Environmental Modelling & Software最新文献

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A reliable deep ensemble hybrid model for urban air quality health index forecasting in maritime Canada 加拿大沿海地区城市空气质量健康指数预报的可靠深度集合混合模型

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

Environmental Modelling & Software

Pub Date : 2025-12-24 DOI: 10.1016/j.envsoft.2025.106837

Mehdi Jamei , Gurjit S. Randhawa , Mumtaz Ali , Masoud Karbasi , Ismail Olumegbon , Saad Javed Cheema , Travis J. Esau , Qamar U. Zaman , Aitazaz A. Farooque

Accurate Air Quality Health Index (AQHI) forecasting is crucial for safeguarding public health and informing policy decisions in coastal urban regions of Maritime Canada. This study introduces a graph-enhanced deep ensemble model that integrates Robust Empirical Mode Decomposition (REMD), Deep Ensemble Random Vector Functional Link (DeepERVFL), graph-based feature selection, and Borda Count multi-criteria decision making for multi-weekly AQHI forecasting. Forecast uncertainty is quantified using bootstrap resampling to ensure confidence in the results. Benchmarking against Recursive LSTM and Histogram-Based Gradient Boosting Ensemble (HBGBE) models shows the superior performance of the REMD-DeepERVFL framework, with BORDA scores of 0.940 (T+1) and 1.06 (T+3) in Halifax, 0.797 (T+3) in Charlottetown, and 0.931 (T+3) in St. John's. The framework supports air-quality early warning systems, public health communication, and climate-health monitoring, offering timely and reliable information. This hybrid approach provides a robust, scalable, and uncertainty-aware solution for regional AQHI forecasting in Atlantic Canada.

准确的空气质量健康指数（AQHI）预报对于保障加拿大沿海城市地区的公众健康和为决策提供信息至关重要。本研究引入了一种图增强深度集成模型，该模型集成了鲁棒经验模态分解（REMD）、深度集成随机向量功能链接（DeepERVFL）、基于图的特征选择和Borda计数多准则决策，用于多周AQHI预测。使用自举重采样对预测不确定性进行量化，以确保结果的置信度。对递归LSTM和基于直方图的梯度增强集成（HBGBE）模型进行基准测试表明，remd - deeperfl框架的性能优越，在Halifax的BORDA得分为0.940 （T+1）和1.06 (T+3)，在Charlottetown的BORDA得分为0.797 (T+3)，在St. John的BORDA得分为0.931 （T+3）。该框架支持空气质量预警系统、公共卫生通信和气候健康监测，提供及时可靠的信息。这种混合方法为加拿大大西洋地区的区域AQHI预报提供了一个强大的、可扩展的和不确定性意识的解决方案。

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引用次数: 0

PyDRGHT: A comprehensive python package for drought analysis pydright：用于干旱分析的全面Python包

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

Environmental Modelling & Software

Pub Date : 2025-12-24 DOI: 10.1016/j.envsoft.2025.106847

Tolga Barış Terzi

Drought is an escalating environmental hazard with profound societal and ecological impacts, intensified by climate change. Effective monitoring and probabilistic assessment require integrated tools capable of capturing both univariate and multivariate characteristics, including the interdependent behavior of multiple hydroclimatic variables. This study introduces PyDRGHT, an open-source Python package for comprehensive drought analysis. PyDRGHT provides a unified framework for computing standardized univariate and multivariate drought indices, identifying drought characteristics, and conducting univariate and copula-based bivariate frequency analyses to enable transparent and reproducible probabilistic assessments. PyDRGHT's utility is demonstrated using long-term precipitation and streamflow records from the Seyhan River Basin, Türkiye (1965–2011), illustrating robust drought detection and characterization. By offering a flexible and robust platform within the Python ecosystem, PyDRGHT advances drought monitoring, risk assessment, and hydroclimatic research.

干旱是一种不断升级的环境危害，具有深远的社会和生态影响，并因气候变化而加剧。有效的监测和概率评估需要能够捕捉单变量和多变量特征的综合工具，包括多个水文气候变量的相互依赖行为。本研究介绍了pydright，一个用于全面干旱分析的开源Python包。pydright提供了一个统一的框架，用于计算标准化的单变量和多变量干旱指数，识别干旱特征，并进行单变量和基于copula的双变量频率分析，以实现透明和可重复的概率评估。pydright的实用性通过使用 rkiye（1965-2011）的塞汉河流域的长期降水和流量记录来证明，说明了强大的干旱检测和表征。通过在Python生态系统中提供一个灵活而强大的平台，pydright推进了干旱监测、风险评估和水文气候研究。

引用次数: 0

Three-dimensional narrative visualization in virtual geographic scenes for enhancing textual information driven by knowledge and semantics 基于知识和语义驱动的虚拟地理场景三维叙事可视化增强文本信息

