Environmental Modelling & Software最新文献

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hydroMOPSO: A flexible and model-independent multi-objective optimisation R package for environmental and hydrological models hydroMOPSO：一个灵活的和模型无关的多目标优化环境和水文模型R包

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

Environmental Modelling & Software

Pub Date : 2026-01-02 DOI: 10.1016/j.envsoft.2025.106851

Rodrigo Marinao , Mauricio Zambrano-Bigiarini , Oscar M. Baez-Villanueva

This article introduces hydroMOPSO, a multi-objective, model-independent R package for the calibration of hydrological and environmental models. It supports both R-based and R-external models through wrapper functions, providing flexibility for a wide range of optimisation problems. The package includes fine-tuning options to generate a Pareto-optimal front. The performance of hydroMOPSO was compared to the caRamel R package using benchmark functions and case studies involving two R-based hydrological models in an Andean catchment. hydroMOPSO outperformed caRamel on benchmarks, with faster convergence in the two hydrological models. An R-external case study demonstrated the flexibility and ease of use of hydroMOPSO, through its application to the calibration of the SWAT+ model. The package also enables the generation of informative outputs for modellers, with particular emphasis on hydrographs and parameter sets from the Pareto-optimal front. hydroMOPSO constitutes a valuable tool for researchers and practitioners seeking to implement multi-objective optimisation in environmental and hydrological modelling.

本文介绍了hydroMOPSO，一个用于水文和环境模型校准的多目标、模型无关的R包。它通过包装器函数支持基于r和外部r的模型，为广泛的优化问题提供了灵活性。该软件包包括微调选项，以产生一个帕累托最优的前面。使用基准函数和案例研究，将hydroMOPSO与caRamel R包的性能进行了比较，这些案例研究涉及安第斯流域的两个基于R的水文模型。在基准测试中，hydroMOPSO的表现优于caRamel，在两个水文模型中收敛速度更快。R-external案例研究表明，通过将hydroMOPSO应用于SWAT+模型的校准，该方法具有灵活性和易用性。该软件包还可以为建模人员生成信息输出，特别强调来自帕累托最优锋面的水文曲线和参数集。对于寻求在环境和水文建模中实现多目标优化的研究人员和实践者来说，hydroMOPSO构成了一个有价值的工具。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-02 DOI: 10.1016/j.envsoft.2026.106855

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

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.

气候变化和人类活动的加速演变及其日益复杂的相互作用导致径流不确定性和不一致性显著增加。了解和评估两者对径流的影响对水资源规划和管理非常重要。本研究基于Budyko框架提出了一种两步精细化时变归因模型，结合对传统方法的修正和对传统归因模型结构的改进，构建了径流归因的通用建模框架。将该模型应用于历下河流域，在多个时空尺度上进行了评价。结果表明，该模型在年尺度和多年平均尺度上分别提高了11.42% ~ 33.46%和5.06% ~ 6.84%的径流变化分离精度。这种双步骤模型有助于准确分离和可推广的建模见解，以评估对气候和人为耦合驱动因素的水文响应。

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引用次数: 0

A distributed data-driven system for dynamic wildfire monitoring and forecasting 用于动态野火监测和预报的分布式数据驱动系统

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

Environmental Modelling & Software

Pub Date : 2026-01-01 DOI: 10.1016/j.envsoft.2026.106854

Mengxia Zha , Zheng Wang , Jie Ji , Jiping Zhu

Traditional data-driven wildfire forecasting frameworks do not incorporate observational data collection, resulting in a disconnect between forecasting and monitoring. This study presents a distributed data-driven system for collaborative monitoring and forecasting to improve the efficiency of wildfire management. A search zone generation method was developed to guide unmanned aerial vehicle (UAV) operations based on FARSITE predictions. Additionally, the distributed Ensemble Transform Kalman Filter was employed for data assimilation (DA), integrating UAV-based observations to dynamically correct the forecast fire perimeter. When applied to the 2022 Dragon Fire, the system achieved 83 % coverage of the fire perimeter—a 51 % improvement over traditional methods. Moreover, the average root mean square error of the forecast fire perimeter was reduced by 53 %. The system requires only 15 s on average to forecast 1 h of wildfire spread, with DA computations taking approximately 5 s on average.

