Assessment of Drought Using Drought Indices and Analysing the Relationship Between Groundwater Levels and Drought Indices: A Case Study of Ahmedabad District

Abstract

Drought is a natural and frequently occurring phenomenon in many parts of the country. Drought also affects a lot to the economy, environment, agriculture, industries, and the community. Also, a great part of the country depends a lot on the groundwater for their water requirement, be it agriculture, industrial, or municipal. Groundwater levels change with the number of parameters like rainfall, temperature, streamflow, etc. Very little work has been done on the effect of drought on groundwater fluctuations. Hence, it becomes important to assess the co-relationship between groundwater levels and drought severity. Viramgam and Dhandhuka talukas of Ahmedabad district in Gujarat state are selected for the present study. In the first part, the quantification of the drought using various drought indices, i.e., Standard Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and drought assessment using their respective values is done. The second part deals with analysing the relationship of drought indices with groundwater levels in the Viramgam and Dhandhuka regions. Rainfall and temperature data from 1981 to 2020 were used for the calculation of SPI and RDI using “DrinC” software. In Viramgam, extreme drought was recorded in 1986–87 and 2017–18, according to both SPI and RDI. In Dhandhuka, extreme drought was recorded in 1981–82 and 1986–87 according to SPI, whereas according to RDI in Dhandhuka extreme drought was recorded in 1986–87. In this paper, a total of eight regression models were developed for the relationship between SPI, RDI, and groundwater (GW) levels for Viramgam and Dhandhuka talukas (two models each for SPI and RDI for both talukas). In Viramgam, correlation coefficients were found to be 0.63, 0.6 and 0.65, 0.63, respectively, for SPI and RDI models. In Dhandhuka, correlation coefficients were found to be 0.64, 0.62 and 0.75, 0.73, respectively, for SPI and RDI models.