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Hybrid cellular automata-based air pollution model for traffic scenario microsimulations Environ. Model. Softw. (IF 4.8) Pub Date : 2025-02-07 Tabea S. Sonnenschein, Zhendong Yuan, Jibran Khan, Jules Kerckhoffs, Roel C.H. Vermeulen, Simon Scheider
Scenario microsimulations like agent-based models can account for feedbacks and spatio-temporal and social heterogeneity when projecting future intervention impacts. Addressing air pollution exposure requires traffic scenario models (i.e. of car-free zones). Traditional air pollution models do not meet all requirements for traffic scenario microsimulation: isolating traffic emission, integrating relevant
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Advanced Systems for Environmental MonitoringJamalMabroukiMouradeAzrourIoT and the application of Artificial Intelligence2024Springer Nature SwitzerlandISBN 978-3-031-50860-8 (eBook) Environ. Model. Softw. (IF 4.8) Pub Date : 2025-02-06 Fransiskus Serfian Jogo, Hanum Khairana Fatmah, Aufaclav Zatu Kusuma Frisky
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Spatiotemporal [formula omitted] forecasting via dynamic geographical Graph Neural Network Environ. Model. Softw. (IF 4.8) Pub Date : 2025-02-06 Qin Zhao, Jiajun Liu, Xinwen Yang, Hongda Qi, Jie Lian
With the growing interest in data-driven methods, Graph Neural Networks (GNNs) have demonstrated strong performance in PM2.5 forecasting as a deep learning architecture. However, GNN-based methods typically construct the graph based solely on the distance between stations, and few methods introduce geographical factors that significantly affect the spatial dispersion of PM2.5, leading to performance
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ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory Environ. Model. Softw. (IF 4.8) Pub Date : 2025-02-04 Michele Licata, Stefano Faga, Giandomenico Fubelli
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
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A geospatial model for real-time predicting rural fire propagation velocity using dynamic algorithms and open data for advanced emergency management Environ. Model. Softw. (IF 4.8) Pub Date : 2025-02-01 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
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Amadeus: Accessing and analyzing large scale environmental data in R Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-31 Mitchell Manware, Insang Song, Eva S. Marques, Mariana Alifa Kassien, Lara P. Clark, Kyle P. Messier
Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the amadeus package for R, a tool to improve access to and utility with large scale environmental
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MANG@COAST: A spatio-temporal modeling approach of muddy shoreline mobility based on mangrove monitoring Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-30 P.E. Augusseau, C. Proisy, A. Gardel, G. Brunier, L. Granjon, T. Maury, A. Mury, A. Staquet, V.F. Santos, R. Walcker, P. Degenne, D. Lo Seen, E.J. Anthony
Highly dynamic wave-exposed muddy coasts harbouring mangrove ecosystems can be subject to both marked accretion and erosion depending on the complex interactions between mud and waves. We propose a multiscale modelling approach and empirical equations calibrated and integrated into a landscape dynamics model implemented on a mud-bank coast using the Ocelet language to simplify the complex processes
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Derivation of characteristic physioclimatic regions through density-based spatial clustering of high-dimensional data Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-28 Sebastian Lehner, Katharina Enigl, Matthias Schlögl
Physioclimatic regions are homogeneous geospatial entities that exhibit similar characteristics in both climatic conditions and the physiographic environment. They provide a foundation for a broad range of analyses in earth system sciences that are conditional on the prevailing climatological properties shaping geographical areas. However, delineating such regions is challenging due to high-dimensional
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HenkSchuttelaarsArnoldHeeminkEricDeleersnijdeThe Mathematics of Marine Modelling: Water, Solute, and Particle Dynamics in Estuaries and Shallow Seas2022SpringerSwitzerland AG324Hardback £139.99: ISBN 9783031095580 Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-27 Anjela Karunia Amalia, Anggun Rosa Ajie Safira, Anak Agung Eka Andiani, Fuji Sintia Armi, Eka Widya Utami
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Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-27 Xingtian Chen, Yuhang Zhang, Aizhong Ye, Jinyang Li, Kuolin Hsu, Soroosh Sorooshian
Pre-trained models like FourCastNet, Pangu and GraphCast have gained popularity in the meteorological field. In hydrology, data-driven rainfall-runoff models based on long short-term memory (LSTM) networks have been successfully applied for various purposes. As large-sample hydrological datasets (e.g., Caravan) continue to grow, it is foreseeable that pre-trained models tailored for hydrology will
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A real-time and modular weather station software architecture based on microservices Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-27 J. Bonilla, J.A. Carballo, V. Abad-Alcaraz, M. Castilla, J.D. Álvarez, J. Fernández-Reche
The increasing demand for accurate and real-time weather data has highlighted the limitations of traditional weather stations, which often lack the flexibility and scalability required for modern applications. This paper introduces a real-time and modular weather station software architecture based on microservices, designed to address these challenges. The proposed system leverages microservices to
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ASTERIX: Module for modeling the water flow on vegetated hillslopes Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-26 Stelian Ion, Dorin Marinescu, Stefan Gicu Cruceanu
The paper presents an open source software for numerical integration of an extended Saint-Venant model used as a mathematical tool to simulate the water flow from laboratory up to large-scale spatial domains applying physically-based principles of fluid mechanics. Many in-situ observations have shown that vegetation plays a key role in controlling the hydrological flux at catchment scale. In case of