传统的数据驱动的野火预测框架不包括观测数据收集，导致预测和监测之间脱节。本研究提出了一个分布式数据驱动的协同监测预报系统，以提高野火管理的效率。提出了一种基于FARSITE预测的无人机搜索区域生成方法。此外，采用分布式集成变换卡尔曼滤波进行数据同化（DA），整合无人机观测数据，对预测火场周长进行动态校正。当应用于2022年的“龙火”导弹时，该系统实现了83%的火力覆盖范围，比传统方法提高了51%。此外，预测火灾周长的平均均方根误差降低了53%。该系统预测1小时的野火蔓延平均只需15秒，而数据处理计算平均大约需要5秒。

引用次数: 0

Origin–destination specific traffic emissions and data-driven NO2 pollution-optimal routing in urban environments 城市环境下交通排放与数据驱动的NO2污染优化路径

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

Environmental Modelling & Software

Pub Date : 2025-12-29 DOI: 10.1016/j.envsoft.2025.106813

Samantha Ivings , James A. King , Alexander Roocroft , Patricio Ortiz , Toby Willis , Maria Val Martin , Hadi Arbabi , Giuliano Punzo

Urban air pollution from traffic poses serious public health risks. Pollution exposure can be minimised through traffic routing systems; these currently rely on detailed local environmental information, which is often difficult to collect or generalise within and across cities. Here, we introduce a new data-driven approach for ready application to different urban road networks by directly relating NO₂ to traffic density in a time-dependent and weather-corrected manner. We demonstrate this application by comparing pollution-optimal routings, using our novel direct NO₂/density approach, to the conventional traffic assignment minimising user travel time, in a case study of Sheffield, UK. There, we find user-optimal traffic flows result in 21% higher total NO₂ concentrations than pollution-optimal routings, while saving only 9% in total travel time: an average of 0.3 min per road. Our generalisable framework offers a practical alternative to current emissions-based models for air-quality-aware traffic control and environmental zone planning.

交通造成的城市空气污染对公众健康构成严重威胁。可透过交通路线系统，尽量减少污染；这些评估目前依赖于详细的当地环境信息，而这些信息往往很难在城市内部和城市之间收集或概括。在这里，我们介绍了一种新的数据驱动方法，通过以时间依赖和天气校正的方式直接将二氧化氮与交通密度联系起来，以便随时应用于不同的城市道路网络。在英国谢菲尔德的一个案例研究中，我们通过比较污染最优路线，使用我们新颖的直接NO2/密度方法，与最小化用户旅行时间的传统交通分配，来展示这一应用。在那里，我们发现用户优化的交通流量导致总二氧化氮浓度比污染优化的路线高21%，而总旅行时间仅节省9%：平均每条路0.3分钟。我们的概括性框架为空气质量意识交通控制和环境区域规划提供了一个实际的替代方案，以取代目前基于排放的模型。

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引用次数: 0

Re-Emission: A free open-source software for estimating, reporting, and visualizing greenhouse gas emissions from reservoirs 重新排放：一个免费的开源软件，用于估计、报告和可视化水库的温室气体排放

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

Environmental Modelling & Software

Pub Date : 2025-12-26 DOI: 10.1016/j.envsoft.2025.106850

Tomasz Janus , Christopher Barry , Xingxing Zhang , Jaise Kuriakose

Reservoirs are significant contributors to anthropogenic greenhouse gas emissions, whose climate impacts require appropriate assessment and reporting. Existing emission modelling tools often demand extensive input data and manual processing, limiting their application to small sets of reservoirs. Moreover, no frameworks currently enable experimentation with new emission models and custom configurations. Here, we present Re-Emission — a free, open-source Python library and command-line tool for streamlined reservoir-emission modelling, including batch processing of multiple reservoirs, custom configurations, and integration with third-party libraries. Its utility is demonstrated through two case studies involving about 250 reservoirs. Re-Emission integrates with a catchment-analysis tool to automate spatially explicit emission assessments and can be embedded in multi-domain frameworks for water-resource and energy planning, addressing a key barrier to the wider adoption of reservoir emission models.

