Environmental Modelling & Software最新文献

英文中文

AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing 人工智能驱动的波斯湾和阿曼湾有害藻华遥感预测

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.2024.106311

Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi

This study develops an artificial intelligence (AI) model to forecast harmful algal blooms (HABs) in the Persian Gulf and Gulf of Oman using freely available remote sensing data, including chlorophyll-a (Chl-a), sea surface temperature (SST), salinity, and wind. The model introduces novel features such as spatial and temporal standard deviations of Chl-a concentration and a derived gradient feature. Correlation analysis indicated that these features enhance predictive capability. A multi-layer artificial neural network (ANN) was trained using a 66%/34% data split for training and testing, achieving 88.7% accuracy in binary classification (bloom/non-bloom) with an area under the ROC curve (AUC) of 90.1%. Overfitting was mitigated by monitoring training and validation loss, both of which consistently decreased over epochs, confirming robust model generalization. The use of standard deviation in SST and salinity highlights their influence on bloom dynamics, providing key insights into algal bloom drivers. The focus on freely available data enables stakeholders to better manage the environmental challenges posed by HABs.

本研究开发了一个人工智能（AI）模型，利用可免费获得的遥感数据，包括叶绿素-a （Chl-a）、海面温度（SST）、盐度和风，预测波斯湾和阿曼湾的有害藻华（HABs）。该模型引入了新的特征，如Chl-a浓度的时空标准偏差和衍生的梯度特征。相关分析表明，这些特征增强了预测能力。采用66%/34%的数据分割率训练多层人工神经网络（ANN）进行训练和测试，二元分类（开花/不开花）准确率达到88.7%，ROC曲线下面积（AUC）为90.1%。通过监测训练和验证损失来减轻过拟合，两者都随着时间的推移而持续下降，证实了模型的鲁棒泛化。使用海温和盐度的标准差强调了它们对水华动态的影响，为了解藻华驱动因素提供了关键见解。将重点放在可免费获得的数据上，使利益攸关方能够更好地管理有害藻华带来的环境挑战。

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引用次数: 0

Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties 基于机器学习的地下生物量从地上生物量和土壤性质预测

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.2024.106313

Yuquan Zhao , Lu Zhang , Shilong Lei , Lirong Liao , Chao Zhang

Precise and accurate quantification of belowground biomass (BGB) is essential for understanding terrestrial carbon dynamics. Traditional methods for estimating BGB suffer from a number of disadvantages, including inability to resolve differences among plant species, high dependence on Diameter at Breast Height, and destructive sampling. To address these issues, we developed a novel machine learning framework to estimate grassland BGB by integrating vegetation and soil data from 294 plots on China's Loess Plateau. An ensemble model combining XGBoost regression, Gradient boosting regression, Ridge regression, and ElasticNet regression outperformed the individual models, achieving a training R² of 0.623 and a testing R² of 0.502, highlighting its superior ability to identify the complex dependencies of BGB. Integration of key features, including soil organic carbon, plant height, and aboveground biomass, significantly improved the predictive accuracy. Nonlinear BGB–environment interactions are commonly underrecognized in traditional models. The model presented herein advances our ability to assess underground carbon stocks and offers insights into the ecological strategies of grassland species under competitive light conditions. By revealing the multifaceted influences of soil and vegetation on BGB, our research refines the understanding of grassland carbon dynamics. This study marks a precedent for harnessing advanced machine learning in ecological modeling to facilitate more accurate predictions of global change.

地下生物量（BGB）的精确定量对理解陆地碳动态至关重要。传统的BGB估算方法存在许多缺点，包括无法解决植物物种之间的差异，高度依赖于胸围直径，以及破坏性采样。为了解决这些问题，我们开发了一个新的机器学习框架，通过整合中国黄土高原294个样地的植被和土壤数据来估计草地BGB。结合XGBoost回归、Gradient boosting回归、Ridge回归和ElasticNet回归的集成模型优于单个模型，训练R2为0.623，测试R2为0.502，突出了其识别BGB复杂依赖关系的卓越能力。整合土壤有机碳、植物高度和地上生物量等关键特征显著提高了预测精度。在传统模型中，bgb环境的非线性相互作用通常被低估。本文提出的模型提高了我们评估地下碳储量的能力，并为了解竞争光照条件下草地物种的生态策略提供了见解。通过揭示土壤和植被对BGB的多方面影响，我们的研究完善了对草地碳动态的认识。这项研究标志着在生态建模中利用先进的机器学习来促进更准确地预测全球变化的先例。

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引用次数: 0

Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics 采用相似度控制合成随机环境时间序列，保持原始信号的统计特征

