Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106321
Floran Clopin , Ilaria Micella , Jorrit P. Mesman , Ma Cristina Paule-Mercado , Marina Amadori , Shuqi Lin , Lisette N. de Senerpont Domis , Jeroen J.M. de Klein
Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models, and 6 hybrid models in different sets of combinations, with a range of objectives (e.g. understanding the natural processes, predicting, and analysing climate change and land-use scenarios, or evaluating the different management options). Some integrated models had multiple applications whereas others were only applied once, with an uneven global geographical distribution.
To aid model selection by future users, we present a support tool discriminating the models by their features and application fields. This study encourages the development of open-source tools aiding interdisciplinary collaborations and further research in the field of integrated modelling.
{"title":"Integrated models of nutrient dynamics in lake and reservoir watersheds: A systematic review and integrated modelling decision pathway","authors":"Floran Clopin , Ilaria Micella , Jorrit P. Mesman , Ma Cristina Paule-Mercado , Marina Amadori , Shuqi Lin , Lisette N. de Senerpont Domis , Jeroen J.M. de Klein","doi":"10.1016/j.envsoft.2025.106321","DOIUrl":"10.1016/j.envsoft.2025.106321","url":null,"abstract":"<div><div>Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models, and 6 hybrid models in different sets of combinations, with a range of objectives (e.g. understanding the natural processes, predicting, and analysing climate change and land-use scenarios, or evaluating the different management options). Some integrated models had multiple applications whereas others were only applied once, with an uneven global geographical distribution.</div><div>To aid model selection by future users, we present a support tool discriminating the models by their features and application fields. This study encourages the development of open-source tools aiding interdisciplinary collaborations and further research in the field of integrated modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106321"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020243","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 : 2025-02-01DOI: 10.1016/j.envsoft.2025.106328
Sümeyye Kaynak , Baran Kaynak , Carlos Erazo Ramirez , Ibrahim Demir
The development of web technologies and their integration into various fields has allowed a new era in data-driven decision-making and public data accessibility, especially through their adoption of monitoring and quantification environmental resources provided by governmental institutions. The use of web technologies has made it possible to create applications that can be accessed and used by a wide user base. However, dealing with the complexity of environmental data and non-standard data formats remains a hindering issue. To overcome these challenges and obtain up-to-date information from different institutions, we present Geo-WC: a web component framework specifically designed for earth and environmental sciences, serving as a bridge across various scientific domains. The Geo-WC utilizes a developer-friendly approach through simple HTML declarative syntax to bring together data in a single interface that is easy for developers to work with, making it accessible to users of varying skill levels. The framework integrates widely used web technologies, facilitating client-side data analysis, visualization, and accessibility within web browsers.
{"title":"Geo-WC: Custom web components for earth science organizations and agencies","authors":"Sümeyye Kaynak , Baran Kaynak , Carlos Erazo Ramirez , Ibrahim Demir","doi":"10.1016/j.envsoft.2025.106328","DOIUrl":"10.1016/j.envsoft.2025.106328","url":null,"abstract":"<div><div>The development of web technologies and their integration into various fields has allowed a new era in data-driven decision-making and public data accessibility, especially through their adoption of monitoring and quantification environmental resources provided by governmental institutions. The use of web technologies has made it possible to create applications that can be accessed and used by a wide user base. However, dealing with the complexity of environmental data and non-standard data formats remains a hindering issue. To overcome these challenges and obtain up-to-date information from different institutions, we present Geo-WC: a web component framework specifically designed for earth and environmental sciences, serving as a bridge across various scientific domains. The Geo-WC utilizes a developer-friendly approach through simple HTML declarative syntax to bring together data in a single interface that is easy for developers to work with, making it accessible to users of varying skill levels. The framework integrates widely used web technologies, facilitating client-side data analysis, visualization, and accessibility within web browsers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106328"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990594","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 : 2025-02-01DOI: 10.1016/j.envsoft.2025.106331
Eun Taek Shin , Se Hyuck An , Sung Won Park , Seung Oh Lee , Chang Geun Song
Accurate parameter selection is crucial for reliable predictions in fluid dynamics, environmental transport, and urban flood prediction. Traditional manual methods are time-consuming and prone to errors. This study introduces an automated algorithm to optimize roughness and viscosity coefficients in two-dimensional flow analysis models. Our algorithm automates the simulation process within specified parameter ranges, using Root Mean Square Error (RMSE) to compare results with experimental data. Applied to a diverging channel and an abruptly widening channel, the algorithm successfully identified optimal parameters, accurately matching experimental observations. Heatmaps visualize RMSE values, facilitating optimal parameter identification. This advancement enhances model efficiency and accuracy, streamlining the parameter determination process and offering a robust method for hydraulic modeling.
