水模拟SW2D软件

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107190

Pascal Finaud-Guyot,Vincent Guinot,Antoine Rousseau,Lilas Bugeau,João Guilherme Caldas Steinstraesser,Carole Delenne

引用次数: 0

Spherical graph neural super-resolution for atmospheric moisture fields with conservation correction and cross-resolution transfer 基于守恒校正和交叉分辨率传递的大气湿度场球面图神经网络超分辨率研究

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107207

Nicholas Hong Sheng Chung,Thalosang Tshireletso,Meghdad Bagheri

引用次数: 0

Sabu: A microservices-based software framework for disaggregated carbon emissions estimation Sabu：一个基于微服务的软件框架，用于分类碳排放估算

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107208

Alireza Adli,Ursula Eicker

引用次数: 0

A Deep Learning–driven Decision Support System for characterization of probability distribution tails of hydrometeorological extremes 水文气象极端事件概率分布尾特征的深度学习驱动决策支持系统

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107213

Sukhsehaj Kaur,Sagar Rohidas Chavan

引用次数: 0

SlideMapper: a Google Earth Engine-based web application for semi-automatic landslide mapping using Sentinel-2 imagery SlideMapper：一个基于b谷歌Earth engine的web应用程序，用于使用Sentinel-2图像进行半自动滑坡测绘

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107205

Xiao-Mi Wang,Yi-Fen Xu,Lei-Lei Liu,Can Wang

引用次数: 0

Toward Near-Real-Time Flood-Risk Mapping with Crowdsourced Data: A Bayesian Neural Network Framework for Uncertainty-Guided Verification 基于众包数据的近实时洪水风险映射：不确定性导向验证的贝叶斯神经网络框架

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-10-01 DOI: 10.1016/j.envsoft.2026.107206

Mengjue Zhu,Barnali Dixon,Subhrajit Guhathakurta

引用次数: 0

Regional vs local LSTM models for short-term streamflow forecasting under operational constraints 区域与本地LSTM模型在操作约束下的短期流量预测

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.envsoft.2026.106897

Jorge Saavedra-Garrido , Jorge Arevalo , Luis De La Fuente , Aldo Tapia , Christopher Paredes-Arroyo , Ana Maria Cordova , Daira Velandia , Pablo Álvarez , Héctor Reyes-Serrano , Rodrigo Salas

{"title":"Regional vs local LSTM models for short-term streamflow forecasting under operational constraints","authors":"Jorge Saavedra-Garrido , Jorge Arevalo , Luis De La Fuente , Aldo Tapia , Christopher Paredes-Arroyo , Ana Maria Cordova , Daira Velandia , Pablo Álvarez , Héctor Reyes-Serrano , Rodrigo Salas","doi":"10.1016/j.envsoft.2026.106897","DOIUrl":"10.1016/j.envsoft.2026.106897","url":null,"abstract":"<div><div>Reliable short-term streamflow forecasting remains a key challenge due to data latency, uncertainty, and other real-world constraints. This study presents a regional Long Short-Term Memory (LSTM) model to predict daily mean and maximum streamflow across 340 points in Chile over a five-day horizon, explicitly accounting for operational limitations such as unavailable recent streamflow and delayed input data. Compared to locally trained models, the regional model demonstrates superior performance in temporal correlation and variance representation, with Kling–Gupta Efficiency (KGE) <span><math><mo>≥</mo></math></span> 0.6 at 156 points. Crucially, high-flow event prediction improves significantly: bias in the Fractional High-flow Volume (FHV) is reduced by <span><math><mo>∼</mo></math></span>50% at the 90th percentile and <span><math><mo>∼</mo></math></span>25% at the 99th percentile, demonstrating strong operational robustness with minimal degradation over the forecast horizon. These findings highlight the potential of regional deep learning models to offer scalable and resilient performance across diverse hydrological settings, supporting flood preparedness and water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106897"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072501","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

MAformer: A multivariate prediction framework with adaptive multi-scale decomposition and phase correction for water quality in aquaculture environments MAformer：具有自适应多尺度分解和相位校正的水产养殖环境水质多元预测框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.envsoft.2026.106905

