Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2024.106308
Amber Spackman Jones , Jeffery S. Horsburgh
Hydrologic Information Systems (HIS) integrate hardware and software to support collection, management, and sharing of hydrologic observations data. Successful HIS facilitate hydrologic monitoring, scientific investigation, watershed management, and communication of hydrologic conditions. Furthermore, HIS support the day-to-day data operations that are essential to organizations that monitor hydrologic systems. As an introductory overview of HIS, this paper reviews the history of HIS development and identifies and describes key components. Based on past HIS literature, patterns emerged for universal and generic HIS functionality and components. The main data pools are collection/acquisition, operational storage, and sharing/publication/dissemination with data flux occurring between pools. Persistent and contemporary challenges for HIS are identified, and examples of current and emerging HIS are described in the context of how they are addressing these challenges. Opportunities remain for coordinated community efforts to address outstanding barriers, advance HIS, and further enable hydrology.
{"title":"Hydrologic information systems: An introductory overview","authors":"Amber Spackman Jones , Jeffery S. Horsburgh","doi":"10.1016/j.envsoft.2024.106308","DOIUrl":"10.1016/j.envsoft.2024.106308","url":null,"abstract":"<div><div>Hydrologic Information Systems (HIS) integrate hardware and software to support collection, management, and sharing of hydrologic observations data. Successful HIS facilitate hydrologic monitoring, scientific investigation, watershed management, and communication of hydrologic conditions. Furthermore, HIS support the day-to-day data operations that are essential to organizations that monitor hydrologic systems. As an introductory overview of HIS, this paper reviews the history of HIS development and identifies and describes key components. Based on past HIS literature, patterns emerged for universal and generic HIS functionality and components. The main data pools are collection/acquisition, operational storage, and sharing/publication/dissemination with data flux occurring between pools. Persistent and contemporary challenges for HIS are identified, and examples of current and emerging HIS are described in the context of how they are addressing these challenges. Opportunities remain for coordinated community efforts to address outstanding barriers, advance HIS, and further enable hydrology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106308"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990587","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.2025.106325
Nur Haznirah Hazman , Rohaizaazira Mohd Zawawi , Ainin Sofia Jusoh , Muhammad Akmal Remli , Marieanne Christie Leong , Mohd Saberi Mohamad , Sarahani Harun
The polar regions hold immense ecological and historical significance, offering insights into biomarker identification, climate history, and natural antifreeze proteins. However, global climate change and scattered datasets threaten effective research in these areas. To address these challenges, we developed PolarBytes, a centralized platform for polar research, focusing on biodiversity, climatology, diseases, and molecular biology. PolarBytes streamlines data access, analysis, and visualization through an intuitive interface and advanced machine learning tools. Its robust API system, including RESTful and Swagger interfaces, eliminates manual downloads, supports automation, and enhances research efficiency. By centralizing data from isolated repositories, PolarBytes simplifies data retrieval and fosters collaboration, enabling researchers to focus on scientific exploration rather than technical hurdles. This user-friendly platform empowers the scientific community to uncover new insights and drive innovation in understanding polar ecosystems and their global impact.
{"title":"PolarBytes: Advancing polar research with a centralized open-source data sharing platform","authors":"Nur Haznirah Hazman , Rohaizaazira Mohd Zawawi , Ainin Sofia Jusoh , Muhammad Akmal Remli , Marieanne Christie Leong , Mohd Saberi Mohamad , Sarahani Harun","doi":"10.1016/j.envsoft.2025.106325","DOIUrl":"10.1016/j.envsoft.2025.106325","url":null,"abstract":"<div><div>The polar regions hold immense ecological and historical significance, offering insights into biomarker identification, climate history, and natural antifreeze proteins. However, global climate change and scattered datasets threaten effective research in these areas. To address these challenges, we developed PolarBytes, a centralized platform for polar research, focusing on biodiversity, climatology, diseases, and molecular biology. PolarBytes streamlines data access, analysis, and visualization through an intuitive interface and advanced machine learning tools. Its robust API system, including RESTful and Swagger interfaces, eliminates manual downloads, supports automation, and enhances research efficiency. By centralizing data from isolated repositories, PolarBytes simplifies data retrieval and fosters collaboration, enabling researchers to focus on scientific exploration rather than technical hurdles. This user-friendly platform empowers the scientific community to uncover new insights and drive innovation in understanding polar ecosystems and their global impact.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106325"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020246","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.2024.106289
Jin Qi , Wenting Lv , Junxia Zhu , Minyu Wang , Zhe Zhang , Guangyuan Zhang , Sensen Wu , Zhenhong Du
The spatiotemporal interpolation model is necessary for generating continuous distributions for spatiotemporally discrete sampling points. However, there remain challenges in spatiotemporal interpolation due to the complex spatiotemporal effect and the imprecise kernel functions. Here, we proposed a spatiotemporal autoregressive neural network interpolation model (STARNN) that incorporates adaptive spatiotemporal distance quantification and supervised learning. The 10-fold cross-validation modelling on sea surface temperature and coastal nutrients demonstrated that the STARNN model performs better than baseline models and can well depict reasonable spatiotemporal distributions for environmental factors. By proposing two stacked neural networks, the STARNN model can accurately integrate spatial and temporal distances and avoids subjective selection of the kernel function. This study developed a novel interpolation model for processing discrete spatiotemporal points by following the data-driven paradigm, which can offer decision support for simulating the spread of sea temperature anomalies and optimizing the distribution of water quality measurement stations.
