Pub Date : 2025-02-06DOI: 10.1016/j.envsoft.2025.106366
T. Kang , S. Lee , H. An , M. Kim , I. Kimura
This study introduces Deb2D, an advanced predictive model that combines Eulerian flow dynamics with Lagrangian driftwood movement to accurately simulate debris flows. It enhances the existing Deb2D framework (An et al., 2019) by integrating a driftwood dynamics module rewritten in C++ (Kang et al., 2020) and a user-friendly Graphical User Interface developed with QtCreator for setup and visualization of simulations. This improvement enables precise two-way interactions between driftwood and debris flows, ensuring detailed visualization of their dynamics. When applied to the 2011 Mt. Umyeon debris flow in South Korea, the model demonstrated high accuracy in replicating observed phenomena. Future developments will focus on adapting this model into a QGIS plugin to broaden its applicability and user base.
{"title":"Development of an advanced numerical simulation program considering debris flow and driftwood behavior","authors":"T. Kang , S. Lee , H. An , M. Kim , I. Kimura","doi":"10.1016/j.envsoft.2025.106366","DOIUrl":"10.1016/j.envsoft.2025.106366","url":null,"abstract":"<div><div>This study introduces Deb2D, an advanced predictive model that combines Eulerian flow dynamics with Lagrangian driftwood movement to accurately simulate debris flows. It enhances the existing Deb2D framework (An et al., 2019) by integrating a driftwood dynamics module rewritten in C++ (Kang et al., 2020) and a user-friendly Graphical User Interface developed with QtCreator for setup and visualization of simulations. This improvement enables precise two-way interactions between driftwood and debris flows, ensuring detailed visualization of their dynamics. When applied to the 2011 Mt. Umyeon debris flow in South Korea, the model demonstrated high accuracy in replicating observed phenomena. Future developments will focus on adapting this model into a QGIS plugin to broaden its applicability and user base.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106366"},"PeriodicalIF":4.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395430","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}
Landslides threaten lives and infrastructure, making accurate inventories crucial for risk management. This study combines expert methods with machine learning to automate and validate landslide detection and timing using Sentinel-2 satellite imagery. We developed ExMAD (Expert-based Multi-temporal AI Detector), an open-source methodological framework (https://github.com/NewGeoProjects/ExMAD) to integrate artificial intelligence with human expertise to detect occurrence timing of a targeted landslide. A U-Net neural network was chosen to effectively test ExMAD in landslide detection over Sentinel-2 worldwide multitemporal satellite imagery sequences, and the model was tested through five evaluations. ExMAD was able to effectively extract timing of target landslides on Sentinel-2 images and was able to correctly detect the presence/absence of landslide, proving the suitability of AI systems in landslide temporal mapping task.
This research proves the potential of hybrid AI-human approaches for landslide risk assessment, integrate human expertise with machine learning offers promising advancements for remote and field mapping of landslide. Furthermore, the ExMAD methodology adheres to the European Union's Artificial Intelligence Act, stressing human oversight in high-risk AI applications to enhance trust, control, and efficiency in landslide inventory creation and risk management.
{"title":"ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory","authors":"Michele Licata, Stefano Faga , Giandomenico Fubelli","doi":"10.1016/j.envsoft.2025.106363","DOIUrl":"10.1016/j.envsoft.2025.106363","url":null,"abstract":"<div><div>Landslides threaten lives and infrastructure, making accurate inventories crucial for risk management. This study combines expert methods with machine learning to automate and validate landslide detection and timing using Sentinel-2 satellite imagery. We developed ExMAD (Expert-based Multi-temporal AI Detector), an open-source methodological framework (<span><span>https://github.com/NewGeoProjects/ExMAD</span><svg><path></path></svg></span>) to integrate artificial intelligence with human expertise to detect occurrence timing of a targeted landslide. A U-Net neural network was chosen to effectively test ExMAD in landslide detection over Sentinel-2 worldwide multitemporal satellite imagery sequences, and the model was tested through five evaluations. ExMAD was able to effectively extract timing of target landslides on Sentinel-2 images and was able to correctly detect the presence/absence of landslide, proving the suitability of AI systems in landslide temporal mapping task.</div><div>This research proves the potential of hybrid AI-human approaches for landslide risk assessment, integrate human expertise with machine learning offers promising advancements for remote and field mapping of landslide. Furthermore, the ExMAD methodology adheres to the European Union's Artificial Intelligence Act, stressing human oversight in high-risk AI applications to enhance trust, control, and efficiency in landslide inventory creation and risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106363"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350438","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}
Improvements in public geospatial datasets provide opportunities for deriving urban drainage networks and simulation models of these networks (UDMs). We present SWMManywhere, which leverages such datasets for generating synthetic UDMs and creating a Storm Water Management Model for any urban area globally. SWMManywhere's modular and parameterised approach enables customisation to explore hydraulicly feasible network configurations. Key novelties of our workflow are in network topology derivation that accounts for combined effects of impervious area and pipe slope. We assess SWMManywhere by comparing pluvial flooding, drainage network outflows, and design with known networks. The results demonstrate high quality simulations are achievable with a synthetic approach even for large networks. Our sensitivity analysis shows that manholes locations, outfalls, and underlying street network are the most sensitive parameters. We find widespread sensitivity across all parameters without clearly defined values that they should take, thus, recommending an uncertainty driven approach to synthetic drainage network modelling.
