Environmental Modelling & Software最新文献_第10页

An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt 一个用于高山流域水文建模的增强Python框架：雪滞回和冰川冰融化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-19 DOI: 10.1016/j.envsoft.2025.106842

Martin Masten , Simon Seelig , Matevž Vremec , Magdalena Seelig , Gerfried Winkler

{"title":"An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt","authors":"Martin Masten , Simon Seelig , Matevž Vremec , Magdalena Seelig , Gerfried Winkler","doi":"10.1016/j.envsoft.2025.106842","DOIUrl":"10.1016/j.envsoft.2025.106842","url":null,"abstract":"<div><div>Simulating snow cover and glacier ice melt is essential for understanding hydrological processes in high-alpine catchments. We present a new Python extension to the Rainfall-Runoff Modeling Playground (RRMPG) that incorporates two key alpine-specific processes: snow cover hysteresis and glacier ice melt. Snow hysteresis captures the asymmetric evolution of snow-covered area between accumulation and melt periods, while glacier melt modeling is crucial in glacierized catchments due to its strong influence on water balance. The model is tested in two catchments in the Ötztal Alps and shows high accuracy in simulating runoff and snow cover dynamics. A multi-objective calibration approach using observed runoff and MODIS snow cover data improves model robustness. Designed for modularity and interoperability, the framework integrates easily with tools for calibration, sensitivity analysis, and data visualization. This open-source extension advances hydrological modeling in complex alpine environments by offering enhanced process representation, flexibility, and compatibility with Python-based workflows.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106842"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785095","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 flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl 一个灵活的、可微分的神经增强水文建模框架：hydromodels的设计、实现和应用

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.envsoft.2025.106802

Xin Jing, Xue Yang, JunGang Luo, GangGang Zuo

{"title":"A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl","authors":"Xin Jing, Xue Yang, JunGang Luo, GangGang Zuo","doi":"10.1016/j.envsoft.2025.106802","DOIUrl":"10.1016/j.envsoft.2025.106802","url":null,"abstract":"<div><div>Integrating deep learning with hydrological models is a popular research direction; however, this field faces significant challenges due to automatic differentiation requirements and interface incompatibilities, leading to many existing hydrological modeling frameworks being unable to perform effective hybrid modeling. To fill this gap, we propose a framework that inherits and enhances the design philosophies of previous modeling frameworks. It utilizes symbolic programming to reduce the difficulty of hydrological modeling, particularly for hybrid models integrating deep learning, supports automatic differentiation for model optimization, and effectively addresses the diverse and evolving needs for both specialized hydrological and hybrid modeling applications. This framework, named HydroModels.jl, is implemented in the Julia programming language, is publicly accessible on GitHub, and is accompanied by detailed documentation. This study describes its architecture and implementation details, and presents two case studies as examples to demonstrate its integration capabilities and applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106802"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613576","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

Visualization of Urban Digital Twins on the web with attribute-driven adaptive tiling 基于属性驱动自适应平铺的城市数字孪生网络可视化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.envsoft.2026.106863

Ziya Usta , Alper Tunga Akın , Ken Arroyo Ohori , Jantien Stoter

引用次数: 0

The HYDRUS model for soil and water management: A brief review of capabilities, trends, and future directions 用于水土管理的HYDRUS模型：能力、趋势和未来方向的简要回顾

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-26 DOI: 10.1016/j.envsoft.2025.106801

V. Jayasuriya , Prabha Susan Philip , T. Jyolsna

引用次数: 0

Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia 揭示中亚地区需水量多因素交互效应的贝叶斯析因分析

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-30 DOI: 10.1016/j.envsoft.2025.106806