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.2024.106283

Ofek Aloni , Gal Perelman , Barak Fishbain

Synthetic datasets are widely used in applications like missing data imputation, simulations, training data-driven models, and system robustness analysis. Typically based on historical data, these datasets need to represent specific system behaviors while being diverse enough to challenge the system with a broad range of inputs. This paper introduces a method using discrete Fourier transform to generate synthetic time series with similar statistical moments to any given signal. The method allows control over the similarity level between the original and synthetic signals. Analytical proof shows that this method preserves the first two statistical moments and the autocorrelation function of the input signal. It is compared to ARMA, GAN, and CoSMoS methods using various environmental datasets with different temporal resolutions and domains, demonstrating its generality and flexibility. A Python library implementing this method is available as open-source software.

合成数据集广泛应用于缺失数据输入、模拟、训练数据驱动模型和系统鲁棒性分析等领域。通常基于历史数据，这些数据集需要表示特定的系统行为，同时具有足够的多样性，以广泛的输入挑战系统。本文介绍了一种利用离散傅立叶变换生成与任意给定信号具有相似统计矩的合成时间序列的方法。该方法允许控制原始信号和合成信号之间的相似度。分析证明，该方法保留了输入信号的前两个统计矩和自相关函数。将其与ARMA、GAN和CoSMoS方法进行了比较，这些方法使用了具有不同时间分辨率和域的各种环境数据集，证明了其通用性和灵活性。实现此方法的Python库可以作为开源软件获得。

引用次数: 0

Community-enabled life-cycle assessment Stormwater Infrastructure Costs (CLASIC) tool 社区支持的生命周期评估雨水基础设施成本（classic）工具

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.2024.106279

Mazdak Arabi , Tyler Dell , Mahshid Mohammad Zadeh , Christine A. Pomeroy , Jennifer M. Egan , Tyler Wible , Sybil Sharvelle

Urbanization, land use change, and climate change have profound effects on urban stormwater. This study develops the Community-enabled Life-cycle Analysis of Stormwater Infrastructure Costs (CLASIC) software to support decisions about stormwater control infrastructure over a range of alternative scenarios at the neighborhood to municipal scales. The tool quantifies hydrologic and stormwater quality performance, life-cycle costs, and triple-bottom-line social, economic, and environmental co-benefits of green, gray, and hybrid green-gray stormwater practices. CLASIC is deployed as a cloud-based web-tool, with a geographical information system (GIS) enabled interface, and built-in computing services to characterize terrain, soil, land use, and climatic conditions using publicly available datasets, and to parameterize and execute the modeling modules. Three community level case studies in the United States illustrate the utility of CLASIC for climate change assessments, green infrastructure implementation for community redevelopment, and assessment of the effects of changes in rainfall characteristics on the performance of stormwater practices.

城市化、土地利用变化和气候变化对城市雨水有着深远的影响。本研究开发了社区支持的雨水基础设施成本生命周期分析（classic）软件，以支持在社区到市政规模的一系列备选方案中有关雨水控制基础设施的决策。该工具量化了水文和雨水质量表现、生命周期成本，以及绿色、灰色和混合绿灰雨水处理的三重底线社会、经济和环境共同效益。classic作为基于云的网络工具部署，具有地理信息系统（GIS）支持的界面，内置计算服务，可以使用公开可用的数据集描述地形、土壤、土地利用和气候条件，并参数化和执行建模模块。美国的三个社区层面的案例研究说明了classic在气候变化评估、社区重建的绿色基础设施实施以及评估降雨特征变化对雨水处理效果的影响方面的效用。

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引用次数: 0

Predicting massive floating macroalgal blooms in a regional sea 预测区域海洋中大量漂浮的大型藻华

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.2024.106310

Fucang Zhou , Zhi Chen , Zaiyang Zhou , Bing Cao , Lili Xu , Dongyan Liu , Ruishan Chen , Karline Soetaert , Jianzhong Ge

Increasingly frequent and severe floating macroalgal blooms present significant challenges to coastal and ocean environments. Here a short-term forecast system of floating macroalgal blooms was developed to predict the physical-biogeochemical environment and macroalgal ecodynamic processes in a regional ocean. Predictions of macroalgal ecodynamic processes are influenced by oceanic conditions (hydrodynamics, temperature, and nutrients), as well as atmospheric conditions (wind). The system's effectiveness is demonstrated by successfully hindcasting the June 2021 green tide bloom event in the Yellow Sea and using real-time satellite data to make reliable and robust continuous short-term predictions for 2022 and 2023. The prediction accuracy of coverage reaches 87.5%, and the minimum transport error of the green tide center of mass is 6.09 nautical miles over an 7-day prediction duration. Supported by regional marine physics and biogeochemistry and macroalgal physiological characteristic datasets, this system may serve as a crucial cornerstone for similar floating macroalgal disaster prevention.