{"title":"SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere","authors":"Barnaby Dobson , Tijana Jovanovic , Diego Alonso-Álvarez , Taher Chegini","doi":"10.1016/j.envsoft.2025.106358","DOIUrl":"10.1016/j.envsoft.2025.106358","url":null,"abstract":"<div><div>Improvements in public geospatial datasets provide opportunities for deriving urban drainage networks and simulation models of these networks (UDMs). We present SWMManywhere, which leverages such datasets for generating synthetic UDMs and creating a Storm Water Management Model for any urban area globally. SWMManywhere's modular and parameterised approach enables customisation to explore hydraulicly feasible network configurations. Key novelties of our workflow are in network topology derivation that accounts for combined effects of impervious area and pipe slope. We assess SWMManywhere by comparing pluvial flooding, drainage network outflows, and design with known networks. The results demonstrate high quality simulations are achievable with a synthetic approach even for large networks. Our sensitivity analysis shows that manholes locations, outfalls, and underlying street network are the most sensitive parameters. We find widespread sensitivity across all parameters without clearly defined values that they should take, thus, recommending an uncertainty driven approach to synthetic drainage network modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106358"},"PeriodicalIF":4.8,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379431","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.2024.106301
Shaokun He , YiBo Wang , Dimitri Solomatine , Xiao Li
Long-term water resource management involving multipurpose coordination requires robust decision-making in water infrastructure cases to cope with various types of uncertainties. Traditional robust optimization methods generally do not explicitly propagate input or parametric uncertainties into estimates of the robustness of solutions, which limits their ability to address uncertainty comprehensively across solution spaces. In this study, we introduce an explicit robust decision-making framework that blends multiobjective search, probabilistic analysis of robustness, and diagnostic verification tools to identify robust optimal solutions to external uncertainty. The proposed framework is illustrated on four diverse robustness formulations, which capture a wide variety of stakeholder attitudes from highly risk-averse to risk-neutral, for the primary operating objectives (hydropower production, water diversion, and hydrological alteration degree) in China's Hanjiang cascade reservoir system. By analyzing the Pareto front propagated from inflow uncertainty, it is found that optimal robust policies with a significantly higher degree of hydrological alteration are preferred in most formulations to achieve relatively lower joint uncertainty of hydropower and water diversion. These policies also yield sufficiently stable model performance in the case of an out-of-sample streamflow set during diagnostic verification. Furthermore, a comparative analysis of four different formulations suggests that a composite normalized robustness indicator (NRI) developed in this study to integrate various robustness metrics can achieve an effective balance for all considered objectives. These findings highlight the benefits of explicit robust optimization for managing hydrological uncertainties in multipurpose cascade reservoirs.