{"title":"Development of optimal parameter determination algorithm for two-dimensional flow analysis model","authors":"Eun Taek Shin , Se Hyuck An , Sung Won Park , Seung Oh Lee , Chang Geun Song","doi":"10.1016/j.envsoft.2025.106331","DOIUrl":"10.1016/j.envsoft.2025.106331","url":null,"abstract":"<div><div>Accurate parameter selection is crucial for reliable predictions in fluid dynamics, environmental transport, and urban flood prediction. Traditional manual methods are time-consuming and prone to errors. This study introduces an automated algorithm to optimize roughness and viscosity coefficients in two-dimensional flow analysis models. Our algorithm automates the simulation process within specified parameter ranges, using Root Mean Square Error (RMSE) to compare results with experimental data. Applied to a diverging channel and an abruptly widening channel, the algorithm successfully identified optimal parameters, accurately matching experimental observations. Heatmaps visualize RMSE values, facilitating optimal parameter identification. This advancement enhances model efficiency and accuracy, streamlining the parameter determination process and offering a robust method for hydraulic modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106331"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020248","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 : 2025-02-01DOI: 10.1016/j.envsoft.2024.106265
M.A. Harris , T.H. Diehl , L.E. Gorman Sanisaca , A.E. Galanter , M.A. Lombard , K.D. Skinner , C. Chamberlin , B.A. McCarthy , R. Niswonger , J.S. Stewart , K.J. Valseth
Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).
{"title":"Automating physics-based models to estimate thermoelectric-power water use","authors":"M.A. Harris , T.H. Diehl , L.E. Gorman Sanisaca , A.E. Galanter , M.A. Lombard , K.D. Skinner , C. Chamberlin , B.A. McCarthy , R. Niswonger , J.S. Stewart , K.J. Valseth","doi":"10.1016/j.envsoft.2024.106265","DOIUrl":"10.1016/j.envsoft.2024.106265","url":null,"abstract":"<div><div>Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106265"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128135","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}
Sea ice in the polar oceans plays a significant role in regulating global climate and biological ecosystems. During the winter months, seawater freezes to form porous ice, which also serves as a habitat for sea ice algae to survive in harsh winter conditions. However, accurate description of mechanisms and interactions associated with formation of ice, and its interaction with photosynthesis and carbon assimilation have not been well understood. This paper presents a modeling framework to describe coupled small scale Physical (P) and BioGeoChemical (BGC) processes associated with sea ice. Critical processes associated with photosynthesis along with growth and loss of algal carbon are considered. Appropriate parametrization for environmental factors such as temperature, light, salinity, and nutrients are employed to model the photosynthetic rate. Summer and winter environmental conditions are presented and discussed in detail. Finally, monthly data is taken from literature to simulate a typical year in the Southern Ocean.
