IMPACT OF RAINFALL AND LULC ON FLOOD HAZARD MODELLING WITH A FOCUS ON ROORKEE AREA

Abstract

A proper risk assessment and management framework is based on the accuracy of data and the estimates prepared in accordance with it. Understanding the risk and stakes involved in different situations helps in reducing the damages by floods. It also helps in preparing for damages which can’t be avoided. But at the same time, inaccuracies in risk assessment may lead to overestimation or underestimation of hazards. The estimations of how much losses to human life and property may occur due to a certain hazard are called risk associated with that hazard. When flooding of any area including water bodies occurs near a city or village due to excessive rainfall, it causes damage to human life and property and also disrupts the day to day life of people living in affected area. Thus, rainfall is a major component in assessment of flood risk and in turn, the accuracy of the rainfall data largely affects the results of the hazard modelling and risk assessment. The study focuses on the study of rainfall data for Roorkee area, and compares the results of runoff generation from observed sub- daily rainfall data with the results from rainfall of smaller durations estimated with the same methods used in Uttarakhand Disaster Risk Project. The basic inputs required for risk assessment models include rainfall patterns and physiographic characteristics of the area. Local rainfall data has been utilized to understand the rainfall distribution, storm intensities and relationship of total storm depths with intensities observed. Based on the results of the 40-year data of recording type rain gauge with 15 minute interval data intensity, duration and frequency relationship has been established. A basic runoff generation modelling has also been carried out with SCS-CN method. Using this method, the effect of LULC on various parts of Roorkee area has been studied. Based on this analysis, the areas likely to be most affected during floods have been identified