Yanxiao Zhou , Yongping Li , Guohe Huang , Zhenyao Shen , Yufei Zhang

{"title":"Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia","authors":"Yanxiao Zhou , Yongping Li , Guohe Huang , Zhenyao Shen , Yufei Zhang","doi":"10.1016/j.envsoft.2025.106806","DOIUrl":"10.1016/j.envsoft.2025.106806","url":null,"abstract":"<div><div>This study advances an integrated Bayesian support vector machine-based two-step factorial analysis (abbreviated as BSVM-TFA) method for revealing the influences of human activities on water demand. The developed method can capture complex nonlinear relationships between human activities and water demand by calibrating SVM hyperparameters through Bayesian optimization, which helps prevent overfitting. BSVM-TFA can also identify the individual and interactive effects of multiple factors on water demand and screen key influencing factors. The BSVM-TFA is then applied to Central Asia, and the results show that by 2050, water demand would range from 75.66 × 10<sup>9</sup> m<sup>3</sup> to 113.23 × 10<sup>9</sup> m<sup>3</sup> under different scenarios, indicating an uncertainty of about 33.18 % driven by human activities. The key factors influencing water demand in Central Asia are GDP and agricultural irrigation efficiency (AIE), with a total contribution of 47.98 %; the water demand would be reduced by 16.42 × 10<sup>9</sup> m<sup>3</sup> with low-growth GDP and increasing AIE.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106806"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619718","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

An analytical methodology to assess epistemic uncertainty of 2D flood models under steady flow conditions 稳定水流条件下二维洪水模型认知不确定性评估的分析方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.envsoft.2025.106849

Vasilis Bellos , Vassilios A. Tsihrintzis

引用次数: 0

Cross-scale separation of climate and human impacts on runoff using a dual-step refined time-varying attribution model 基于双步精细时变归因模型的气候和人类对径流影响的跨尺度分离

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.envsoft.2026.106855

Ziqin Zheng , Zengchuan Dong , Wenzhuo Wang , Jinyu Meng , Hao Ke , You Zhang

{"title":"Cross-scale separation of climate and human impacts on runoff using a dual-step refined time-varying attribution model","authors":"Ziqin Zheng , Zengchuan Dong , Wenzhuo Wang , Jinyu Meng , Hao Ke , You Zhang","doi":"10.1016/j.envsoft.2026.106855","DOIUrl":"10.1016/j.envsoft.2026.106855","url":null,"abstract":"<div><div>The accelerated evolution of climate change and human activities as well as their increasingly complex interactions have led to a significant increase in runoff uncertainty and non-consistency. Understanding and assessing the impacts of both on runoff will be important for water resources planning and management. This study develops a general modelling framework for runoff attribution by proposing a dual-step refined time-varying attribution model based on Budyko framework, which combine revisions of traditional methods and improvements of the structure of the traditional attribution model. The proposed model is evaluated through application to the Lixia River Basin across multiple spatio-temporal scales. Results demonstrate that the model enhances the accuracy of runoff change separation by 11.42 %–33.46 % at annual scales and by 5.06 %–6.84 % at the multi-year average scales. This dual-step model contributes an accurate separation and generalizable modelling insights for assessment of hydrological responses to coupled climatic and anthropogenic drivers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106855"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894760","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

Water in computable general equilibrium models: Review, synthesis and avenues for future research 可计算一般平衡模型中的水：综述、综合及未来研究方向

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.envsoft.2025.106839

Saba Al Hosni , Scott J. McGrane , Gioele Figus , Cecilia Tortajada

{"title":"Water in computable general equilibrium models: Review, synthesis and avenues for future research","authors":"Saba Al Hosni , Scott J. McGrane , Gioele Figus , Cecilia Tortajada","doi":"10.1016/j.envsoft.2025.106839","DOIUrl":"10.1016/j.envsoft.2025.106839","url":null,"abstract":"<div><div>Water-extended Computable General Equilibrium (CGE) models are a class of economy-wide models widely used as tools to address research and policy questions for various water-related topics. This systematic review analyses 100 applications of water-CGE models, categorising them into key areas based on their structure and aims, including agricultural, industrial, combination of agricultural and industrial, energy, and combination of energy and agriculture, to examine the methodological approaches of incorporating water into CGE models, and to explore the various themes of the applications. Findings suggest that improvements in incorporating water in CGE models require improvements in the quality and detail of water data, explicitly specifying water as a factor of production, constructing models at smaller spatial scales, accounting for water seasonality, and improving transparency of calibration and validation methods. Addressing these challenges will enhance the representation of water in CGE models that can provide critical insights in addressing water-economy interconnections.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106839"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785083","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