水库是人为温室气体排放的重要来源，其气候影响需要适当的评估和报告。现有的排放模拟工具通常需要大量的输入数据和人工处理，这限制了它们在小范围油藏中的应用。此外，目前没有任何框架能够试验新的排放模型和定制配置。在这里，我们介绍了Re-Emission——一个免费的、开源的Python库和命令行工具，用于简化库排放建模，包括多个库的批量处理、自定义配置和与第三方库的集成。通过涉及约250个水库的两个案例研究证明了该方法的实用性。reemission与流域分析工具集成，可自动进行空间明确的排放评估，并可嵌入水资源和能源规划的多领域框架，解决了广泛采用水库排放模型的关键障碍。

引用次数: 0

HR-MM Segformer: Enhancing land use and land cover semantic segmentation through transformer-based multisource remote sensing feature fusion HR-MM分割器：基于变压器的多源遥感特征融合增强土地利用和土地覆盖语义分割

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

Environmental Modelling & Software

Pub Date : 2025-12-26 DOI: 10.1016/j.envsoft.2025.106848

Junfu Fan , Zongwen Shi , Yujie Du , Can Zhuang

Automatic land use and land cover (LULC) semantic segmentation is constrained by spectral confusion and the semantic gap inherent in independent-branch multisource and multisensor (MM) data fusion architectures. To address these limitations, this study proposes HR-MM SegFormer, a framework incorporating a unified transformer encoder. Unlike conventional dual-stream approaches, the architecture projects heterogeneous high-resolution (HR) optical imagery and MM auxiliary data (multispectral/DSM) into a shared semantic manifold via a feature correction (FC) layer. Additionally, a multimodal cross-attention fusion (MCAF) module dynamically retrieves complementary spectral and geometric contexts guided by HR spatial structures. Experimental evaluations of the HR-MS LULC and HR-DSM Potsdam datasets yield mean intersection over union (mIoU) scores of 88.50 % and 87.84 %, respectively. These results correspond to improvements of 8.89 % and 12.67 % over single-modal baselines, accompanied by a 6.3 % increase in computational overhead. This study substantiates that the unified full-attention paradigm bridges cross-modal disparities, providing an effective solution for fine-grained Earth observations.

独立分支多源多传感器（MM）数据融合架构中固有的频谱混淆和语义缺口限制了土地利用和土地覆盖（LULC）自动语义分割。为了解决这些限制，本研究提出了HR-MM SegFormer，这是一个包含统一变压器编码器的框架。与传统的双流方法不同，该架构通过特征校正（FC）层将异构高分辨率（HR）光学图像和MM辅助数据（多光谱/DSM）投影到共享的语义流形中。此外，多模态交叉注意融合（MCAF）模块在HR空间结构的引导下动态检索互补的光谱和几何背景。HR-MS LULC和HR-DSM波茨坦数据集的实验评估结果显示，平均mIoU分数分别为88.50%和87.84%。这些结果与单模态基线相比分别提高了8.89%和12.67%，同时计算开销增加了6.3%。该研究证实了统一的全注意范式可以弥合跨模态差异，为细粒度地球观测提供了有效的解决方案。

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引用次数: 0

A coupled land use change-ecohydrological model for multi-seasonal arid agricultural systems: an Egyptian case study 多季节干旱农业系统的土地利用变化-生态水文耦合模型：埃及案例研究