{"title":"A coupled multiscale description of seasonal Physical–BioGeoChemical dynamics in Southern Ocean Marginal Ice Zone","authors":"Raghav Pathak , Seyed Morteza Seyedpour , Bernd Kutschan , Silke Thoms , Tim Ricken","doi":"10.1016/j.envsoft.2024.106270","DOIUrl":"10.1016/j.envsoft.2024.106270","url":null,"abstract":"<div><div>Sea ice in the polar oceans plays a significant role in regulating global climate and biological ecosystems. During the winter months, seawater freezes to form porous ice, which also serves as a habitat for sea ice algae to survive in harsh winter conditions. However, accurate description of mechanisms and interactions associated with formation of ice, and its interaction with photosynthesis and carbon assimilation have not been well understood. This paper presents a modeling framework to describe coupled small scale Physical (P) and BioGeoChemical (BGC) processes associated with sea ice. Critical processes associated with photosynthesis along with growth and loss of algal carbon are considered. Appropriate parametrization for environmental factors such as temperature, light, salinity, and nutrients are employed to model the photosynthetic rate. Summer and winter environmental conditions are presented and discussed in detail. Finally, monthly data is taken from literature to simulate a typical year in the Southern Ocean.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106270"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874847","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 : 2025-02-01DOI: 10.1016/j.envsoft.2024.106287
Gerardo Grelle , Luigi Guerriero , Domenico Calcaterra , Diego Di Martire , Chiara Di Muro , Enza Vitale , Giuseppe Sappa
The VERE framework was designed and developed in Python to generate hazard confidence maps for seismic-induced landslides, leveraging advanced data analysis and machine learning capabilities. A Virtual Environment (VE) and a Real Environment (RE) containing, respectively, datasets and map sets, are the core of the framework. The Virtual Environment (VE) comprises datasets including morphometric, geotechnical, and hydrological metadata, which are generated assuming a normal distribution, based on representative recurrent values of these parameters in the study area. The Real Environment (RE) includes grid datasets with a common resolution, obtained through analytical preprocessing of various spatial data distributions, including InSAR (Interferometric Synthetic Aperture Radar) data. This data is processed to detect ongoing slope instability and the activity state of surveyed landslides. The framework employs numerical machine learning, trained on meta-solutions derived from an advanced simplified physical model. The model accounts for viscoplastic behavior as well as the reduction of shear strengths toward the residual state during seismic-induced sliding. Hazard confidence maps are produced through an ML-based prediction, considering co-seismic displacements and post-seismic mobility under different initial porewater pressures and seismicity scenarios. The test-site region is the Sele River valley located in an inter-Apennine sector of southern Italy, a seismic-prone area known for its recent seismic activity.
VERE 框架是用 Python 设计和开发的，目的是利用先进的数据分析和机器学习能力，生成地震诱发的滑坡的危险置信度地图。虚拟环境（VE）和真实环境（RE）分别包含数据集和地图集，是该框架的核心。虚拟环境（VE）由数据集组成，其中包括形态测量、岩土工程和水文元数据，这些数据集是根据研究区域中这些参数的代表性经常值假设正态分布生成的。真实环境 (RE) 包括具有通用分辨率的网格数据集，这些数据集是通过对各种空间数据分布（包括 InSAR（干涉合成孔径雷达）数据）进行分析预处理而获得的。这些数据经过处理后，可用于检测正在发生的斜坡不稳定性和已勘测滑坡的活动状态。该框架采用了数值机器学习技术，根据高级简化物理模型得出的元解决方案进行训练。该模型考虑了粘塑性行为以及地震诱发滑动过程中剪切强度向残余状态的降低。考虑到不同初始孔隙水压力和地震强度情况下的共震位移和震后流动性，通过基于 ML 的预测生成了灾害置信度图。试验地点区域是位于意大利南部亚平宁山脉间的塞勒河流域，该地区是地震多发区，近期地震活动频繁。
{"title":"VERE Py-framework: Dual environment for physically-informed machine learning in seismic landslide hazard mapping driven by InSAR","authors":"Gerardo Grelle , Luigi Guerriero , Domenico Calcaterra , Diego Di Martire , Chiara Di Muro , Enza Vitale , Giuseppe Sappa","doi":"10.1016/j.envsoft.2024.106287","DOIUrl":"10.1016/j.envsoft.2024.106287","url":null,"abstract":"<div><div>The VERE framework was designed and developed in Python to generate hazard confidence maps for seismic-induced landslides, leveraging advanced data analysis and machine learning capabilities. A Virtual Environment (VE) and a Real Environment (RE) containing, respectively, datasets and map sets, are the core of the framework. The Virtual Environment (VE) comprises datasets including morphometric, geotechnical, and hydrological metadata, which are generated assuming a normal distribution, based on representative recurrent values of these parameters in the study area. The Real Environment (RE) includes grid datasets with a common resolution, obtained through analytical preprocessing of various spatial data distributions, including InSAR (Interferometric Synthetic Aperture Radar) data. This data is processed to detect ongoing slope instability and the activity state of surveyed landslides. The framework employs numerical machine learning, trained on meta-solutions derived from an advanced simplified physical model. The model accounts for viscoplastic behavior as well as the reduction of shear strengths toward the residual state during seismic-induced sliding. Hazard confidence maps are produced through an ML-based prediction, considering co-seismic displacements and post-seismic mobility under different initial porewater pressures and seismicity scenarios. The test-site region is the Sele River valley located in an inter-Apennine sector of southern Italy, a seismic-prone area known for its recent seismic activity.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106287"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825339","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 : 2025-01-31DOI: 10.1016/j.envsoft.2025.106352
Mitchell Manware , Insang Song , Eva S. Marques , Mariana Alifa Kassien , Lara P. Clark , Kyle P. Messier
Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the amadeus package for R, a tool to improve access to and utility with large scale environmental data, primarily covering the United States. amadeus aims to reduce the learning curve for conducting spatial data analyses in R by providing functions which download, process, and calculate covariates from various publicly available environmental data sources. The functions promote interoperability with popular spatial data R packages and integration across other programming languages. Created and maintained with test-driven development, amadeus supports the reproducibility of environmental data acquisition and preparation. The amadeus package has diverse data access and integration applications, ranging from health-oriented studies in environmental epidemiology to ecology and climatology.
