Pub Date : 2025-01-22DOI: 10.1016/j.envsoft.2025.106334
Hae Na Yoon, Lucy Marshall, Ashish Sharma, Seokhyeon Kim
The surrogate river discharge model (SRM) uses remote sensing surrogates of river discharge (SR) to estimate streamflow in ungauged basins. Integrating SR derived from L-band microwave data with climate inputs of rainfall and potential evapotranspiration, the model operates within a hydrological framework. While SR is strongly correlated with streamflow, it is unitless and requires calibration for physical coherence. Calibration translates SR into an actual discharge value using the average or mean discharge (QM) derived from the Budyko framework. A novel likelihood approach employing SR and QM eliminates reliance on direct discharge observations. Validation across three Australian catchments demonstrates satisfactory performance, with NSE >0.6 and KGE >0.6, highlighting its applicability in data-scarce regions. The SRM software includes tools for L-band microwave data acquisition, SR generation, and hydrological model calibration, enabling global application in river discharge estimation.
{"title":"Doing hydrology when no in-situ data exists: Surrogate River discharge Model (SRM)","authors":"Hae Na Yoon, Lucy Marshall, Ashish Sharma, Seokhyeon Kim","doi":"10.1016/j.envsoft.2025.106334","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106334","url":null,"abstract":"The surrogate river discharge model (SRM) uses remote sensing surrogates of river discharge (SR) to estimate streamflow in ungauged basins. Integrating SR derived from L-band microwave data with climate inputs of rainfall and potential evapotranspiration, the model operates within a hydrological framework. While SR is strongly correlated with streamflow, it is unitless and requires calibration for physical coherence. Calibration translates SR into an actual discharge value using the average or mean discharge (QM) derived from the Budyko framework. A novel likelihood approach employing SR and QM eliminates reliance on direct discharge observations. Validation across three Australian catchments demonstrates satisfactory performance, with NSE >0.6 and KGE >0.6, highlighting its applicability in data-scarce regions. The SRM software includes tools for L-band microwave data acquisition, SR generation, and hydrological model calibration, enabling global application in river discharge estimation.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"27 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055237","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 : 2025-01-21DOI: 10.1016/j.envsoft.2025.106338
Michael Edidem, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang
The increasing availability of High-Resolution Digital Elevation Models (HRDEMs) allows accurate delineation of stream and drainage flowlines at the field scale. However, the presence of digital flow barriers like roads effectively impedes hydrological connectivity represented on the HRDEMs. Conventional methods for locating these artificial barriers such as on-screen digitization and field surveying are cost prohibitive over large geographic areas. Thus, a database of drainage crossings under roads is a crucial input for refining flowlines derived from HRDEMs. In this study, we developed advanced deep learning models for detecting the locations of drainage crossing structures in agricultural areas. Our method assesses the performance of a two-stage object detector, Faster R-CNN and a single-stage object detector, YOLOv5. The models were trained using random HRDEM tiles and ground truth labels developed for the West Fork Big Blue Watershed, Nebraska. The Faster R-CNN and YOLOv5 achieved an average F1-score of 0.78. The best-fit models in Nebraska were then transferred to three other watersheds in Illinois, North Dakota, and California. These findings show effective spatial detection of these drainage crossing features, attributed to their distinct topographic patterns. Such spatial object detection approaches offer a promising avenue for automated integration of drainage crossings into HRDEMs with minimal manual interventions, thereby enhancing the delineation of elevation-derived hydrographic features for regional applications.
