Environmental Modelling & Software最新文献

英文中文

An efficient multi-objective optimization method for inter-basin water diversion based on the rotation vector method for exploring the convex objective space 基于旋转矢量法探索凸目标空间的高效跨流域调水多目标优化方法

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

Environmental Modelling & Software

Pub Date : 2026-01-08 DOI: 10.1016/j.envsoft.2026.106858

Zekun Li , Bin Xu , Ping-an Zhong , Jiaying Tan , Ran Mo , Xinrong Wang , Zichen Ren , Guoqing Wang , Jianyun Zhang , Jiangyuan Li , Scott E. Boyce

Inter-basin water diversion systems involve multiple sources, users, and objectives with complex interrelations and numerous decision variables. Classical

ε

-constraint method often produce infeasible or redundant solutions in multi-objective optimization. To address this, the limitations of the classical

ε

-constraint method are analyzed, and long-term water diversion models with convex objectives are reformulated as convex programming problems. Based on convex programming characteristics, a rotation vector method is proposed to explore the boundary range of the objective space and optimize

ε

-constraint threshold combinations, reducing infeasibility and redundancy. The method is applied to testing functions and the South-to-North Water Diversion Eastern Route Project in China, targeting minimization of water shortage, diversion volume, and ecological water supplementation. Results indicate that the main conflicts arise from competition between water supply and ecological demands. Compared with the classical

ε

-constraint method, the proposed algorithm increases the Pareto set hypervolume by 35 % and decreases spacing by 15 %, enhancing diversity and uniformity.

跨流域调水系统涉及多个水源、用户和目标，具有复杂的相互关系和众多的决策变量。经典的ε-约束方法在多目标优化问题中往往产生不可行解或冗余解。针对这一问题，分析了经典ε约束方法的局限性，将具有凸目标的长期调水模型重新表述为凸规划问题。基于凸规划的特点，提出了一种旋转矢量法来探索目标空间的边界范围，优化ε约束阈值组合，减少了不可行性和冗余性。将该方法应用于中国的南水北调东线工程和功能测试中，以实现水资源短缺最小化、引水量最小化、生态补水最小化为目标。结果表明，水资源供给与生态需求之间的竞争是主要矛盾。与经典的ε-约束方法相比，该算法使Pareto集超体积增大35%，间隔减小15%，增强了多样性和均匀性。

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引用次数: 0

AI-driven knowledge synthesis for food web parameterisation 人工智能驱动的食物网参数化知识综合

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

Environmental Modelling & Software

Pub Date : 2026-01-07 DOI: 10.1016/j.envsoft.2026.106865

Scott Spillias , Elizabeth A. Fulton , Fabio Boschetti , Cathy Bulman , Joanna Strzelecki , Rowan Trebilco

We introduce a proof-of-concept framework, Synthesizing Parameters for Ecosystem modelling with LLMs (SPELL), that automates species grouping and diet matrix generation to accelerate food web construction for ecosystem models. SPELL retrieves species lists, classifies them into functional groups, and synthesizes trophic interactions by integrating global biodiversity databases (e.g., FishBase, GLOBI), species interaction repositories, and optionally curated local knowledge using Large Language Models (LLMs). We validate the approach through a marine case study across four Australian regions, achieving high reproducibility in species grouping (

<

99.7%) and moderate consistency in trophic interactions (51%–59%). Comparison with an expert-derived food web for the Great Australian Bight indicates strong but incomplete ecological accuracy: 92.6% of group assignments were at least partially correct and 82% of trophic links were identified. Specialized groups such as benthic organisms, parasites, and taxa with variable feeding strategies remain challenging. These findings highlight the importance of expert review for fine-scale accuracy and suggest SPELL is a generalizable tool for rapid prototyping of trophic structures in marine and potentially non-marine ecosystems.

