Environmental Modelling & Software最新文献_第3页

Freshwater Modeling in Aotearoa New Zealand: Current Practice and Future Directions 新西兰奥特罗阿淡水建模：当前实践和未来方向

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

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106820

Katharina Dost, Kohji Muraoka, Anne-Gaelle Ausseil, Rubianca Benavidez, Brendon Blue, Nic Conland, Chris Daughney, Annette Semadeni-Davies, Linh Hoang, Anna Hooper, Theodore Alfred Kpodonu, Tapuwa Marapara, Richard McDowell, Trung Nguyen, Dang Anh Nguyet, Ned Norton, Deniz Özkundakci, Lisa Pearson, James Rolinson, Ra Smith, Tom Stephens, Reina Tamepo, Ken Taylor, Vincent van Uitregt, Bethanna Jackson, Theo Sarris, Alexander Elliott, Jörg Wicker

引用次数: 0

Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties 水库-湖泊系统洪水风险分析的混合高维藤copula -贝叶斯网络框架：处理多源不确定性

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

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106818

Xuesong Yang , Bin Xu , Huili Wang , Xinman Qin , Xinrong Wang , Zichen Ren , Yao Yao , Siying Zhou , Yao Liu , Ping Chang

Complex flood control systems which comprise reservoirs, lakes, and external rivers, frequently encounter multifaceted risk sources that are spatiotemporally interconnected, resulting in diverse flood risks. This study developed a comprehensive risk analysis framework integrating stochastic simulation and Bayesian networks to facilitate refined risk prediction and diagnosis. Vine copula and Monte Carlo methods were used for probabilistic modeling and simulation, while Bayesian network was used for bidirectional risk assessment. A case study of Chaohu Lake Basin (China) show that vine copula effectively elucidates both intervariable correlations and single variable characteristics. The lateral inflow volume of lake and the external river water levels are dominant risk sources. When the maximum water level of lake increases from 9.5 m to 11.5 m, the posterior probability of dominant risk sources exceeding the design value at 20 % increases by 46.12 % and 32.22 %. This study represents an innovative approach to risk analysis for complex reservoir-lake systems.

复杂的防洪系统包括水库、湖泊和外部河流，经常遇到多方面的风险源，这些风险源在时空上相互关联，导致不同的洪水风险。本研究建立了一个综合的风险分析框架，将随机模拟和贝叶斯网络相结合，以方便精确的风险预测和诊断。采用Vine copula和Monte Carlo方法进行概率建模和仿真，采用贝叶斯网络进行双向风险评估。以巢湖流域为例，研究表明，藤蔓copula可以有效地解释变量间相关性和单变量特征。湖侧入水量和外河水位是主要风险源。当湖泊最高水位从9.5 m增加到11.5 m时，优势风险源超过设计值20%的后验概率分别增加了46.12%和32.22%。本研究为复杂水库-湖泊系统的风险分析提供了一种创新的方法。

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引用次数: 0

Ensemble deep learning towards high-resolution soil-moisture mapping for enhanced water management in California's Central Valley 面向高分辨率土壤湿度测绘的集成深度学习，以增强加州中央山谷的水资源管理

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

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106824

Ali Azedou , Aouatif Amine , Said Lahssini , Gordon Osterman , Mauricio Arboleda-Zapata , Michael Cosh , Isaya Kisekka

Soil moisture (SM) plays a vital role in both hydrological and agricultural processes and is critical for achieving groundwater sustainability in agriculture through demand management. NASA's Soil Moisture Active Passive (SMAP) satellite measures the SM across the Earth and provides data on both the surface and root zone SM but at a coarse spatial resolution of 9 km, thereby limiting detailed analyses. This study aimed to develop an optimized deep ensemble learning framework to downscale the resolution of SMAP observations of California's Central Valley from 9 km to 30 m for both the surface and root-zone SM. Sensitivity analysis was employed to identify key explanatory variables. The models were then combined into an ensemble DNN trained on multiscale SMAP data and validated against in-situ SM measurements. The results demonstrated that the ensemble model achieved the highest coefficients of determination (

R^{2}

) of 0.789 and 0.683 for surface and root-zone SM, respectively, with the lowest root mean square errors of 0.0281 and 0.0814 cm3/cm3, respectively, along with the highest NSE scores of 0.50 and 0.433, thereby reliably capturing spatial patterns and predictive accuracy. A sensitivity analysis identified precipitation, LST, topographic factors, land cover, and vegetation indices as key predictors for SSM, while organic matter, silt content, precipitation, and DEM were the most influential for RZSM. Seasonal analysis revealed distinct patterns linked to climate and management practices at a spatial resolution of 30 m, thereby capturing seasonal variations in soil moisture among major crops. Additionally, SM maps can be used to refine the estimated evapotranspiration resulting from applied irrigation water sourced from groundwater pumping, allowing for better monitoring of water use. SM can also be used to inform agronomic practices, such as delayed irrigation in early spring, which can reduce groundwater demand.

