<![CDATA[River flooding during rainy season is partly resulted from land cover changes. This study analyzes the impact of land cover changes on flood hydrographs using Curve Number (CN), Impervious (I), and Initial Abstraction (Ia). Land cover data (2009 and 2022) were obtained from the Ministry of Environment and Forestry, while the 2035 scenario was modeled with QGIS MOLUSCE (ANN). CN and I values were then applied in HEC-HMS simulations with SCS and Snyder Unit Hydrograph methods. Results show major land conversion by 2035 is particularly from dryland to rice fields, built-up areas, and forest plantations. The 2035 land cover prediction had minimum overall error of 0.0332 and Kappa coefficient of 0.765, indicating good model reliability. Composite CN increased from 67.9 (2009) to 68.0 (2022) and 68.4 (2035); I values from 5.6 to 5.7 and 6.4; while Ia decreased from 24.0 to 23.9 and 23.5 (2035). Flood discharges with the SCS method rise from 617.2 m³/s (2009) to 623.8 m³/s (2022) and 641.3 m³/s (2035), while the Snyder method produced 621.3, 621.6, and 630.5 m³/s. Statistical comparison between simulated and frequency-based design flood discharge results in PBIAS values of 0.1–0.2 (very good) and NSE of 1.0 (very good). The discharge increases of 1.1–2.8% indicate that land cover changes contribute to higher flood potential, but still in moderate level as most conversion is to rice fields, which function as temporary water storage and delay direct runoff.]]>
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