Assessment of land use and land cover dynamics and its impact in direct runoff generation estimation using SCS CN method

Thiruchelve, S R (2024) Assessment of land use and land cover dynamics and its impact in direct runoff generation estimation using SCS CN method. Acta Geophysica. ISSN 1895-7455

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Abstract

The Madurai local planning authority, encompassing a land area of 726.34 sq. km, has encountered challenges of droughts and flash floods during the north–east monsoon season. These issues have arisen as a result of notable alterations in land use and the swift pace of urbanization. This comprehensive study aims to assess the effects of land use changes on direct runoff within the study area over a span of 40 years, from 1980 to 2020. This study attempted to understand the evolution of land use and land cover change in the Madurai LPA region over the past 2 decades and its corresponding impact on runoff generation. The study also predicted the trend of LULC in 2040. LANDSAT images from different years were acquired to create land use and land cover maps using ERDAS IMAGINE 9.1 and Arc GIS Version 10.1. Four hydrological soil groups were determined using data from the Madurai Atlas, and the surface runoff was calculated using the soil conservation service–curve number. The accuracy of the land use and land cover maps was evaluated using an error matrix and kappa index. LULC predictions for 2040 were made using the cellular automata and artificial neural network model. The analysis showed that agricultural land increased by 5.9% between 1980 and 2020, while forest cover decreased by 0.2% and urban settlements grew by 7.4% in the D hydrological soil group. The predicted land use for 2040 indicates that agricultural land will account for 54.1%, followed by 1% forest cover and 15.8% urban areas. The accuracy of the predicted land use map was validated using the 2020 map, with a 91% accuracy and a kappa coefficient of 0.8. The Madurai region has experienced a notable surge in urbanization, highlighting the urgency for effective flood management and the implementation of urban development strategies that prioritize the creation of green spaces and efficient storm water drainage systems.

Item Type: Article
Subjects: B Civil Engineering > Urban Engineering
C Computer Science and Engineering > Neural Networks
Divisions: Civil Engineering
Depositing User: Users 5 not found.
Date Deposited: 22 Aug 2024 11:09
Last Modified: 22 Aug 2024 11:10
URI: https://ir.psgitech.ac.in/id/eprint/987

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