Environmental Modelling & Software最新文献

Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series 纯外生预测的时间分布深度学习模型：使用天气图像时间序列进行地下水位预测的应用

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-03 DOI: 10.1016/j.envsoft.2025.106568

Matteo Salis , Abdourrahmane M. Atto , Stefano Ferraris , Rosa Meo

{"title":"Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series","authors":"Matteo Salis , Abdourrahmane M. Atto , Stefano Ferraris , Rosa Meo","doi":"10.1016/j.envsoft.2025.106568","DOIUrl":"10.1016/j.envsoft.2025.106568","url":null,"abstract":"<div><div>Deep Learning (DL) models have revealed to be very effective in hydrology, especially in handling spatially distributed data (e.g. raster data). We have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piedmont, IT) using only exogenous weather image time series. Both the models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the images into hidden vectors. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted for handling output of different length and completely shifted in the future to the input. Both models have shown remarkable results focusing on different learnable information: TDC-LSTM has focused more on bias while the TDC-UnPWaveNet more on the temporal dynamics maximizing correlation <span><math><mi>ρ</mi></math></span>, achieving mean BIAS (and standard deviation) −0.18(0.05), −0.25(0.19) and <span><math><mi>ρ</mi></math></span> 0.93(0.03), 0.96(0.01) respectively over all the sensors.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106568"},"PeriodicalIF":4.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification of wind gust predictions in the northeast United States: An evidential neural network and explainable artificial intelligence approach 美国东北部阵风预测的不确定性量化：证据神经网络和可解释的人工智能方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-28 DOI: 10.1016/j.envsoft.2025.106595

Israt Jahan , John S. Schreck , David John Gagne , Charlie Becker , Marina Astitha

{"title":"Uncertainty quantification of wind gust predictions in the northeast United States: An evidential neural network and explainable artificial intelligence approach","authors":"Israt Jahan , John S. Schreck , David John Gagne , Charlie Becker , Marina Astitha","doi":"10.1016/j.envsoft.2025.106595","DOIUrl":"10.1016/j.envsoft.2025.106595","url":null,"abstract":"<div><div>Machine learning algorithms have shown promise in reducing bias in wind gust predictions, while still underpredicting high gusts. Uncertainty quantification (UQ) supports this issue by identifying when predictions are reliable or need cautious interpretation. Using data from 61 extratropical storms in the Northeastern USA, we introduce evidential neural network (ENN) as a novel approach for UQ in gust predictions, leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model. Explainable AI techniques suggested that key predictive features contributed to higher uncertainty, which correlated strongly with storm intensity and spatial gust gradients. Compared to WRF, ENN demonstrated a 47 % reduction in RMSE and allowed the construction of gust prediction intervals without an ensemble, successfully capturing at least 95 % of observed gusts at 179 out of 266 stations. From an operational perspective, providing gust forecasts with quantified uncertainty enhances stakeholders’ confidence in risk assessment and response planning for extreme gust events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106595"},"PeriodicalIF":4.8,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling solute transport in rivers: Analytical and numerical solutions 河流中溶质运移的模拟：解析和数值解

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-27 DOI: 10.1016/j.envsoft.2025.106580

Mohamed Bey-Zekkoub, Pablo Tassi, Carine Lucas, Norinda Chhim

{"title":"Modeling solute transport in rivers: Analytical and numerical solutions","authors":"Mohamed Bey-Zekkoub, Pablo Tassi, Carine Lucas, Norinda Chhim","doi":"10.1016/j.envsoft.2025.106580","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106580","url":null,"abstract":"This study presents a novel analytical framework for modeling one-dimensional solute transport in rivers, integrating advection, rate-limited adsorption on suspended sediments, and first-order degradation. Analytical solutions are used to validate the numerical scheme’s accuracy under idealized conditions, tested for instantaneous and continuous pollutant discharges. The research importantly investigates short-term solute accumulation in riverbeds, a critical yet understudied process that affects sediment transport and pollutant fate. Applicable to a wide range of contaminants (e.g., nutrients, pesticides), the framework aids water quality assessment, pollution control, and risk mitigation. Implemented in the open-source SWASHES library, these solutions provide practical tools for decision-support systems and serve as reliable benchmarks to validate numerical models. By addressing transient and persistent pollutant scenarios, this work enhances predictive capabilities for environmental management. The approach bridges analytical and numerical methods, offering a robust foundation for simulating solute transport across industrial and ecological contexts, advancing sustainable water resource management.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"152 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing flood forecasts: A comprehensive neural network approach for groundwater flooding in lowland karst areas 加强洪水预报：低地喀斯特地区地下水洪水的综合神经网络方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-26 DOI: 10.1016/j.envsoft.2025.106591

