Environmental Modelling & Software最新文献

英文中文

A Surrogate-Assisted Calibration Framework for Computationally Expensive Urban Fluvial Flood Models 一个计算代价昂贵的城市河流洪水模型的代理辅助校准框架

IF 4.9 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-02-05 DOI: 10.1016/j.envsoft.2026.106902

Congji Han, Takahiro Koshiba, Keiko Wada, Kenji Kawaike

引用次数: 0

STPredict: A python package automizing spatio-temporal predictions STPredict：一个自动化时空预测的python包

IF 4.9 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-02-05 DOI: 10.1016/j.envsoft.2026.106901

Arash Mari Oriyad, Arezoo Haratian, Mahdi Naderi, Nasrin Rafiei, Maryam Meghdadi, Zeinab Maleki, Pouria Ramazi

引用次数: 0

Development of a hybrid Bayesian-Hyperband optimization procedure: GeoAI-driven hyperparameter tuning of AdaBoost for enhancing Mineral Prospectivity Mapping 混合贝叶斯-超带优化程序的开发：geoai驱动的AdaBoost超参数调优，用于增强矿产远景图

IF 4.9 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-02-04 DOI: 10.1016/j.envsoft.2026.106883

Mahsa Hajihosseinlou, Abbas Maghsoudi, Reza Ghezelbash

引用次数: 0

evapoRe: An R-based application for exploratory data analysis of evapotranspiration 蒸散发：一个基于r的探索性数据分析应用程序

IF 4.9 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-29 DOI: 10.1016/j.envsoft.2026.106884

Akbar Rahmati Ziveh, Mijael Rodrigo Vargas Godoy, Vishal Thakur, Johanna R. Thomson, Martin Hanel, Yannis Markonis

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 4.9 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub 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

引用次数: 0

Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions 在数据稀缺地区使用基于聚类的机器学习技术推进水位预测

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-27 DOI: 10.1016/j.envsoft.2026.106899

SangHyun Lee, Taeil Jang

Accurate and scalable water level forecasting is essential for effective water resources management, particularly in regions with limited long-term records. We present a clustering-based framework for one- and three-day-ahead water level prediction in the Saemangeum Watershed, South Korea. Twenty-five monitoring stations were grouped into six hydrologically similar clusters using k-means clustering with wavelet-entropy features. Within each cluster, multilayer perceptron (MLP) models were trained using two strategies: (1) training only at the centroid station and (2) training at the station with the longest record in each cluster. The longest-record strategy showed strong agreement with observations, achieving mean Nash–Sutcliffe efficiency and root-mean-square error values of 0.97 and 0.06 for one-day-ahead forecasts, and 0.83 and 0.14 for three-day-ahead forecasts across all stations. By training one MLP per cluster and transferring it to all member stations, the framework reduces computational cost and provides a practical solution for large-scale water level forecasting in data-scarce environments.

准确和可扩展的水位预报对于有效的水资源管理至关重要，特别是在长期记录有限的地区。我们提出了一个基于聚类的框架，用于预测韩国新万金流域1天和3天前的水位。采用具有小波熵特征的k-均值聚类方法将25个监测站划分为6个水文相似的聚类。在每个聚类中，多层感知器（MLP）模型使用两种策略进行训练：(1)仅在质心站进行训练；(2)在每个聚类中记录最长的站进行训练。记录时间最长的策略与观测结果表现出很强的一致性，在所有台站中，提前一天预报的平均纳什-萨特克利夫效率和均方根误差分别为0.97和0.06，提前三天预报的误差分别为0.83和0.14。通过每个集群训练一个MLP并将其传递到所有成员站，该框架降低了计算成本，并为数据稀缺环境下的大规模水位预测提供了实用的解决方案。

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引用次数: 0

A Transformer-based neural network for global short-range dust forecasting 基于变压器的神经网络全球短期粉尘预报

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-27 DOI: 10.1016/j.envsoft.2026.106898

Shikang Du , Siyu Chen , Jiaqi He , Yu Fu , Lulu Lian

Dust forecasting holds significant scientific and societal value. Traditional numerical weather prediction (NWP) models predict dust by solving differential equations that simulate the physicochemical processes of dust aerosols. However, uncertainties in initial and boundary conditions, coupled with the complexity of modeling dust processes, result in significant challenges in both accuracy and computational cost. In this study, we introduce DustReal, a Transformer-based short-range dust forecasting model that leverages deep learning for enhanced accuracy and efficiency. DustReal takes MERRA-2 reanalysis data as input to generate hourly forecasts of surface dust concentration (DUSMASS) and dust optical depth at 550 nm (DOD) over the next 24 h, with a spatial resolution of 0.5° × 0.625°. Evaluation on an independent test set from 2022 to 2023 demonstrates robust forecasting accuracy, with DustReal outperforming three operational NWP dust forecast systems in Asia and excelling at capturing the fine-scale spatiotemporal evolution of dust events. Our results highlight that DustReal can deliver high-quality dust forecasts at a fraction of the computational cost required by traditional NWP models. As a lightweight, deep learning-based short-range model, DustReal offers a practical solution for sectors such as aviation and solar energy, facilitating the development of operational dust forecasting systems.