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Enhancing hydrological modeling of ungauged watersheds through machine learning and physical similarity-based regionalization of calibration parameters Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-22 Arun Bawa, Katie Mendoza, Raghavan Srinivasan, Fearghal O'Donchha, Deron Smith, Kurt Wolfe, Rajbir Parmar, John M. Johnston, Joel Corona
This study enhances hydrological modeling in ungauged watersheds by employing physical similarity and machine learning-based clustering for regionalizing the Soil and Water Assessment Tool (SWAT) model parameters at the HUC12 (hydrological unit code) watershed scale within a HUC02 basin. Eleven features, including environmental, topographical, soil, and hydrological properties, were utilized to identify
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Doing hydrology when no in-situ data exists: Surrogate River discharge Model (SRM) Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-22 Hae Na Yoon, Lucy Marshall, Ashish Sharma, Seokhyeon Kim
The surrogate river discharge model (SRM) uses remote sensing surrogates of river discharge (SR) to estimate streamflow in ungauged basins. Integrating SR derived from L-band microwave data with climate inputs of rainfall and potential evapotranspiration, the model operates within a hydrological framework. While SR is strongly correlated with streamflow, it is unitless and requires calibration for
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GeoAI-based drainage crossing detection for elevation-derived hydrographic mapping Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-21 Michael Edidem, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang
The increasing availability of High-Resolution Digital Elevation Models (HRDEMs) allows accurate delineation of stream and drainage flowlines at the field scale. However, the presence of digital flow barriers like roads effectively impedes hydrological connectivity represented on the HRDEMs. Conventional methods for locating these artificial barriers such as on-screen digitization and field surveying
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A systemic approach to managing uncertainties in repetitive multibeam bathymetric surveys Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-18 Gaétan Sauter, Stefano C. Fabbri, Corine Frischknecht, Flavio S. Anselmetti, Katrina Kremer
Multibeam Echo Sounder systems have enhanced the precision of modern bathymetric mapping, enabling the creation of high-resolution digital bathymetry models that characterise ocean and lake floors. However, the inferred models contain uncertainties that necessitate consideration, especially when conducting quantitative temporal comparisons. By exploring the results of two bathymetric surveys targeting
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Simple analysis of biodiversity response functions and multipliers for biodiversity offsetting and other applications Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-18 Atte Moilanen, Pauli Lehtinen
Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions
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A framework for assessing the computational reproducibility of geo-simulation experiments Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-17 Zhiyi Zhu, Min Chen, Guangjin Ren, Yuanqing He, Lingzhi Sun, Fengyuan Zhang, Yongning Wen, Songshan Yue, Guonian Lü
Recent advances in computational technologies have enhanced geo-simulation experiments (GSEs), making computational reproducibility assessments increasingly critical. However, existing methods often focus on isolated aspects, lacking a comprehensive framework. This study proposes an integrated framework for assessing reproducibility in GSEs, structured into two parts: (1) evaluating overall computational
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Integrated models of nutrient dynamics in lake and reservoir watersheds: A systematic review and integrated modelling decision pathway Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-17 Floran Clopin, Ilaria Micella, Jorrit P. Mesman, Ma Cristina Paule-Mercado, Marina Amadori, Shuqi Lin, Lisette N. de Senerpont Domis, Jeroen J.M. de Klein
Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models
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Evaluating the influence of topography data resolution on lake hydrodynamic model under a simulation uncertainty analysis framework Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-16 Quan Han, Ling Zhou, Wenchao Sun, Jinqiang Wang, Chi Ma
Spatial resolution of topography data significantly impacts computational time of lake hydrodynamic modelling. This study proposes a calibration tool to examine impacts of topography data resolution on simulation uncertainty, evolving from the Generalized Likelihood Uncertainty Analysis framework. Using the EFDC hydrodynamic model, BaiYangDian Lake in North China was simulated at three resolutions:
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Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-16 Lei Yao, Jiangjiang Zhang, Chenglong Cao, Feifei Zheng
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
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Development of optimal parameter determination algorithm for two-dimensional flow analysis model Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-16 Eun Taek Shin, Se Hyuck An, Sung Won Park, Seung Oh Lee, Chang Geun Song
Accurate parameter selection is crucial for reliable predictions in fluid dynamics, environmental transport, and urban flood prediction. Traditional manual methods are time-consuming and prone to errors. This study introduces an automated algorithm to optimize roughness and viscosity coefficients in two-dimensional flow analysis models. Our algorithm automates the simulation process within specified
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Towards a more robust implementation of the so-called “triangle” method: A new add-on to the SimSphere SVAT model Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-15 George P. Petropoulos, Spyridon E. Detsikas, Christina Lekka
