Multi-Temporal Analysis of Curve Number (CN) Variation Due to Land Cover Change in the Ake Kobe River Basin, Indonesia

¹ Faculty of Engineering, Hasanuddin University Makassar, Indonesia
² Department of Civil Engineering, Hasanuddin University Makassar, Indonesia

^* Corresponding author's email address: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Land cover change has become a major driver affecting hydrological responses in River Basins, particularly in increasing surface runoff potential and flood risk. This study aims to analyze the multi-temporal variation of Curve Numbers caused by land cover changes in the Ake Kobe Watershed, Indonesia. The research methodology combines remote sensing techniques and Geographic Information Systems by utilizing multispectral satellite imagery. Land cover classification applies an Object-Based Image Analysis approach and Machine Learning algorithms to produce LULC maps with an accuracy of 0.90 and a kappa coefficient of 0.92. Estimation of the CN value was carried out using the Soil Conservation Service Curve Number method, taking into account the combination of land cover and Hydrologic Soil Groups. The research results reveal substantial land change dynamics, marked by a decrease in natural vegetation area from 2019 to 2024 by 66.31 km² or 9.78%, as well as an expansion of open land by 49.13 km² or 166.6% and built-up areas by 11.28 km². The change directly causes an increase in the CN value from 64 to 75 in 2024 to CN 75 in 2024 across the entire watershed, indicating a decrease in soil infiltration capacity. Spatiotemporal analysis indicates that the conversion of forests into industrial or residential areas significantly increases the potential for surface runoff, making the Ake Kobe watershed increasingly vulnerable to extreme rainfall. These findings underscore the urgency of sustainable watershed management strategies and ecosystem-based land use planning to mitigate the adverse impacts of hydrological changes and future disaster risks.