{"title":"Amadeus: Accessing and analyzing large scale environmental data in R","authors":"Mitchell Manware , Insang Song , Eva S. Marques , Mariana Alifa Kassien , Lara P. Clark , Kyle P. Messier","doi":"10.1016/j.envsoft.2025.106352","DOIUrl":"10.1016/j.envsoft.2025.106352","url":null,"abstract":"<div><div>Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the <em>amadeus</em> package for R, a tool to improve access to and utility with large scale environmental data, primarily covering the United States. <em>amadeus</em> aims to reduce the learning curve for conducting spatial data analyses in R by providing functions which download, process, and calculate covariates from various publicly available environmental data sources. The functions promote interoperability with popular spatial data R packages and integration across other programming languages. Created and maintained with test-driven development, <em>amadeus</em> supports the reproducibility of environmental data acquisition and preparation. The <em>amadeus</em> package has diverse data access and integration applications, ranging from health-oriented studies in environmental epidemiology to ecology and climatology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106352"},"PeriodicalIF":4.8,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377302","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 : 2025-01-30DOI: 10.1016/j.envsoft.2025.106353
Guhan Li , Peng Shi , Simin Qu , Lingzhong Kong , Xiaohua Xiang , Qian Yang , Yu Qiao , Shiyu Lu
Parameter inversion in hydrological models aims to estimate parameters from observed data, improving accuracy and understanding of the system. This process typically involves optimization algorithms to identify optimal parameter combinations, often resulting in significant computational costs due to the necessity for numerous model runs, particularly in complex hydrological models. To address this challenge, this study introduces the Adaptive Surrogate Model Assisted Swarm Intelligence (ASMA-SI) framework. ASMA-SI uses the iterative traces of swarm intelligence (SI) as a training sample set, fostering a tightly coupling between SI and the surrogate model while minimizing computational demands and enhancing search efficiency. The framework was applied to enhance three prominent SI algorithms: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Synthetic experiments and a case study were conducted to evaluate the inversion efficacy of ASMA-SI. In the synthetic experiments, ASMA-SI demonstrated faster convergence to the ‘true value’, while in the real-world case study, it outperformed in nearly all of the nine test groups, achieving better average performance metrics.