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

Environmental Modelling & Software

Pub Date : 2025-12-26 DOI: 10.1016/j.envsoft.2025.106845

Aimen Sattar , Simon Moulds , Calum Brown , Mark Rounsevell , Peter Alexander

Modelling interactions between climate, water, crops, and human decision-making requires coupling of biophysical and socioeconomic processes to model outcomes and explore potential futures. This study presents a novel coupled model of land-use change and ecohydrological processes in arid agricultural systems. The model links SWAT+, which simulates ecohydrological processes, including crop growth and irrigation water use, with CRAFTY, an agent-based framework that allocates land according to agent characteristics and resource conditions. Egypt is used as a case study where climate and socioeconomic stressors constrain agricultural production. The coupling captures how shifts in potential yields, driven by elevated CO₂ and warming, shape land-use change. Crop yields vary by crop and scenario, with the largest gains – and declines – under high-emission futures, while water use efficiency consistently improves, especially at higher CO₂ concentrations. Relying on open global datasets, the model provides a transferable approach for exploring climate adaptation in data-scarce, water-limited regions.

模拟气候、水、作物和人类决策之间的相互作用需要将生物物理和社会经济过程耦合起来，以模拟结果并探索潜在的未来。提出了一种新的干旱农业系统土地利用变化与生态水文过程耦合模型。该模型将模拟生态水文过程（包括作物生长和灌溉用水）的SWAT+与基于主体的框架CRAFTY联系起来，后者根据主体特征和资源条件分配土地。埃及被用作气候和社会经济压力因素制约农业生产的案例研究。这种耦合反映了在二氧化碳浓度升高和气候变暖的驱动下，潜在产量的变化是如何影响土地利用变化的。作物产量因作物和情景而异，在未来高排放的情况下，收益和降幅最大，而水利用效率持续提高，特别是在二氧化碳浓度较高的情况下。该模型依赖于开放的全球数据集，为探索数据稀缺、水资源有限地区的气候适应提供了一种可转移的方法。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-26 DOI: 10.1016/j.envsoft.2025.106849

Vasilis Bellos , Vassilios A. Tsihrintzis

We discuss the epistemic uncertainty observed in the output of 2D flood models in a steady-state conditions using an idealized benchmark setup. First, we propose a new taxonomy in uncertainty sources defining five drivers: a) forcing, b) geometric, c) physical, d) computational, e) structural. Then, we perform a sensitivity analysis to investigate the influence of the drivers’ variables to model outcome and an uncertainty quantification using several metrics, to include the Coefficient of Variation, the skewness and the newly proposed Uncertainty Index to quantify the contribution of every driver in the total uncertainty and its characteristics. We found that the driver with the major impact is forcing, followed by the geometric and the physical drivers, while the computational and the structural drivers have negligible impact, at the main channel. Given that in our era the accuracy of topographic information is high, future research shall focus on forcing and physical driver.

我们讨论了在稳态条件下使用理想基准设置的二维洪水模型输出中观察到的认知不确定性。首先，我们提出了一种新的不确定性源分类方法，定义了五个驱动因素：a)强迫，b)几何，c)物理，d)计算，e)结构。然后，我们进行了敏感性分析，以研究驱动变量对模型结果的影响，并使用几个指标进行了不确定性量化，包括变异系数，偏度和新提出的不确定性指数，以量化每个驱动因素在总不确定性及其特征中的贡献。我们发现，在主通道上，具有主要影响的驱动因素是强迫，其次是几何和物理驱动因素，而计算和结构驱动因素的影响可以忽略不计。在我们这个时代，地形信息的精度很高，未来的研究重点应该放在强迫和物理驱动上。

引用次数: 0

Data-driven parameterization of SWAT+ reservoir module without access to operation rules 无操作规则的SWAT+油藏模块数据驱动参数化

