**GIS Models to address the effect of Climate Change**

The role of geospatial technology and Geographic Information (GI) sciences has been immense in the present era. Be it the reasons that cause climate change or its effect now and in the future or the early warning preparedness models, one cannot ignore the role of geospatial technology and GI sciences. This research essay presents four different geospatial models that can be used in various applications including the preparedness against the climate change issues. Technical aspect and main focus on application aspect has been presented assuming the geospatial data has already been acquired.

**1. ****Process Models**

A **process model** integrates existing knowledge about the environmental processes in the real world into a set of relationships and equations for quantifying the processes (Beck et al. 1993). Some of these models may use mathematical equations that are derived from empirical data, whereas others may use equations derived from laws in physics.

One such process model is the **Soil Erosion Model.** A well-known model of soil erosion is the Revised Universal Soil Loss Equation (RUSLE), the updated version of the Universal Soil Loss Equation (USLE) (Wischmeier and Smith 1965, 1978; Renard et al. 1997). RUSLE predicts the average soil loss carried by runoff from specific field slopes in specified cropping and management systems and from rangeland.

Other process model includes the **AGNPS (Agricultural Nonpoint Source) model** and **SWAT (Soil and Water Assessment Tool) model**, etc.

**2. ****Regression Models**

A regression model relates a dependent variable to a number of independent (explanatory) variables in an equation, which can then be used for prediction or estimation (Rogerson 2001). There are two types of regression model: linear regression and logistic regression.

Linear regression makes various assumptions of the predicted value and the actual value (and their differences: errors or residues). While no such assumption is made in logistic regression. Commercial products like ArcGIS and IDRISI have commands to build raster-based linear or logistic models. Open source like GRASS has a command to build linear regression models.

For example: Chang and Li (2000) use linear regression to **model snow accumulation** using snow water equivalent as dependent variable on location and topographic variables as independent variables.

Similarly, Pereira and Itami (1991) developed the **habitat suitability model** for red squirrel using logistic regression model.

**3. ****Index Models**

Index model calculate the index value for each unit area and produces a ranked map based on the index values. There are various methods like weighted linear combination methods and various other methods that can be used to calculate the index value.

For example: Chuvieco and Congalton (1989) construct a **forest fire hazard index model** for a study area in the Mediterranean coast of Spain by using the following five factors:

- Vegetation species, classified according to fuel class, stand conditions, and site.
- Elevation.
- Slope.
- Aspect.
- Proximity to roads and trails, campsites, or housing.

**4. ****Binary Models**

Binary model uses binary logical expression to select the spatial feature from a composite vector layers or multiple rasters. The output of the model will be either 1(if condition is true) or 0(if condition is false).

A simple but powerful application of binary model is the **change detection**.** **Due to the effect of climate change, the forested area that been deserted can be found out.

Another application of binary model is the **site selection**. Suppose we are required to find the potential industrial site that meets the following criteria:

- At least 50km
^{2}in size. - Commercial zone.
- Not subjected to flood.
- Less that 15 percent slope.

Various layers overlay operation and then query can provide us the desired result.

To nutshell, the geospatial and GI sciences models have wide range of application. Mentioned are only a few. The effect of climate change on forest, agricultural land, wildlife inhabitant, etc has been discussed in above topic with different modeling technique to represent them.

Chapter Revision from a GIS Book.

## Neeru Menthol

Great blog, good work, keep it up.