{"title":"On the future of hydroecological models of everywhere","authors":"Keith Beven","doi":"10.1016/j.envsoft.2025.106431","DOIUrl":"10.1016/j.envsoft.2025.106431","url":null,"abstract":"<div><div>This paper addresses the potential for hydroecological models of everywhere to be used, in conjunction with interaction with local stakeholders, as a way of learning about places as well as being used as predictive tools. The importance of facilitating stakeholder involvement in defining assumptions and uncertainties, and in model evaluation is stressed. The potential for using data science and real-time updating in using the internet of things to contribute to a models of everywhere framework is also discussed.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106431"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642323","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}
{"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}
Francesco Zuccarello, Giuseppe Bilotta, Flavio Cannavò, Annalisa Cappello, Roberto Guardo, Gaetana Ganci
{"title":"A Markov Chain Monte Carlo approach for complex lava flow simulations driven by satellite-derived data","authors":"Francesco Zuccarello, Giuseppe Bilotta, Flavio Cannavò, Annalisa Cappello, Roberto Guardo, Gaetana Ganci","doi":"10.1016/j.envsoft.2025.106426","DOIUrl":"10.1016/j.envsoft.2025.106426","url":null,"abstract":"<div><div>We present a novel optimization strategy for the numerical simulation of lava flows that automatically find the best combination of input parameters to fit observed flows considering their uncertainties. The approach is based on the Metropolis algorithm, a Monte Carlo Markov Chain (MCMC) method that performs a sequence of simulations aiming to refine the sampling of unknown parameters to determine their probability distributions. Using this algorithm, we predict the most likely path of lava flows during ongoing eruptions, taking input parameters such as vent locations and Time Average Discharge Rates from satellite imagery. The approach has been validated against synthetic tests on an inclined plane and the 27 February–01 March 2017 eruption at Mt. Etna. This method is the first attempt to use a MCMC method for lava flow modeling, providing several advantages in constraining best-fit values in high-dimensional spaces with complex likelihood functions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106426"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643039","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}
{"title":"Models and the common good","authors":"Andrea Saltelli","doi":"10.1016/j.envsoft.2025.106430","DOIUrl":"10.1016/j.envsoft.2025.106430","url":null,"abstract":"<div><div>How can models do well for the common good? Narratives and counter-narratives are possible to answer the question. The latter are taken here to argue that models may misuse their epistemic authority for reasons of occasion, opportunity and interest. A solution calls for the involvement of more disciplines and actors, but these are not by themselves sufficient due to the present governance, practice and circumstances of science. Short of a radical reset, or Reformation, change will be difficult.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106430"},"PeriodicalIF":4.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678388","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}
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}
{"title":"Uncertainty estimation for environmental multimodel predictions: The BLUECAT approach and software","authors":"Alberto Montanari , Demetris Koutsoyiannis","doi":"10.1016/j.envsoft.2025.106419","DOIUrl":"10.1016/j.envsoft.2025.106419","url":null,"abstract":"<div><div>An extension of the BLUECAT approach and software for uncertainty assessment of environmental predictions is presented, allowing the application to multimodel outputs. BLUECAT operates by transforming a point prediction provided by deterministic models to a corresponding stochastic formulation, thereby allowing the estimation of a bias corrected expected value along with confidence limits. In this paper we also propose to use BLUECAT for model selection in the context of multimodel predictions, by using a measure of uncertainty as selection criterion. We emphasise here the value of BLUECAT for gaining an improved understanding of the underlying environmental systems and multimodel combination. Two examples of applications are presented, highlighting the benefits attainable through uncertainty driven integration of several prediction models. These case studies can be reproduced through the BLUECAT software, that is available in the public domain along with help facilities and instructions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106419"},"PeriodicalIF":4.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643026","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}
{"title":"A hybrid framework for regional climate seasonality study and trend analysis","authors":"Masooma Suleman, Peter A. Khaiter","doi":"10.1016/j.envsoft.2025.106429","DOIUrl":"10.1016/j.envsoft.2025.106429","url":null,"abstract":"<div><div>One of the profound effects produced by climate change is shifting the seasons in terms of both duration and start/end dates. It is important for sustainable management to detect and predict any such seasonal changes as they may trigger earlier-than-usual timing of plant phenology, animal migration, and other ecological, environmental, economic, and social implications. In this study, we are using meteorological data recorded in four cities across Southern Ontario, Canada over the past 70 years (1953–2022) to explore regional relationship between climate variables and seasonal shifts. Applying a combination of statistical and machine learning (ML) algorithms, a novel hybrid framework is suggested for detecting, quantifying, and visualizing seasonal clusters and trends. A comparative analysis of different ML clustering algorithms to identify variations in seasonality timing and to establish phenological seasons is conducted. The resultant seasonal clusters are then used to detect shifts in seasonality dynamics and trends in climate parameters.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106429"},"PeriodicalIF":4.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678383","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}
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}
{"title":"PyTorchFire: A GPU-accelerated wildfire simulator with Differentiable Cellular Automata","authors":"Zeyu Xia , Sibo Cheng","doi":"10.1016/j.envsoft.2025.106401","DOIUrl":"10.1016/j.envsoft.2025.106401","url":null,"abstract":"<div><div>Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce <span>PyTorchFire</span>, an open-access, <span>PyTorch</span>-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our <span>PyTorchFire</span> simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. <span>PyTorchFire</span> has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106401"},"PeriodicalIF":4.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592244","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}
Yifan Zhang, Mengyu Ma, Jun Li, Anran Yang, Qingren Jia, Zebang Liu
{"title":"Efficient and fine-grained viewshed analysis in a three-dimensional urban complex environment","authors":"Yifan Zhang, Mengyu Ma, Jun Li, Anran Yang, Qingren Jia, Zebang Liu","doi":"10.1016/j.envsoft.2025.106359","DOIUrl":"10.1016/j.envsoft.2025.106359","url":null,"abstract":"<div><div>Performing efficient and fine-grained viewshed analysis in 3D complex urban models, particularly when handling large-scale datasets, presents a significant challenge in Geographic Information Systems (GIS). Existing methods are primarily designed for 2.5D raster models and struggle to effectively manage large-scale data. Furthermore, the commonly utilized approaches for 3D models need large display memory and lack statistical analyses. To address these challenges, this paper adopted a results-oriented approach that diverged from the traditional data-driven paradigm by reformulating the conventional viewshed computation problem as a spatial query problem. Building on this premise, we proposed the Q-View method for oblique photogrammetry data, which involved creating an indexing model for large-scale datasets and enabled parallel querying between the line-of-sight (LOS) and the model. The Q-View method enables efficient and spatially exhaustive analysis, effectively overcoming the complexities associated with traditional viewshed computations. Experimental results showed that our approach achieved a query rate of up to 4 million visibility queries per second on a dataset with 17.6 million triangular meshes. Compared to the latest methods, it offered a 72.45% improvement in operational efficiency and superior accuracy relative to the GPU-rated method. These findings indicated that the proposed method substantially improved both the accuracy and efficiency of viewshed analysis in complex urban environments, providing decision support for urban planning and environmental monitoring.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106359"},"PeriodicalIF":4.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577051","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}