土壤湿度在水文和农业过程中都起着至关重要的作用，对于通过需求管理实现农业地下水的可持续性至关重要。美国宇航局的土壤湿度主动式被动卫星（SMAP）测量了整个地球的土壤湿度，并提供了地表和根部土壤湿度的数据，但空间分辨率只有9公里，因此限制了详细分析。本研究旨在开发一个优化的深度集成学习框架，将加利福尼亚州中央山谷地表和根区SM的SMAP观测分辨率从9 km降至30 m。采用敏感性分析确定关键的解释变量。然后将这些模型结合到一个基于多尺度SMAP数据训练的集成深度神经网络中，并根据原位SM测量结果进行验证。结果表明，集合模型对地表和根区SM的决定系数（R2）最高，分别为0.789和0.683，均方根误差最低，分别为0.0281和0.0814 cm3/cm3， NSE得分最高，分别为0.50和0.433，能够可靠地捕捉空间格局和预测精度。敏感性分析表明，降水、地表温度、地形因子、土地覆被和植被指数是主要的SSM预测因子，而有机质、粉土含量、降水和DEM对RZSM的影响最大。季节分析揭示了在30米空间分辨率下与气候和管理实践相关的独特模式，从而捕捉了主要作物土壤湿度的季节变化。此外，SM地图还可用于精确估算来自地下水抽水的灌溉用水所产生的蒸散量，从而更好地监测用水情况。SM还可以用于为农艺实践提供信息，例如在早春推迟灌溉，这可以减少地下水需求。

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引用次数: 0

PMAR: A Lagrangian approach to the modelling of anthropogenic pressures for marine management 海洋管理中人为压力建模的拉格朗日方法

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

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106822

Sofia Bosi , Alessandra Raffaetà , Marta Simeoni , Nikola Bobchev , Dimitar Berov , Andrea Barbanti , Stefano Menegon

PMAR (Pressure models for MARine activities) is a modelling framework and open-source Python-based software designed to assess anthropogenic pressures for marine management. PMAR uses Lagrangian trajectories calculated from ocean models to simulate pressure dispersion. An explicit link between pressures and their sources is established through a weight-based mechanism, which allows to rapidly explore different pressure source scenarios. Here, PMAR is adopted to investigate the distribution of surface macroplastics in the Black Sea under two scenarios, yielding results consistent with previous modelling studies and observations. Over 50% of the macroplastics in each Black Sea country after a yearly run came from elsewhere, stressing the importance of cross-boundary cooperation. Compared to other pressure modelling frameworks, PMAR emerges as a balanced compromise between computational efficiency and predictive accuracy. Future work will focus on making PMAR accessible to decision-makers and aligning it with requirements of Digital Twin of the Ocean applications.

PMAR（海洋活动压力模型）是一个建模框架和基于python的开源软件，旨在评估海洋管理的人为压力。PMAR使用从海洋模型计算出的拉格朗日轨迹来模拟压力弥散。通过基于权重的机制，在压力和压力源之间建立了明确的联系，从而可以快速探索不同的压力源场景。在这里，采用PMAR研究了黑海表面宏观塑料在两种情景下的分布，得出的结果与先前的建模研究和观测结果一致。经过一年的运行，每个黑海国家超过50%的宏观塑料来自其他地方，强调了跨境合作的重要性。与其他压力建模框架相比，PMAR在计算效率和预测精度之间取得了平衡。未来的工作将侧重于使决策者能够访问PMAR，并使其与海洋数字孪生应用的要求保持一致。

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引用次数: 0

Improve the estimation of forest wind vulnerability through remote sensed data: a new methodology 利用遥感数据改进森林风脆弱性估算：一种新方法

