Environmental Modelling & Software最新文献_第2页

WigglyRivers: A tool to characterize the multiscale nature of meandering channels

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-14 DOI: 10.1016/j.envsoft.2025.106423

Daniel Gonzalez-Duque , Jesus D. Gomez-Velez

{"title":"WigglyRivers: A tool to characterize the multiscale nature of meandering channels","authors":"Daniel Gonzalez-Duque , Jesus D. Gomez-Velez","doi":"10.1016/j.envsoft.2025.106423","DOIUrl":"10.1016/j.envsoft.2025.106423","url":null,"abstract":"<div><div>Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS’s High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106423"},"PeriodicalIF":4.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642324","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

A new conditional generative adversarial neural network approach for statistical downscaling of the ERA5 reanalysis over the Italian Peninsula

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-14 DOI: 10.1016/j.envsoft.2025.106427

Ilenia Manco , Walter Riviera , Andrea Zanetti , Marco Briscolini , Paola Mercogliano , Antonio Navarra

{"title":"A new conditional generative adversarial neural network approach for statistical downscaling of the ERA5 reanalysis over the Italian Peninsula","authors":"Ilenia Manco , Walter Riviera , Andrea Zanetti , Marco Briscolini , Paola Mercogliano , Antonio Navarra","doi":"10.1016/j.envsoft.2025.106427","DOIUrl":"10.1016/j.envsoft.2025.106427","url":null,"abstract":"<div><div>State-of-the-art General Circulation Models (GCMs) typically operate at a coarse spatial resolution, requiring a refinement to assess regional climate changes and their impacts. This weakness is mainly known for representing regional-scale topography and meteorological processes, particularly those responsible for extreme events. Dynamical downscaling methods are computationally demanding. In contrast, though computationally efficient, statistical approaches often sacrifice spatial consistency. To address these limitations, this work introduces an innovative and robust Conditional Generative Adversarial Neural Network (cGAN) architecture for statistical downscaling, discussing the methodology, advantages, and contributions to refining predictions at a finer scale. By leveraging a generator-discriminator architecture, the cGAN developed permits to downscale ERA5 reanalysis at the local scale to obtain a new high-resolution dataset (∼2.2 km), ERA5-DownGAN. The results obtained show the cGAN's architecture presented accurately reproduces the patterns, value range, and extreme values generated by dynamical models for the 2-m temperature over the Italian Peninsula.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106427"},"PeriodicalIF":4.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the future of hydroecological models of everywhere

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-13 DOI: 10.1016/j.envsoft.2025.106431

Keith Beven

引用次数: 0

Innovative knowledge-based system for streamflow hindcasting: A comparative assessment of Gaussian Process-Integrated Neural Network with LSTM and GRU models

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-13 DOI: 10.1016/j.envsoft.2025.106433

Arathy Nair G R , Adarsh S

{"title":"Innovative knowledge-based system for streamflow hindcasting: A comparative assessment of Gaussian Process-Integrated Neural Network with LSTM and GRU models","authors":"Arathy Nair G R , Adarsh S","doi":"10.1016/j.envsoft.2025.106433","DOIUrl":"10.1016/j.envsoft.2025.106433","url":null,"abstract":"<div><div>Lack of historical data is a major bottleneck for hydrologists to proceed with reliable climate change studies. This work proposes Gaussian Process-Integrated Neural Network (GAUSNET) technique for streamflow hindcasting by considering significant hydrological variables and Global climatic oscillations (GCO) identified by Variance Inflation Factor as system inputs. Dynamic Time Warping based Interpolation is utilized to align monthly GCOs with daily streamflows, followed by feature selection and auto-correlation using Gradient Boosting Machines. On applying for streamflow hindcasting of Greater Pamba, Kerala, India, GAUSNET consistently outperformed Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) across all of the input scenarios with an average Nash-Sutcliffe Efficiency (NSE) of 0.93. GAUSNET based hindcasting can overcome issues of data shortage, fill the data gaps and capture extreme events. Moreover, its ability for uncertainty quantification enhances the reliability and make it as robust tool for hydrological modeling, flood risk assessment, and sustainable water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106433"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678382","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

A Markov Chain Monte Carlo approach for complex lava flow simulations driven by satellite-derived data

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-13 DOI: 10.1016/j.envsoft.2025.106426

Francesco Zuccarello, Giuseppe Bilotta, Flavio Cannavò, Annalisa Cappello, Roberto Guardo, Gaetana Ganci

