Environmental Modeling System Research Articles

Topsoil porosity prediction across habitats at large scales using environmental variables

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their ‘dynamic’ influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 &n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions.Classification.Environmental Sciences.

Simulation and Application of Water Environment in Highly Urbanized Areas: A Case Study in Taihu Lake Basin

In the wake of frequent and intensive human activities, highly urbanized areas consistently grapple with severe water environmental challenges. It becomes imperative to establish corresponding water environment models for simulating and forecasting regional water quality, addressing the associated environmental risks. The distributed framework water environment modeling system (DF-WEMS) incorporates fundamental principles, including the distributed concept and node concentration mass conservation. It adeptly merges point source and non-point source pollution load models with zero-dimensional, one-dimensional, and two-dimensional water quality models. This integration is specifically tailored for various Hydrological Feature Units (HFUs), encompassing lakes, reservoirs, floodplains, paddy fields, plain rivers, and hydraulic engineering structures. This holistic model enables the simulation and prediction of the water environment conditions within the watershed. In the Taihu Lake basin of China, a highly urbanized region featuring numerous rivers, lakes and gates, the DF-WEMS is meticulously constructed, calibrated, and validated based on 26 key water quality monitoring stations. The results indicate a strong alignment between the simulation of water quality indicators (WQIs) and real-world conditions, demonstrating the model’s reliability. This model proves applicable to the simulation, prediction, planning, and management of the water environment within the highly urbanized watershed.

Book Review of Modeling and Simulation of Environmental Systems:A Computation Approach

Water and air are important elements of the interactions that make up the environmental continuum, and these factors are critical to our lifestyles, economic well-being, and the health and integrity of ecosystems. With the development of advanced computational modelling, numerical techniques and fast computers, some complex environmental management problems can be solved by computational modelling. Modeling and Simulation of Environmental Systems: A Computation Approach is an English-language monograph in the field of modeling and simulation of environmental systems (published by CRC Press) , which is divided into four chapters, Water Resources, Air Pollution, Internet of Things and Environmental Systems, and Future Algorithms in Environmental Systems. Future Algorithms in Environmental Systems, presents four chapters on modeling and simulation of environmental systems and related case studies, covering computational methods used to solve air and water pollution problems, a general approach to spatio-temporal analytical modeling, the use of formulas, algorithms, and techniques to build mathematical modeling, and a pathway to the implementation of formulas for complex modeling problems. This paper briefly introduces the authors of the work, systematically sorts out the chapter structure of the book, and analyzes the application of some of the environmental models as well as the opportunities and challenges of using them. The work demonstrates how models, metrics, and ecological processes can be directly applied to environmental subdisciplines and can provide guidance and reference for environmental practitioners and research engineers.

An integrated SWJ-LSTM-ETS modeling strategy for investigating upper morphology dynamics of a stochastic laboratory delta with environmental changes

Accurately modeling delta morphological evolution remains challenging because of the complexity of interacting processes (e.g., runoff, wave and tidal flow). This study develops an integrated framework combining a shallow water jet (SWJ) based model, long short-term memory (LSTM) neural networks, and exponential smoothing (ETS) time series models to predict the cross-sectional topographic dynamics of a stochastic experimental delta's upper plain. The integrated approach enhances explanatory power compared to individual models by assimilating mechanistic and empirical techniques. Hindcasting and forecasting under vegetation and intermittent discharge impacts yield insights aligning with validated findings on fluvio-morphological responses. Notably, new interactions are revealed like discharge inducing central deposition while vegetation drives bi-lateral erosion near the inlet. Farther downstream, discharge transitions result in alternating and pronounced patterns of erosion and deposition, with abrupt changes in discharge having a greater impact than gradual ones, particularly considering the mitigating effect of vegetation on these dynamics. This demonstrates an effective strategy widely applicable to integrating physics-based and data-driven models for analogous environmental systems involving both deterministic and random elements. The key contributions are developing a versatile integrated modeling methodology through synergizing physical simulators, statistical learners, and stochastic representations. This provides enhanced capabilities for elucidating complex delta dynamics.

Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA)global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

Comparing containerization-based approaches for reproducible computational modeling of environmental systems

Creating online data repositories that follow findable, accessible, interoperable, and reusable (FAIR) principles has been a significant focus in the research community to address the reproducibility crisis facing many computational fields, including environmental modeling. However, less work has focused on another reproducibility challenge: capturing modeling software and computational environments needed to reproduce complex modeling workflows. Containerization technology offers an opportunity to address this need, and there are a growing number of strategies being put forth that leverage containerization to improve the reproducibility of environmental modeling. This research compares ten such approaches using a hydrologic model application as a case study. For each approach, we use both quantitative and qualitative metrics for comparing the different strategies. Based on the results, we discuss challenges and opportunities for containerization in environmental modelling and recommend best practices across both research and educational use cases for when and how to apply the different containerization-based strategies.

Effects of Cold Plasma on the Excitation of Internally Driven ULF Waves by Ring Current Ions Based On the Magnetosphere‐Ionosphere Coupled Model

AbstractInternally driven Pc4‐5 waves are excited in the plasmaspheric drainage plume (PDP) and near the plasmapause. The excitation of ultralow frequency (ULF) waves was investigated by using the magnetosphere‐ionosphere coupled model between Geospace Environment Modeling System for Integrated Studies‐Ring Current (GEMSIS‐RC) and GEMSIS‐POTential solver (GEMSIS‐POT). In order to investigate the effects of cold plasma on the wave excitation, the simulation code to describe the dynamics of cold plasma was included in the model. The model can reproduce the shrink of the plasmasphere on the nightside and the formation of the PDP on the dayside. First, fundamental Pc5 waves are excited through the drift resonance on the dayside. The waves are caused by positive energy gradient of ion phase space density (PSD) at 50–130 keV. Second harmonic waves (drift‐bounce resonance) are generated outside the plasmapause. These two types of ULF waves are also seen in the case of constant density. Unlike the case of constant density, localized eastward propagating Pc4 waves (drift resonance) are seen on the dawnside associated with the azimuthal density gradient. The azimuthal wave number of Pc4 waves is about 70 and anti‐earthward gradient of PSD about 10 keV contributes to wave growth. We also detect fundamental Pc4‐5 waves near the plasmapause on the nightside in the drift resonance with 100–150 keV ions. Simulation results suggest that the plasmapause has an effect to sustain the excitation of Pc4‐5 ULF wave through the drift resonance.

Evaluation and Modelling of Reference Evapotranspiration Using Different Machine Learning Techniques for a Brazilian Tropical Savanna

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.

Implications of Extended Environmental Multimedia Modeling System (EEMMS) on Water Allocation Management: Tritium Numerical Case Study

Tritium waste deposition in air-unsaturated groundwater zones poses great challenges to optimal water allocation. This paper reviews the research progress of air-unsaturated-groundwater interaction. Traditional interaction studies typically model the fate and migration of pollutants in different regions. This can lead to biased results and simulation errors. The development of air-unsaturated-ground integrated modeling will be a breakthrough and a hotspot in tritium management. In this paper, the fate and migration of tritium leakage is further studied using the existing extended Environment Multimedia Modeling System (EEMMS). Moreover, to better understand its distribution in three zones, using tritium as a typical pollutant, it is necessary to consider its characteristics in different zones, especially its migration from unsaturated zones to groundwater and air zones. The result shows that the tritiated water vapor transfer in unsaturated groundwater areas decreases and part of the tritiated water vapor transfers to atmospheric areas as tritiated gas vapor. Compared with the analytical test accuracy (5 pCi mL−1), the accuracy of the tritium modeling using the finite element method can reach the minimum concentration limit of 0 pCi mL−1. The study of its distribution in air-unsaturated-groundwater zones can provide reference for other similar tritium management or NAPLs distribution across multimedia area.

Automation of a National Noise Model

The United Kingdom Department for Food and Rural Affairs (Defra) commissioned the design and build of an environmental noise modelling system (NMS). The NMS has been developed to support Defra in developing its environmental noise evidence base by preparing national road and railway noise models. This paper discusses the automation aspects of the NMS with respect to the various elements required for a noise model. This includes the development of sophisticated data processing engines and the techniques utilised to automate the delivery of a 3-dimensional noise propagation environment, and road and railway noise emissions. It highlights several innovative methods which are now possible due to the data landscape in England and how through open data standards the NMS has the potential to improve the fidelity of national noise models.