{"title":"A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors","authors":"Jin Qi , Wenting Lv , Junxia Zhu , Minyu Wang , Zhe Zhang , Guangyuan Zhang , Sensen Wu , Zhenhong Du","doi":"10.1016/j.envsoft.2024.106289","DOIUrl":"10.1016/j.envsoft.2024.106289","url":null,"abstract":"<div><div>The spatiotemporal interpolation model is necessary for generating continuous distributions for spatiotemporally discrete sampling points. However, there remain challenges in spatiotemporal interpolation due to the complex spatiotemporal effect and the imprecise kernel functions. Here, we proposed a spatiotemporal autoregressive neural network interpolation model (STARNN) that incorporates adaptive spatiotemporal distance quantification and supervised learning. The 10-fold cross-validation modelling on sea surface temperature and coastal nutrients demonstrated that the STARNN model performs better than baseline models and can well depict reasonable spatiotemporal distributions for environmental factors. By proposing two stacked neural networks, the STARNN model can accurately integrate spatial and temporal distances and avoids subjective selection of the kernel function. This study developed a novel interpolation model for processing discrete spatiotemporal points by following the data-driven paradigm, which can offer decision support for simulating the spread of sea temperature anomalies and optimizing the distribution of water quality measurement stations.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106289"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797866","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.2025.106317
Wenying Du , Chang Liu , Qingyun Xia , Mengtian Wen , Ying Hu , Zeqiang Chen , Lei Xu , Xiang Zhang , Berhanu Keno Terfa , Nengcheng Chen
Flooding is the most frequent natural disaster globally, resulting in the highest economic losses. Efficient resource retrieval is crucial for improving flood response. Constructing a knowledge graph aids in the precise discovery of flood observation resources. However, current research faces issues: phased flood process observation is neglected, and effective correlation among disaster elements, such as tasks, data, methods, and sensors, is lacking. To address this, we construct the Ontology for Flood Process Observation (OFPO) and develop the Knowledge Graph for Flood Process Observation (KGFPO), providing integrated management and decision-making support. These are validated using the “7–20 Henan Extremely Heavy Rainfall” and “7-21 Xinxiang Extremely Heavy Rainfall” cases. OFPO and KGFPO achieve integrated management of flood observation resources, improve retrieval efficiency and accuracy, facilitate decision-making, and support other natural disasters.
{"title":"OFPO & KGFPO: Ontology and knowledge graph for flood process observation","authors":"Wenying Du , Chang Liu , Qingyun Xia , Mengtian Wen , Ying Hu , Zeqiang Chen , Lei Xu , Xiang Zhang , Berhanu Keno Terfa , Nengcheng Chen","doi":"10.1016/j.envsoft.2025.106317","DOIUrl":"10.1016/j.envsoft.2025.106317","url":null,"abstract":"<div><div>Flooding is the most frequent natural disaster globally, resulting in the highest economic losses. Efficient resource retrieval is crucial for improving flood response. Constructing a knowledge graph aids in the precise discovery of flood observation resources. However, current research faces issues: phased flood process observation is neglected, and effective correlation among disaster elements, such as tasks, data, methods, and sensors, is lacking. To address this, we construct the Ontology for Flood Process Observation (OFPO) and develop the Knowledge Graph for Flood Process Observation (KGFPO), providing integrated management and decision-making support. These are validated using the “7–20 Henan Extremely Heavy Rainfall” and “7-21 Xinxiang Extremely Heavy Rainfall” cases. OFPO and KGFPO achieve integrated management of flood observation resources, improve retrieval efficiency and accuracy, facilitate decision-making, and support other natural disasters.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106317"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935455","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.2025.106355
Carlos Brys , David Luis La Red Martínez , Marcelo Marinelli
When a fire is detected in a rural environment, it is imperative to know the dynamics of the fire's development. Knowing the fire's trajectory is vital since the firefront will have shifted when first responders reach the ignition site. We developed a fast rural fire propagation calculation algorithm that can predict the fire front trajectory 6 h from the time of detection, taking as input data only the latitude and longitude coordinates of the detected hot spot, and obtaining all the necessary data from open online sources. In response to the pressing demand for effective fire control strategies in rural areas, this paper introduces a computational analytical model to predict the fire speed of rural fire behavior. By integrating topographic, meteorological, and land use data, our system offers on-demand fire behavior forecasts, addressing a critical need in the field. With the key component, a predictor, our system identifies patterns and provides crucial information to decision-makers. This comprehensive approach positions our system as an invaluable tool for rescue teams and decision-makers engaged in the proactive battle against rural fires.
