Friday, August 19, 2016

Software Scenario Functions: Environmental modelling

A model is an abstraction of reality. This helps by representing complex reality in the simplest way. A change in any parameter of the model can be used to visualise the impacts on the entire model. This is the purpose of modeling. The best model is always that which achieves the greatest match between model outputs and real-world observations. Modeling is a powerful tool to understanding observations and can be used to develop and test theories. Moreover, it is faster to get a result by modeling than to actually spend time, energy and resources on the field. Environmental modeling is a powerful tool to understand the interactions between the environment, ecosystems and populations of animals. This is essential for monitoring and management of sustainable means of human dependency on environmental systems.

Environmental models integrate both time and space to understand the nature and functioning of the ecosystem under study. Environment models are multi-component in nature requiring the understanding of interactions between the biotic and abiotic systems. The complexity increases with the increasing number of components and an understanding of these systems requires breaking them into manageable components, combining them and explicitly describing the interactions occurring.

Environmental models cannot be built in the laboratory to adequately represent them. Environmental problems are multivariate, non-linear and complex. Modeling provides an integrated framework in which the individual disciplines can work on different aspects of the research problem and provide a module for integrating within the modelling framework.

GIS and environmental modeling have been used for decision making, planning and environmental management. This combination has been used along with environmental models for applications like:

  • monitoring of deforestation
  • agro-ecological zonation
  • ozone layer depletion
  • flood early warning systems
  • climate and weather prediction
  • ocean monitoring and mapping
  • soil mapping
  • wetland degradation
  • natural disaster & hazard assessment and mapping
  • land cover for input to global climate models

GIS models may be varied in space, in time or in state variables. GIS and remote sensing provide tools to extrapolate models in space as well as upscale models to smaller scales.

A few examples of environmental models used in GIS are listed below with brief descriptions:

  1. RUSLE - The Revised Universal Soil Loss Equation was successfully used with GIS. The process uses raster processing capabilities of the Map Analysis and Processing System (MAPS) to overlay data themes containing spatially distributed values for different RUSLE factors. This technique produces a map of relative levels of soil erosion potential caused due to rainfall, soil type, terrain, vegetation and erosion control practice. The terrain factor from DEM helps calculate soil loss potential for large areas.Thus the RUSLE and GIS interface can be used for soil degradation studies over a large scale.
  2. BIOCLIM - The BIOCLIM system determines the distribution of plants and animals based on climatic surfaces. Bioclimatic variables are used in species distribution modeling and related ecological modeling techniques. Worldclim is a set of global climate layers (gridded climate data) with a spatial resolution of about 1 km2. This data can be used for mapping and spatial modeling. GIS can be used in conjunction with BIOCLIM to make grid maps of distribution of biological diversity or it can also be used to find areas that have high, low or complementary levels of diversity. GIS can also be used to map and query climate data. BIOCLIM and GIS can also be used to predict species distribution. 
  3. CART -The Classification And Regression Tree (CART) model is a binary partioning methods yielding a class of models called tree -based models. The method is applied to several environemtal and ecological studies due to its capability of handling both continuous and discrete variables, its ability to model interactions among predictors and its hierarchical structure. When used in combination with GIS, the CART model output was converted into suitability maps that show the abrupt transitions between areas of high and low suitability.
  4. Monte Carlo simulation - The Monte Carlo method involves generation of random number of parameters to explore the behaviour of a complex process. The numbers are generated using a probability distribution function that describes the occurrence probability of an event. The power of this method lies in the number of simulated samples. The Monte Carlo simulation provides an answer to what may happen and the probability associated with each scenario. The Monte Carlo simulation technique is widely used in spatial analysis. It finds applications in spatial data disaggregation and statistical testing.