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Dinamika Teknik Sipil: Majalah Ilmiah Teknik SipilDinamika Teknik Sipil: Majalah Ilmiah Teknik Sipil

Drought poses a significant threat to water supply in regions like Kulon Progo, Indonesia. Seasonal water shortages lead to agricultural losses and increased disaster vulnerability. This study aims to improve groundwater exploration methods for drought mitigation by analyzing the correlation between lineament density and groundwater using a dataset of 127 observations from Samigaluh and Kalibawang subdistricts using Pearson Correlation. The research uncovers a non-linear threshold at 4 km/km². Below the threshold, groundwater occurrence increases with density (r = 0.949), showing increasing infiltration. But above the threshold, occurrence decreases (r = -1.000), suggesting over-fracturing reduces storability. Maximum depths follow a similar pattern (r = 0.883 below, r = -1.000 above). These findings challenge conventional scoring methods in groundwater potential zoning, which often assume a linear positive relationship with lineament density. The study provides a novel framework for targeted exploration, prioritizing moderate-density zones to mitigate drought impacts and build resilience against climate-induced disasters.

This research highlighted the important effect of lineament density threshold on groundwater occurrence and depth in Samigaluh and Kalibawang Subdistricts, Kulon Progo.The study found that the correlation between lineament density and groundwater was non-linear, with a threshold at 4 km/km².Prioritizing groundwater infrastructure development in moderate lineament density zones is recommended to enhance drought resilience and sustainable water management.

Future research should consider spatiotemporal analyses combining remote sensing with field validation to increase model accuracy. Furthermore, investigating the influence of geological formations and hydrogeological parameters beyond lineament density is crucial for a more comprehensive understanding of groundwater dynamics. Finally, exploring the application of advanced modeling techniques, such as machine learning algorithms, could improve the prediction of groundwater potential and optimize drought mitigation strategies in similar geological settings.

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