{"title":"An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty","authors":"Shaokun He , YiBo Wang , Dimitri Solomatine , Xiao Li","doi":"10.1016/j.envsoft.2024.106301","DOIUrl":"10.1016/j.envsoft.2024.106301","url":null,"abstract":"<div><div>Long-term water resource management involving multipurpose coordination requires robust decision-making in water infrastructure cases to cope with various types of uncertainties. Traditional robust optimization methods generally do not explicitly propagate input or parametric uncertainties into estimates of the robustness of solutions, which limits their ability to address uncertainty comprehensively across solution spaces. In this study, we introduce an explicit robust decision-making framework that blends multiobjective search, probabilistic analysis of robustness, and diagnostic verification tools to identify robust optimal solutions to external uncertainty. The proposed framework is illustrated on four diverse robustness formulations, which capture a wide variety of stakeholder attitudes from highly risk-averse to risk-neutral, for the primary operating objectives (hydropower production, water diversion, and hydrological alteration degree) in China's Hanjiang cascade reservoir system. By analyzing the Pareto front propagated from inflow uncertainty, it is found that optimal robust policies with a significantly higher degree of hydrological alteration are preferred in most formulations to achieve relatively lower joint uncertainty of hydropower and water diversion. These policies also yield sufficiently stable model performance in the case of an out-of-sample streamflow set during diagnostic verification. Furthermore, a comparative analysis of four different formulations suggests that a composite normalized robustness indicator (<em>NRI</em>) developed in this study to integrate various robustness metrics can achieve an effective balance for all considered objectives. These findings highlight the benefits of explicit robust optimization for managing hydrological uncertainties in multipurpose cascade reservoirs.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106301"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825336","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.2024.106305
Ruijie Jiang , Hui Lu , Kun Yang , Hiroshi Cho , Dai Yamazaki
Accurate flood modelling is crucial for disaster prevention. Fine-resolution global routing models can offer more detailed flood information, but balancing model efficiency with accuracy remains challenging. This study examines the conditions under which a fine-resolution model outperforms a coarser one, using the CaMa-Flood model at 0.05°, 0.083°, 0.1°, and 0.25° resolutions across the contiguous United States. The results indicate finer resolution does not improve the simulation of flood timing, but better simulates the daily river discharge and flood peak flow due to better representation of the river network in small rivers. Notably, the improvement in daily discharge simulation is greater than that in peak flow. Nevertheless, uncertainties in channel parameters mean that a more detailed river network does not necessarily yield better flood simulations. For rivers with upstream drainage areas greater than 500 km2, a 0.25° model is sufficient if high-precision channel parameters are unavailable.
{"title":"Analysis and comparison of the flood simulations with the routing model CaMa-Flood at different spatial resolutions in the CONUS","authors":"Ruijie Jiang , Hui Lu , Kun Yang , Hiroshi Cho , Dai Yamazaki","doi":"10.1016/j.envsoft.2024.106305","DOIUrl":"10.1016/j.envsoft.2024.106305","url":null,"abstract":"<div><div>Accurate flood modelling is crucial for disaster prevention. Fine-resolution global routing models can offer more detailed flood information, but balancing model efficiency with accuracy remains challenging. This study examines the conditions under which a fine-resolution model outperforms a coarser one, using the CaMa-Flood model at 0.05°, 0.083°, 0.1°, and 0.25° resolutions across the contiguous United States. The results indicate finer resolution does not improve the simulation of flood timing, but better simulates the daily river discharge and flood peak flow due to better representation of the river network in small rivers. Notably, the improvement in daily discharge simulation is greater than that in peak flow. Nevertheless, uncertainties in channel parameters mean that a more detailed river network does not necessarily yield better flood simulations. For rivers with upstream drainage areas greater than 500 km<sup>2</sup>, a 0.25° model is sufficient if high-precision channel parameters are unavailable.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106305"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884321","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.2024.106259
Aadityan Sridharan , Meerna Thomas , Georg Gutjahr , Sundararaman Gopalan
Earthquake events that are often accompanied by prolonged rainfall before, during, or after the mainshock, usually result in thousands of landslides. To estimate landslide trigger factors in such scenarios, we propose a hybrid model combining a statistical model for cumulative rainfall with a physical model for coseismic landslide displacement. The statistical model is a Distributed Lag Nonlinear Model (DLNM) and the physical model is a rigorous Newmark's analysis. The chain of events that led to landsliding following the 2011 Sikkim earthquake is used as a case study. Trigger information of 164 landslide points from field investigations were used to train the model and predict the trigger for 1196 satellite-based landslide points. The hybrid model significantly improves predictions over generalized additive models. Cumulative rainfall shows a significant spatial correlation with trigger factors and heavy rainfall three weeks before the earthquake played a key role in preparing the ground for landslides.
