Sensitivity of hydrological hazard estimates to precipitation representativeness in a data-scarce mountainous basin in Iran

Estimates of hydrological hazards such as floods and landslides are highly sensitive to the spatial representativeness of precipitation inputs in data-scarce mountainous basins, where precipitation heterogeneity is strong and observational coverage is limited. This study assesses how precipitation dataset choice and station-network configuration affect SWAT streamflow simulations in the Barandouz catchment, a mountainous sub-basin of Lake Urmia in northwest Iran. The 1158 km² basin is characterized by elevations ranging from 1298 to 3483 m a.s.l. and predominantly pasture land cover.

Daily gauge precipitation from four stations was combined with ERA5-Land reanalysis data to construct three forcing scenarios: (S1) observed in-catchment gauges; (S2) gauges augmented with two upstream virtual stations forced by ERA5-Land; and (S3) the same configuration with bias-adjusted ERA5-Land precipitation. Comparison of daily gauge and ERA5-Land precipitation shows moderate agreement (R² ≈ 0.33–0.44), improving after bias adjustment (R² ≈ 0.43–0.47).

Daily streamflow simulations were evaluated at three main-stem gauges. Model calibration and uncertainty analysis are being conducted using SWAT-CUP (SUFI-2) with split-sample calibration (2006–2013) and validation (2014–2022). Preliminary simulations indicate systematic underestimation of observed discharge across all forcing scenarios, pointing to remaining inconsistencies in precipitation forcing and/or runoff generation. Ongoing calibration will quantify the extent to which these biases can be reduced and identify the precipitation forcing configuration that yields the most robust daily streamflow estimates, with direct implications for hydrometeorological hazard assessment in the Lake Urmia basin.