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

Environmental Modelling & Software

Pub Date : 2025-12-25 DOI: 10.1016/j.envsoft.2025.106852

S. Sreeraj , P. Athira , Kristin Peters , Jens Kiesel

Reservoirs play a pivotal role in water resources engineering, yet their simulation in hydrologic models is often constrained by a lack of operational rule data. This study presents a data-driven approach using simulated annealing for parameterizing reservoir module in the process based hydrological model SWAT + since the information on reservoir operation is lacking. Using only observed release flow time series, key reservoir decision table entries and hydrologic parameters were optimized for the Cedar Creek Reservoir (U.S.). The approach yielded substantial improvements in modeled reservoir outflow (+163.7 %, KGE = 0.72), storage (+588.2 %, KGE = 0.83), and evaporation (+85.3 %, KGE = 0.63), relative to default parameterizations. The results demonstrate that this data-driven method enables accurate SWAT + simulations even though reservoir operation rules are unavailable, thereby supporting its application in hydrologic engineering and water resources planning in data-scarce situations.

水库在水利工程中起着举足轻重的作用，但其在水文模型中的模拟往往受到缺乏运行规律数据的制约。针对基于过程的水文模型SWAT +中水库运行信息缺乏的问题，提出了一种数据驱动的模拟退火方法来参数化水库模块。仅使用观测到的释放流量时间序列，就对美国Cedar Creek水库的关键油藏决策表项和水文参数进行了优化。与默认参数化相比，该方法在模拟水库流出量（+ 163.7%,KGE = 0.72）、库容（+ 588.2%,KGE = 0.83）和蒸发（+ 85.3%,KGE = 0.63）方面取得了实质性的改善。结果表明，该方法能够在没有水库运行规律的情况下实现SWAT +的精确模拟，支持了该方法在数据匮乏情况下的水文工程和水资源规划中的应用。

引用次数: 0

Hydro3DJS: A modular web-based library for real-time 3D visualization of watershed dynamics and digital twin integration Hydro3DJS：一个基于web的模块化库，用于流域动态和数字孪生集成的实时3D可视化

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

Environmental Modelling & Software

Pub Date : 2025-12-25 DOI: 10.1016/j.envsoft.2025.106853

Ramteja Sajja , Omer Mermer , Yusuf Sermet , Ibrahim Demir

Effective visualization of hydrological data is essential for interpreting flood risk and supporting environmental decision-making. This study presents Hydro3DJS, a lightweight, browser-native 3D visualization library that integrates real-time environmental data with interactive digital-twin capabilities. Built with JavaScript, WebGL, and the Google Maps API, the system renders rainfall, floodwater dynamics, and insfrastructure exposure within spatially accurate environments while incorporating live data from USGS and NOAA. Its modular design supports terrain-aware water animation, customizable 3D infrastructure models, and scenario-based simulations suitable for research, planning, and communication. Two use cases, a rural multi-hazard simulation and an urban 100-year flood event with stream gauge overlays, demonstrate cross-context applicability. A usability study with 20 domain experts highlights the platform's strengths and identifies opportunities for enhanced interactivity and performance. Hydro3DJS contributes an accessible open-source framework for real-time 3D hydrological visualization, modular environmental data integration, and improved stakeholder communication in flood and watershed analysis.

水文数据的有效可视化对于解释洪水风险和支持环境决策至关重要。这项研究提出了Hydro3DJS，这是一个轻量级的浏览器原生3D可视化库，它将实时环境数据与交互式数字孪生功能集成在一起。该系统使用JavaScript、WebGL和谷歌Maps API构建，在空间精确的环境中呈现降雨、洪水动态和基础设施暴露，同时结合来自USGS和NOAA的实时数据。它的模块化设计支持地形感知水动画，可定制的3D基础设施模型，以及适合研究，规划和通信的基于场景的模拟。两个用例，一个农村多灾害模拟和一个城市百年一遇的洪水事件，展示了跨上下文的适用性。一项由20位领域专家参与的可用性研究强调了该平台的优势，并确定了增强交互性和性能的机会。Hydro3DJS提供了一个可访问的开源框架，用于实时三维水文可视化，模块化环境数据集成，以及改善洪水和流域分析中的利益相关者沟通。

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