Pub Date : 2025-11-27DOI: 10.1016/j.envsoft.2025.106799
Jamiu Adekunle Idowu , Ayman Alfahid
Floods are among the world's most devastating hazards, yet progress in predicting and managing flood risk remains limited by pervasive uncertainties at every stage of the modelling pipeline. This systematic review identifies eight open problems in uncertainty quantification for flood modelling, including: long-term prediction errors, poor calibration of predictive intervals, incomplete representation of uncertainties, inadequate handling of spatial and temporal variability, non-linearity, data scarcity and integration issues, high computational costs, and failure to capture uncertainties in extreme events. These challenges reflect a system-level mismatch between the dynamic complexity of floods and the fragmented nature of current modelling practice. Real progress in flood risk science demands a shift from siloed, modular workflows to seamless, end-to-end probabilistic pipelines – integrating heterogeneous data, hybridizing process-based and data-driven models, rigorously quantifying uncertainty at all stages, and communicating actionable risk information for policy and emergency response.
{"title":"Open problems in uncertainty quantification for flood modelling: A systematic review","authors":"Jamiu Adekunle Idowu , Ayman Alfahid","doi":"10.1016/j.envsoft.2025.106799","DOIUrl":"10.1016/j.envsoft.2025.106799","url":null,"abstract":"<div><div>Floods are among the world's most devastating hazards, yet progress in predicting and managing flood risk remains limited by pervasive uncertainties at every stage of the modelling pipeline. This systematic review identifies eight open problems in uncertainty quantification for flood modelling, including: long-term prediction errors, poor calibration of predictive intervals, incomplete representation of uncertainties, inadequate handling of spatial and temporal variability, non-linearity, data scarcity and integration issues, high computational costs, and failure to capture uncertainties in extreme events. These challenges reflect a system-level mismatch between the dynamic complexity of floods and the fragmented nature of current modelling practice. Real progress in flood risk science demands a shift from siloed, modular workflows to seamless, end-to-end probabilistic pipelines – integrating heterogeneous data, hybridizing process-based and data-driven models, rigorously quantifying uncertainty at all stages, and communicating actionable risk information for policy and emergency response.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106799"},"PeriodicalIF":4.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611928","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 : 2025-11-26DOI: 10.1016/j.envsoft.2025.106792
Ana Trisovic, Jan Range, Philip Durbin, Katherine Mika, Amber Leahey, Wei Li, Danielle Braun
{"title":"Advancing geospatial data infrastructure in dataverse via metadata automation, interactive tools and LLM case study","authors":"Ana Trisovic, Jan Range, Philip Durbin, Katherine Mika, Amber Leahey, Wei Li, Danielle Braun","doi":"10.1016/j.envsoft.2025.106792","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106792","url":null,"abstract":"","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"41 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609228","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 : 2025-11-26DOI: 10.1016/j.envsoft.2025.106801
V. Jayasuriya , Prabha Susan Philip , T. Jyolsna
The HYDRUS software suite is a cornerstone of modern soil and water science, having evolved from a specialized numerical solver into a comprehensive platform for simulating complex vadose zone processes. A bibliometric analysis of 3154 peer-reviewed articles (1993–2024) quantifies this trajectory, revealing distinct eras of growth and shifting research themes. Key applications in irrigation optimization, nutrient management, and contaminant fate are examined, highlighting the critical role of specialized add-on modules for simulating advanced processes like preferential flow and reactive transport. This review synthesizes persistent scientific challenges, including model parameterization, the representation of nonequilibrium phenomena, and the need for rigorous validation. Future research directions point toward enhanced computational efficiency and deeper integration with GIS, remote sensing, and machine learning to address existing limitations and explore emerging environmental problems.
