Pub Date : 2025-03-01DOI: 10.1016/j.envsoft.2025.106374
Yang Xu , Heng Li , Yuqian Hu , Chunxiao Zhang , Bingli Xu
In deep learning (DL)-based regionalized streamflow modeling, basin similarity has demonstrated to be effective for sharing hydrological information. However, the differences in the use of hydrological information by DL due to different basin similarity strategies remain underexplored. For this, we cluster and regionalize 222 Australian basins based on hydrology, climate, landscape, and position characteristics, and compare their performance with the benchmark model. The results reveal: (1) Basin similarity strategies-based models outperform the benchmark model, demonstrating the effectiveness of basin similarity strategies; (2) Hydrology similarity yields the best model, while climate similarity is relatively stable, suggesting that the key hydrological information for improving DL performance comes from hydrology and climate characteristics; (3) The differences in DL’s utilization of hydrological information are influenced by the combined effects of basin climate, hydrology, soil, and vegetation conditions. This study provides insights into how DL-based regionalized streamflow modeling more effectively utilize basin hydrological information.
{"title":"Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity","authors":"Yang Xu , Heng Li , Yuqian Hu , Chunxiao Zhang , Bingli Xu","doi":"10.1016/j.envsoft.2025.106374","DOIUrl":"10.1016/j.envsoft.2025.106374","url":null,"abstract":"<div><div>In deep learning (DL)-based regionalized streamflow modeling, basin similarity has demonstrated to be effective for sharing hydrological information. However, the differences in the use of hydrological information by DL due to different basin similarity strategies remain underexplored. For this, we cluster and regionalize 222 Australian basins based on hydrology, climate, landscape, and position characteristics, and compare their performance with the benchmark model. The results reveal: (1) Basin similarity strategies-based models outperform the benchmark model, demonstrating the effectiveness of basin similarity strategies; (2) Hydrology similarity yields the best model, while climate similarity is relatively stable, suggesting that the key hydrological information for improving DL performance comes from hydrology and climate characteristics; (3) The differences in DL’s utilization of hydrological information are influenced by the combined effects of basin climate, hydrology, soil, and vegetation conditions. This study provides insights into how DL-based regionalized streamflow modeling more effectively utilize basin hydrological information.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106374"},"PeriodicalIF":4.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535264","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}
Pub Date : 2025-03-01DOI: 10.1016/j.envsoft.2025.106403
Marek Kruk
The aim of this work is to find an effective combination of modelling based on the boosting technique and Shapley value computation with the practise of evaluating an undirected graph model. To this end, we created an XGBoost-SHAP regression model in which the target variable is the cyanobacteria concentration and the model variables consist of 20 environmental factors. Two partial correlation-based graphs were then created. Firstly, a preliminary network containing all the features (with the target variable) with the original datasets of the parameters, and secondly, a network called SHAP-NET based on the Shapley values of the independent variables from the SHAP model. It seems that by using new combined machine learning and network tools such as SHAP-NET, it will be possible to further improve the idea of explainability of models in the field of XAI (eXplainable Artificial Intelligence), and attempts to solve practical domain problems, as in this work, can contribute to progress in this area.