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

Environmental Modelling & Software

Pub Date : 2025-12-23 DOI: 10.1016/j.envsoft.2025.106844

Yukun Guo , Jun Zhu , Zhihao Guo , Jianlin Wu , Jinbin Zhang

The narrative visualization of geographic textual information significantly facilitates information dissemination and enhances public understanding. However, current visualization approaches suffer from inaccurate text parsing, inefficient visualization construction processes, and unintuitive visual outputs. To address these issues, this paper proposes a 3D narrative visualization method that integrates semantic information and knowledge with virtual geographic scenes. The method includes the construction of a geographic narrative knowledge graph, the design of a multi-level spatiotemporal narrative visualization model, and a collaborative narrative visualization strategy using large language models and knowledge graphs. Experimental analyses were conducted using texts describing natural disasters and social events in Luding County. Results showed effectiveness scores consistently above 3.5, cognitive accuracy improvements of up to 16 %, and cognitive processing time reduced by approximately half. These findings verify that the proposed method effectively transforms textual geographic information into narrative visualizations, significantly improving public comprehension and cognitive efficiency, thus demonstrating its practical potential for broader applications in geographic information communication.

地理文本信息的叙事可视化极大地促进了信息的传播，增强了公众的理解。然而，当前的可视化方法存在文本解析不准确、可视化构建过程效率低下和可视化输出不直观等问题。为了解决这些问题，本文提出了一种将语义信息和知识与虚拟地理场景相结合的三维叙事可视化方法。该方法包括构建地理叙事知识图谱，设计多层次时空叙事可视化模型，以及基于大语言模型和知识图谱的协同叙事可视化策略。实验分析采用泸定县自然灾害和社会事件描述文本。结果显示，有效性得分一直在3.5以上，认知准确性提高了16%，认知处理时间减少了大约一半。这些研究结果验证了该方法有效地将文本地理信息转化为叙事可视化，显著提高了公众的理解和认知效率，从而显示了其在地理信息传播中更广泛应用的实际潜力。

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引用次数: 0

Producing Earth science data for impact: Improved commercial cloud usability of archive model data 为影响生产地球科学数据：改进归档模型数据的商业云可用性

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

Environmental Modelling & Software

Pub Date : 2025-12-23 DOI: 10.1016/j.envsoft.2025.106846

Amy McNally , Lucas Sterzinger , Ian Carroll

This overview introduces concepts, challenges, and solutions for analysis of Earth System Model data in cloud computing environments. We highlight how NASA's Earth Science Data System is migrating data to the cloud, but existing data formats are not yet optimized for cloud performance. Specifically, there is significant performance degradation in the on-cloud analysis due to fragmented metadata and small data chunk size. Using data from two NASA Land Data Assimilation Systems we present a case study comparing on-premises vs. commercial cloud workflows. The case study demonstrates that re-chunking archival data may be necessary, as well as consolidating metadata and generating metadata sidecar files, for enhanced cloud performance. We also recommend resources and tools for users interested in cloud-based analysis and shifts in practices for data producers. These changes will allow for successful cloud migration of NASA Earthdata and improve data discoverability and usability for critical Earth science research and applications.

本概述介绍了云计算环境中地球系统模型数据分析的概念、挑战和解决方案。我们强调了NASA的地球科学数据系统如何将数据迁移到云端，但现有的数据格式尚未针对云性能进行优化。具体来说，由于碎片化的元数据和较小的数据块大小，在云上分析中存在显著的性能下降。使用来自两个NASA陆地数据同化系统的数据，我们提出了一个比较内部部署与商业云工作流程的案例研究。案例研究表明，为了增强云性能，重新分组归档数据以及合并元数据和生成元数据侧车文件可能是必要的。我们还为对基于云的分析感兴趣的用户推荐资源和工具，并为数据生产者推荐实践转变。这些变化将允许NASA地球数据的成功云迁移，并提高关键地球科学研究和应用的数据可发现性和可用性。

引用次数: 0

Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction 驯服非线性：改进洪峰预测的迭代概念路径模型

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

Environmental Modelling & Software

Pub Date : 2025-12-22 DOI: 10.1016/j.envsoft.2025.106843

Ekant Sarkar , Akshay Kadu , S.L. Kesav Unnithan , Basudev Biswal

The magnitude and timing of flood peaks are strongly influenced by the non-linear relationship between flow velocity and discharge. Traditional routing models struggle to account for the variation in flow velocity, particularly with time, due to computational constraints. This study proposes a novel Iterative Routing Model (IRM) that updates flow velocity as a function of streamflow magnitude. The IRM was applied to seven gauging stations in the Godavari River Basin, India. It outperformed two other models used in this study in simulating peak discharge and timing, with the lowest average absolute deviations of 29.98 % and 0.2 days, respectively. The IRM achieved the highest median NSE (0.78) and KGE (0.79) values across all stations. Moreover, the calibrated Manning's roughness from the proposed model appears more realistic compared to that given by other models. Overall, our findings highlight the potential of the proposed model to improve flood peak predictions in large river basins.