{"title":"The HYDRUS model for soil and water management: A brief review of capabilities, trends, and future directions","authors":"V. Jayasuriya , Prabha Susan Philip , T. Jyolsna","doi":"10.1016/j.envsoft.2025.106801","DOIUrl":"10.1016/j.envsoft.2025.106801","url":null,"abstract":"<div><div>The HYDRUS software suite is a cornerstone of modern soil and water science, having evolved from a specialized numerical solver into a comprehensive platform for simulating complex vadose zone processes. A bibliometric analysis of 3154 peer-reviewed articles (1993–2024) quantifies this trajectory, revealing distinct eras of growth and shifting research themes. Key applications in irrigation optimization, nutrient management, and contaminant fate are examined, highlighting the critical role of specialized add-on modules for simulating advanced processes like preferential flow and reactive transport. This review synthesizes persistent scientific challenges, including model parameterization, the representation of nonequilibrium phenomena, and the need for rigorous validation. Future research directions point toward enhanced computational efficiency and deeper integration with GIS, remote sensing, and machine learning to address existing limitations and explore emerging environmental problems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106801"},"PeriodicalIF":4.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609266","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 : 2025-11-25DOI: 10.1016/j.envsoft.2025.106798
Dongjun Lee , Ritesh Karki , Latif Kalin
Urbanization alters land use patterns, influencing hydrological and biochemical cycles. However, most watershed-scale hydrologic models, including the Soil and Water Assessment Tool (SWAT), use simplified urban classifications that fail to capture the spatial heterogeneity of impervious surfaces. To address this limitation, we developed NLCD-Imp, a QGIS plugin that enhances National Land Cover Database (NLCD) land use/land cover (LULC) maps by integrating detailed urban characteristics. We applied NLCD-Imp to SWAT to assess its impact on hydrological and biochemical responses in an urban watershed. The NLCD-Imp inputs increased surface runoff, reduced evapotranspiration, and led to a two-to fourfold increase in simulated nutrient loads in highly impervious urban areas. A threshold-based method showed that a 2 % imperviousness threshold balances model accuracy and complexity. NLCD-Imp improves urban LULC representation in SWAT and can be adapted for other models, enhancing simulation reliability and supporting sustainable urban water management.
{"title":"Introducing NLCD-Imp: A QGIS plugin to better replicate urban characteristics in land use/cover maps for SWAT","authors":"Dongjun Lee , Ritesh Karki , Latif Kalin","doi":"10.1016/j.envsoft.2025.106798","DOIUrl":"10.1016/j.envsoft.2025.106798","url":null,"abstract":"<div><div>Urbanization alters land use patterns, influencing hydrological and biochemical cycles. However, most watershed-scale hydrologic models, including the Soil and Water Assessment Tool (SWAT), use simplified urban classifications that fail to capture the spatial heterogeneity of impervious surfaces. To address this limitation, we developed NLCD-Imp, a QGIS plugin that enhances National Land Cover Database (NLCD) land use/land cover (LULC) maps by integrating detailed urban characteristics. We applied NLCD-Imp to SWAT to assess its impact on hydrological and biochemical responses in an urban watershed. The NLCD-Imp inputs increased surface runoff, reduced evapotranspiration, and led to a two-to fourfold increase in simulated nutrient loads in highly impervious urban areas. A threshold-based method showed that a 2 % imperviousness threshold balances model accuracy and complexity. NLCD-Imp improves urban LULC representation in SWAT and can be adapted for other models, enhancing simulation reliability and supporting sustainable urban water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106798"},"PeriodicalIF":4.6,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593067","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 : 2025-11-25DOI: 10.1016/j.envsoft.2025.106793
Boguslaw Twarog , Przemyslaw Hawro , Tadeusz Kwater
The protection of water reservoirs requires precise monitoring of quality parameters, which is a crucial aspect of water resource management. This article proposes an algorithm for real-time reconstruction of unmeasured signals using only easily accessible measurement data. The proposed method relies on a specially designed latch mechanism and a mathematical river model. The river model under consideration is described by nonlinear ordinary differential equations obtained via a transformation of partial differential equations. The task was accomplished through the application of an adaptive signal estimation algorithm specifically developed for this purpose, with additional tuning of dynamic properties achieved through precise placement of eigenvalues. The results of simulation studies confirmed improved accuracy in reproducing dynamic processes, particularly for signals that are challenging to measure directly, compared to other analysed methods. As a practical application, the proposed algorithm is implemented as a soft sensor for monitoring a biochemically polluted river.