Evaluation of Aerosol Optical Depth Forecasts from NOAA’s Global Aerosol Forecast Model (GEFS-Aerosols)

Abstract The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model. Significance Statement The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

Exploring the evolution of student interest: Investigation of the scientific aspects of learning physics towards renewable energy

Science teaching in today’s schools has important scientific implications for attracting students and ensuring continuous, sustainable, and successful schooling, across the different components of the education system. These implications are mainly linked to the development of scientific literacy among all students. The present work highlights the interest of Moroccan secondary school students in physics as an important discipline in the science field. The main objective of this research is to explore the changing interest of these students by identifying environmental education and the mathematization of physics as an important scientific context in learning the discipline. To achieve this goal, we conducted an empirical study among students in the Fez-Meknes region of Morocco. The results showed that exploring the mathematization of physics can develop students’ interest in the discipline. In addition, the engagement of physics with environmental education will influence favorably the students’ understanding and therefore motivation for scientific careers in mathematical modeling of environmental systems.

Identifying Heat-Susceptible Stallions Using a Novel Environmental and Statistical Modelling System

Identifying Heat-Susceptible Stallions Using a Novel Environmental and Statistical Modelling System

Assessing Coastal Flood Risk in a Changing Climate for Dublin, Ireland

With increasing urban expansion and population growth, coastal urban areas will be increasingly affected by climate change impacts such as extreme storm events, sea level rise and coastal flooding. To address coastal inundation risk for impact studies, integrated approaches accounting for flood hazard modelling, exposure and vulnerability of human and environmental systems are crucial. In this study, we model the impacts of sea level rise on coastal inundation depth for County Dublin, the most extensively urbanized area in Ireland, for the current period and for 2100 under two Representative Concentration Pathways RCP 4.5 and 8.5. A risk-centred approach has been considered by linking the information on coastal flood-prone areas to the exposure of the urban environment, in terms of potential future land cover changes, and to the socioeconomic vulnerability of the population. The results suggest significant challenges for Dublin city and the surrounding coastal areas, with an increase of around 26% and 67% in the number of administrative units considered at very high risk by the end of the century under a RCP 4.5 and 8.5, respectively. This study aims to contribute to existing coastal inundation research undertaken for Ireland by (i) providing a first-level screening of flooding hazards in the study area, (ii) demonstrating how land cover changes and socioeconomic vulnerability can contribute to the level of experienced risk and (iii) informing local authorities and at-risk communities so as to support them in the development of plans for adaptation and resilience.

Experimental Determination Of Parameters Of Flow Structure Of Filtration Currents Through Layers Of Adsorbent Granules In Ecological Mass Exchange Equipment

A new method of processing experimental data and identifying parameters of flow structure by gas phase under conditions of filtration currents through layers of adsorbent granules in ecological mass exchange equipment is presented. Experimental data of gas phase velocity profiles and procedure of conducting experimental studies are presented. Basic design dependencies are given for determining parameters of flow structure by gas phase, based on treatment of experimental dependencies of non-uniformity of fields of flows of filtration currents through layers of adsorbents. An example is given of constructing C- and F-response curves that allow you to recognize models of flow structures by a continuous gas phase and increase the accuracy of mathematical modeling of mass exchange environmental systems.

International Network for Public Health and Environmental Tracking (INPHET) and its role to Environmental Epidemiology

Background and aim: INPHET was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Our aim is to show how this has supported environmental epidemiology practice. Methods: A "Community of Practice”, INPHET main goals focus on sharing experiences and expertise; addressing common challenges to implementation and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. Results: The network, initiated in ISEE meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this Symposium (Georgia, Ghana, Zambia), and highlight the EPHS/Tracking (EPHT) model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers. Conclusions: Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. INPHET aims to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. Its objectives and work support environmental epidemiology in its role as the pillar science that provides evidence for decision making and intervention aimed at reducing and preventing environmental related diseases, and contributes to an overall vision for public health.