Ruhhee Tabbussum , Bidroha Basu , Patrick Morrissey , Laurence Gill

引用次数: 0

Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin 测试机器学习算法作为小流域水文气象建模的后处理工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-26 DOI: 10.1016/j.envsoft.2025.106592

Chengjing Xu , Ping-an Zhong , Silvio Davolio , Oxana Drofa , Enrico Gambini , Giovanni Ravazzani , Alessandro Ceppi

{"title":"Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin","authors":"Chengjing Xu , Ping-an Zhong , Silvio Davolio , Oxana Drofa , Enrico Gambini , Giovanni Ravazzani , Alessandro Ceppi","doi":"10.1016/j.envsoft.2025.106592","DOIUrl":"10.1016/j.envsoft.2025.106592","url":null,"abstract":"<div><div>Accurate runoff forecasting in downstream urban areas of small river basins is crucial for flood warning and risk management. This study proposes a hybrid runoff forecasting framework that integrates a process-driven hydro-meteorological model with data-driven post-processing using ensemble machine learning (ML) algorithms. First, the MOLOCH meteorological model is used to predict weather conditions, and it is coupled with the FEST hydrological model, which translates meteorological output into hydrological responses. Finally, multiple ML algorithms are employed for error correction, and the results are integrated using the Stacking ensemble strategy. The case study indicates that: The Stacking model consistently outperforms traditional autoregression (AR) and long short term memory (LSTM) models in both overall accuracy and flood-event-specific performance metrics. Particularly, the post-processing framework exhibits comparable effectiveness when applied to both the coupled hydro-meteorological forecasting chain (CHMFC) and the FEST model, confirming its flexibility and potential to improve forecast lead time. While forecast performance naturally degrades over longer lead times, the Stacking model maintains better robustness and slower performance decay. The proposed hybrid framework combines the interpretability of process-driven models with the nonlinear capture capabilities of data-driven models, offering a promising solution for enhancing real-time runoff forecasting in flood-prone small river basins.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106592"},"PeriodicalIF":4.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic assessment of a simplified water quality model and its linkage to the hydrological characteristics of lakes 一个简化水质模型的概率评估及其与湖泊水文特征的联系。

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-26 DOI: 10.1016/j.envsoft.2025.106593

Joana Postal Pasqualini, Fernando Mainardi Fan

{"title":"Probabilistic assessment of a simplified water quality model and its linkage to the hydrological characteristics of lakes","authors":"Joana Postal Pasqualini, Fernando Mainardi Fan","doi":"10.1016/j.envsoft.2025.106593","DOIUrl":"10.1016/j.envsoft.2025.106593","url":null,"abstract":"<div><div>Simple models can be valuable for supporting the management of engineering applications across various fields, such as water resources and environmental sanitation. However, the inherent uncertainty associated with these approaches should be recognized and quantified to better support decision-making. This study assessed variability arising from two primary factors: the adoption of different numerical methods and parameter variability within a complete stirring tank reactor (CSTR) model applied for phosphorous predictions across four different hydrological conditions. An ensemble approach, integrating multiple numeric methods, was employed to evaluate numerical uncertainty. Parametric uncertainty was assessed through Monte Carlo simulations. The study focused solely on characterizing uncertainties without evaluating model performance or comparing the results from the applied models to observational data. The numerical and parametric uncertainty ranges were similar in reservoirs with low-volume fluctuations and differed in reservoirs where volume increased over time. The overall magnitude of observed uncertainty depended on the hydrodynamics during the analysis period. The choice of numerical approach is particularly important in assessing cases involving seasonal shifts in volumetric storage or potential impacts from climate change. Results supported the theory that a probabilistic approach supports decision-making by encompassing a wider range of possible results. Moreover, due to its simplified methodology, probabilistic analysis enhances the confidence in the modeling results, even with the inherent simplifications of zero-dimensional modeling. This added layer of analysis helps to account for uncertainties, improving the overall reliability of the model despite its simplicity.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106593"},"PeriodicalIF":4.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imputation for spatiotemporal PM2.5 data via Varying-Coefficient Autoregressive Adversarial Network 基于变系数自回归对抗网络的PM2.5时空数据反演

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-25 DOI: 10.1016/j.envsoft.2025.106564