日益频繁和严重的浮藻华对沿海和海洋环境构成了重大挑战。为了预测区域海洋浮游藻华的物理-生物地球化学环境和生态动力学过程，建立了浮游藻华短期预报系统。大藻生态动力学过程的预测受到海洋条件（水动力学、温度和营养物）以及大气条件（风）的影响。该系统的有效性通过成功预测2021年6月黄海绿潮事件以及使用实时卫星数据对2022年和2023年进行可靠和稳健的连续短期预测来证明。覆盖预报精度达到87.5%，7 d预报周期内绿潮质心最小输运误差为6.09海里。在区域海洋物理、生物地球化学和大型藻生理特征数据集的支持下，该系统可作为类似大型藻漂浮灾害预防的重要基石。

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引用次数: 0

Solving the Master Equation on river networks: A computer algebra approach 求解河网主方程：一种计算机代数方法

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.2024.106288

Samuele De Bartolo , Gaetano Napoli , Stefano Rizzello , Raffaele Vitolo

We describe the algorithms and the software that have been used in a new computational method based on the use of Master Equations. Our computer algebra procedures simulate the diffusion of a pollutant in river networks. The representation of river networks as trees makes the derivation of governing equations for pollutant transport an easy task. This includes mass balance equations that account for the sources, sinks, and transport of pollutants in the river network. In two previous papers we described the model and some simulations obtained from our software. In this paper we describe two software libraries, respectively for the Reduce and the Mathematica computer algebra systems, that have been developed on the basis of our model. The libraries can be found in our GitHub repository.

我们描述了基于主方程的一种新的计算方法所使用的算法和软件。我们的计算机代数程序模拟了污染物在河网中的扩散。将河网表示为树使得推导污染物运移的控制方程变得容易。这包括解释河网中污染物的源、汇和输送的质量平衡方程。在之前的两篇论文中，我们描述了该模型以及用我们的软件得到的一些模拟结果。本文描述了基于该模型开发的Reduce和Mathematica计算机代数系统的两个软件库。这些库可以在我们的GitHub库中找到。

引用次数: 0

A simple method for the enhancement of river bathymetry in LiDAR DEM

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.2025.106354

Gabriele Farina , Marco Pilotti , Luca Milanesi , Giulia Valerio

The preparation of an accurate bathymetry is crucial for flood modeling and is usually done using a LiDAR-derived Digital Elevation Model (DEM). However, a recurrent flaw of LiDAR DEM is the presence of water along rivers, that prevents a careful reproduction of the river bed and channel conveyance. This paper provides a simple and effective algorithm to tackle this problem when ground surveyed cross sections are available to complement DEM data. In contrast to most interpolation approaches, the algorithm is physically-based, using a 2D Shallow Water Equations solver in the identification of the wetted river bed perimeter. The method was applied to a 37 km long stretch of the Mella River (Northern Italy) providing satisfactory results. Further examples show the potential of the method in cases of increasing complexity of riverbed bathymetry. The procedure is explained step by step in the supplementary material, using two widely used freeware software.

引用次数: 0

SIMRA: An interactive web-based tool for single integrated microbial risk assessment and management within the national water security framework (NWSF) SIMRA：在国家水安全框架（NWSF）内进行单一综合微生物风险评估和管理的交互式网络工具

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.2024.106304

A. Murei, M.N.B. Momba

In this study, we present a single integrated microbial risk assessment (SIMRA) tool, which is a web-based application that offers a multi-tiered approach to microbial risk assessment of water resources, and accommodates three different user proficiency levels for microbial risk assessment based on resource availability. The main objective was to integrate the components of water safety plans and sanitation safety plans and develop a single web-based application platform that enables the assessment, management, and monitoring of possible risks associated with various types of water. Key parameters in the risk assessment process in the tool are concentration of indicator microorganisms and pathogens. The SIMRA web-based tool enables comprehensive water and sanitation analysis, risk assessment, geospatial mapping of water resources, and generates feedback on the level of safety of the water source. The web server is publicly accessible at https://simraweb.web.app/. Here, we outline the development of SIMRA and its main features and demonstrate its applicability.