The use of simulation process models combined with Earth Observation (EO) datasets provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions (LSIs). This is achieved by combining the horizontal coverage and spectral resolution of EO data with the vertical coverage and fine temporal continuity of those models. A particular
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Geo-WC: Custom web components for earth science organizations and agencies Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-14 Sümeyye Kaynak, Baran Kaynak, Carlos Erazo Ramirez, Ibrahim Demir
The development of web technologies and their integration into various fields has allowed a new era in data-driven decision-making and public data accessibility, especially through their adoption of monitoring and quantification environmental resources provided by governmental institutions. The use of web technologies has made it possible to create applications that can be accessed and used by a wide
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A process-based framework for validating forest landscape modeling outcomes Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-13 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
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PolarBytes: Advancing polar research with a centralized open-source data sharing platform Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-10 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
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An integrated modeling approach to assess water-energy nexus in a semi-arid watershed Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-10 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
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Modelling evapotranspiration in urban green stormwater infrastructures: Importance of sensitivity analysis and calibration strategies with a hydrological model Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-08 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
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Land-N2N: An effective and efficient model for simulating the demand-driven changes in multifunctional lands Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-07 Yifan Gao, Changqing Song, Zhifeng Liu, Sijing Ye, Peichao Gao
Land is multifunctional. Among all land change models, the only model capable of modeling multifunctional land changes is the CLUMondo model. However, the CLUMondo model is ineffective and inefficient. In the study, we addressed the problems by improving the CLUMondo model through four strategies, resulting in the improved version named “Land-N2N”. To evaluate the Land-N2N model, we designed six comparative
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OFPO & KGFPO: Ontology and knowledge graph for flood process observation Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-03 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
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The Concawe NO2 source apportionment viewer: A web-application to mitigate NO2 pollution from traffic and other sources Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-03 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
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Hydrologic information systems: An introductory overview Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-02 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
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Cascading effect modelling of integrating geographic factors in interdependent systems Environ. Model. Softw. (IF 4.8) Pub Date : 2025-01-01 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
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Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-29 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
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Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-29 Sreeni Chadalavada, Oliver Faust, Massimo Salvi, Silvia Seoni, Nawin Raj, U. Raghavendra, Anjan Gudigar, Prabal Datta Barua, Filippo Molinari, Rajendra Acharya
Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted
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A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-28 Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan
The commonly used random sampling method in machine learning-based flood susceptibility studies has two major issues: a default invalid assumption of spatial homogeneity and an inadequate number of non-flood samples. To address these issues, this study proposed a novel sample-enhancement framework to improve the quality of training samples on both flood and non-flood sides. Three one-way enhancements
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AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-25 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
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Predicting massive floating macroalgal blooms in a regional sea Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-24 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
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Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-22 Prince Agyemang, Ebenezer M. Kwofie, Jamie I. Baum, Dongyi Wang, Emmanuel A. Kwofie
To generate evidence to address food system challenges, we developed an adaptable framework for multimodel assessment of the convergence effect of health and environmental drivers in food systems. We achieved this goal by developing a modeling framework that facilitates testing and applying four deep-learning algorithms using a case study of the United States's food system. Among the models tested
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SIMRA: An interactive web-based tool for single integrated microbial risk assessment and management within the national water security framework (NWSF) Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-21 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
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Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-20 Mohamed M. Fathi, Zihan Liu, Anjali M. Fernandes, Michael T. Hren, Dennis O. Terry, C. Nataraj, Virginia Smith
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
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Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-20 Ehsan Foroumandi, Hamid Moradkhani, Witold F. Krajewski, Fred L. Ogden