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Unmanned technology is an important development project of today’s cutting-edge science and technology, which has a significant impact on social and economic development, national defense construction, and scientific and technological development. The rapid development of industrial information technology has driven the unmanned intelligent vehicle system to innovate and gradually enter the public’s view, and at the same time, the driving safety of unmanned intelligent vehicles is also widely concerned. Target information perception system is the foundation of unmanned system and the fundamental guarantee of safety and intelligence of unmanned vehicles. There are three key problems of target recognition in unmanned driving, namely, target classification, localization, and attitude determination. In the implementation of a networked virtual environment system, a flexible and complete perception model is needed as the guiding model of the system. Since 3D point cloud data can provide more spatial information than 2D RGB image data, it is more beneficial to determine the target category, position, and pose in 3D. In this paper, based on the existing research of unmanned intelligent vehicle perception system, we combine the application of fusion of 3D target information perception model and develop a nighttime unmanned system based on multiview fusion of 3D target information perception model. This system can simultaneously perform the detection of multiple categories of objects and predict the center point, length, width, height, and orientation of the objects, so that the unmanned car can sense the location of the surrounding objects when driving in the actual scene.

Near-real-time MODIS-derived vegetation index data products and online services for CONUS based on NASA LANCE

This paper describes a set of Near-Real-Time (NRT) Vegetation Index (VI) data products for the Conterminous United States (CONUS) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from Land, Atmosphere Near-real-time Capability for EOS (LANCE), an openly accessible NASA NRT Earth observation data repository. The data set offers a variety of commonly used VIs, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Mean-referenced Vegetation Condition Index (MVCI), Ratio to Median Vegetation Condition Index (RMVCI), and Ratio to previous-year Vegetation Condition Index (RVCI). LANCE enables the NRT monitoring of U.S. cropland vegetation conditions within 24 hours of observation. With more than 20 years of observations, this continuous data set enables geospatial time series analysis and change detection in many research fields such as agricultural monitoring, natural resource conservation, environmental modeling, and Earth system science. The complete set of VI data products described in the paper is openly distributed via Web Map Service (WMS) and Web Coverage Service (WCS) as well as the VegScape web application (https://nassgeodata.gmu.edu/VegScape/).

Environmental Accounting System Model Based on Artificial Intelligence Blockchain and Embedded Sensors.

With the rapid development of communication technology and automation technology, sensors are becoming more and more intelligent. This study proposes an environmental accounting system model based on artificial intelligence blockchain and embedded sensors. A high-precision sensor system based on embedded technology is first built, which not only avoids the shortcomings of analog data transmission, but also has high performance and price advantages. At the same time, it can be easily combined into a simple sensor network, and array measurement can be realized through the development of blockchain technology. However, the current blockchain system has the disadvantage of storage limitation. The problem is particularly serious when a large amount of data needs to be stored in the blockchain, especially when the blockchain is combined with big data. As a comprehensive field of accounting, ecology, and environmental science, environmental accounting has made great contributions to sustainable development in the field of accounting. The theoretical research in the field of environmental accounting in China started late, and a unified environmental accounting system has not actually been established. Under the conditions of immature theoretical and policy basis, companies have almost no actual surveys on environmental accounting. The characteristics of blockchain technology, such as decentralization, transparency, and changes in information unavailability, can fully solve the problem that the current accounting information system cannot consolidate transaction information and the accounting process. The reliability assurance mechanism of the accounting information system based on blockchain technology can greatly ensure the reliability of the accounting information system, effectively suppress accounting fraud, and improve the transparency of information.

Editorial to theme section on open environmental software systems modeling

This theme section aims to disseminate the latest research results in the area of open environmental software systems modeling. Software-intensive systems, such as cyber-physical systems and self-adaptive systems, need to be able to constantly operate in open and dynamic environments, which requires them to continuously evolve, handle internal and external uncertainties, and so on. Traditional software engineering methodologies need to be extended for tackling these new challenges, and modeling is a promising technique to do that. This theme section contains two papers that tackle these issues, that were accepted after a thorough peer-reviewing process. Moreover, it contains an “Expert’s Voice” contribution on the uncertainty interaction problem, which is a relevant issue in the systems considered in this theme section.