Environmental Modelling & Software最新文献

英文中文

Hydrologic information systems: An introductory overview 水文信息系统：概论

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 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.

水文信息系统（HIS）集成了硬件和软件，支持水文观测数据的收集、管理和共享。成功的HIS有助于水文监测、科学调查、流域管理和水文条件的交流。此外，信息系统还支持对监测水文系统的组织至关重要的日常数据操作。本文对信息系统的发展历史进行了回顾，并对关键组成部分进行了识别和描述。基于过去的HIS文献，出现了用于通用和通用HIS功能和组件的模式。主要的数据池是收集/获取、操作存储和共享/发布/传播，池之间发生数据流。确定了卫生信息系统面临的持续和当代挑战，并在如何应对这些挑战的背景下描述了当前和正在出现的卫生信息系统的例子。在解决突出的障碍、推进HIS和进一步实现水文学方面，社区协调努力仍有机会。

引用次数: 0

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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

极地地区具有巨大的生态和历史意义，为生物标志物鉴定、气候历史和天然抗冻蛋白提供了见解。然而，全球气候变化和分散的数据集威胁着这些领域的有效研究。为了应对这些挑战，我们开发了PolarBytes，这是一个极地研究的集中平台，专注于生物多样性、气候学、疾病和分子生物学。PolarBytes通过直观的界面和先进的机器学习工具简化了数据访问、分析和可视化。其强大的API系统，包括RESTful和Swagger接口，消除了手动下载，支持自动化，提高了研究效率。通过将来自孤立存储库的数据集中，PolarBytes简化了数据检索并促进了协作，使研究人员能够专注于科学探索，而不是技术障碍。这个用户友好的平台使科学界能够在了解极地生态系统及其全球影响方面发现新的见解并推动创新。

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引用次数: 0

A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors 离散环境因素的时空自回归神经网络插值法

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 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.

时空插值模型是产生时空离散采样点连续分布的必要条件。然而，由于时空效应的复杂性和核函数的不精确性，在时空插值方面仍然存在挑战。本文提出了一种结合自适应时空距离量化和监督学习的时空自回归神经网络插值模型（STARNN）。对海表温度和海岸营养物的10倍交叉验证模型表明，STARNN模型优于基线模型，能较好地描述环境因子的合理时空分布。通过提出两个堆叠神经网络，STARNN模型可以准确地整合时空距离，避免了核函数的主观选择。本研究建立了一种基于数据驱动的离散时空点插值模型，为模拟海温异常扩散和优化水质测量站分布提供决策支持。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

洪水是全球最常见的自然灾害，造成的经济损失最大。有效的资源检索是提高洪水响应能力的关键。知识图谱的构建有助于洪水观测资源的精确发现。然而，目前的研究面临的问题是：忽视了洪水过程的阶段性观测，缺乏任务、数据、方法和传感器等灾害要素之间的有效关联。为此，我们构建了洪水过程观测本体（OFPO），开发了洪水过程观测知识图谱（KGFPO），为洪水过程观测提供综合管理和决策支持。以“7-20河南特大暴雨”和“7-21新乡特大暴雨”为例进行了验证。OFPO和KGFPO实现了洪水观测资源的综合管理，提高了检索效率和准确性，便于决策，支持其他自然灾害。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 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.

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

为了减轻空气污染，污染源分配是设计有效措施的关键要素。然而，源分配通常涉及只有专家用户才能访问的复杂模型链。在本文中，我们提出了一个新的web应用程序，Concawe NO2源分配查看器。它允许专家和非专家用户以快速和用户友好的方式评估不同部门的贡献以及道路运输部门措施对欧盟27国+英国当前和未来二氧化氮污染的影响。degraeuwe等人（2024）在之前的一篇论文中描述了观看者背后的方法。这里我们描述了用户界面并给出了一些例子；不同部门对3136个监测站NO2浓度的贡献，以及特定交通政策（如欧7/VII标准、城市通行条例）对948个欧洲城市NO2浓度的影响。

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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 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.

能源和水系统之间的协同作用和冲突需要这些部门之间的合作。有效管理相互依赖的能源和水系统需要一种联系的方法，承认这些相互联系，而不是将它们视为不同的系统。我们采用了一种综合建模方法，基于水消耗、能源生产和二氧化碳排放等多种标准来评估水-能源关系。模拟结果表明，将火电厂湿式冷却系统转换为干式冷却系统可节约96%的用水。此外，如果为了减少二氧化碳排放而关闭tpp，水力发电厂只能满足总发电量的16%。因此，在确保能源安全的同时减少二氧化碳排放是一项具有挑战性的任务。尽管仅生产总能源的10-15%，但在所有情况下，HPPs占总用水量的70-100%。

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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 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 (

1 0^{4}

–

1 0^{5}

m) satellite images to sub-microscale (

1 0^{0}

–

1 0^{1}

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.

野火发生的频率和严重程度不断增加，这凸显了对精确的火灾和烟羽蔓延模型的需求。我们介绍了一种方法，该方法可有效隔离和跟踪各种时空尺度和图像类型中的火灾和烟羽行为，识别系统中的物理现象，并提供对开发和验证模型有用的见解。我们的方法结合了图像分割和图论来划分火锋和烟羽边界。我们证明，该方法能有效地将火灾和烟羽与视觉上相似的物体区分开来。结果表明，该方法可成功隔离和跟踪各种图像来源中的火灾和羽流动态，包括从同步尺度（104-105 米）卫星图像到接近火灾环境的亚微观尺度（100-101 米）图像。此外，该方法还利用了图像内绘和时空数据集生成技术，可用于统计和机器学习模型。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

大多数种植系统模型模拟的是概念土壤有机质（SOM）库，如无法测量的主动、被动和缓慢库，使模型校准复杂化。在现实中，SOM可以用可量化的颗粒有机质（POM）和矿物相关有机质（MAOM）池来描述，它们对管理和气候的响应不同。我们提出了集成在种植系统建模框架（SIMPLACE）中的AMPSOM模型。AMPSOM模拟MAOM和POM中碳氮动态，以响应作物生长和管理，以及土壤质地，水氮含量和温度。模拟土壤有机碳（SOC）的放射性碳同位素（14C），以约束缓慢循环的有机碳库的周转时间。对德国勃兰登堡地区36种沙质和壤土进行了模型标定和评价。结果表明，AMPSOM能沿不同土壤类型重现POM和MAOM土壤有机碳和氮储量的分布规律。

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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 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

大规模土地利用变化模拟对于理解土地动态、调查气候变化和制定政策法规至关重要。然而，由于高计算需求，在大尺度上进行高分辨率土地利用变化模拟具有挑战性。相反，使用粗分辨率数据的土地利用变化模拟会扭曲空间细节，从而降低模拟性能。并行计算可以减少计算需求，但需要大量的计算资源。混合单元CA模型提供了平衡仿真性能和计算强度的解决方案。使用不同分辨率土地利用数据集的对比实验表明，混合元胞CA模型，即使是使用粗分辨率数据的混合元胞CA模型，也可以获得与使用精细分辨率数据的纯元胞CA模型相当的结果，但可以显著缩短模拟时间。这突出了混合单元CA模型在较低计算强度下实现相当性能的效率。此外，本研究还提供了一种混合单元CA模型不确定度的测量方法。综上所述，本研究揭示了混合单元CA模型在高效大规模土地利用模拟中的独特优势，从而为未来的土地利用管理和政策决策提供了有价值的见解和指导。

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