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

Environmental Modelling & Software

Pub Date : 2025-12-04 DOI: 10.1016/j.envsoft.2025.106825

Tommaso Baggio , Maximiliano Costa , Niccolò Marchi , Tommaso Locatelli , Emanuele Lingua

Windstorms are the primary cause of damage to European forests. Although different mechanistic and probabilistic models have been developed to estimate the vulnerability of forests to wind, their practical application remains limited. This study presents a new, semi-automated methodology for deriving tree and forest characteristics over large areas through the analysis of Canopy Height Model (CHM) data. By integrating the semi-mechanistic model ForestGALES, the developed algorithm uses these data to calculate spatially explicit maps of Critical Wind Speed (CWS). The presented methodology is applied to a real case study to calculate the CWS of forests in the Italian Eastern Alps. Results show that adding detailed and spatially distributed forest cover information improves the CWS calculations, thereby enhancing the reliability of models to assess forest wind vulnerability. Forest practitioners can take advantage of this new methodology to enhance the resistance and resilience of their forests through specific management techniques.

风暴是破坏欧洲森林的主要原因。虽然已经开发了不同的机制和概率模型来估计森林对风的脆弱性，但它们的实际应用仍然有限。本研究提出了一种新的、半自动化的方法，通过分析冠层高度模型（Canopy Height Model， CHM）数据来获得大面积的树木和森林特征。通过整合半机械模式ForestGALES，该算法利用这些数据计算临界风速（CWS）的空间显式地图。将该方法应用于意大利东阿尔卑斯地区森林CWS的实际计算。结果表明，加入详细的、空间分布的森林覆盖信息可以改善CWS计算，从而提高模型评估森林风脆弱性的可靠性。森林从业人员可以利用这一新方法，通过具体的管理技术提高森林的抵抗力和复原力。

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引用次数: 0

A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States 一个分季节到季节性的气候预测通知灌溉调度工具为美国本土

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

Environmental Modelling & Software

Pub Date : 2025-12-04 DOI: 10.1016/j.envsoft.2025.106819

Haiyang Shi , Ximing Cai , Xinchen Hu , Alaa Jamal , Donghui Li , Chao Sun , Xin-Zhong Liang

Irrigation accounts for a significant share of global freshwater use, and optimizing scheduling is crucial for improving water use efficiency. Current methods rely on short-term weather forecasts, limiting long-term planning. Additionally, most models are site-specific due to data constraints, and lacking national applicability. This study develops a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS). By integrating sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model, the tool optimizes irrigation scheduling at any day in the season, balancing water cost and crop yield. A human-computer interaction framework provides real-time irrigation recommendations while incorporating farmer feedback. S2S-informed scheduling improves water use efficiency and net profit compared to default SWAP schedules. Various up-to-date CONUS-scale datasets helped to reduce dependence on in-situ observations and extend the applicability of the tool to diverse field conditions in the CONUS.

灌溉占全球淡水使用的很大一部分，优化调度对提高用水效率至关重要。目前的方法依赖于短期天气预报，限制了长期规划。此外，由于数据的限制，大多数模型都是针对特定地点的，缺乏全国适用性。本研究开发了一种实时灌溉调度工具，用于美国连续玉米地（CONUS）。通过将分季节到季节（S2S）气候预测与土壤水大气植物（SWAP）模型相结合，该工具可以在季节的任何一天优化灌溉计划，平衡水成本和作物产量。人机交互框架提供实时灌溉建议，同时结合农民的反馈。与默认SWAP计划相比，s2s通知调度提高了用水效率和净利润。各种最新的CONUS尺度数据集有助于减少对原位观测的依赖，并将该工具的适用性扩展到CONUS的不同现场条件。

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引用次数: 0

Optimizing significant wave height forecasting through Ensemble Patch-TST and attention-enhanced recurrent models 通过集合Patch-TST和注意增强循环模型优化显著波高预报

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

Environmental Modelling & Software

Pub Date : 2025-12-02 DOI: 10.1016/j.envsoft.2025.106803

Zaharaddeeen Karami Lawal , Hayati Yassin , Daphne Teck Ching Lai , Azam Che Idris

