Assessment of Simulated Meteorological Data Applicability for Hydrological Modelling in Low Land River Catchments

Hydrological modelling in lowland catchments is often constrained by flat terrain and sparce meteorological station networks, which limits the accuracy of spatial interpolation of precipitation and temperature. In these conditions, the nearest available station may be located tens of kilometres away, making interpolated meteorological inputs highly uncertain and prone to systematic bias. This study aims to improve interpolated meteorological data for hydrological applications by developing and evaluating a practical bias correction approach suitable for low-relief regions with insufficient station density. Long-term temperatures and precipitation records from 18 meteorological stations in Lithuania (1961–2020) were used as reference data. Meteorological fields were reconstructed using Ordinary Kriging and Spline interpolation and evaluated against observations at monthly and daily time scales using correlation (r), Root Mean Square Error (RMSE), Percent Bias (PBIAS), Nash–Sutcliffe Efficiency (NSE), and Probability of Detection (POD) for precipitation. Bias correction was applied to interpolated datasets using inverse distance weighting (IDW) based on one to four neighbouring stations, reflecting typical distances of 50–70 km between observation sites. The results show that while the interpolation method strongly influences precipitation accuracy, bias correction substantially reduces systematic errors without altering temporal structure. The most robust improvements were obtained using two to three neighbouring stations and an IDW power parameter of one, particularly under flat terrain conditions. When applied as input to the HBV rainfall–runoff model for three representative lowland catchments, bias-corrected interpolated meteorological data consistently improved runoff simulations, bringing model performance closer to that achieved using historical station observations. The findings demonstrate that targeted bias correction is an effective and computationally simple strategy for improving interpolated meteorological data in data-sparse lowland regions. The proposed approach provides practical guidance for hydrological modelling where dense observation networks are unavailable and reliance on interpolation is unavoidable.