{"title":"Signal estimation adaptive algorithm with latch-mechanism for real-time water quality monitoring","authors":"Boguslaw Twarog , Przemyslaw Hawro , Tadeusz Kwater","doi":"10.1016/j.envsoft.2025.106793","DOIUrl":"10.1016/j.envsoft.2025.106793","url":null,"abstract":"<div><div>The protection of water reservoirs requires precise monitoring of quality parameters, which is a crucial aspect of water resource management. This article proposes an algorithm for real-time reconstruction of unmeasured signals using only easily accessible measurement data. The proposed method relies on a specially designed latch mechanism and a mathematical river model. The river model under consideration is described by nonlinear ordinary differential equations obtained via a transformation of partial differential equations. The task was accomplished through the application of an adaptive signal estimation algorithm specifically developed for this purpose, with additional tuning of dynamic properties achieved through precise placement of eigenvalues. The results of simulation studies confirmed improved accuracy in reproducing dynamic processes, particularly for signals that are challenging to measure directly, compared to other analysed methods. As a practical application, the proposed algorithm is implemented as a soft sensor for monitoring a biochemically polluted river.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106793"},"PeriodicalIF":4.6,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598636","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 : 2025-11-24DOI: 10.1016/j.envsoft.2025.106800
S.J.H.A. Gradussen , A.L. de Jongste , V. Chavarrías
When modelling the morphological response to river interventions, deterministic discharge time series are commonly used as a cost-effective alternative for approximating the most likely morphological changes resulting from Monte Carlo (MC) analyses. The conventional deterministic approach cycles long-term discharge statistics through a Cycled Annual Hydrograph (CyAH), neglecting inter-annual discharge variability. We introduce a new deterministic method, the Multiple Annual Hydrograph (MuAH), that accounts for such inter-annual discharge variations. Using a one-dimensional model, we simulate morphodynamic changes resulting from a hypothetical intervention in an alluvial river to demonstrate the application of these deterministic time series and to evaluate their performance. We find that MuAH time series result in both long-term (quasi-static) evolution and seasonal (dynamic) morphological changes that more closely match MC results and natural discharge time series, compared with the CyAH approach. This enables more accurate assessments of morphological change induced by river interventions when using deterministic time series.
{"title":"Deterministic discharge time series for morphodynamic assessments of river interventions","authors":"S.J.H.A. Gradussen , A.L. de Jongste , V. Chavarrías","doi":"10.1016/j.envsoft.2025.106800","DOIUrl":"10.1016/j.envsoft.2025.106800","url":null,"abstract":"<div><div>When modelling the morphological response to river interventions, deterministic discharge time series are commonly used as a cost-effective alternative for approximating the most likely morphological changes resulting from Monte Carlo (MC) analyses. The conventional deterministic approach cycles long-term discharge statistics through a Cycled Annual Hydrograph (CyAH), neglecting inter-annual discharge variability. We introduce a new deterministic method, the Multiple Annual Hydrograph (MuAH), that accounts for such inter-annual discharge variations. Using a one-dimensional model, we simulate morphodynamic changes resulting from a hypothetical intervention in an alluvial river to demonstrate the application of these deterministic time series and to evaluate their performance. We find that MuAH time series result in both long-term (quasi-static) evolution and seasonal (dynamic) morphological changes that more closely match MC results and natural discharge time series, compared with the CyAH approach. This enables more accurate assessments of morphological change induced by river interventions when using deterministic time series.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106800"},"PeriodicalIF":4.6,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593069","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 : 2025-11-22DOI: 10.1016/j.envsoft.2025.106796
Zhaocai Wang , Cheng Ding , Nannan Xu , Weilong Wang , Xingxing Zhang
Accurate streamflow forecasts are critically important for monitoring flood disasters and managing water resources. The factors influencing streamflow are complex, characterized by significant non-linearity and intricacy. Developing a data-driven hybrid deep learning model for streamflow prediction represents an effective strategy. Consequently, this study introduces an enhanced deep learning model, named CEEMDAN-ISMA-CNN-LSTM-AM-RF (CICLAR), for predicting both streamflow and extreme flood events. This study integrates multi-source heterogeneous data, including remote sensing, meteorological, hydrological, and streamflow data. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to reduce the complexity, and then multi-source data are input into the CNN-LSTM-AM model. Additionally, the Improved Slime Mould Algorithm (ISMA) is employed to optimize the neural network's hyperparameters. Finally, Random Forest (RF) is used to perform non-linear summation. The study conducted daily streamflow predictions at 11 stations located in the upstream, midstream, and downstream sections of the Jialing River in China, demonstrating that the CICLAR model significantly outperforms other benchmark models. Taking the Beibei Hydrological Station as an example, compared to the conventional Long Short-Term Memory (LSTM) model, the Nash-Sutcliffe Efficiency Coefficient (NSE) of the CICLAR model's prediction results increased by 30 %, and the Mean Absolute Error (MAE) decreased by 75 %. For extreme flood forecasting, compared to the LSTM, the CICLAR model reduced the Mean Relative Error (MRE) by 0.86 and improved the Qualification Rate (QR) by 150 %. The results of this study show that the CICLAR model has significant application value in extreme flood forecasting and water resources management.
