Environmental Modelling & Software最新文献_第10页

PolarBytes: Advancing polar research with a centralized open-source data sharing platform PolarBytes：通过集中的开源数据共享平台推进极地研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 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

{"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}

引用次数: 0

OFPO & KGFPO: Ontology and knowledge graph for flood process observation OFPO & KGFPO：洪水过程观测的本体与知识图谱

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 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

{"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}

引用次数: 0

A geospatial model for real-time predicting rural fire propagation velocity using dynamic algorithms and open data for advanced emergency management

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106355

Carlos Brys , David Luis La Red Martínez , Marcelo Marinelli

{"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}

引用次数: 0

Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework 时序深度学习框架中使用卷积神经网络的时空洪水深度和速度动态

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106307

Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith

{"title":"Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework","authors":"Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith","doi":"10.1016/j.envsoft.2024.106307","DOIUrl":"10.1016/j.envsoft.2024.106307","url":null,"abstract":"<div><div>Computational hydrodynamic models support river science and management. However, current physics-based models face computational challenges; they require extensive processing time for large-scale two-dimensional flood simulations. Despite the success of Deep Learning (DL) applications in generating inundation maps, accurate prediction of unsteady flood hydrodynamic maps remains challenging. This paper compares traditional approaches to a novel DL approach, which integrates convolutional neural networks with long short-term memory, to deliver precise, rapid, and continuous simulation of the spatiotemporal dynamics of river floods. This is the first DL framework able to generate essential hydrodynamic variables: water depth, velocity magnitude, and flow direction maps. Water depth and velocity magnitude predictions across the testing dataset are robust, with average RMSE of 0.14 m and 0.02 m/s, respectively. The DL predictions are 415 times faster compared to traditional computational approaches, representing a paradigm shift in hydrodynamics modeling that advances long-term flood simulations and resilient river management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106307"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905661","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}

引用次数: 0

Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates 基于深度学习和局部集合更新的数据同化方法的降雨径流模型参数估计和不确定性量化

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106332

Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng

{"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":"10.1016/j.envsoft.2025.106332","url":null,"abstract":"<div><div>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<sub>DL</sub> with a deep learning-based update, and ES<sub>LU</sub> 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<sub>DL</sub> and ES<sub>LU</sub>, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106332"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","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}

引用次数: 0

The Concawe NO2 source apportionment viewer: A web-application to mitigate NO2 pollution from traffic and other sources Concawe二氧化氮来源分配查看器：一个减少交通和其他来源的二氧化氮污染的网络应用程序

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106315

Bart Degraeuwe , Robin Houdmeyers , Stijn Janssen , Wouter Lefebvre , Athanasios Megaritis

引用次数: 0

An integrated modeling approach to assess water-energy nexus in a semi-arid watershed 半干旱流域水-能关系综合建模方法研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106326

Zeynep Özcan , Merih Aydınalp Köksal , Emre Alp

引用次数: 0

Fire dynamic vision: Image segmentation and tracking for multi-scale fire and plume behavior 火灾动态视觉：多尺度火灾和烟羽行为的图像分割与跟踪

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106286

Daryn Sagel, Bryan Quaife

{"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}

引用次数: 0

AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems 一个可测量的耕地种植系统土壤有机碳和氮模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106291

Inès Astrid Tougma , Marijn Van de Broek , Johan Six , Thomas Gaiser , Maire Holz , Isabel Zentgraf , Heidi Webber

{"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}

引用次数: 0

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations 平衡大规模土地利用变化模拟中 CA 模型的模拟性能和计算强度

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106293

Zhewei Liang , Xun Liang , Xintong Jiang , Tingyu Li , Qingfeng Guan

{"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}

引用次数: 0