{"title":"SHAP-NET, a network based on Shapley values as a new tool to improve the explainability of the XGBoost-SHAP model for the problem of water quality","authors":"Marek Kruk","doi":"10.1016/j.envsoft.2025.106403","DOIUrl":"10.1016/j.envsoft.2025.106403","url":null,"abstract":"<div><div>The aim of this work is to find an effective combination of modelling based on the boosting technique and Shapley value computation with the practise of evaluating an undirected graph model. To this end, we created an XGBoost-SHAP regression model in which the target variable is the cyanobacteria concentration and the model variables consist of 20 environmental factors. Two partial correlation-based graphs were then created. Firstly, a preliminary network containing all the features (with the target variable) with the original datasets of the parameters, and secondly, a network called SHAP-NET based on the Shapley values of the independent variables from the SHAP model. It seems that by using new combined machine learning and network tools such as SHAP-NET, it will be possible to further improve the idea of explainability of models in the field of XAI (eXplainable Artificial Intelligence), and attempts to solve practical domain problems, as in this work, can contribute to progress in this area.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106403"},"PeriodicalIF":4.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549399","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}
Pub Date : 2025-02-28DOI: 10.1016/j.envsoft.2025.106405
Frederik Priem , Marianne Jilge , Uta Heiden , Ben Somers , Frank Canters
Spectral libraries link surface reflectance characteristics to thematic cover type interpretation. Despite their potential, spectral libraries are rarely used beyond their original application or analysis. In this paper we introduce the concept of a Generic Urban Spectral Library (GUSL). A GUSL is a thoroughly labelled collection of multi-site, -sensor and -temporal spectral libraries that supports urban mapping. The GUSL is envisioned as an open data source equipped with tools that facilitate deployment for different mapping purposes and image types. We introduce the GUSL concept, present a preliminary implementation containing eight spectral libraries and discuss two use case experiments performed with the spectral libraries and the GSL-tools currently included in the GUSL. The first use case focuses on GUSL expansion with image-derived spectral endmembers. From a 200 by 200-pixel hyperspectral urban image, we extract 229 endmember spectra. By comparing the extracted spectra to the spectra already included in the experimental GUSL, we demonstrate how the GUSL can reduce the burden of endmember labelling and identify spectra that are novel to the GUSL. In the second use case the experimental GUSL is used for mapping urban land cover from airborne hyperspectral data in two cities (Munich, Brussels) for which local spectra are included in the GUSL, and in a third city (Pavia) for which no local spectra are present in the library. Generalized material group and more detailed artificial material type mapping yield average user and producer accuracies ranging between 0.72 and 0.96. When mapping is performed on the Pavia image, the obtained material group accuracies remain reasonable, i.e., around 0.80. Our experimental results confirm the potential of the GUSL for urban library building and mapping. Future research avenues are proposed to move towards an operational GUSL.
{"title":"Generic spectral library framework for urban land cover mapping with optical remote sensing imagery","authors":"Frederik Priem , Marianne Jilge , Uta Heiden , Ben Somers , Frank Canters","doi":"10.1016/j.envsoft.2025.106405","DOIUrl":"10.1016/j.envsoft.2025.106405","url":null,"abstract":"<div><div>Spectral libraries link surface reflectance characteristics to thematic cover type interpretation. Despite their potential, spectral libraries are rarely used beyond their original application or analysis. In this paper we introduce the concept of a Generic Urban Spectral Library (GUSL). A GUSL is a thoroughly labelled collection of multi-site, -sensor and -temporal spectral libraries that supports urban mapping. The GUSL is envisioned as an open data source equipped with tools that facilitate deployment for different mapping purposes and image types. We introduce the GUSL concept, present a preliminary implementation containing eight spectral libraries and discuss two use case experiments performed with the spectral libraries and the GSL-tools currently included in the GUSL. The first use case focuses on GUSL expansion with image-derived spectral endmembers. From a 200 by 200-pixel hyperspectral urban image, we extract 229 endmember spectra. By comparing the extracted spectra to the spectra already included in the experimental GUSL, we demonstrate how the GUSL can reduce the burden of endmember labelling and identify spectra that are novel to the GUSL. In the second use case the experimental GUSL is used for mapping urban land cover from airborne hyperspectral data in two cities (Munich, Brussels) for which local spectra are included in the GUSL, and in a third city (Pavia) for which no local spectra are present in the library. Generalized material group and more detailed artificial material type mapping yield average user and producer accuracies ranging between 0.72 and 0.96. When mapping is performed on the Pavia image, the obtained material group accuracies remain reasonable, i.e., around 0.80. Our experimental results confirm the potential of the GUSL for urban library building and mapping. Future research avenues are proposed to move towards an operational GUSL.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106405"},"PeriodicalIF":4.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577048","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}
Pub Date : 2025-02-27DOI: 10.1016/j.envsoft.2025.106404
W.D. Dimuth P. Welivitiya , G.R. Hancock
Aerial and ground-based survey routinely employs technology such as digital photogrammetry, Light Detecting and Ranging (LiDAR) and Terrestrial Laser Scanning (TLS). These systems produce huge data sets with varying accuracy and reliability. At present there are no guidelines for the grid size dimension needed to accurately and reliably represent common features such as rills, gullies and contour drains. Here, synthetic landscapes with a very high density of points (10,000 pt m−2) are created. Coordinate data capture error is also examined. Results demonstrate that for the reliable representation of a gully or contour drain, the DEM grid spacing needs to be at least 1/3 the width of the feature of interest. Typical coordinate errors inherent within the data do not significantly affect the definition of gullies or contour drains. The findings here provide a defensible guide for the coordinate density required to hydrologically and geomorphically represent a landscape surface.