我们引入了一个概念验证框架，使用LLMs （SPELL）进行生态系统建模的综合参数，该框架可自动进行物种分组和饮食矩阵生成，以加速生态系统模型的食物网构建。通过整合全球生物多样性数据库（如FishBase、GLOBI）、物种相互作用库和使用大型语言模型（llm）的可选管理的本地知识，SPELL检索物种列表，将它们分类到功能组，并综合营养相互作用。我们通过澳大利亚四个地区的海洋案例研究验证了该方法，在物种分组中实现了高重复性（99.7%），在营养相互作用中实现了中等一致性（51%-59%）。与大澳大利亚湾的专家衍生食物网的比较表明，生态准确性很强，但不完整：92.6%的群体分配至少部分正确，82%的营养联系被确定。专门的群体，如底栖生物、寄生虫和具有可变摄食策略的分类群仍然具有挑战性。这些发现强调了专家审查对精细尺度准确性的重要性，并表明SPELL是海洋和潜在的非海洋生态系统中营养结构快速原型的通用工具。

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引用次数: 0

Effects of rising CO2 concentrations on water dynamics and yields for C3 and C4 crops under both irrigated and dryland conditions in the Texas High Plains 德州高平原灌溉和旱地条件下CO2浓度上升对C3和C4作物水分动态和产量的影响

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

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2026.106859

Na Wen , Junyu Qi , Yue Wang , Gary W. Marek , Srinivasulu Ale , Puyu Feng , De Li Liu , Raghavan Srinivasan , Yong Chen

Elevated CO₂ affect crop growth and water dynamics by altering stomatal conductance (g_s, m s⁻¹) and leaf area index (LAI). However, the effects on C3 and C4 crops under different water conditions remain unclear. This study employed a modified SWAT model, incorporating a nonlinear g_s equation and a LAI function, to evaluate elevated CO₂ impacts on actual evapotranspiration (ET), irrigation, and crop yields. Results indicated that solely elevated CO₂ reduced ET by 6.8%–20.7% under irrigated conditions, but had no apparent effect on ET under dryland conditions. Elevated CO₂ enhanced crop yields, with its effect more pronounced under dryland conditions. Under future climate scenarios (2041–2100), ET increased by 6.7%–9.4% for dryland crops, while irrigated winter wheat ET declined by 0.6%–8.6%. Future crop yields generally increased, except for irrigated sorghum, which declined by up to 11.9% under high emission scenario. C3 crops were more positive response to future climate than C4 crops.

CO2升高通过改变气孔导度（gs, m s−1）和叶面积指数（LAI）影响作物生长和水分动力学。然而，不同水分条件对C3和C4作物的影响尚不清楚。本研究采用改进的SWAT模型，结合非线性gs方程和LAI函数来评估CO2升高对实际蒸散发（ET）、灌溉和作物产量的影响。结果表明，在灌溉条件下，CO2浓度升高可使土壤蒸散发减少6.8% ~ 20.7%，而在旱地条件下，CO2浓度升高对土壤蒸散发无明显影响。二氧化碳浓度升高可提高作物产量，其影响在旱地条件下更为明显。在未来气候情景下（2041-2100），旱地作物的蒸散发增加6.7% ~ 9.4%，而灌溉冬小麦的蒸散发减少0.6% ~ 8.6%。在高排放情景下，除灌溉高粱产量下降高达11.9%外，未来作物产量普遍增加。C3作物对未来气候的响应比C4作物更积极。

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引用次数: 0

The importance of system interactions in hydrodynamic models of parts of complex interconnected deltas 系统相互作用在复杂互联三角洲部分水动力模型中的重要性

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

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2025.106838

Niels M. Welsch, Jord J. Warmink, Suzanne J.M.H. Hulscher, Denie C.M. Augustijn

Climate change affects river deltas worldwide. Hydrodynamic models are used to study these effects. However, choosing the spatial scale and boundary conditions for these models is complex due to interconnectivity within river deltas. We study how boundary conditions of a model covering only part of such systems are impacted by changing conditions outside of the domain. We couple different components of the Dutch river delta into a model covering the complete delta, and force it with a range of river discharges and sea levels. Results show that the impact depends on the distance to the boundaries, as well as the relative (upstream) discharge in the considered rivers. As these differences are found to propagate far upstream, these findings underline the importance of choosing appropriate downstream boundaries when modelling water levels in parts of interconnected systems influenced by changing conditions outside the modelled domain (e.g. sea level rise or changing hydrographs).