Haoran Xing, Ying Li, Dashe Li, Huanhai Yang

{"title":"MAformer: A multivariate prediction framework with adaptive multi-scale decomposition and phase correction for water quality in aquaculture environments","authors":"Haoran Xing, Ying Li, Dashe Li, Huanhai Yang","doi":"10.1016/j.envsoft.2026.106905","DOIUrl":"10.1016/j.envsoft.2026.106905","url":null,"abstract":"<div><div>Accurate prediction of dissolved oxygen (DO) is crucial for intelligent decision-making in aquaculture. However, achieving this goal is challenging due to nonstationarity, multi-period aliasing, and local phase shifts inherent in DO series. We propose the Multi-scale Adaptive transformer (MAformer) for water quality prediction. First, the hierarchical adaptive smoothing decomposer stabilizes long-term patterns while preserving short-term details. Second, a multi-period phase-aligned attention module achieves cross-period synchronization. Third, the phase-shift correction attention module enhances robustness to short-term disturbances. Experiments on six marine ranching datasets from diverse geographical and climatic regions demonstrate that MAformer significantly outperforms seven state-of-the-art baseline models. For instance, it achieves an average reduction of 9.59% in MAE and 7.96% in RMSE, alongside improvements of 8.39% in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and 6.25% in KGE. These results confirm MAformer’s superior capability as a reliable and generalizable tool for intelligent aquaculture management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106905"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209274","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 python framework for differentiable hydrological modeling and research workflow automation 可微分水文建模和研究工作流自动化的Python框架

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.2026.106895

Wenyu Ouyang , Shuolong Xu , Yikai Chai , Laihong Zhuang , Zhihong Liu , Lei Ye , Xinzhuo Wu , Yong Peng , Chi Zhang

{"title":"A python framework for differentiable hydrological modeling and research workflow automation","authors":"Wenyu Ouyang , Shuolong Xu , Yikai Chai , Laihong Zhuang , Zhihong Liu , Lei Ye , Xinzhuo Wu , Yong Peng , Chi Zhang","doi":"10.1016/j.envsoft.2026.106895","DOIUrl":"10.1016/j.envsoft.2026.106895","url":null,"abstract":"<div><div>This study introduces a Python-based framework for constructing differentiable hydrological models with a modular design to streamline research workflows. The framework integrates five key modules: hydrodataset and hydrodatasource for data preprocessing, hydromodel and torchhydro for traditional and differentiable modeling, and HydroDHM for orchestrating integrated workflows. The data modules automate preparation of diverse datasets, including open-access and proprietary resources. Hydromodel supports process-based model calibration and evaluation, while torchhydro enables neural network integration for differentiable models. HydroDHM coordinates these components through a unified interface for configuring and executing end-to-end modeling pipelines. Case studies in CAMELS basins demonstrate that differentiable models achieve comparable streamflow simulation performance to traditional approaches. By decoupling data handling from model development and providing uv-installable (and pip-compatible) modules, the framework ensures reproducibility, scalability, and adaptability across diverse hydrological contexts.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106895"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047987","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

Modeling hydrologic response to wildfires in the Pacific Northwest with a modified calibration technique 用改进的校准技术模拟太平洋西北地区野火的水文响应

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.2026.106896

Hyunwoo Kang , Cameron E. Naficy , Kevin D. Bladon

{"title":"Modeling hydrologic response to wildfires in the Pacific Northwest with a modified calibration technique","authors":"Hyunwoo Kang , Cameron E. Naficy , Kevin D. Bladon","doi":"10.1016/j.envsoft.2026.106896","DOIUrl":"10.1016/j.envsoft.2026.106896","url":null,"abstract":"<div><div>The 2020 Labor Day fires in the Western Cascades of Oregon, USA, burned extensive forested areas, which altered hydrologic processes, water quality, aquatic ecosystems, and drinking water resources. Understanding wildfire severity effects on hydrologic processes is crucial for improved water resource management. Our study assessed wildfire severity impacts on hydrology using a modified calibration method for the Soil and Water Assessment Tool (SWAT) model. Calibration incorporated evapotranspiration and leaf area index to represent vegetation loss and hydrologic impacts. We also integrated a wildfire module to simulate fire effects on soil and vegetation parameters. This improved modeling approach effectively captured post-fire hydrologic behavior, especially increased high streamflows and reduced evapotranspiration, with greater changes linked to higher burn severity. These findings emphasize the importance of considering fire severity in hydrologic modeling, aiding proactive management and mitigation strategies to protect water supply and enhance ecosystem resilience in wildfire-prone regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106896"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047988","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