Rainfall Indices for MCDM models in ArcGIS: how and Why?
Preparation of 10 important rainfall indices for MCDM models and time series analysis using Excel, ArcGIS and QGIS
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What You Will Learn!
- Step by step procedure from data download, handling, selection, visualization and produce rainfall indices map
- Comprehensive understanding of rainfall indices i.e. MFI, rainfall erosivity factor, RDI, PCI, RSI, RAI, RVI, CVR and PNPI
- Complete guide to prepare rainfall indices for MCDM models
- My continuous support, taking your hand step-by-step to develop rainfall indices using real data
Description
In this course, I have shown a complete process about how to download rainfall data, process data, convert daily to monthly rainfall data, step by step guide of 10 important rainfall indices and 14 maps such as long term average annual rainfall (High resolution 0.04 X 0.04), Rainfall Intensity Index (by MFI), Rainfall erosivity factor (R), Rainfall deviation Index ( RDI), Precipitation concentration Index (PCI), Rainfall seasonality Index (RSI), Rainfall Anomaly Index (RAI), Rainfall variability index (RVI), Co-efficient of the variability of Rainfall (CVR), Percent of normal precipitation index (PNPI) in excel and produced map for MCDM models using ArcGIS.
The Rainfall intensity is one of the main factors due to its significant impact on the flood magnitude, flash flood, desertification, Bank erosion, Gully Erosion, land degradation, sediment flux, soil erosion, etc.
The rainfall erosivity factor (R) is developed by Wischmeier and Smith (1978) and modified by Arnoldus (1980). It is determined as a function of the volume, intensity and duration of the rainfall and can be computed from a single storm, or a series of storms to include cumulative erosivity from any time period
The Precipitation Concentration Index (PCI) was developed by Oliver (1980) to quantify the periodic variation of the rainfall, concentration of rainfall and rainfall erosivity.
Rainfall seasonality Index (RSI) developed by Walsh and Lawler (1981), refers to the degree of variability in monthly rainfall through the year; it assesses seasonal contrasts in rainfall amounts rather than whether months are ‘dry’ or ‘wet’ in an absolute sense.
Rainfall Anomaly Index (RAI) developed by van Rooy (1965) is used in depicting periods of dryness and wetness in the area.
The rainfall variability index (RVI) is the ratio between anomalies over the standard deviation of the long period of rainfall data.
The coefficient of variation (CV) is a statistical measure of the dispersion of data points in a data series around the mean.
The Percent of normal precipitation index (PNPI) is one of the most straightforward measures of rainfall deviation from its long-term mean. ‘Normal’ may be and is usually set to a long-term mean precipitation value at a location.
After completing this course, you will be efficiently able to prepare these parameters for MCDM models using Excel and ArcMap.
Who Should Attend!
- Students, researchers and professionals of Natural hazards, Environmental Science, Ecology, and Geography
- Students, researchers and professionals who work on: Hazards, vulnerability and risk [flooding, landslides, drought], susceptibility [Groundwater potentiality, vulnerability] and Suitability [Agricultural suitability, Irrigation suitability]
- Anyone interested in learning rainfall indices for MCDM models Using Step by Step Approach