The National Weather Service (NWS) operates the National Water Model (NWM) to provide continental-scale streamflow forecasting across the United States. Despite the broad scope of NWM, it faces limitations in delivering operational-level predictions. To overcome these limitations, the NWS embarked on development of the Next Generation Water Resources Modeling Framework (NextGen). However, a key shortcoming
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Analysis and comparison of the flood simulations with the routing model CaMa-Flood at different spatial resolutions in the CONUS Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-19 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
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Modelling wildfire spread and spotfire merger using conformal mapping and AAA-least squares methods Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-18 Samuel J. Harris, N.R. McDonald
A two-dimensional model of wildfire spread and merger is presented. Three features affect the wildfire propagation: (i) a constant basic rate of spread term accounting for radiative and convective heat transfer, (ii) the unidirectional, constant ambient wind, and (iii) a fire-induced pyrogenic wind. Two numerical methods are proposed to solve for the pyrogenic potential. The first utilises the conformal
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Solving the Master Equation on river networks: A computer algebra approach Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-17 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
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A coupled multiscale description of seasonal Physical–BioGeoChemical dynamics in Southern Ocean Marginal Ice Zone Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-17 Raghav Pathak, Seyed Morteza Seyedpour, Bernd Kutschan, Silke Thoms, Tim Ricken
Sea ice in the polar oceans plays a significant role in regulating global climate and biological ecosystems. During the winter months, seawater freezes to form porous ice, which also serves as a habitat for sea ice algae to survive in harsh winter conditions. However, accurate description of mechanisms and interactions associated with formation of ice, and its interaction with photosynthesis and carbon
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Fire dynamic vision: Image segmentation and tracking for multi-scale fire and plume behavior Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-12 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
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An open framework for analysing future flood risk in urban areas Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-11 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
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An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-10 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
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A novel operational water quality mobile prediction system with LSTM-Seq2Seq model Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-09 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
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Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-06 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
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EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-06 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
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AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-06 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
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VERE Py-framework: Dual environment for physically-informed machine learning in seismic landslide hazard mapping driven by InSAR Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-06 Gerardo Grelle, Luigi Guerriero, Domenico Calcaterra, Diego Di Martire, Chiara Di Muro, Enza Vitale, Giuseppe Sappa
The VERE framework was designed and developed in Python to generate hazard confidence maps for seismic-induced landslides, leveraging advanced data analysis and machine learning capabilities. A Virtual Environment (VE) and a Real Environment (RE) containing, respectively, datasets and map sets, are the core of the framework. The Virtual Environment (VE) comprises datasets including morphometric, geotechnical
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A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors Environ. Model. Softw. (IF 4.8) Pub Date : 2024-12-04 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
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Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics Environ. Model. Softw. (IF 4.8) Pub Date : 2024-11-30 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
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QUAL2K water quality model: A comprehensive review of its applications, and limitations Environ. Model. Softw. (IF 4.8) Pub Date : 2024-11-30 Siti Salwa Mohamad Noor, Noor Aida Saad, Muhammad Fitri Mohd Akhir, Muhamad Syafiq Abd Rahim
Achieving Sustainable Development Goals (SDG 6), focused on ensuring the availability and sustainable water management, is a critical global priority. Attaining this target requires sustainable water management, balancing economic, social, and environmental needs to ensure long term water availability and quality. Water quality models help analyse, anticipate, and manage factors affecting water bodies
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Community-enabled life-cycle assessment Stormwater Infrastructure Costs (CLASIC) tool Environ. Model. Softw. (IF 4.8) Pub Date : 2024-11-28 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
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Cascade method for water level measurement based on computer vision Environ. Model. Softw. (IF 4.8) Pub Date : 2024-11-28 Di Zhang, Jingyan Qiu
Computer vision-based methods of water level measurement that utilize cameras to capture and process images of water bodies and their surroundings are gaining attention due to their advantages over non-visual sensors. This study aims to improve the generalization ability of the water level measurement algorithm based on computer vision to promote the application of the method in a broader range of