粉尘预报具有重要的科学和社会价值。传统的数值天气预报（NWP）模式通过求解模拟沙尘气溶胶物理化学过程的微分方程来预测沙尘。然而，初始条件和边界条件的不确定性，加上粉尘过程建模的复杂性，在精度和计算成本方面都带来了重大挑战。在本研究中，我们介绍了DustReal，一种基于变压器的短期粉尘预测模型，该模型利用深度学习来提高准确性和效率。DustReal以MERRA-2再分析数据为输入，生成未来24小时550 nm （DOD）表面尘埃浓度（DUSMASS）和尘埃光学深度的逐小时预报，空间分辨率为0.5°× 0.625°。在2022 - 2023年的独立测试集上进行的评估表明，DustReal的预测精度很高，优于亚洲三个正在运行的NWP沙尘预报系统，并擅长捕捉沙尘事件的精细时空演变。我们的研究结果强调，DustReal可以以传统NWP模型所需的一小部分计算成本提供高质量的尘埃预测。作为一种轻量级、基于深度学习的短程模型，DustReal为航空和太阳能等行业提供了实用的解决方案，促进了可操作粉尘预报系统的发展。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub 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

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.

本研究介绍了一个基于python的框架，用于构建具有模块化设计的可微分水文模型，以简化研究工作流程。该框架集成了五个关键模块：用于数据预处理的hydrodataset和hydrodatasource，用于传统和可微分建模的hydromodel和torchhydro，以及用于编排集成工作流的HydroDHM。数据模块自动准备各种数据集，包括开放访问和专有资源。Hydromodel支持基于过程的模型校准和评估，而torchhydro支持可微分模型的神经网络集成。HydroDHM通过一个统一的接口来协调这些组件，用于配置和执行端到端建模管道。骆驼盆地的案例研究表明，可微分模型与传统方法相比具有相当的流量模拟性能。通过将数据处理与模型开发分离，并提供uv安装（和pip兼容）模块，该框架确保了在不同水文环境下的再现性、可扩展性和适应性。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-24 DOI: 10.1016/j.envsoft.2026.106896

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

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.

2020年劳动节，美国俄勒冈州西部瀑布发生火灾，烧毁了大片森林地区，改变了水文过程、水质、水生生态系统和饮用水资源。了解野火严重程度对水文过程的影响对于改善水资源管理至关重要。本研究使用改良的土壤和水评估工具（SWAT）模型校准方法评估了野火严重程度对水文的影响。校正采用蒸散和叶面积指数来表示植被损失和水文影响。我们还集成了一个野火模块来模拟火灾对土壤和植被参数的影响。这种改进的建模方法有效地捕获了火灾后的水文行为，特别是增加的高流量和减少的蒸散量，这些变化与更高的烧伤严重程度有关。这些发现强调了在水文建模中考虑火灾严重程度的重要性，有助于主动管理和缓解战略，以保护供水，增强野火易发地区的生态系统恢复能力。

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引用次数: 0

A catalogue of Do's and Don'ts in the modeling of environmental systems 环境系统建模中该做与不该做的目录

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-24 DOI: 10.1016/j.envsoft.2026.106893

Xifu Sun , Anthony Jakeman , Serena H. Hamilton , Volker Grimm , Randall J. Hunt , Sondoss El Sawah , Hsiao-Hsuan Wang , Barry Croke , Min Chen

Modeling plays a vital role in understanding and managing complex environmental systems, but its credibility and quality depend heavily on a comprehensive set of defensible model activities and practices, especially when the system of interest is plagued with uncertainties and conflicting stakeholder perspectives. This paper proposes a catalogue of Do's and Don'ts to guide modelers in addressing the many pertinent considerations through the whole modeling cycle. This practical tool provides advice on approaching modeling effectively through adhering to good modeling practice. It emphasizes model choices that align with the model purpose and context, and the justification and documentation of modeling decisions and assumptions. Managing uncertainty is a core consideration. The identification, assessment and reporting of these uncertainties is important across the entire modeling process, which spans problem framing, technical design, implementation and application phases. Such good practices are critical for transparency and reliability of the modeling.

建模在理解和管理复杂的环境系统中起着至关重要的作用，但是它的可信度和质量在很大程度上依赖于一套全面的可辩护的模型活动和实践，特别是当感兴趣的系统受到不确定性和利益相关者观点冲突的困扰时。本文提出了一个应该做和不应该做的目录，以指导建模者在整个建模周期中解决许多相关的考虑因素。这个实用的工具提供了通过坚持良好的建模实践来有效地进行建模的建议。它强调与模型目的和上下文一致的模型选择，以及建模决策和假设的证明和文档。管理不确定性是一个核心考虑。在整个建模过程中，识别、评估和报告这些不确定性是很重要的，它跨越了问题框架、技术设计、实现和应用阶段。这样的良好实践对于建模的透明性和可靠性至关重要。

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引用次数: 0

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Environmental Modelling & Software

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