{"title":"What is the optimal digital elevation model grid size to best capture hillslope gullies and contour drains?","authors":"W.D. Dimuth P. Welivitiya , G.R. Hancock","doi":"10.1016/j.envsoft.2025.106404","DOIUrl":"10.1016/j.envsoft.2025.106404","url":null,"abstract":"<div><div>Aerial and ground-based survey routinely employs technology such as digital photogrammetry, Light Detecting and Ranging (LiDAR) and Terrestrial Laser Scanning (TLS). These systems produce huge data sets with varying accuracy and reliability. At present there are no guidelines for the grid size dimension needed to accurately and reliably represent common features such as rills, gullies and contour drains. Here, synthetic landscapes with a very high density of points (10,000 pt m<sup>−2</sup>) are created. Coordinate data capture error is also examined. Results demonstrate that for the reliable representation of a gully or contour drain, the DEM grid spacing needs to be at least 1/3 the width of the feature of interest. Typical coordinate errors inherent within the data do not significantly affect the definition of gullies or contour drains. The findings here provide a defensible guide for the coordinate density required to hydrologically and geomorphically represent a landscape surface.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106404"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583089","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}
Pub Date : 2025-02-27DOI: 10.1016/j.envsoft.2025.106391
Junjie Yu , Yuan Sun , Sarah Lindley , Caroline Jay , David O. Topping , Keith W. Oleson , Zhonghua Zheng
The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool used to interface with the containerized CLMU and create urban surface and atmospheric forcing data. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies.
{"title":"Integration and execution of Community Land Model Urban (CLMU) in a containerized environment","authors":"Junjie Yu , Yuan Sun , Sarah Lindley , Caroline Jay , David O. Topping , Keith W. Oleson , Zhonghua Zheng","doi":"10.1016/j.envsoft.2025.106391","DOIUrl":"10.1016/j.envsoft.2025.106391","url":null,"abstract":"<div><div>The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool used to interface with the containerized CLMU and create urban surface and atmospheric forcing data. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106391"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528666","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}
Pub Date : 2025-02-26DOI: 10.1016/j.envsoft.2025.106402
Philipp Brun , Lucienne de Witte , Manuel Richard Popp , Damaris Zurell , Dirk Nikolaus Karger , Patrice Descombes , Riccardo de Lutio , Jan Dirk Wegner , Christophe Bornand , Stefan Eggenberg , Tasko Olevski , Niklaus E. Zimmermann
Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.
{"title":"FlorID – A nationwide identification service for plants from photos and habitat information","authors":"Philipp Brun , Lucienne de Witte , Manuel Richard Popp , Damaris Zurell , Dirk Nikolaus Karger , Patrice Descombes , Riccardo de Lutio , Jan Dirk Wegner , Christophe Bornand , Stefan Eggenberg , Tasko Olevski , Niklaus E. Zimmermann","doi":"10.1016/j.envsoft.2025.106402","DOIUrl":"10.1016/j.envsoft.2025.106402","url":null,"abstract":"<div><div>Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106402"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562381","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}
Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analysing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia.
{"title":"Modelling bushfire severity and predicting future trends in Australia using remote sensing and machine learning","authors":"Shouthiri Partheepan , Farzad Sanati , Jahan Hassan","doi":"10.1016/j.envsoft.2025.106377","DOIUrl":"10.1016/j.envsoft.2025.106377","url":null,"abstract":"<div><div>Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analysing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106377"},"PeriodicalIF":4.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547082","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}
Pub Date : 2025-02-25DOI: 10.1016/j.envsoft.2025.106393
Lu Yang , Jingming Hou , Xinhong Wang , Pan Wang , Yongwei Wang
This paper presents a high-resolution 2D eco-hydraulics model accelerated by GPU technology, specifically designed for a spawning ground of Gymnocypris eckloni located downstream of the B hydropower station in the Upper Yellow River. The model evaluates the quality of the spawning habitat from April to June during a typical year. The calculation efficiency is improved by 12.1 times eco-hydraulics on a GPU device compared with the simulation on CPU device. The results indicate that while the concentration of dissolved oxygen (DO) meets the spawning requirements, water temperature significantly affects the habitat quality. Through the analysis of Weighted Useable Area (WUA) values and considering the impact of flood pulses on fish, an ecological scheduling scheme for the B hydropower station was developed to simulate the natural runoff process from April 24 to May 24. The implementation of this ecological scheduling scheme facilitates the coordinated development of economic activities and river ecological health. Additionally, it serves as an important reference for the development of ecological flow management measures and programs for other reservoirs.