洪峰的大小和时间受水流速度与流量的非线性关系的强烈影响。由于计算的限制，传统的路线模型很难考虑到流速的变化，特别是随时间的变化。本文提出了一种新的迭代路由模型（IRM），该模型将流速作为流量大小的函数进行更新。IRM应用于印度戈达瓦里河流域的七个测量站。在峰值放电和时序模拟方面，该模型的平均绝对偏差最小，分别为29.98%和0.2天。IRM的NSE中位数（0.78）和KGE中位数（0.79）在所有站点中最高。此外，与其他模型相比，所提出模型的校准曼宁粗糙度似乎更真实。总的来说，我们的发现突出了所提出的模型在改善大型河流流域洪峰预测方面的潜力。

引用次数: 0

Supervised learning-based water quality prediction and ecological risk factor mining across China’s 12 major river basins 基于监督学习的中国12大流域水质预测与生态风险因子挖掘

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

Environmental Modelling & Software

Pub Date : 2025-12-19 DOI: 10.1016/j.envsoft.2025.106840

Jishu Guo, Yimin Huang, Yun Zhang

Surface water quality underpins ecosystem stability, regional security, and public health, yet capturing spatio-temporal heterogeneity from historical monitoring remains challenging. We propose a Spatio-Temporal Aware Neural Network (SANN) that couples high-order spatial structure learning with explicit temporal modeling to represent nonlinear interactions among 11 physicochemical variables across China’s 12 major river basins. Using 15,855 samples from the National Surface Water Monitoring Network, SANN is benchmarked against ten traditional, deep, and graph-based models, attaining a mean accuracy of 91.87%, an F1-score of 91.15%, and a precision of 91.32%, outperforming the state of the art water quality prediction model. Feature-importance analysis reveals distinct, time-varying regional drivers: total phosphorus dominates the eight eastern–southern basins, whereas the permanganate index prevails in the four western–northern basins. The framework clarifies spatio-temporal heterogeneity in water-quality controls and provides actionable guidance for basin-specific, time-aware pollution mitigation and ecological restoration. The source code is available at: https://github.com/FengLiuii/SANN.

地表水质量是生态系统稳定、区域安全和公共卫生的基础，但从历史监测中捕捉时空异质性仍然具有挑战性。本文提出了一个时空感知神经网络（SANN），该网络将高阶空间结构学习与显式时间建模相结合，以表征中国12个主要流域11个物理化学变量之间的非线性相互作用。使用来自国家地表水监测网络的15,855个样本，SANN与10个传统的、深度的和基于图形的模型进行基准测试，平均准确率为91.87%，f1得分为91.15%，精度为91.32%，优于最先进的水质预测模型。特征重要性分析揭示了明显的时变区域驱动因素：东部-南部8个盆地以总磷指数为主，而西部-北部4个盆地以高锰酸盐指数为主。该框架阐明了水质控制的时空异质性，并为流域特定的、有时间意识的污染缓解和生态恢复提供了可操作的指导。源代码可从https://github.com/FengLiuii/SANN获得。

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引用次数: 0

Enhancing flood forecasting with deep learning: A scalable alternative to traditional hydrodynamic models 用深度学习增强洪水预报：传统水动力模型的可扩展替代方案

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

Environmental Modelling & Software

Pub Date : 2025-12-19 DOI: 10.1016/j.envsoft.2025.106841

Weeraphat Duangkhwan , Chaiwat Ekkawatpanit , Chanchai Petpongpan , Duangrudee Kositgittiwong , So Kazama , Yusuke Hiraga , Chai Jaturapitakkul

Flooding, intensified by climate change, necessitates advanced prediction models. Traditional hydrodynamic simulation, HEC-RAS 1D/2D is computationally intensive, limiting real-time flood forecasting. This study proposes an integrated deep learning framework to emulate HEC-RAS 1D/2D, significantly reducing computational demands. The framework comprises an LSTM for river water level prediction and a CNN for flood inundation mapping. To ensure physical consistency, the CNN learns the relationship between river water levels and flood inundation by mimicking overflow results of 1D/2D models. Training uses observed hydrological data and flood inundation maps from 1D/2D simulations. Results indicate the LSTM achieving good accuracy to predict water level. The CNN effectively translates water level predictions into flood depth maps, demonstrating close agreement with HEC-RAS outputs. Overall, the AI-based framework significantly accelerates flood simulations while maintaining high accuracy, making it a promising tool for real-time flood prediction and large-scale flood risk assessment.