{"title":"A geospatial model for real-time predicting rural fire propagation velocity using dynamic algorithms and open data for advanced emergency management","authors":"Carlos Brys , David Luis La Red Martínez , Marcelo Marinelli","doi":"10.1016/j.envsoft.2025.106355","DOIUrl":"10.1016/j.envsoft.2025.106355","url":null,"abstract":"<div><div>When a fire is detected in a rural environment, it is imperative to know the dynamics of the fire's development. Knowing the fire's trajectory is vital since the firefront will have shifted when first responders reach the ignition site. We developed a fast rural fire propagation calculation algorithm that can predict the fire front trajectory 6 h from the time of detection, taking as input data only the latitude and longitude coordinates of the detected hot spot, and obtaining all the necessary data from open online sources. In response to the pressing demand for effective fire control strategies in rural areas, this paper introduces a computational analytical model to predict the fire speed of rural fire behavior. By integrating topographic, meteorological, and land use data, our system offers on-demand fire behavior forecasts, addressing a critical need in the field. With the key component, a predictor, our system identifies patterns and provides crucial information to decision-makers. This comprehensive approach positions our system as an invaluable tool for rescue teams and decision-makers engaged in the proactive battle against rural fires.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106355"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143336704","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}
To mitigate air pollution, source apportionment is a key element for the design of effective measures. However, source apportionment often involves complex model chains only accessible to expert users. In this paper we present a new web-application, the Concawe NO2 source apportionment viewer. It allows experts and non-expert users to evaluate the contributions of different sectors and the impact of measures in the road transport sector on current and future NO2 pollution in the EU27+UK in a fast and user-friendly way. The methodology behind the viewer was described in a previous paper byDegraeuwe et al. (2024). Here we describe the user interface and give some examples; the contribution of different sectors to the NO2 concentrations in the 3136 monitoring stations, and the impact of specific transport policies (e.g., Euro 7/VII standard, urban access regulations) on the NO2 concentrations in 948 European cities.
{"title":"The Concawe NO2 source apportionment viewer: A web-application to mitigate NO2 pollution from traffic and other sources","authors":"Bart Degraeuwe , Robin Houdmeyers , Stijn Janssen , Wouter Lefebvre , Athanasios Megaritis","doi":"10.1016/j.envsoft.2024.106315","DOIUrl":"10.1016/j.envsoft.2024.106315","url":null,"abstract":"<div><div>To mitigate air pollution, source apportionment is a key element for the design of effective measures. However, source apportionment often involves complex model chains only accessible to expert users. In this paper we present a new web-application, the Concawe NO<sub>2</sub> source apportionment viewer. It allows experts and non-expert users to evaluate the contributions of different sectors and the impact of measures in the road transport sector on current and future NO<sub>2</sub> pollution in the EU27+UK in a fast and user-friendly way. The methodology behind the viewer was described in a previous paper byDegraeuwe et al. (2024). Here we describe the user interface and give some examples; the contribution of different sectors to the NO<sub>2</sub> concentrations in the 3136 monitoring stations, and the impact of specific transport policies (e.g., Euro 7/VII standard, urban access regulations) on the NO<sub>2</sub> concentrations in 948 European cities.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106315"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935447","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.2025.106326
Zeynep Özcan , Merih Aydınalp Köksal , Emre Alp
The synergies and conflicts between the energy and water systems, necessitate the collaboration between these sectors. Effective management of the interdependent energy and water systems requires a nexus approach that acknowledges these interconnections, as opposed to regarding them as distinct systems. We applied an integrated modeling approach for evaluating the Water-Energy Nexus based on a variety of criteria as water consumption, energy production, and CO2 emissions. According to the simulations, 96% reduction in water savings can be achieved when wet cooling systems of the thermal power plant (TPP) are converted to dry. Moreover, if the TPPs are shut down to reduce CO2 emissions, the hydroelectric power plants can only cover 16% of the total electricity production. Hence, securing energy while reducing CO2 emissions is a challenging task. Despite producing only 10–15% of total energy, HPPs account for 70–100% of total water consumption in all scenarios.