{"title":"GeoAI-based drainage crossing detection for elevation-derived hydrographic mapping","authors":"Michael Edidem, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang","doi":"10.1016/j.envsoft.2025.106338","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106338","url":null,"abstract":"The increasing availability of High-Resolution Digital Elevation Models (HRDEMs) allows accurate delineation of stream and drainage flowlines at the field scale. However, the presence of digital flow barriers like roads effectively impedes hydrological connectivity represented on the HRDEMs. Conventional methods for locating these artificial barriers such as on-screen digitization and field surveying are cost prohibitive over large geographic areas. Thus, a database of drainage crossings under roads is a crucial input for refining flowlines derived from HRDEMs. In this study, we developed advanced deep learning models for detecting the locations of drainage crossing structures in agricultural areas. Our method assesses the performance of a two-stage object detector, Faster R-CNN and a single-stage object detector, YOLOv5. The models were trained using random HRDEM tiles and ground truth labels developed for the West Fork Big Blue Watershed, Nebraska. The Faster R-CNN and YOLOv5 achieved an average F1-score of 0.78. The best-fit models in Nebraska were then transferred to three other watersheds in Illinois, North Dakota, and California. These findings show effective spatial detection of these drainage crossing features, attributed to their distinct topographic patterns. Such spatial object detection approaches offer a promising avenue for automated integration of drainage crossings into HRDEMs with minimal manual interventions, thereby enhancing the delineation of elevation-derived hydrographic features for regional applications.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"23 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049850","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 : 2025-01-18DOI: 10.1016/j.envsoft.2025.106333
Gaétan Sauter, Stefano C. Fabbri, Corine Frischknecht, Flavio S. Anselmetti, Katrina Kremer
Multibeam Echo Sounder systems have enhanced the precision of modern bathymetric mapping, enabling the creation of high-resolution digital bathymetry models that characterise ocean and lake floors. However, the inferred models contain uncertainties that necessitate consideration, especially when conducting quantitative temporal comparisons. By exploring the results of two bathymetric surveys targeting a lacustrine delta, this study examines how geomorphological changes can effectively be interpreted through repetitive multi-temporal bathymetric surveys. We propose to use a workflow for Geographic Information System aiming at providing the basis for diverse studies that will implement bathymetric difference maps, also ensuring consistency. The proposed methodology incorporates the use of confidence intervals, based on the estimated uncertainties. The groundwork for interpretation relies on: (i) qualitative display using multivariate choropleth, (ii) quantitative assessment with the calculation of volumes of raw changes in cubic metres (m³), along with confidence intervals (±m³) and (iii) volumetric histograms accompanied with error bars.
{"title":"A systemic approach to managing uncertainties in repetitive multibeam bathymetric surveys","authors":"Gaétan Sauter, Stefano C. Fabbri, Corine Frischknecht, Flavio S. Anselmetti, Katrina Kremer","doi":"10.1016/j.envsoft.2025.106333","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106333","url":null,"abstract":"Multibeam Echo Sounder systems have enhanced the precision of modern bathymetric mapping, enabling the creation of high-resolution digital bathymetry models that characterise ocean and lake floors. However, the inferred models contain uncertainties that necessitate consideration, especially when conducting quantitative temporal comparisons. By exploring the results of two bathymetric surveys targeting a lacustrine delta, this study examines how geomorphological changes can effectively be interpreted through repetitive multi-temporal bathymetric surveys. We propose to use a workflow for Geographic Information System aiming at providing the basis for diverse studies that will implement bathymetric difference maps, also ensuring consistency. The proposed methodology incorporates the use of confidence intervals, based on the estimated uncertainties. The groundwork for interpretation relies on: (i) qualitative display using multivariate choropleth, (ii) quantitative assessment with the calculation of volumes of raw changes in cubic metres (m³), along with confidence intervals (±m³) and (iii) volumetric histograms accompanied with error bars.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"120 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049852","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 : 2025-01-18DOI: 10.1016/j.envsoft.2025.106322
Atte Moilanen, Pauli Lehtinen
Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions. Here we collate and develop simple calculations for the determination of offset size (area) in the context of so-called multiplier approaches to offsets. We focus on the analysis of the response of habitat condition to action, which is a critical component of multiplier calculations, because the effectiveness and speed of different conservation actions and interventions can vary significantly. An excel application and R-code are included that implement calculations on offset response functions. The proposed methods are also relevant for other applications, including the generation of biodiversity credits for biodiversity credit markets.