引用次数: 0

Models and the common good

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106430

Andrea Saltelli

引用次数: 0

Analysis of SWAT+ model performance: A comparative study using different software and algorithms

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106425

Samanta Tolentino Cecconello , Danielle Bressiani , Maria Cândida Moitinho Nunes , Luís Carlos Timm

{"title":"Analysis of SWAT+ model performance: A comparative study using different software and algorithms","authors":"Samanta Tolentino Cecconello , Danielle Bressiani , Maria Cândida Moitinho Nunes , Luís Carlos Timm","doi":"10.1016/j.envsoft.2025.106425","DOIUrl":"10.1016/j.envsoft.2025.106425","url":null,"abstract":"<div><div>The Soil and Water Assessment Tool plus (SWAT+) model is widely used to analyze water dynamics in hydrological processes. It improves upon the earlier SWAT version by incorporating decision tables that allow for the specification of different land use management activities and scenarios. However, accurate watershed representation requires proper calibration and validation. Among the available open-source tools for this purpose, SWAT + Toolbox and R-SWAT remain underexplored in scientific literature. This study aimed to compare and evaluate the performance of SWAT + using R-SWAT and SWAT + Toolbox by applying different calibration and validation algorithms in a rural watershed in southern Rio Grande do Sul, Brazil. The Sequential Uncertainty Fitting 2 (SUFI-2) and Dynamically Dimensioned Search (DDS) algorithms were employed to calibrate and validate both monthly and daily streamflow from 2015 to 2017, using 23 monthly and 825 daily observations. Model performance was assessed using the Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics. The results showed that SWAT + Toolbox outperformed R-SWAT, achieving better accuracy in monthly calibration (NSE = 0.69; KGE = 0.78) and daily validation (NSE = 0.74; KGE = 0.78). These findings highlight the greater precision of SWAT + Toolbox in streamflow modeling, although both tools exhibited limitations in representing baseflow. Additionally, the uncertainty analysis underscored the need for more precise input data, particularly regarding soil characterization. Future research should explore the implementation of more advanced calibration algorithms available in R-SWAT and improvements in soil characterization to enhance hydrological simulation accuracy, reduce uncertainties, and improve model reliability. Moreover, a comparative analysis between SWAT+ and its predecessor is recommended.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106425"},"PeriodicalIF":4.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637427","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 estimation for environmental multimodel predictions: The BLUECAT approach and software

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106419

Alberto Montanari , Demetris Koutsoyiannis

引用次数: 0

A hybrid framework for regional climate seasonality study and trend analysis

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-11 DOI: 10.1016/j.envsoft.2025.106429

Masooma Suleman, Peter A. Khaiter

引用次数: 0

Dust storm detection for ground-based stations with imbalanced machine learning

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-08 DOI: 10.1016/j.envsoft.2025.106420

Shikang Du , Siyu Chen , Shanling Cheng , Jiaqi He , Dan Zhao , Xusheng Zhu , Lulu Lian , Xingxing Tu , Qinghong Zhao , Yue Zhang

{"title":"Dust storm detection for ground-based stations with imbalanced machine learning","authors":"Shikang Du , Siyu Chen , Shanling Cheng , Jiaqi He , Dan Zhao , Xusheng Zhu , Lulu Lian , Xingxing Tu , Qinghong Zhao , Yue Zhang","doi":"10.1016/j.envsoft.2025.106420","DOIUrl":"10.1016/j.envsoft.2025.106420","url":null,"abstract":"<div><div>Dust storms, common meteorological hazard in arid and semi-arid regions, have significant environmental and societal impacts. Rapid and accurate detecting dust storms is critical for early warning systems. Over the past few decades, dust storm detection primarily relied on satellite remote sensing techniques using multi-channel imagery, but these methods have limitations in temporal resolution. With the recent expansion of China's observation network, the dense distribution of ground-based sensors offers a promising data source for real-time dust storm detection. This study proposes a machine learning approach to detect dust storms using ground-based sensor networks. By combining undersampling strategies and ensemble algorithms, this method improves model's performance in detecting dust storms. Compared with the state-of-the-art models, this approach improves the Recall rates for different dust storm levels by 24.32% and the G-Mean by 18.58%, achieving superior dust storm detection performance. This approach can offer the near-real-time, hourly updated dust storm detection products.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106420"},"PeriodicalIF":4.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637426","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