{"title":"Application of the 2D high-resolution eco-hydraulics model based on GPU acceleration technology in the Upper Yellow River","authors":"Lu Yang , Jingming Hou , Xinhong Wang , Pan Wang , Yongwei Wang","doi":"10.1016/j.envsoft.2025.106393","DOIUrl":"10.1016/j.envsoft.2025.106393","url":null,"abstract":"<div><div>This paper presents a high-resolution 2D eco-hydraulics model accelerated by GPU technology, specifically designed for a spawning ground of <em>Gymnocypris eckloni</em> located downstream of the B hydropower station in the Upper Yellow River. The model evaluates the quality of the spawning habitat from April to June during a typical year. The calculation efficiency is improved by 12.1 times eco-hydraulics on a GPU device compared with the simulation on CPU device. The results indicate that while the concentration of dissolved oxygen (DO) meets the spawning requirements, water temperature significantly affects the habitat quality. Through the analysis of Weighted Useable Area (WUA) values and considering the impact of flood pulses on fish, an ecological scheduling scheme for the B hydropower station was developed to simulate the natural runoff process from April 24 to May 24. The implementation of this ecological scheduling scheme facilitates the coordinated development of economic activities and river ecological health. Additionally, it serves as an important reference for the development of ecological flow management measures and programs for other reservoirs.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106393"},"PeriodicalIF":4.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535283","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}
Pub Date : 2025-02-24DOI: 10.1016/j.envsoft.2025.106382
Miguel López-Otal , Fernando Domínguez-Castro , Borja Latorre , Javier Vela-Tambo , Jorge Gracia
Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts.
To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.
{"title":"SeqIA: A Python framework for extracting drought impacts from news archives","authors":"Miguel López-Otal , Fernando Domínguez-Castro , Borja Latorre , Javier Vela-Tambo , Jorge Gracia","doi":"10.1016/j.envsoft.2025.106382","DOIUrl":"10.1016/j.envsoft.2025.106382","url":null,"abstract":"<div><div>Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts.</div><div>To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106382"},"PeriodicalIF":4.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508306","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}
Pub Date : 2025-02-23DOI: 10.1016/j.envsoft.2025.106396
Xueying Li , Xiaofan Yang
porousRTFoam v1.0 is a software developed to solve pore-scale hydro-bio-geochemical processes in porous media. It is developed based on OpenFOAM® by using the micro-continuum approach, which is adopted to solve a system of equations, including the Darcy-Brinkman-Stokes equation, the advection-diffusion equation with geochemical source terms, as well as biomass evolution with Monod kinetics for biofilm growth. Calcite dissolution and biofilm formation are used as benchmark cases to demonstrate the capabilities of porousRTFoam, with results compared against existing numerical packages and experimental data. The software is further demonstrated to be adaptable for building specific models, such as, mineral dissolution and precipitation in porous media and microbially induced calcite carbonate precipitation (MICP) in fractured media. The new software has the potential to promote fundamental understanding of various pore-scale reactive transport mechanisms, including but not limited to cell aggregation as well as cotransport of contaminants and bacteria in subsurface environments.
{"title":"porousRTFoam v1.0: An open-source numerical platform for simulating pore-scale reactive transport processes in porous media","authors":"Xueying Li , Xiaofan Yang","doi":"10.1016/j.envsoft.2025.106396","DOIUrl":"10.1016/j.envsoft.2025.106396","url":null,"abstract":"<div><div>porousRTFoam v1.0 is a software developed to solve pore-scale hydro-bio-geochemical processes in porous media. It is developed based on OpenFOAM® by using the micro-continuum approach, which is adopted to solve a system of equations, including the Darcy-Brinkman-Stokes equation, the advection-diffusion equation with geochemical source terms, as well as biomass evolution with Monod kinetics for biofilm growth. Calcite dissolution and biofilm formation are used as benchmark cases to demonstrate the capabilities of porousRTFoam, with results compared against existing numerical packages and experimental data. The software is further demonstrated to be adaptable for building specific models, such as, mineral dissolution and precipitation in porous media and microbially induced calcite carbonate precipitation (MICP) in fractured media. The new software has the potential to promote fundamental understanding of various pore-scale reactive transport mechanisms, including but not limited to cell aggregation as well as cotransport of contaminants and bacteria in subsurface environments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106396"},"PeriodicalIF":4.8,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511636","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}
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