气候变化影响着全世界的河流三角洲。水动力模型用于研究这些效应。然而，由于河流三角洲内部的相互联系，这些模型的空间尺度和边界条件的选择是复杂的。我们研究了仅覆盖此类系统的一部分的模型的边界条件如何受到域外变化条件的影响。我们将荷兰河三角洲的不同组成部分结合成一个覆盖整个三角洲的模型，并将其与一系列河流流量和海平面相结合。结果表明，影响取决于与边界的距离，以及所考虑河流的相对（上游）流量。由于发现这些差异向上游传播得很远，这些发现强调了在对受模拟域外变化条件（例如海平面上升或变化的水文曲线）影响的互联系统部分的水位进行建模时选择适当的下游边界的重要性。

引用次数: 0

An ensemble method for integrating rainfall forecast products based on average mutual information decomposition 基于平均互信息分解的降雨预报产品集成方法

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

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2026.106864

Wei Ding , Minglei Ren , Yawei Ning , Xuan Li , Jinnan Zhang , Huicheng Zhou

Accurate rainfall forecasting is vital for reservoir operation, shifting focus from minimizing missed detections in flood season to reducing false alarms post-flood. Conventional Multi-Model Ensemble (MME) methods use static weights, ignoring forecast skill variations across rainfall intensities. This study proposes MME-MID, a novel framework using Normalized Average Mutual Information Decomposition to dynamically weight forecasts by categorized rainfall intensities. Two decomposition approaches are applied: (1) Uncertainty decomposition prioritizes higher-accuracy categories to reduce post-flood false alarms; (2) Information decomposition enhances detection of high-intensity events during flood season. Validation in Dahuofang and Huanren basins shows MME-MID reduces miss ratio by 5.5 % in flood season and false alarm ratio by 11 % post-flood versus conventional MME, without increasing overall error, significantly improving decision reliability.

准确的降雨预报对水库运行至关重要，它将重点从减少汛期的漏报转移到减少汛期后的误报。传统的多模式集成（MME）方法使用静态权重，忽略了降雨强度之间的预测技能变化。本研究提出了一种新的框架MME-MID，该框架使用归一化平均互信息分解对降雨强度分类动态加权预测。采用两种分解方法：(1)不确定性分解优先考虑精度较高的分类，以减少灾后误报；(2)信息分解增强了汛期高强度事件的检测。在大火坊和桓仁流域的验证表明，与传统的MME相比，MME- mid在汛期减少了5.5%的漏报率，在灾后减少了11%的误报率，而总体误差没有增加，显著提高了决策可靠性。

引用次数: 0

The transformative role of artificial intelligence in water resources engineering: A comprehensive review 人工智能在水资源工程中的变革作用：综述

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

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2026.106857

Muhammad Rashid , Adan Saeed , Mohiq Khalid , Aniqa Murtaza , Muhammad Waqar Saleem

Artificial intelligence (AI) is reshaping water resources engineering by tackling complex forecasting, control, and decision problems across the hydrologic cycle. This review synthesizes how core AI families, machine learning (supervised, unsupervised, reinforcement), deep learning (ANNs, CNNs, RNNs, Transformers), fuzzy logic, and hybrid schemes are used for streamflow and rainfall–runoff prediction, water-quality assessment, flood and drought risk, precision irrigation, urban water operations (demand, leakage, treatment), groundwater management, climate-impact analysis, and reservoir scheduling. Reported gains include higher predictive skill, operational efficiency, and cost savings that support more sustainable management. We also surface persistent constraints: sparse and noisy data, limited interpretability, deployment cost and skill barriers, and ethical concerns around bias, accountability, and privacy. Looking ahead, priorities include Explainable AI, resilient hybrid physics, ML models, tighter coupling with IoT and remote sensing, and principled strategies for data scarcity. We argue for rigorous, transparent, and interdisciplinary practice to realize AI's benefits while safeguarding water security and equity.