气候变化加剧的洪水需要先进的预测模型。传统的水动力模拟，HEC-RAS 1D/2D计算量大，限制了实时洪水预报。本研究提出了一个集成的深度学习框架来模拟HEC-RAS 1D/2D，显著降低了计算需求。该框架包括用于河流水位预测的LSTM和用于洪水淹没制图的CNN。为了保证物理一致性，CNN通过模拟一维/二维模型的溢流结果来学习河流水位与洪水淹没之间的关系。训练使用观测到的水文数据和1D/2D模拟的洪水淹没图。结果表明，LSTM预测水位具有较好的精度。CNN有效地将水位预测转化为洪水深度图，显示出与HEC-RAS输出的密切一致。总体而言，基于人工智能的框架在保持较高精度的同时显著加快了洪水模拟速度，使其成为实时洪水预测和大规模洪水风险评估的有前景的工具。

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引用次数: 0

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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-19 DOI: 10.1016/j.envsoft.2025.106842

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

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.

模拟积雪和冰川融化对于了解高高山流域的水文过程至关重要。我们为降雨径流建模游乐场（RRMPG）提供了一个新的Python扩展，该扩展包含两个关键的高山特定过程：积雪滞后和冰川冰融化。积雪滞后反映了积雪覆盖面积在积累期和融化期之间的不对称演变，而冰川融化模拟由于其对水平衡的强烈影响而对冰川化集水区至关重要。该模型在Ötztal阿尔卑斯山脉的两个集水区进行了测试，在模拟径流和积雪动态方面显示出较高的准确性。利用实测径流和MODIS积雪数据的多目标校准方法提高了模型的鲁棒性。该框架专为模块化和互操作性而设计，可轻松集成用于校准、灵敏度分析和数据可视化的工具。这个开源扩展通过提供增强的过程表示，灵活性和与基于python的工作流的兼容性，在复杂的高山环境中推进水文建模。

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引用次数: 0

AI-assisted voice enabled computing framework for hydrological analysis 用于水文分析的ai辅助语音计算框架

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

Environmental Modelling & Software

Pub Date : 2025-12-18 DOI: 10.1016/j.envsoft.2025.106833

Carlos Erazo Ramirez , Ibrahim Demir

This work presents a web-based, voice-enabled, no-code platform for AI-assisted hydrological analysis. The system allows users to interact through natural language—via both text and speech—to retrieve data, utilize hydrological functions, and visualize spatial and analytical outputs. Core components include a conversational AI assistant utilizing Large Language Models, a modular analysis engine based on HydroSuite, and direct integration with hydrological data from federal agencies using HydroShare and other data and web services. Structured intent parsing, persistent session state, and dynamic map-layer control support multi-turn interactions and reproducible workflows. A case study over the Mississippi River Delta demonstrates how the platform enables guided exploration, layered data integration, and low-latency execution with minimal technical overhead. The platform lowers barriers for research, education, and decision-making in hydrology by combining AI reasoning with a transparent, accessible user interface. By enabling natural language interaction, data integration, and reproducible, multi-turn task processing, this system lays the foundation for automated hydrological research and operational workflows.

这项工作提出了一个基于网络的、支持语音的、无代码的平台，用于人工智能辅助水文分析。该系统允许用户通过文本和语音进行自然语言交互，检索数据，利用水文功能，可视化空间和分析输出。核心组件包括使用大型语言模型的会话AI助手，基于HydroSuite的模块化分析引擎，以及使用HydroShare和其他数据和web服务与联邦机构的水文数据直接集成。结构化意图解析、持久会话状态和动态映射层控制支持多回合交互和可重复的工作流。密西西比河三角洲的一个案例研究演示了该平台如何以最小的技术开销实现引导勘探、分层数据集成和低延迟执行。该平台通过将人工智能推理与透明、易用的用户界面相结合，降低了水文学研究、教育和决策的障碍。通过实现自然语言交互、数据集成和可重复的多轮任务处理，该系统为自动化水文研究和操作工作流程奠定了基础。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-18 DOI: 10.1016/j.envsoft.2025.106839

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

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.

水扩展可计算一般均衡（CGE）模型是一类经济范围内的模型，被广泛用作解决各种与水相关主题的研究和政策问题的工具。本系统综述分析了100种水-CGE模型的应用，根据其结构和目标将其分类为关键领域，包括农业、工业、农业与工业结合、能源、能源与农业结合，以检验将水纳入CGE模型的方法方法，并探讨应用的各种主题。研究结果表明，将水纳入CGE模型的改进需要提高水数据的质量和细节，明确地将水作为生产要素，在更小的空间尺度上构建模型，考虑水的季节性，以及提高校准和验证方法的透明度。解决这些挑战将增强CGE模型中水的代表性，从而为解决水与经济的相互联系提供关键见解。

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引用次数: 0

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