{"title":"Interpretable deep learning hybrid streamflow prediction modeling based on multi-source data fusion","authors":"Zhaocai Wang , Cheng Ding , Nannan Xu , Weilong Wang , Xingxing Zhang","doi":"10.1016/j.envsoft.2025.106796","DOIUrl":"10.1016/j.envsoft.2025.106796","url":null,"abstract":"<div><div>Accurate streamflow forecasts are critically important for monitoring flood disasters and managing water resources. The factors influencing streamflow are complex, characterized by significant non-linearity and intricacy. Developing a data-driven hybrid deep learning model for streamflow prediction represents an effective strategy. Consequently, this study introduces an enhanced deep learning model, named CEEMDAN-ISMA-CNN-LSTM-AM-RF (CICLAR), for predicting both streamflow and extreme flood events. This study integrates multi-source heterogeneous data, including remote sensing, meteorological, hydrological, and streamflow data. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to reduce the complexity, and then multi-source data are input into the CNN-LSTM-AM model. Additionally, the Improved Slime Mould Algorithm (ISMA) is employed to optimize the neural network's hyperparameters. Finally, Random Forest (RF) is used to perform non-linear summation. The study conducted daily streamflow predictions at 11 stations located in the upstream, midstream, and downstream sections of the Jialing River in China, demonstrating that the CICLAR model significantly outperforms other benchmark models. Taking the Beibei Hydrological Station as an example, compared to the conventional Long Short-Term Memory (LSTM) model, the Nash-Sutcliffe Efficiency Coefficient (NSE) of the CICLAR model's prediction results increased by 30 %, and the Mean Absolute Error (MAE) decreased by 75 %. For extreme flood forecasting, compared to the LSTM, the CICLAR model reduced the Mean Relative Error (MRE) by 0.86 and improved the Qualification Rate (QR) by 150 %. The results of this study show that the CICLAR model has significant application value in extreme flood forecasting and water resources management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106796"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575521","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 : 2025-11-22DOI: 10.1016/j.envsoft.2025.106780
Shiqi Zhang , Peihao Peng , Ning Li , Juan Wang , Xuefeng Peng , Zhaozhi Luo
Fine-scale vegetation zoning of evergreen broad-leaved forests (EBFs) is essential for ecological understanding and forest management, yet expert-led schemes often under-represent environmental complexity. This study proposes a multi-source modelling framework that bridges vegetation classification and zonation, demonstrated in Sichuan Province, China. Vegetation classification data for primary units were derived from Landsat-8 and Sentinel-1 imagery using a hierarchical multi-label network, and for secondary units from kernel density estimation of constructive species. A vegetation–environment relationship model then quantifies the influence of climate, topography and soil, converting discrete classes into the continuous indicators required for zoning. Finally, zoning thresholds were defined using confidence interval and Jenks natural breaks, and boundaries refined through upscaling and Gaussian filtering. The resulting map delineates three vegetation areas and fifteen vegetation districts, capturing spatial heterogeneity across the Sichuan Basin-Tibetan Plateau ecotone. The framework provides a replicable tool for vegetation zoning in complex mountain systems.
{"title":"From vegetation classification to vegetation zonation: a multi-source modelling framework for evergreen broad-leaved forests in complex terrain","authors":"Shiqi Zhang , Peihao Peng , Ning Li , Juan Wang , Xuefeng Peng , Zhaozhi Luo","doi":"10.1016/j.envsoft.2025.106780","DOIUrl":"10.1016/j.envsoft.2025.106780","url":null,"abstract":"<div><div>Fine-scale vegetation zoning of evergreen broad-leaved forests (EBFs) is essential for ecological understanding and forest management, yet expert-led schemes often under-represent environmental complexity. This study proposes a multi-source modelling framework that bridges vegetation classification and zonation, demonstrated in Sichuan Province, China. Vegetation classification data for primary units were derived from Landsat-8 and Sentinel-1 imagery using a hierarchical multi-label network, and for secondary units from kernel density estimation of constructive species. A vegetation–environment relationship model then quantifies the influence of climate, topography and soil, converting discrete classes into the continuous indicators required for zoning. Finally, zoning thresholds were defined using confidence interval and Jenks natural breaks, and boundaries refined through upscaling and Gaussian filtering. The resulting map delineates three vegetation areas and fifteen vegetation districts, capturing spatial heterogeneity across the Sichuan Basin-Tibetan Plateau ecotone. The framework provides a replicable tool for vegetation zoning in complex mountain systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106780"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575240","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}
Nature-based solutions (NBS) have emerged as sustainable approaches to urban water management, addressing critical challenges like wastewater treatment and stormwater management while delivering additional environmental and social benefits. A significant advantage of NBS is their potential to decentralize urban water management, enabling cities to distribute water treatment and storage systems across multiple locations, thereby alleviating pressure on traditional centralized infrastructure. However, the wide array of NBS options, each tailored to specific contexts, presents a considerable challenge for decision-makers. The Nat4Wat decision-support system (DSS) was developed to help stakeholders select, compare, and evaluate NBS options for wastewater treatment and stormwater management. Nat4Wat streamlines decision-making by integrating a transparent multicriteria evaluation with an open knowledge base that links performance, costs, and cobenefits. By integrating multicriteria decision analysis (MCDA), Nat4Wat evaluates factors —such as cost-effectiveness, environmental performance, operational requirements, and social benefits— guiding users toward the most suitable and sustainable NBS for their specific needs. This paper details the co-development of Nat4Wat, in collaboration with technology providers and decision-makers, showcasing its application in two case studies: wastewater reuse at a rural hotel and stormwater mitigation in an urban area. These examples demonstrate how the tool streamlines decision-making, enhances transparency, and fosters stakeholder participation. As urban areas face increasing water-related challenges driven by climate change and population growth, Nat4Wat serves as a relevant resource for integrating NBS into resilient and sustainable urban water management strategies.