人工智能（AI）正在通过解决整个水文循环中复杂的预测、控制和决策问题来重塑水资源工程。这篇综述综合了核心AI家族、机器学习（监督、无监督、强化）、深度学习（ann、cnn、rnn、transformer）、模糊逻辑和混合方案如何用于溪流和降雨径流预测、水质评估、洪水和干旱风险、精准灌溉、城市用水运营（需求、泄漏、处理）、地下水管理、气候影响分析和水库调度。报告的收益包括更高的预测技能、操作效率和成本节约，支持更可持续的管理。我们还提出了持续存在的约束：稀疏和嘈杂的数据，有限的可解释性，部署成本和技能障碍，以及关于偏见，问责制和隐私的道德问题。展望未来，优先事项包括可解释的人工智能、弹性混合物理、机器学习模型、与物联网和遥感的紧密耦合，以及针对数据稀缺的原则策略。我们主张严格、透明和跨学科的实践，以实现人工智能的好处，同时维护水安全和公平。

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引用次数: 0

Blockchain-based carbon informatics for multiple stakeholders using spatial-temporal analytics 使用时空分析为多个利益相关者提供基于区块链的碳信息

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

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2026.106861

Songyang Ruan , Xinlai Liu , H. Oliver Gao

Effective carbon disclosure is critical for climate action but is often constrained by limited transparency, low credibility, and fragmented data. This study develops a blockchain-based carbon informatics platform integrated with spatial-temporal analytics to enhance traceability and accountability in the transportation sector. Following a stakeholder-oriented design architecture, the platform is implemented on a permissioned blockchain to meet the needs of carbon entities, regulators, investors, auditors, and the public. Leveraging transportation emissions data from over 3000 counties in the United States (1990–2020), the proposed blockchain platform presents transparent data recording, secure data exchange, and dynamic visualization through interactive carbon dashboard services. Spatial panel econometric analysis identifies significant spatial-temporal autocorrelation in county-level emissions, wherein clean fuel adoption, higher income, and greater educational attainment correspond to lower emission levels. This study demonstrates the integration of blockchain-based informatics with spatial-temporal analytics can support credible carbon disclosure and facilitate data-driven climate governance.

有效的碳信息披露对气候行动至关重要，但往往受到透明度有限、可信度低和数据碎片化的限制。本研究开发了一个基于区块链的碳信息平台，与时空分析相结合，以增强运输部门的可追溯性和问责制。遵循以利益相关者为导向的设计架构，该平台在许可的区块链上实施，以满足碳实体、监管机构、投资者、审计人员和公众的需求。利用美国3000多个县（1990-2020年）的交通排放数据，提议的区块链平台通过交互式碳仪表板服务提供透明的数据记录、安全的数据交换和动态可视化。空间面板计量经济分析发现，县级排放存在显著的时空自相关性，其中清洁燃料的采用、较高的收入和较高的教育程度对应于较低的排放水平。本研究表明，基于区块链的信息学与时空分析的整合可以支持可信的碳披露，并促进数据驱动的气候治理。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2026-01-06 DOI: 10.1016/j.envsoft.2026.106863

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

Despite growing use of 3D city models (3DCMs) and urban digital twins (UDTs), web tools for their processing and visualization remain scarce. We present an interoperable, high-performance web application composed of a 3D tiler and a WebGPU viewer that enables scalable conversion, streaming, and rendering of urban datasets in compliance with open standards. The proposed system allows users to explore large-scale 3DCMs interactively without local installations. A showcase visualizing quality-validation results for a 3DCM demonstrates practical value. Experiments confirm that 3D Tiles 1.1 standard enables scalable data management and richer interaction, whereas WebGPU offers up to 7x better rendering performance on modern hardware. By presenting this solution and usage example, we aim to foster development of next-generation web-based 3D geospatial, digital-twin, and metaverse solutions.