Soft computing techniques for atmospheric pollution and traffic emission prediction 大气污染与交通排放预测的软计算技术

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.envsoft.2025.106828

Vivek Mathur , Divya Srivastava , Jaya Pandey , Vivek Mishra

{"title":"Soft computing techniques for atmospheric pollution and traffic emission prediction","authors":"Vivek Mathur , Divya Srivastava , Jaya Pandey , Vivek Mishra","doi":"10.1016/j.envsoft.2025.106828","DOIUrl":"10.1016/j.envsoft.2025.106828","url":null,"abstract":"<div><div>Air pollution, especially from traffic emissions, poses a significant threat to public health and environmental sustainability. Traditional monitoring systems are resource-intensive, prompting a shift toward computational forecasting techniques. This mini-review evaluates the application of soft computing methods—such as Artificial Neural Networks (ANN), Fuzzy Logic, Genetic Algorithms, and hybrid models—for atmospheric pollution and traffic emission prediction. These models offer flexibility, adaptability, and high accuracy in handling nonlinear and uncertain data. The review compares model architectures, input features, and performance metrics, emphasizing the superior predictive ability of hybrid and deep learning models. Additionally, the potential integration of these models with IoT and smart city frameworks is discussed. Key limitations, including lack of model generalizability and uncertainty handling, are highlighted alongside suggestions for future improvement. This work provides a concise overview of emerging data-driven strategies for air quality forecasting, offering direction for researchers and policymakers in sustainable urban planning.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106828"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785087","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

Watershed boundary extraction from digital elevation models using RBM-SegNet 基于RBM-SegNet的数字高程模型流域边界提取

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-01-30 Epub Date: 2025-11-29 DOI: 10.1016/j.envsoft.2025.106805

Huanyu Yang , Hongming Zhang , Yuwei Sun , Lu Du , Weilin Xu , Jincheng Ni , Qiankun Chen , Chunmei Wang , Qinke Yang , Haijing Shi

{"title":"Watershed boundary extraction from digital elevation models using RBM-SegNet","authors":"Huanyu Yang , Hongming Zhang , Yuwei Sun , Lu Du , Weilin Xu , Jincheng Ni , Qiankun Chen , Chunmei Wang , Qinke Yang , Haijing Shi","doi":"10.1016/j.envsoft.2025.106805","DOIUrl":"10.1016/j.envsoft.2025.106805","url":null,"abstract":"<div><div>Accurately extracting watershed boundaries is critical for hydrological modeling and environmental management. Traditional extraction methods from Digital Elevation Models (DEMs) rely on manually defined thresholds and supplementary terrain features, limiting adaptability and efficiency. To address these issues, this study developed a watershed boundaries extraction framework based on a Residual Bottleneck Attention Multi-feature Fusion Network (RBM-SegNet). The framework consists of three components: an input layer, a semantic segmentation model, and a post-processing module. Key contributions include: (1) utilizing the [DEM, Slope, Hillshade, and Aspect] functions as the optimal input combination; (2) introducing residual connections and the Bottleneck Attention Module (BAM) to enhance feature transmission and suppress irrelevant regions; (3) incorporating multi-feature fusion to refine structural and detail prediction; and (4) incorporating post-processing to improve output-completeness and hydrological consistency. The experimental results show that RBM-SegNet outperforms traditional and existing deep learning methods in accuracy, demonstrating strong potential for practical applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106805"},"PeriodicalIF":4.6,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613575","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