{"title":"Adaptive Surrogate Model Assisted Swarm Intelligence for Parameter Inversion of complex hydrological models","authors":"Guhan Li , Peng Shi , Simin Qu , Lingzhong Kong , Xiaohua Xiang , Qian Yang , Yu Qiao , Shiyu Lu","doi":"10.1016/j.envsoft.2025.106353","DOIUrl":"10.1016/j.envsoft.2025.106353","url":null,"abstract":"<div><div>Parameter inversion in hydrological models aims to estimate parameters from observed data, improving accuracy and understanding of the system. This process typically involves optimization algorithms to identify optimal parameter combinations, often resulting in significant computational costs due to the necessity for numerous model runs, particularly in complex hydrological models. To address this challenge, this study introduces the Adaptive Surrogate Model Assisted Swarm Intelligence (ASMA-SI) framework. ASMA-SI uses the iterative traces of swarm intelligence (SI) as a training sample set, fostering a tightly coupling between SI and the surrogate model while minimizing computational demands and enhancing search efficiency. The framework was applied to enhance three prominent SI algorithms: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Synthetic experiments and a case study were conducted to evaluate the inversion efficacy of ASMA-SI. In the synthetic experiments, ASMA-SI demonstrated faster convergence to the ‘true value’, while in the real-world case study, it outperformed in nearly all of the nine test groups, achieving better average performance metrics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106353"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388453","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 : 2025-01-30DOI: 10.1016/j.envsoft.2025.106345
P.E. Augusseau , C. Proisy , A. Gardel , G. Brunier , L. Granjon , T. Maury , A. Mury , A. Staquet , V.F. Santos , R. Walcker , P. Degenne , D. Lo Seen , E.J. Anthony
Highly dynamic wave-exposed muddy coasts harbouring mangrove ecosystems can be subject to both marked accretion and erosion depending on the complex interactions between mud and waves. We propose a multiscale modelling approach and empirical equations calibrated and integrated into a landscape dynamics model implemented on a mud-bank coast using the Ocelet language to simplify the complex processes driving sea-mangrove coastline dynamics and quantity them with 10 years of satellite observations of mangrove shoreline fluctuations.
We find that fluctuations in seafront mangroves can be simulated with acceptable accuracy along 200 km of coastline. In the absence of mud banks, seasonal wave forcing resulted in erosion rates reaching 1100 m/y. Our findings indicate that wave energy can be reduced by 90% at all locations when the width of mud banks exceeds 2000 m in front of the mangroves. Finally, we discuss the potential of this modeling approach for anticipating coastal changes.
{"title":"MANG@COAST: A spatio-temporal modeling approach of muddy shoreline mobility based on mangrove monitoring","authors":"P.E. Augusseau , C. Proisy , A. Gardel , G. Brunier , L. Granjon , T. Maury , A. Mury , A. Staquet , V.F. Santos , R. Walcker , P. Degenne , D. Lo Seen , E.J. Anthony","doi":"10.1016/j.envsoft.2025.106345","DOIUrl":"10.1016/j.envsoft.2025.106345","url":null,"abstract":"<div><div>Highly dynamic wave-exposed muddy coasts harbouring mangrove ecosystems can be subject to both marked accretion and erosion depending on the complex interactions between mud and waves. We propose a multiscale modelling approach and empirical equations calibrated and integrated into a landscape dynamics model implemented on a mud-bank coast using the Ocelet language to simplify the complex processes driving sea-mangrove coastline dynamics and quantity them with 10 years of satellite observations of mangrove shoreline fluctuations.</div><div>We find that fluctuations in seafront mangroves can be simulated with acceptable accuracy along 200 km of coastline. In the absence of mud banks, seasonal wave forcing resulted in erosion rates reaching 1100 m/y. Our findings indicate that wave energy can be reduced by 90% at all locations when the width of mud banks exceeds 2000 m in front of the mangroves. Finally, we discuss the potential of this modeling approach for anticipating coastal changes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106345"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077716","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 : 2025-01-28DOI: 10.1016/j.envsoft.2025.106324
Sebastian Lehner , Katharina Enigl , Matthias Schlögl
Physioclimatic regions are homogeneous geospatial entities that exhibit similar characteristics in both climatic conditions and the physiographic environment. They provide a foundation for a broad range of analyses in earth system sciences that are conditional on the prevailing climatological properties shaping geographical areas. However, delineating such regions is challenging due to high-dimensional input data and nonlinear processes in nature. We introduce a nonparametric clustering methodology to derive geospatial clusters with similar physioclimatic attributes, using a comprehensive dataset of climatological and geomorphometric indices from Austria. Our analysis workflow includes (1) Principal Component Analysis (PCA) for linear dimension reduction, (2) Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimension reduction, (3) Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for clustering and (4) random forest for feature importance assessment. Results show both agreement and differences compared to reference classification, thereby highlighting the need for quantitative performance evaluation and synoptic plausibility assessment. Findings include the identification of two characteristic clusters for inneralpine valleys in Western Austria and interfluves in the Styrian basin. This workflow offers a blueprint for delineating consistent geospatial regions for various applications. Clusters obtained with this approach may assist in unearthing new perspectives on regionalisation, provide new insights in the underlying characteristics determining these regions, and thus aid in the understanding of complex environmental patterns.
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