scholarly journals Environmental modeling framework invasiveness: Analysis and implications

2011 ◽  
Vol 26 (10) ◽  
pp. 1240-1250 ◽  
Author(s):  
W. Lloyd ◽  
O. David ◽  
J.C. Ascough ◽  
K.W. Rojas ◽  
J.R. Carlson ◽  
...  
Keyword(s):  
Environmental Modeling ◽  
Modeling Framework

The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE

2021 ◽  
Author(s):  
Peter H. Verburg ◽  
Žiga Malek ◽  
Sean P. Goodwin ◽  
Cecilia Zagaria
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Use Changes ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Regional Modelling ◽  
Model Application ◽  
Climatic Regions ◽  
Single Region

The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model application, which then allocates land use change across different regions. This facilitates integration with the Integrated Economic-Environmental Modeling (IEEM) Platform for subnational assessments and increases the efficiency of the IEEM and Ecosystem Services Modeling (IEEMESM) workflow. Multi-regional modelling is particularly useful in larger and diverse countries, where we can expect different spatial distributions in land use changes in different regions: regions of different levels of achieved socio-economic development, regions with different topographies (flat vs. mountainous), or different climatic regions (dry vs humid) within a same country. Accounting for such regional differences also facilitates developing ecosystem services models that consider region specific biophysical characteristics. This manual, and the data that is provided with it, demonstrates multi-regional land use change modeling using the country of Colombia as an example. The user will learn how to prepare the data for the model application, and how the multi-regional run differs from a single-region simulation.

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

scholarly journals Development of an ESMF Based Flexible Coupling Application of ADCIRC and WAVEWATCH III for High Fidelity Coastal Inundation Studies

2020 ◽  
Vol 8 (5) ◽  
pp. 308 ◽  
Author(s):  
Saeed Moghimi ◽  
Andre Van der Westhuysen ◽  
Ali Abdolali ◽  
Edward Myers ◽  
Sergey Vinogradov ◽  
...  
Keyword(s):  
System Modeling ◽  
Atlantic Coast ◽  
Storm Track ◽  
Circulation Model ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Earth System Modeling ◽  
Coastal Inundation ◽  
Wavewatch Iii ◽  
Fully Implicit

To enable flexible model coupling in coastal inundation studies, a coupling framework based on the Earth System Modeling Framework (ESMF) and the National Unified Operational Prediction Capability (NUOPC) technologies under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed. The framework is essentially a software wrapper around atmospheric, wave and storm surge models that enables its components communicate seamlessly, and efficiently to run in massively parallel environments. For the first time, we are introducing the flexible coupled application of the ADvanced CIRCulation model (ADCIRC) and unstructured fully implicit WAVEWATCH III including NUOPC compliant caps to read Hurricane Weather Research and Forecasting Model (HWRF) generated forcing fields. We validated the coupled application for a laboratory test and a full scale inundation case of the Hurricane Ike, 2008, on a high resolution mesh covering the whole US Atlantic coast. We showed that how nonlinear interaction between surface waves and total water level results in significant enhancements and progression of the inundation and wave action into land in and around the hurricane landfall region. We also presented that how the maximum wave setup and maximum surge regions may happen at the various times and locations depending on the storm track and geographical properties of the landfall area.

scholarly journals TOWARDS IMPLEMENTATION OF COMPONENT-BASED MODELING IN THE PLATFORM FOR CREATING KNOWLEDGE-BASED SYSTEMS

2022 ◽  
pp. 21-30
Author(s):  
Александр Борисович Столбов ◽  
Анна Ананьевна Лемперт ◽  
Александр Иннокентьевич Павлов
Keyword(s):  
Environmental Modeling ◽  
Complex Model ◽  
Balance Model ◽  
Knowledge Based Systems ◽  
Complex Objects ◽  
Modeling Framework ◽  
Knowledge Based ◽  
Modeling Process ◽  
Model Components

В статье исследуются проблемы автоматизации и интеллектуальной поддержки процесса математического и имитационного моделирования сложных объектов за счёт комбинации компонентно-ориентированного и онтологического подходов. В качестве основной прикладной области для применения обсуждаемых методов и средств предполагается использовать такое направление, как комплексное моделирование окружающей среды. В контексте изучаемых вопросов рассмотрены современные подходы к автоматизации компонентно-ориентированного моделирования. При интеграции компонентов-моделей в единую результирующую комплексную модель разработчику необходимо не только обеспечить формальное согласование со стандартами используемого каркаса моделирования, но и учитывать различные типы семантической и синтаксической неоднородности компонентов. В связи с этим выполнена классификация типов интеграции комплексных моделей, обсуждаются особенности реализации компонентно-ориентированного моделирования в авторской платформе создания систем, основанных на знаниях. В качестве иллюстративного примера рассматривается гидролого-экологическая балансовая модель. The article considers the problems of automation and intellectual support of the mathematical and simulation modeling process of complex objects via a combination of component-based and ontological approaches. As the main application area for the discussed methods and tools, it is proposed to use the integrated environmental modeling domain. In this context, modern approaches to the automation of component-based modeling are considered. To couple model components into a final complex model, the developer needs not only to ensure formal agreement with the standards of the modeling framework but also to take into account various types of semantic and syntactic heterogeneity of components. In this regard, the classification of the integration types for complex modeling is carried out, the related implementation features in the author's platform for creating knowledge-based systems are discussed. The hydrological-ecological balance model is considered an illustrative example.