{"title":"Simple analysis of biodiversity response functions and multipliers for biodiversity offsetting and other applications","authors":"Atte Moilanen, Pauli Lehtinen","doi":"10.1016/j.envsoft.2025.106322","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106322","url":null,"abstract":"Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions. Here we collate and develop simple calculations for the determination of offset size (area) in the context of so-called multiplier approaches to offsets. We focus on the analysis of the response of habitat condition to action, which is a critical component of multiplier calculations, because the effectiveness and speed of different conservation actions and interventions can vary significantly. An excel application and R-code are included that implement calculations on offset response functions. The proposed methods are also relevant for other applications, including the generation of biodiversity credits for biodiversity credit markets.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"206 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020242","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 : 2025-01-17DOI: 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.
{"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":"https://doi.org/10.1016/j.envsoft.2025.106321","url":null,"abstract":"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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"74 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-17","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in computational technologies have enhanced geo-simulation experiments (GSEs), making computational reproducibility assessments increasingly critical. However, existing methods often focus on isolated aspects, lacking a comprehensive framework. This study proposes an integrated framework for assessing reproducibility in GSEs, structured into two parts: (1) evaluating overall computational workflows, and (2) investigating individual processes to identify inconsistencies. The framework employs a detailed assessment model using hierarchical dimensions and metrics that combine quantitative measures (e.g., output consistency) and qualitative evaluations (e.g., clarity of descriptions). These components address both broad and granular aspects of computational processes. The framework is implemented in a prototype system to support reproducibility assessments and demonstrated through practical applications. This systematic approach provides a robust and adaptable method for assessing reproducibility, promoting the resolution of challenges in existing methods.
{"title":"A framework for assessing the computational reproducibility of geo-simulation experiments","authors":"Zhiyi Zhu, Min Chen, Guangjin Ren, Yuanqing He, Lingzhi Sun, Fengyuan Zhang, Yongning Wen, Songshan Yue, Guonian Lü","doi":"10.1016/j.envsoft.2025.106323","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106323","url":null,"abstract":"Recent advances in computational technologies have enhanced geo-simulation experiments (GSEs), making computational reproducibility assessments increasingly critical. However, existing methods often focus on isolated aspects, lacking a comprehensive framework. This study proposes an integrated framework for assessing reproducibility in GSEs, structured into two parts: (1) evaluating overall computational workflows, and (2) investigating individual processes to identify inconsistencies. The framework employs a detailed assessment model using hierarchical dimensions and metrics that combine quantitative measures (e.g., output consistency) and qualitative evaluations (e.g., clarity of descriptions). These components address both broad and granular aspects of computational processes. The framework is implemented in a prototype system to support reproducibility assessments and demonstrated through practical applications. This systematic approach provides a robust and adaptable method for assessing reproducibility, promoting the resolution of challenges in existing methods.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"38 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049658","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 : 2025-01-16DOI: 10.1016/j.envsoft.2025.106332
Lei Yao, Jiangjiang Zhang, Chenglong Cao, Feifei Zheng
Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ESDL with a deep learning-based update, and ESLU with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ESDL and ESLU, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.
{"title":"Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates","authors":"Lei Yao, Jiangjiang Zhang, Chenglong Cao, Feifei Zheng","doi":"10.1016/j.envsoft.2025.106332","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106332","url":null,"abstract":"Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ES<ce:inf loc=\"post\">DL</ce:inf> with a deep learning-based update, and ES<ce:inf loc=\"post\">LU</ce:inf> with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ES<ce:inf loc=\"post\">DL</ce:inf> and ES<ce:inf loc=\"post\">LU</ce:inf>, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"38 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020245","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 : 2025-01-16DOI: 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":"https://doi.org/10.1016/j.envsoft.2025.106331","url":null,"abstract":"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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"50 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-16","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.envsoft.2025.106330
Quan Han, Ling Zhou, Wenchao Sun, Jinqiang Wang, Chi Ma
Spatial resolution of topography data significantly impacts computational time of lake hydrodynamic modelling. This study proposes a calibration tool to examine impacts of topography data resolution on simulation uncertainty, evolving from the Generalized Likelihood Uncertainty Analysis framework. Using the EFDC hydrodynamic model, BaiYangDian Lake in North China was simulated at three resolutions: 200, 500, and 1000 m. The first two models show similar accuracy, outperforming the 1000-m model. The parameter space constrained by water level observations and the simulation uncertainties in water level, water age, and velocity from 500-m model closely resembled those from 200-m model, while requiring only 16.7% of the latter's computational time, indicating a feasible spatial resolution range where model performance matches the high-resolution model but with significantly less computational time. The study highlights the importance of calibration with multiple observations and demonstrates potentials of the proposed tool to identify effects of model settings on simulation uncertainty.