{"title":"Estimating landslide trigger factors using distributed lag nonlinear models","authors":"Aadityan Sridharan , Meerna Thomas , Georg Gutjahr , Sundararaman Gopalan","doi":"10.1016/j.envsoft.2024.106259","DOIUrl":"10.1016/j.envsoft.2024.106259","url":null,"abstract":"<div><div>Earthquake events that are often accompanied by prolonged rainfall before, during, or after the mainshock, usually result in thousands of landslides. To estimate landslide trigger factors in such scenarios, we propose a hybrid model combining a statistical model for cumulative rainfall with a physical model for coseismic landslide displacement. The statistical model is a Distributed Lag Nonlinear Model (DLNM) and the physical model is a rigorous Newmark's analysis. The chain of events that led to landsliding following the 2011 Sikkim earthquake is used as a case study. Trigger information of 164 landslide points from field investigations were used to train the model and predict the trigger for 1196 satellite-based landslide points. The hybrid model significantly improves predictions over generalized additive models. Cumulative rainfall shows a significant spatial correlation with trigger factors and heavy rainfall three weeks before the earthquake played a key role in preparing the ground for landslides.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106259"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128147","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.2024.106290
Lizi Xie , Yanxin Zhao , Pan Fang , Meiling Cheng , Zhuo Chen , Yonggui Wang
An adequate water quality prediction mobile system is crucial for real-time, proactive, and convenient water environment monitoring through mobile devices to reduce or prevent water environmental threats. After exploring the feasibility and superiority of the LSTM-seq2seq model for predicting various water quality indicators, the optimal time step range for different length predictions was proposed. To verify the generalizability and reusability of the model, the performance differences of migrating models was investigated. Based on the entire process, we have developed a cost-effective, widely applicable, and sustainable operational prediction system framework. It was successfully applied in the Huangshui River Basin for two years. Results indicated that the model can achieve an NSE of above 0.5 for indicators with high coefficient of variation and above 0.75 for more stable indicators. When carrying out transfer applications, the model can achieve an NSE performance of above 0.5 for most sites in short to medium-term forecasting.
{"title":"A novel operational water quality mobile prediction system with LSTM-Seq2Seq model","authors":"Lizi Xie , Yanxin Zhao , Pan Fang , Meiling Cheng , Zhuo Chen , Yonggui Wang","doi":"10.1016/j.envsoft.2024.106290","DOIUrl":"10.1016/j.envsoft.2024.106290","url":null,"abstract":"<div><div>An adequate water quality prediction mobile system is crucial for real-time, proactive, and convenient water environment monitoring through mobile devices to reduce or prevent water environmental threats. After exploring the feasibility and superiority of the LSTM-seq2seq model for predicting various water quality indicators, the optimal time step range for different length predictions was proposed. To verify the generalizability and reusability of the model, the performance differences of migrating models was investigated. Based on the entire process, we have developed a cost-effective, widely applicable, and sustainable operational prediction system framework. It was successfully applied in the Huangshui River Basin for two years. Results indicated that the model can achieve an <em>NSE</em> of above 0.5 for indicators with high coefficient of variation and above 0.75 for more stable indicators. When carrying out transfer applications, the model can achieve an <em>NSE</em> performance of above 0.5 for most sites in short to medium-term forecasting.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106290"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797864","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.2024.106316
Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu
Cascading effects from global disruptions such as natural disasters and pandemics have attracted significant research attention. Current approaches face challenges in adequately integrating geographic and systemic factors, limiting their ability to simulate the intricate dynamics of interdependent systems. Here, we proposed a novel Interdependency Network-based Geographic Cascade (INGC) model, coupling geographic factors to capture cascading shocks across global interdependent networks. By integrating macro-level interdependencies and typical dynamic network modelling approaches, the INGC enables more accurate simulations of hazard damage and shock propagation, highlighting critical nodes and pathways essential for informed policy-making. Through the global lockdown case analysis, the INGC model demonstrated its advantages in identifying critical sectors and regions by revealing heterogenous cascading patterns and their details robustly. This approach offers a scalable framework for future research and policy, ensuring greater resilience in the face of complex global extreme events.