The GEOframe system: a modular, expandible, open-source system for doing hydrology by computer according to the open science paradigms.

2021 ◽  
Author(s):  
Riccardo Rigon ◽  
Marialaura Bancheri ◽  
Giuseppe Formetta ◽  
Francesco Serafin ◽  
Michele Bottazzi ◽  
...  
Keyword(s):  
Open Source ◽  
Hydrological Modeling ◽  
Hydrological Cycle ◽  
A Priori ◽  
Environmental Modeling ◽  
Radiation Budget ◽  
Model Parameters ◽  
Network Graph ◽  
Modeling Framework ◽  
Modeling Tools

<p>The scope of this work is to present new insights of the <span>GEOframe system.</span> GEOframe is an open-source, semi-distributed, component-based hydrological modeling system. It is developed in Java and in Python and based on the environmental modeling framework Object Modeling System V3 (OMS3). Each part of the hydrological cycle is implemented in a self-contained building block, commonly called component. Components can be joined together to obtain multiple modeling solutions that can accomplish from simple to very complicated tasks. More than 50 components are available for the estimation of all the variables of the hydrological cycle. Starting from the geomorphic and DEM analyses, <span>GEOframe allows the spatial interpolation of the </span>meteorological forcing data, the simulation of the radiation budget, the estimation of the ET and of the snow processes. Runoff production is performed by using the Embedded Reservoir Model (ERM) or a combination of its reservoirs. Model parameters can be calibrated using two algorithms and several objective functions. The graph-based structure, called NET3, is employed for the management of process simulations. NET3 is designed using a river network/graph structure analogy, where each HRU is a node of the graph, and the channel links are the connections between the nodes. In any NET3 node, a different modeling solution can be implemented and nodes (HRUs or channels) can be connected or disconnected at run time through scripting.<span>  </span>Thanks to its solid informatics infrastructure and physical base, GEOframe proved a great flexibility and a great robustness in several applications, from small to big scale catchments. GEOframe is open source, is chain of development is based on open source products, and its codes are engineered to be inspectionable. This because it helps the reproducibility and replicability of research. Developers and users can easily collaborate, share documentation, and archive examples and data within the GEOframe community. We believe that these are a priori condition to verify the reliability and the robustness of models. GEOframe modular structure allows for the fair comparison of model structure units and algorithms implementations because just the component performing that specific task has to be changed. In this contribution we list the components available and discuss some applications at different scales whit different modeling tools which return what we think realistic results. We show that there exist no perfect model of a process but that the modelling art and science can<span>  </span>be made more evolutionary even when they are revolutionary.<span> </span></p>

An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

2014 ◽  
Vol 55 ◽  
pp. 77-91 ◽  
Author(s):  
Gene Whelan ◽  
Keewook Kim ◽  
Mitch A. Pelton ◽  
Jeffrey A. Soller ◽  
Karl J. Castleton ◽  
...  
Keyword(s):  
Environmental Modeling ◽  
Risk Assessments ◽  
Modeling Framework ◽  
Microbial Risk

scholarly journals The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

2016 ◽  
Vol 97 (7) ◽  
pp. 1229-1247 ◽  
Author(s):  
Gerhard Theurich ◽  
C. DeLuca ◽  
T. Campbell ◽  
F. Liu ◽  
K. Saint ◽  
...  
Keyword(s):  
General Circulation ◽  
The United States ◽  
Ocean Model ◽  
Environmental Modeling ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Modeling Framework ◽  
The Earth ◽  
Metadata Standards

Abstract The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open-source terms or to credentialed users. The ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the United States. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. This shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multiagency development of coupled modeling systems; controlled experimentation and testing; and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NAVGEM), the Hybrid Coordinate Ocean Model (HYCOM), and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model.

scholarly journals Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard

2020 ◽  
Vol 12 (15) ◽  
pp. 2436
Author(s):  
Noa Ohana-Levi ◽  
Kyle Knipper ◽  
William P. Kustas ◽  
Martha C. Anderson ◽  
Yishai Netzer ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Soil Type ◽  
Irrigation Management ◽  
Spatial Association ◽  
Environmental Modeling ◽  
Topographic Wetness Index ◽  
Modeling Framework ◽  
Error Matrix ◽  
Management Zones

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.

A software engineering perspective on environmental modeling framework design: The Object Modeling System

2013 ◽  
Vol 39 ◽  
pp. 201-213 ◽  
Author(s):  
O. David ◽  
J.C. Ascough ◽  
W. Lloyd ◽  
T.R. Green ◽  
K.W. Rojas ◽  
...  
Keyword(s):  
Software Engineering ◽  
Environmental Modeling ◽  
Object Modeling ◽  
Modeling Framework ◽  
Framework Design

Design of a component-based integrated environmental modeling framework

2014 ◽  
Vol 55 ◽  
pp. 1-24 ◽  
Author(s):  
Gene Whelan ◽  
Keewook Kim ◽  
Mitch A. Pelton ◽  
Karl J. Castleton ◽  
Gerard F. Laniak ◽  
...  
Keyword(s):  
Environmental Modeling ◽  
Modeling Framework
Sign in / Sign up

Export Citation Format

Share Document