在这项研究中，我们提出了一个单一的综合微生物风险评估（SIMRA）工具，这是一个基于web的应用程序，提供了一个多层次的水资源微生物风险评估方法，并根据资源可用性适应三种不同的用户熟练程度进行微生物风险评估。主要目标是整合水安全计划和卫生安全计划的组成部分，并开发一个基于网络的单一应用平台，从而能够评估、管理和监测与各种类型的水相关的可能风险。该工具风险评估过程中的关键参数是指标微生物和病原体的浓度。SIMRA基于网络的工具可以进行全面的水和卫生分析、风险评估、水资源的地理空间测绘，并对水源的安全水平产生反馈。web服务器可以在https://simraweb.web.app/上公开访问。在这里，我们概述了SIMRA的发展及其主要特点，并展示了它的适用性。

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引用次数: 0

An open framework for analysing future flood risk in urban areas 分析市区未来洪水风险的开放架构

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.2024.106302

Olivia Butters , Craig Robson , Fergus McClean , Vassilis Glenis , James Virgo , Alistair Ford , Christos Iliadis , Richard Dawson

A combination of climate change and urban development are increasing flood risk in cities worldwide, however analysing both drivers of risk is especially complex as new buildings alter surface water flows changing flood events. This paper provides an overview of the approaches, algorithms, design, and capabilities of the OpenCLIM urban flooding workflow which attempts to address this, coupling building-scale models of urban development with high-resolution simulations of urban flooding. The workflow retrieves and processes national-scale datasets, automatically configuring data and models, thereby significantly reducing user effort in commissioning simulations of risk analysis. A demonstration for Newcastle-upon-Tyne (UK) reveals hotspots of changes in risk and exposure are altered by both urban development and changes in rainfall, with climate change the most significant driver. The workflow is made accessible and transparent via DAFNI (www.dafni.ac.uk), a national computing facility, providing a vital tool for routine and repeatable urban flood risk analysis.

气候变化和城市发展的结合正在增加世界各地城市的洪水风险，然而，分析这两个风险驱动因素尤其复杂，因为新建筑改变了地表水的流动，从而改变了洪水事件。本文概述了OpenCLIM城市洪水工作流的方法、算法、设计和功能，该工作流试图解决这一问题，将城市发展的建筑尺度模型与城市洪水的高分辨率模拟相结合。该工作流检索和处理全国范围的数据集，自动配置数据和模型，从而大大减少了用户在调试风险分析模拟时的工作量。泰恩河畔纽卡斯尔（英国）的一项示范显示，城市发展和降雨变化改变了风险和暴露变化的热点，其中气候变化是最重要的驱动因素。工作流程可通过DAFNI （www.dafni.ac.uk）透明访问，DAFNI是一个国家计算设施，为常规和可重复的城市洪水风险分析提供了重要工具。

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引用次数: 0

Modelling evapotranspiration in urban green stormwater infrastructures: Importance of sensitivity analysis and calibration strategies with a hydrological model 城市绿色雨水基础设施的蒸散模拟：水文模型敏感性分析和校准策略的重要性

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.2025.106319

Ahmeda Assann Ouédraogo , Emmanuel Berthier , Jérémie Sage , Marie-Christine Gromaire

Evapotranspiration (ET) is crucial for urban runoff management, the cooling efficiency of green stormwater infrastructure (GSI), and vegetation resilience. This research investigates the ability of a commonly used hydrological ET scheme, implemented in HYDRUS-1D, to accurately replicate ET fluxes within GSI, including green roofs (GRs) and rain gardens (RGs), in the Paris region, France. Application of the Sobol sensitivity analysis method indicates that, vegetation height and stomatal resistance are key elements in Penman-Monteith potential ET calculations, while substrate water retention parameters are essential for actual ET simulations. Soil cover fraction, substrate pressure head during the anaerobic phase, and interception parameter also influence ET. Calibration using extensive datasets (water content, ET, drainage) demonstrates improved model accuracy for GRs with thicker substrates compared to those with thinner substrates and for RG setups. Drainage calibration ensures long-term ET simulation accuracy, while calibration with water content or ET observations is recommended during prolonged dry periods.

蒸散发（ET）对城市径流管理、绿色雨水基础设施（GSI）的冷却效率和植被恢复能力至关重要。本研究调查了在HYDRUS-1D中实施的常用水文ET方案在法国巴黎地区的GSI（包括绿色屋顶（GRs）和雨水花园（RGs））中精确复制ET通量的能力。Sobol敏感性分析方法的应用表明，植被高度和气孔阻力是Penman-Monteith潜在蒸散发计算的关键因素，而底物保水参数是实际蒸散发模拟的关键参数。土壤覆盖度、厌氧阶段的基质压头和截留参数也会影响ET。使用广泛的数据集（含水量、ET、排水）进行校准表明，与基质较薄的GRs和RG设置相比，基质较厚的GRs的模型精度更高。排水校准确保长期蒸散发模拟的准确性，而建议在长时间干旱期间使用含水量或蒸散发观测值进行校准。

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引用次数: 0

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