Environmental Systems Applications

Ecological & Biological

Ecosystem and Biological Systems Modeling

Ecological & Biological

Predicting behavior of ecological and biological systems is inherently complex and uncertain since they involve systems made up of many component parts that are interrelated, the components interact in complex ways with numerous feedback mechanisms, and in many cases, the systems are poorly characterized. In addition, such systems are often controlled by stochastic variables (i.e., precipitation, temperature) and involve uncertain processes, parameters, and events.

The challenge when evaluating such systems is to find an approach that can incorporate all the knowledge available to planners and scientists into a quantitative framework that can be used to predict the outcome of alternative management approaches, policies and plans. To be effective, the framework needs to be both flexible (so that it can accurately represent the systems) and transparent (so the models can be easily explained to decision-makers and stakeholders).

Salmon model

By combining the power and flexibility of a general-purpose and highly-graphical probabilistic simulation framework with specialized modules to support water quality modeling, GoldSim allows you to create realistic models of complex, real-world multi-media environmental systems for ecological and biological modeling. Using Monte Carlo simulation, you can explicitly represent the uncertainty inherent is these systems in order to carry out risk analyses, evaluate potential environmental impacts, and make better resource management decisions. GoldSim has the power and flexibility to build ‘total system” models of many kinds of ecological or biological systems, ranging from individual organisms to wetlands or entire ecosystems.

Some biological and ecological applications with GoldSim utilize the GoldSim CT (Contaminant Transport) Module, which includes specialized and powerful elements for the simulation of mass transport (e.g., of nutrients or contaminants) within a range of environmental media. Others require only those elements available in the basic GoldSim framework.



Technical Papers

  • Managing Complex Water Resource Systems for Ecological Integrity: Evaluating Tradeoffs and Uncertainty

    Master's Thesis, University of Mexico – May 2014

    Ryan Richard Morrison

    This is a masters thesis describing the methodology used to develop a probabilistic, dynamic simulation model that evaluates the impacts of environmental flow alternatives. These alternatives implement various water use schemes in the Rio Chama basin in New Mexico. This work evaluates the influence of various flows on cottonwood recruitment, reservoir storage, hydropower production, and whitewater boating. This project used multiple tools with GoldSim's role focusing on the probabilistic and dynamic simulation of the hydroclimatic uncertainties related to management operations in the water system.


  • Marine Biogeochemical and Ecosystem Modeling

    Course Material for "Introduction to Climate Modeling", University of Bremen –

    Michael Schulz, University of Bremen

    This web page includes links to a presentation describing the use of GoldSim for climate models and also includes a number of example GoldSim Player models.

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  • Mathematic Models to Simulate the Introduction and Spread of Moniliasis of Cacao in Brazil (in Portuguese)

    Report for the Ministry of Agriculture, Livestock, and Supply – March 2010

    Ricardo Sgrillo, CEPLAC

    This report (in Portugese) describes the development of several stochastic and deterministic models to evaluate the risk of introduction and spread of the cacao frosty pod rot disease, from Peru to Brazil.


  • Stochastic Simulation of Rumen Degradable Protein Surplus in Grazing Dairy Cows

    Animal Feed Science and Technology, Volume 143, Issue 1, pgs. 280-295 – May 2008

    Pacheco, David, AgResearch Limited, Food, Metabolism and Microbiology Section, Grasslands Research Centre

    This paper describes an application of GoldSim that simulates the many factors influencing the quality of a cows' diet, including the weather, level of fertilization, and the age of plants. Stochastic variables for pasture chemical composition and dry matter intake were incorporated in the model. Stochastic simulation may be useful to explore the likelihood of responses to management scenarios designed to increase the efficiency of dietary nitrogen use in pastoral systems characterized by uncertainty and variability.

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