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The AAPG/Datapages Combined Publications Database

Environmental Geosciences (DEG)

Abstract

DOI: 10.1306/eg.01091918005

Assessment of natural groundwater recharge: A case study of North Chennai Aquifer

T. Siva Subramanian,1 and Marykutty Abraham2

1Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, India; [email protected]
2Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, India; [email protected]

ABSTRACT

Groundwater is the major source of drinking water in both urban and rural India. Estimation of natural groundwater recharge is essential for the sustainable development of groundwater. Natural recharge was estimated by various methods, such as the water level fluctuation method, water balance method, linear regression model, and Previous HitnonlinearNext Hit regression model. The recharge estimates by the water balance method was compared with the recharge obtained from the water level fluctuation method for the study area and found to be in good agreement.

Estimation of recharge by the water level fluctuation method is laborious, and envisaging the difficulties in the availability and reliability of data, the water balance method is taken as the standard for developing regression equations in the present study. Simpler linear and Previous HitnonlinearNext Hit regression models were developed for the study area to estimate natural recharge by correlating with the water balance model. The models were calibrated with 10-yr data and validated with 5-yr data. The statistical analysis showed that no significant difference exists between the recharge estimate by the water balance method and the two estimates of natural recharges, such as linear regression and Previous HitnonlinearNext Hit regression models. The average recharge percentages from the water level fluctuation method, water balance method, linear regression model, and Previous HitnonlinearTop regression model are 15.09%, 14.92%, 14.62%, and 14.57%, respectively, for the watershed during the study period. The study proves that regression equations can be efficiently used in recharge computation with proper calibration for ungauged basins, and laborious data-intensive computation methods can be eliminated.

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