{"title":"Evaluating the influence of topography data resolution on lake hydrodynamic model under a simulation uncertainty analysis framework","authors":"Quan Han, Ling Zhou, Wenchao Sun, Jinqiang Wang, Chi Ma","doi":"10.1016/j.envsoft.2025.106330","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106330","url":null,"abstract":"Spatial resolution of topography data significantly impacts computational time of lake hydrodynamic modelling. This study proposes a calibration tool to examine impacts of topography data resolution on simulation uncertainty, evolving from the Generalized Likelihood Uncertainty Analysis framework. Using the EFDC hydrodynamic model, BaiYangDian Lake in North China was simulated at three resolutions: 200, 500, and 1000 m. The first two models show similar accuracy, outperforming the 1000-m model. The parameter space constrained by water level observations and the simulation uncertainties in water level, water age, and velocity from 500-m model closely resembled those from 200-m model, while requiring only 16.7% of the latter's computational time, indicating a feasible spatial resolution range where model performance matches the high-resolution model but with significantly less computational time. The study highlights the importance of calibration with multiple observations and demonstrates potentials of the proposed tool to identify effects of model settings on simulation uncertainty.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"38 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049851","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 : 2025-01-15DOI: 10.1016/j.envsoft.2025.106329
George P. Petropoulos, Spyridon E. Detsikas, Christina Lekka
The use of simulation process models combined with Earth Observation (EO) datasets provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions (LSIs). This is achieved by combining the horizontal coverage and spectral resolution of EO data with the vertical coverage and fine temporal continuity of those models. A particular promising simulation model is SimSphere,h a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth's land surface. Its use is at present continually expanding worldwide both as a stand-alone application or synergistically with EO data and it is already used as an educational and as a research tool for scientific investigations. Herein, the advancements recent introduced to SimSphere are presented, aiming at making its use more robust when integrated with EO data via the “triangle” method.The use of the recently introduced add-on to the SimSphere model is illustrated herein using a variety of examples that involve both satellite and UAV data. The availability of this newly introduced so-called “Convolution” add-on functionality to SimSphere model is of key significance to the users' community of the “triangle” method, as between other, significantly reduces the time required for its implementation. The release of this tool is also very timely, given that variants of the “triangle” are under consideration for deriving operationally regional estimates of energy fluxes and surface soil moisture from EO data provided by non-commercial vendors.
{"title":"Towards a more robust implementation of the so-called “triangle” method: A new add-on to the SimSphere SVAT model","authors":"George P. Petropoulos, Spyridon E. Detsikas, Christina Lekka","doi":"10.1016/j.envsoft.2025.106329","DOIUrl":"https://doi.org/10.1016/j.envsoft.2025.106329","url":null,"abstract":"The use of simulation process models combined with Earth Observation (EO) datasets provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions (LSIs). This is achieved by combining the horizontal coverage and spectral resolution of EO data with the vertical coverage and fine temporal continuity of those models. A particular promising simulation model is SimSphere,h a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth's land surface. Its use is at present continually expanding worldwide both as a stand-alone application or synergistically with EO data and it is already used as an educational and as a research tool for scientific investigations. Herein, the advancements recent introduced to SimSphere are presented, aiming at making its use more robust when integrated with EO data via the “triangle” method.The use of the recently introduced add-on to the SimSphere model is illustrated herein using a variety of examples that involve both satellite and UAV data. The availability of this newly introduced so-called “Convolution” add-on functionality to SimSphere model is of key significance to the users' community of the “triangle” method, as between other, significantly reduces the time required for its implementation. The release of this tool is also very timely, given that variants of the “triangle” are under consideration for deriving operationally regional estimates of energy fluxes and surface soil moisture from EO data provided by non-commercial vendors.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"48 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049853","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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