Accurate significant wave height (SWH) forecasting is pivotal for maritime safety, coastal engineering, and offshore operations. This study explores three advanced deep learning models: Ensemble Patch-TST, LSTM with Attention Mechanism (SWH-AT-LSTM), and GRU with Attention Mechanism (SWH-AT-GRU) for SWH forecasting across multiple forecast horizons. The innovative Ensemble Patch-TST model combines the strengths of ensemble learning and transfer learning to achieve state-of-the-art performance, particularly in medium- and long-term predictions. Using datasets from four distinct offshore stations, the study evaluates each model's ability to capture temporal dependencies and generalize across diverse environmental conditions. Results demonstrate that the Ensemble Patch-TST outperforms baseline Patch-TST and other state-of-the-art models, achieving superior accuracy and robustness, particularly for datasets with limited training samples. This work not only highlights the transformative potential of advanced deep learning techniques in enhancing maritime forecasting systems but also emphasizes their scalability and efficiency, providing a practical and effective approach for real-world applications.

准确的有效波高（SWH）预报对海上安全、海岸工程和海上作业至关重要。本研究探索了三种先进的深度学习模型：集成Patch-TST、带注意机制的LSTM （SWH- at -LSTM）和带注意机制的GRU (SWH- at -GRU)，用于多预测视界的SWH预测。创新的集成补丁- tst模型结合了集成学习和迁移学习的优势，以实现最先进的性能，特别是在中长期预测方面。利用来自四个不同海上站点的数据集，该研究评估了每个模型捕获时间依赖性和在不同环境条件下进行推广的能力。结果表明，集成Patch-TST优于基线Patch-TST和其他最先进的模型，实现了卓越的准确性和鲁棒性，特别是对于有限训练样本的数据集。这项工作不仅突出了先进的深度学习技术在增强海事预报系统方面的变革潜力，而且强调了它们的可扩展性和效率，为现实世界的应用提供了一种实用而有效的方法。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-11-30 DOI: 10.1016/j.envsoft.2025.106806

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

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 × 10⁹ m³ to 113.23 × 10⁹ m³ 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 × 10⁹ m³ with low-growth GDP and increasing AIE.

本研究提出了一种基于贝叶斯支持向量机的两步析因分析方法（简称BSVM-TFA）来揭示人类活动对水资源需求的影响。该方法通过贝叶斯优化校正SVM超参数，捕捉人类活动与需水量之间复杂的非线性关系，防止过拟合。BSVM-TFA还可以识别多个因素对需水量的个体和交互影响，筛选关键影响因素。结果表明，到2050年，中亚地区不同情景下的需水量变化范围为75.66 × 109 m3 ~ 113.23 × 109 m3，受人类活动驱动的不确定性约为33.18%。影响中亚地区需水量的关键因素是GDP和农业灌溉效率（AIE），两者的总贡献率为47.98%；在GDP低增长和AIE增加的情况下，需水量将减少16.42 × 109 m3。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub 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

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.

准确提取流域边界对水文建模和环境管理至关重要。传统的数字高程模型（dem）提取方法依赖于人工定义的阈值和补充地形特征，限制了适应性和效率。为了解决这些问题，本研究开发了一种基于残余瓶颈注意力多特征融合网络（RBM-SegNet）的分水岭边界提取框架。该框架由三个部分组成：输入层、语义分割模型和后处理模块。主要贡献包括：(1)利用[DEM， Slope, Hillshade， and Aspect]函数作为最优输入组合；(2)引入残差连接和瓶颈注意模块（BAM），增强特征传输，抑制不相关区域；(3)结合多特征融合对结构和细节预测进行精细化；(4)结合后处理，提高输出的完整性和水文一致性。实验结果表明，RBM-SegNet在准确性上优于传统和现有的深度学习方法，具有很强的实际应用潜力。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-11-28 DOI: 10.1016/j.envsoft.2025.106802

Xin Jing, Xue Yang, JunGang Luo, GangGang Zuo

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.

将深度学习与水文模型相结合是一个热门的研究方向；然而，由于自动区分要求和接口不兼容，这一领域面临着巨大的挑战，导致许多现有的水文建模框架无法进行有效的混合建模。为了填补这一空白，我们提出了一个框架，它继承并增强了以前建模框架的设计哲学。它利用符号编程来降低水文建模的难度，特别是集成深度学习的混合模型，支持模型优化的自动微分，并有效地满足专业水文和混合建模应用的多样化和不断发展的需求。这个框架被命名为HydroModels。jl，是用Julia编程语言实现的，可以在GitHub上公开访问，并附有详细的文档。本文描述了其体系结构和实现细节，并给出了两个案例研究作为示例来演示其集成能力和适用性。

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引用次数: 0