尽管3D城市模型（3dcm）和城市数字双胞胎（udt）的使用越来越多，但用于处理和可视化的网络工具仍然很少。我们提出了一个可互操作的高性能web应用程序，该应用程序由3D贴片和WebGPU查看器组成，可以根据开放标准对城市数据集进行可扩展的转换、流式传输和渲染。该系统允许用户在没有本地安装的情况下交互式地探索大型3d数据中心。一个展示3DCM质量验证结果的展示展示了它的实用价值。实验证实，3D Tiles 1.1标准支持可扩展的数据管理和更丰富的交互，而WebGPU在现代硬件上提供了高达7倍的渲染性能。通过展示该解决方案和使用示例，我们旨在促进下一代基于web的3D地理空间、数字孪生和元宇宙解决方案的发展。

引用次数: 0

Multi-source uncertainty quantification of water quality index models based on two-dimensional Monte Carlo simulation 基于二维蒙特卡罗模拟的水质指标模型多源不确定性量化

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

Environmental Modelling & Software

Pub Date : 2026-01-05 DOI: 10.1016/j.envsoft.2026.106860

Xizhi Nong , Jiaojiao Tian , Lihua Chen , Jiahua Wei

This study addresses the subjectivity in weight determination of traditional Water Quality Index (WQI) models and their limited consideration of input uncertainty. A framework integrating machine learning methods and two-dimensional Monte Carlo simulation (2D MCS) is proposed for water quality uncertainty analysis, using the robustness monitoring data in Hanjiang River Basin, China, as a case study. Algorithms like Random Forest (RF), XGBoost, Recursive Feature Elimination (RFE), and Support Vector Machines (SVM) are used to assess parameter importance, select key indices, and assign weights for an improved WQI model. Four uncertainty scenarios are evaluated, and a two-layer Monte Carlo simulation examines the model's stability. Results show that the optimized WQI model is more robust and adaptable, especially under combined uncertainty, highlighting the risk of synergistic effects.

本研究解决了传统水质指数（WQI）模型在权重确定中的主观性及其对输入不确定性的有限考虑。以汉江流域水质鲁棒性监测数据为例，提出了一种将机器学习方法与二维蒙特卡罗模拟（2D MCS）相结合的水质不确定性分析框架。使用随机森林（RF）、XGBoost、递归特征消除（RFE）和支持向量机（SVM）等算法来评估参数重要性、选择关键指标并为改进的WQI模型分配权重。评估了四种不确定性情景，并用两层蒙特卡罗模拟检验了模型的稳定性。结果表明，优化后的WQI模型具有更强的鲁棒性和适应性，特别是在组合不确定性下，突出了协同效应的风险。

引用次数: 0

What drives reservoir infrastructure upgrade decisions under deep uncertainty? 在深度不确定性下，是什么推动了油藏基础设施升级决策？

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

Environmental Modelling & Software

Pub Date : 2026-01-05 DOI: 10.1016/j.envsoft.2026.106862

Jiajia Huang , Wenyan Wu , Holger R. Maier , Justin Hughes , Quan J. Wang , Yuan Cao

Huang et al. (2026) recently developed a comprehensive framework to jointly optimize reservoir operation policies and infrastructure upgrades under deep uncertainty. Building on this framework, this commentary provides several new, yet important, insights into reservoir planning and management under deep uncertainty. First, while the discount rate is frequently treated as a constant economic input, high discount rates may create a false sense of security by making delayed infrastructure upgrades economically preferable. In addition, initially undersized key infrastructure increases the risks of locking systems into long-term underperformance that subsequent infrastructure upgrades and operational adaptations cannot fully rectify. Furthermore, the presence or absence of key infrastructure within the system can reshape infrastructure upgrade priorities, highlighting the importance of having a clear system boundary at the planning stage. These new insights are essential for supporting credible and defensible infrastructure decisions in a climate-sensitive planning environment.

Huang等人（2026）最近开发了一个综合框架，用于在深度不确定性下共同优化油藏运营政策和基础设施升级。在此框架的基础上，本评论为深度不确定性下的油藏规划和管理提供了一些新的、重要的见解。首先，虽然贴现率经常被视为一个恒定的经济投入，但高贴现率可能会使延迟的基础设施升级在经济上更可取，从而产生一种虚假的安全感。此外，最初规模较小的关键基础设施增加了将系统锁定在长期性能不佳的风险，而后续的基础设施升级和操作调整无法完全纠正这种风险。此外，系统内关键基础设施的存在或缺失会重塑基础设施升级的优先级，突出了在规划阶段拥有明确系统边界的重要性。这些新的见解对于在气候敏感的规划环境中支持可信和可防御的基础设施决策至关重要。

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