{"title":"Nat4Wat: a co-developed decision-support system for resilient urban water management with nature-based solutions","authors":"Josep Pueyo-Ros , Esther Mendoza , Gianluigi Buttiglieri , Joaquim Comas","doi":"10.1016/j.envsoft.2025.106797","DOIUrl":"10.1016/j.envsoft.2025.106797","url":null,"abstract":"<div><div>Nature-based solutions (NBS) have emerged as sustainable approaches to urban water management, addressing critical challenges like wastewater treatment and stormwater management while delivering additional environmental and social benefits. A significant advantage of NBS is their potential to decentralize urban water management, enabling cities to distribute water treatment and storage systems across multiple locations, thereby alleviating pressure on traditional centralized infrastructure. However, the wide array of NBS options, each tailored to specific contexts, presents a considerable challenge for decision-makers. The Nat4Wat decision-support system (DSS) was developed to help stakeholders select, compare, and evaluate NBS options for wastewater treatment and stormwater management. Nat4Wat streamlines decision-making by integrating a transparent multicriteria evaluation with an open knowledge base that links performance, costs, and cobenefits. By integrating multicriteria decision analysis (MCDA), Nat4Wat evaluates factors —such as cost-effectiveness, environmental performance, operational requirements, and social benefits— guiding users toward the most suitable and sustainable NBS for their specific needs. This paper details the co-development of Nat4Wat, in collaboration with technology providers and decision-makers, showcasing its application in two case studies: wastewater reuse at a rural hotel and stormwater mitigation in an urban area. These examples demonstrate how the tool streamlines decision-making, enhances transparency, and fosters stakeholder participation. As urban areas face increasing water-related challenges driven by climate change and population growth, Nat4Wat serves as a relevant resource for integrating NBS into resilient and sustainable urban water management strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106797"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575238","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 : 2025-11-20DOI: 10.1016/j.envsoft.2025.106789
Luciano Ortenzi , Jacopo Aguzzi , Damianos Chatzievangelou , Eugenio Nerio Nemmi , Michele Ferrari , Ivan Masmitja , Morane Clavel-Henry , Nixon Bahamon , Nathan J. Robinson , Giacomo Picardi , Paula Espina , Simona Violino , Riccardo De Angelis , Simone Figorilli , Lavinia Moscovini , Matteo Gallici , Francesca Antonucci , Alessandro Mei , Corrado Costa
To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management.
{"title":"Generative artificial intelligence and marine ecological monitoring","authors":"Luciano Ortenzi , Jacopo Aguzzi , Damianos Chatzievangelou , Eugenio Nerio Nemmi , Michele Ferrari , Ivan Masmitja , Morane Clavel-Henry , Nixon Bahamon , Nathan J. Robinson , Giacomo Picardi , Paula Espina , Simona Violino , Riccardo De Angelis , Simone Figorilli , Lavinia Moscovini , Matteo Gallici , Francesca Antonucci , Alessandro Mei , Corrado Costa","doi":"10.1016/j.envsoft.2025.106789","DOIUrl":"10.1016/j.envsoft.2025.106789","url":null,"abstract":"<div><div>To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106789"},"PeriodicalIF":4.6,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560063","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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