{"title":"Cascading effect modelling of integrating geographic factors in interdependent systems","authors":"Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu","doi":"10.1016/j.envsoft.2024.106316","DOIUrl":"10.1016/j.envsoft.2024.106316","url":null,"abstract":"<div><div>Cascading effects from global disruptions such as natural disasters and pandemics have attracted significant research attention. Current approaches face challenges in adequately integrating geographic and systemic factors, limiting their ability to simulate the intricate dynamics of interdependent systems. Here, we proposed a novel Interdependency Network-based Geographic Cascade (INGC) model, coupling geographic factors to capture cascading shocks across global interdependent networks. By integrating macro-level interdependencies and typical dynamic network modelling approaches, the INGC enables more accurate simulations of hazard damage and shock propagation, highlighting critical nodes and pathways essential for informed policy-making. Through the global lockdown case analysis, the INGC model demonstrated its advantages in identifying critical sectors and regions by revealing heterogenous cascading patterns and their details robustly. This approach offers a scalable framework for future research and policy, ensuring greater resilience in the face of complex global extreme events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106316"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935471","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.2025.106327
Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma
Forest landscape models (FLMs) simulate forest dynamics by integrating stand- and landscape-scale processes. Thus, evaluating FLMs simulations necessitates including both processes. Thus far, stand-scale processes were evaluated in some FLMs, whereas landscape-scale processes were rarely evaluated. This study presents a framework that evaluates both stand- and landscape-scale processes. For the stand-scale processes, we proposed using stand density management diagrams to evaluate the simulated stand development trajectories that encapsulate the interplay of tree growth, competition, and mortality. For the landscape-scale processes, we evaluated seed dispersal, the basic spatial process driving forest landscape dynamics and not evaluated previously, through comparing simulated tree species colonization pattern against tree age distribution data from inventory data. We demonstrated the applicability of the framework to a 300-year historical forest landscape reconstructed using LANDIS. Given the common features, the framework is applicable to other FLMs or terrestrial ecosystem models operating at large scales.
{"title":"A process-based framework for validating forest landscape modeling outcomes","authors":"Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma","doi":"10.1016/j.envsoft.2025.106327","DOIUrl":"10.1016/j.envsoft.2025.106327","url":null,"abstract":"<div><div>Forest landscape models (FLMs) simulate forest dynamics by integrating stand- and landscape-scale processes. Thus, evaluating FLMs simulations necessitates including both processes. Thus far, stand-scale processes were evaluated in some FLMs, whereas landscape-scale processes were rarely evaluated. This study presents a framework that evaluates both stand- and landscape-scale processes. For the stand-scale processes, we proposed using stand density management diagrams to evaluate the simulated stand development trajectories that encapsulate the interplay of tree growth, competition, and mortality. For the landscape-scale processes, we evaluated seed dispersal, the basic spatial process driving forest landscape dynamics and not evaluated previously, through comparing simulated tree species colonization pattern against tree age distribution data from inventory data. We demonstrated the applicability of the framework to a 300-year historical forest landscape reconstructed using LANDIS. Given the common features, the framework is applicable to other FLMs or terrestrial ecosystem models operating at large scales.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106327"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990596","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.2024.106292
Zhouyayan Li , Yusuf Sermet , Ibrahim Demir
Recently, there are attempts to expand the current usage of satellite Earth surface observation images to forward-looking applications to support decision-making and fast response against future natural hazards. Specifically, deep learning techniques were employed to synthesize Earth surface images at the pixel level. Those studies found that precipitation and soil moisture play non-trivial roles in Earth surface condition prediction tasks. However, unlike many well-defined and well-studied topics, such as change detection, for which many benchmark datasets are openly available, there are limited public datasets for the abovementioned topic for fast prototyping and comparison. To close this gap, we introduced a comprehensive dataset containing SAR images, precipitation, soil moisture, land cover, Height Above Nearest Drainage (HAND), DEM, and slope data collected during the 2019 Central US Flooding events. Deep-learning-based SAR image synthesis and flood mapping with the synthesized images were presented as sample use cases of the dataset.
{"title":"EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis","authors":"Zhouyayan Li , Yusuf Sermet , Ibrahim Demir","doi":"10.1016/j.envsoft.2024.106292","DOIUrl":"10.1016/j.envsoft.2024.106292","url":null,"abstract":"<div><div>Recently, there are attempts to expand the current usage of satellite Earth surface observation images to forward-looking applications to support decision-making and fast response against future natural hazards. Specifically, deep learning techniques were employed to synthesize Earth surface images at the pixel level. Those studies found that precipitation and soil moisture play non-trivial roles in Earth surface condition prediction tasks. However, unlike many well-defined and well-studied topics, such as change detection, for which many benchmark datasets are openly available, there are limited public datasets for the abovementioned topic for fast prototyping and comparison. To close this gap, we introduced a comprehensive dataset containing SAR images, precipitation, soil moisture, land cover, Height Above Nearest Drainage (HAND), DEM, and slope data collected during the 2019 Central US Flooding events. Deep-learning-based SAR image synthesis and flood mapping with the synthesized images were presented as sample use cases of the dataset.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106292"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825338","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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