Lingxiao Xiang, Haitao Zheng

{"title":"Imputation for spatiotemporal PM2.5 data via Varying-Coefficient Autoregressive Adversarial Network","authors":"Lingxiao Xiang, Haitao Zheng","doi":"10.1016/j.envsoft.2025.106564","DOIUrl":"10.1016/j.envsoft.2025.106564","url":null,"abstract":"<div><div>Fine particulate matter (PM2.5) poses risks to environmental health, and missing data due to equipment failures and technical issues hinders pollution analysis. To address this issue, this study proposes Varying-Coefficient Autoregressive Adversarial Network (VCAAN) framework to impute these missing values effectively. First, a Varying-Coefficient Autoregressive (VCA), based on vector autoregression and B-spline approximation of time-varying coefficients, is proposed to capture dynamic spatiotemporal dependencies while reducing model complexity. Next, a Convolutional Discriminative Network (CDN) is designed for spatiotemporal imputation. This network leverages convolutional operations to learn spatiotemporal patterns and assess the quality of the imputed values. In addition, a dynamic adversarial loss weighting mechanism is introduced, enabling VCA and CDN to engage in dynamic adversarial interaction and ultimately converge to a balanced solution. Finally, extensive experiments on Beijing PM2.5 data confirm the proposed method’s superiority, demonstrating its strong adaptability to various missing scenarios and effectiveness even under a high missing rate.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106564"},"PeriodicalIF":4.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TRISH: Tree-ring integrated system for hydrology, a web-based tool for reconstruction TRISH：水文树木年轮综合系统，一个基于网络的重建工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-24 DOI: 10.1016/j.envsoft.2025.106590

Alexander A. Prusevich , David M. Meko , Irina P. Panyushkina , Alexander I. Shiklomanov , Richard B. Lammers , Stanley J. Glidden , Richard D. Thaxton

{"title":"TRISH: Tree-ring integrated system for hydrology, a web-based tool for reconstruction","authors":"Alexander A. Prusevich , David M. Meko , Irina P. Panyushkina , Alexander I. Shiklomanov , Richard B. Lammers , Stanley J. Glidden , Richard D. Thaxton","doi":"10.1016/j.envsoft.2025.106590","DOIUrl":"10.1016/j.envsoft.2025.106590","url":null,"abstract":"<div><div>TRISH (Tree-Ring Integrated System for Hydrology), a new web-based tool for reconstruction of water-balance variables from tree-ring proxies is described. The tool makes use of a mapping application, a global water balance model and R-based reconstruction software. Long time series of water balance variables can be reconstructed by regression or analog statistical methods from tree-ring data uploaded by the user or available in TRISH as previously uploaded public datasets. A predictand hydroclimatic time series averaged or summed over a river basin or arbitrary polygon can be generated interactively by clicking on the map. Control over reconstruction modeling includes optional lagging of predictors, transformation of predictand, and reduction of predictors by principal component analysis. Output includes displayed and downloadable graphics, statistics, and time series. The two-stage reconstruction approach in TRISH allows assessment of the strength of the hydroclimatic signal in individual chronologies in addition to providing a reconstruction based on the tree-ring network. TRISH facilitates the testing of sensitivity of reconstructions to modeling choices and allows a user to explore hydrologic reconstruction in ungauged basins. The R software for reconstruction is available for running offline in the RStudio development environment. TRISH is an open-science resource designed to be shared broadly across the Earth Science research community and to engage water resource management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106590"},"PeriodicalIF":4.8,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a data-driven automatic unstructured mesh generation tool suitable for compound flooding studies 适于复合驱研究的数据驱动非结构化网格自动生成工具的开发

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-21 DOI: 10.1016/j.envsoft.2025.106587

Jaime R. Calzada , Y. Joseph Zhang , Fei Ye , Linlin Cui

引用次数: 0

Improving image-based water-level monitoring by coupling water-line detection techniques and the segment anything model 结合水线检测技术和分段任意模型改进基于图像的水位监测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-20 DOI: 10.1016/j.envsoft.2025.106566

Chenqi Fang , Kai Duan , Zhipeng Lv , Juncai Huang , Qirui Zhong , Jing Chen , Di Long

{"title":"Improving image-based water-level monitoring by coupling water-line detection techniques and the segment anything model","authors":"Chenqi Fang , Kai Duan , Zhipeng Lv , Juncai Huang , Qirui Zhong , Jing Chen , Di Long","doi":"10.1016/j.envsoft.2025.106566","DOIUrl":"10.1016/j.envsoft.2025.106566","url":null,"abstract":"<div><div>Image-based water level measurements offer cost-effective alternatives to traditional methods but often face challenges from environmental disturbances. We present a novel gauge image segmentation method integrating water-line detection techniques with the Segment Anything Model (adaptive-SAM) for continuous water level monitoring. The method uses adaptive prompt points derived from water line detection to generate high-quality masks of staff gauges and retrieve real-time water level data. Experiments conducted at an urban lake in southern China demonstrated the effectiveness of this integration, with the mean error reduced to 0.79 cm. The adaptive locating strategy facilitates the generation of accurate prompt points to guide SAM, thus significantly enhances its resilience to complex environmental disturbances (i.e., water texture, darkness, reflection, and overexposure) and achieves a substantial reduction in maximum errors by 22.2–41.1 cm. This simple yet robust approach provides an accessible tool for non-professionals, potentially increasing hydrological data density when traditional sensors fail.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106566"},"PeriodicalIF":4.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0