Impacts of seasonal vegetation dynamics on water balance and groundwater recharge: a monthly leaf area index (LAI)-driven WetSpass-M application in the Sapgyocheon basin, South Korea

Groundwater recharge is a key indicator for sustainable groundwater use and water resources management. Although groundwater recharge depends on climatic and land-surface conditions, previous WetSpass-M applications have often underestimated the role of vegetation because they typically assume static vegetation. To address this gap, this study quantitatively evaluated the impact of vegetation dynamics—represented by Sentinel-2–derived leaf area index (LAI)—on the water balance and groundwater recharge in the Sapghyocheon basin, South Korea. LAI showed strong spatiotemporal and seasonal variability, and its magnitude and temporal dynamics differed among vegetation types, thereby affecting individual water balance components. In particular, in paddy fields, agricultural practices (e.g., transplanting, crop growth, and harvesting) led to distinct changes in interception, with a marked decrease after harvest. During 2020–2021, the mean annual groundwater recharge ratio was approximately 22% (20–23%). Simulated annual recharge ranged from 294 to 335 mm/year across the basin, and the model reasonably reproduced observed surface runoff (R2 = 0.95). When examined by land-use type, the groundwater recharge fraction in summer in coniferous forests (31%) was about twice that in fields (16%), indicating a clear difference in groundwater-recharge potential among vegetation types. These findings suggest that variations in vegetation type and phenological timing can directly and indirectly affect groundwater recharge. Therefore, explicitly accounting for vegetation dynamics can improve the detail and reliability of water balance and groundwater recharge assessments. However, because the analysis is based on a relatively short (2020–2021) study period and limited observational data, the conclusions should be viewed as an initial step rather than fully generalizable results.