{"title":"An integrated modeling approach to assess water-energy nexus in a semi-arid watershed","authors":"Zeynep Özcan , Merih Aydınalp Köksal , Emre Alp","doi":"10.1016/j.envsoft.2025.106326","DOIUrl":"10.1016/j.envsoft.2025.106326","url":null,"abstract":"<div><div>The synergies and conflicts between the energy and water systems, necessitate the collaboration between these sectors. Effective management of the interdependent energy and water systems requires a nexus approach that acknowledges these interconnections, as opposed to regarding them as distinct systems. We applied an integrated modeling approach for evaluating the Water-Energy Nexus based on a variety of criteria as water consumption, energy production, and CO<sub>2</sub> emissions. According to the simulations, 96% reduction in water savings can be achieved when wet cooling systems of the thermal power plant (TPP) are converted to dry. Moreover, if the TPPs are shut down to reduce CO<sub>2</sub> emissions, the hydroelectric power plants can only cover 16% of the total electricity production. Hence, securing energy while reducing CO<sub>2</sub> emissions is a challenging task. Despite producing only 10–15% of total energy, HPPs account for 70–100% of total water consumption in all scenarios.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106326"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975673","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.2024.106286
Daryn Sagel, Bryan Quaife
The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale (– m) satellite images to sub-microscale (– m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.
{"title":"Fire dynamic vision: Image segmentation and tracking for multi-scale fire and plume behavior","authors":"Daryn Sagel, Bryan Quaife","doi":"10.1016/j.envsoft.2024.106286","DOIUrl":"10.1016/j.envsoft.2024.106286","url":null,"abstract":"<div><div>The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale (<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>–<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> m) satellite images to sub-microscale (<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>0</mn></mrow></msup></mrow></math></span>–<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow></msup></mrow></math></span> m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106286"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825321","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.2024.106291
Inès Astrid Tougma , Marijn Van de Broek , Johan Six , Thomas Gaiser , Maire Holz , Isabel Zentgraf , Heidi Webber
Most cropping system models simulate conceptual soil organic matter (SOM) pools, such as active, passive and slow pools that cannot be measured, complicating model calibration. In reality, SOM can be described in terms of quantifiable pools of particulate organic matter (POM) and mineral-associated organic matter (MAOM) which respond differently to management and climate. We present the AMPSOM model, integrated in a cropping system modelling framework (SIMPLACE). AMPSOM simulates carbon and nitrogen dynamics in MAOM and POM in response to crop growth and management, as well as soil texture, water and nitrogen content and temperature. It also simulates the radiocarbon isotope (14C) of soil organic carbon (SOC) to constrain the turnover time of slowly cycling SOC pools. Model calibration and evaluation were performed for thirty six sandy and loamy arable soils in Brandenburg, Germany. Results show that AMPSOM can reproduce observed patterns of SOC and nitrogen stocks in POM and MAOM along depth profiles across different soil types.
{"title":"AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems","authors":"Inès Astrid Tougma , Marijn Van de Broek , Johan Six , Thomas Gaiser , Maire Holz , Isabel Zentgraf , Heidi Webber","doi":"10.1016/j.envsoft.2024.106291","DOIUrl":"10.1016/j.envsoft.2024.106291","url":null,"abstract":"<div><div>Most cropping system models simulate conceptual soil organic matter (SOM) pools, such as active, passive and slow pools that cannot be measured, complicating model calibration. In reality, SOM can be described in terms of quantifiable pools of particulate organic matter (POM) and mineral-associated organic matter (MAOM) which respond differently to management and climate. We present the AMPSOM model, integrated in a cropping system modelling framework (SIMPLACE). AMPSOM simulates carbon and nitrogen dynamics in MAOM and POM in response to crop growth and management, as well as soil texture, water and nitrogen content and temperature. It also simulates the radiocarbon isotope (<sup>14</sup>C) of soil organic carbon (SOC) to constrain the turnover time of slowly cycling SOC pools. Model calibration and evaluation were performed for thirty six sandy and loamy arable soils in Brandenburg, Germany. Results show that AMPSOM can reproduce observed patterns of SOC and nitrogen stocks in POM and MAOM along depth profiles across different soil types.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106291"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825340","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}
Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.
{"title":"Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations","authors":"Zhewei Liang , Xun Liang , Xintong Jiang , Tingyu Li , Qingfeng Guan","doi":"10.1016/j.envsoft.2024.106293","DOIUrl":"10.1016/j.envsoft.2024.106293","url":null,"abstract":"<div><div>Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106293"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825337","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}