Environmental Modelling: Understanding, Advantages, Disadvantages, and Applications

Apeksha Rathnayaka -

Introduction to Environmental Modelling

Environmental modeling is the process of creating and using computer programs and mathematical equations to represent and simulate the behavior of natural systems. These models are used to examine, predict, and understand the processes and phenomena that occur within a particular environment, and can be applied at various scales, from local to global.

What is a Model?

A model is a program that has been developed to represent the way a system works in the real world. It incorporates equations, relationships, and mathematical formulas based on historical data, and is used to interpret the processes that are likely to occur in the future. It is important that a model should not be a "black box," meaning that it should be transparent and understandable to those using it.

Advantages of Modelling

Modelling has several advantages, including:

  • The ability to examine a system without having to physically create it in a real-world situation
  • The ability to predict what might occur to a system in the future
  • The ability to train people to use a system without putting them at risk through on-ground observations and hands-on experiments
  • The ability to examine a system in great detail regarding a particular event

Disadvantages of Modelling

There are also some disadvantages to consider when using models, including:

  • The results of the model depend on the quality of the model (i.e., the ability of the model developer) and the amount of data used to create it
  • Models and simulations can never completely recreate real-life situations for investigations
  • Every possible situation in the environment may not have been included in the model
  • Trained staff with special skills and equipment are needed to use the model effectively, which can be costly

The Potential of Modelling

Models have the potential to:

  • Quantify expected results (e.g., using past rainfall patterns to predict future cultivation in a dry zone)
  • Compare the effects of two alternative theories (e.g., determining the cause of mass fish and turtle deaths)
  • Extrapolate results to other situations (e.g., predicting the spread of a variant of COVID-19 in one country based on its spread in another)
  • Predict future events (forecasting) (e.g., estimating the global human population in 2030)
  • Describe the effects of complex factors, such as random variations in inputs (e.g., assessing the uncertainty of carbon dioxide emissions on climate change predictions)
  • Translate science into a form that can be easily understood and used by non-experts (e.g., using complex meteorological science in weather forecasting)

What is the Scale of a Model?

The scale of a model refers to the size or scope of the system being studied. Environmental models can be applied at three main scales:

  • Local (e.g., studying the impact of a new development on a specific watershed)
  • Regional (e.g., examining the effects of a drought on a particular region)
  • Global (e.g., predicting the impact of climate change on the planet)

What defines the scale of a model

The scale of models in environmental sciences depends on

- Regulatory needs- Scope of potential impacts

- Complexity

- Data availability

What is the structure of a model?

The model structure which has defined boundaries, includes processes, time scale and spatial scale. The system of interest which is within the boundaries indicates

- How the model can be applied

- To which situations the model can be applied

- To which systems of interest the model can be applied Models need

- Input variables which represent a changing value used to characterize what physically or logically goes into the system/model.

- Parameters which are constant values influencing system/model behaviour.

- Output variables which represent a behavioural/operational characteristic of interest of the system or model.

What are the different model types

The models that are being used in the analysis of system behaviour can be categorized as

· Physical models

· Empirical models

· Mechanistic models

· Computational models

· Conceptual models

· Mathematical models

· Numerical models

· Deterministic models

· Stochastic models

· Static models

· Dynamic models

The type of models which are geometrically and dynamically similar to the large-scale system is called as physical models.

Example - a laboratory-scale wastewater treatment plant

The empirical model is a type of model where the structure is determined by the observed relationship among experimental data. The empirical models can be used to develop relationships for forecasting and describing trends in particular scenarios. These developed relationships and trends are not necessarily mechanistically relevant.

As an example of an empirical model: investigating the relationship of inflowing nutrients in a lake to algal biomass production (eutrophication). Most early lake eutrophication models are based on statistical relationships between the mass loading of nutrients and average algal biomass.

The mechanistic model is a model that has a structure that explicitly represents an understanding of biological, chemical, and/or physical processes. Quantifying phenomena by their underlying causal mechanisms is the attempt of these models.

Example – The model for banded iron formation in continental shelf

The computational model is a mathematical model in computational science that requires extensive computational resources to study the behaviour of a complex system by computer simulation.

A computational modeller implements an algorithm in a computer program, which is a set of instructions written in a programming language (or computer language) that particular computers know how to interpret. Computational models seek to gain an understanding of science through the use of mathematical models on HP computers. Weather research and forecast models can be shown as an example.

Mathematical models are a highly idealized approximations of the real-world system involving many simplifying assumptions based on knowledge of the system, experience and professional judgment.

Numerical models are a type of mathematical models.  Numerical models use some sort of numerical time-stepping procedure in obtaining the models behaviour over time.

A mathematical model which contains no random components; consequently, each component and input is determined exactly can be called as deterministic models. If something is deterministic, you have all of the data necessary to predict (determine) the outcome with 100% certainty. So the model assess what if scenarios.

In stochastic models, the model is based on the theory of probability or the fact that randomness plays a role in predicting future events. Model parameters are estimates with some variation. Therefor the output has variation. As an example, A population model for migratory birds.

The static models describe relationships that do not change with respect to time while dynamic models describe time varying relationships.

Modelling in Environmental Sciences

The model in environmental sciences is "A systematic method for analyzing real-world data and translating them into a meaningful simulation that can be used for system Analysis and future prediction. " These models can be used to inform a variety of activities including

- Research

- Toxicity screening

- Policy analysis

- National regulatory decision making

- Implementation applications

The Importance of Modelling in Environmental Sciences

· To gain a better understanding of and glean insight into environmental processes and their influence on the fate and transport of pollutants in the environment.

· To confirm short and semi permanent chemical concentrations within the numerous compartments of the ecosphere to be used in regularity, social control, and within the assessment of exposures, impacts, and risks of existing also as planned chemicals. To simulate the complex systems at real, compressed, or expanded time horizons that may be more dangerous, expensive, or elaborate to study under real conditions.

· To predict future environmental concentrations of particular pollutants under different loadings of waste and/or management alternatives of waste.

· To use in hypothesis testing relating to different processes, pollution control alternatives and other relevant aspects.

· To satisfy governing and statutory obligations relating to environmental emissions, discharges, transfers, and releases of controlled pollutants.

· To generate data for post-processing, such as statistical analysis, visualization analysis, and animation, for better understanding, communication, and dissemination of scientific information.

· To use environmental impact assessment of proposed new activities that is currently non-existent.