Filling Meteorological Data Gaps for Catchment Modelling in Western Slovakia: Does Method Complexity Matter?

Continuous meteorological records are essential for hydrological studies, yet station records often contain gaps. This study benchmarks 31 statistical, geostatistical, machine-learning and deep-learning methods for reconstructing isolated missing values and short gaps in daily precipitation and air temperature records in the Myjava River Basin, western Slovakia (1981–2020; 19 precipitation and 3 air temperature stations retained after excluding two stations dominated by multi-year interruptions). After diagnosing the structure and mechanism of missing data, all methods were evaluated by 20 repetitions of Monte Carlo cross-validation, in which every method was refitted, using error metrics with uncertainty estimates and significance tests, together with wet-day occurrence, dry and wet spells, extremes and physical plausibility. For air temperature, multiple imputation by chained equations (MICE) and several of the best-performing machine-learning methods were significantly more accurate than distance-based methods (RMSE 0.97–1.00 versus 1.38–1.41 °C), which do not account for systematic differences between stations at different elevations. For precipitation, the distance-based normal ratio, inverse-distance k-nearest-neighbour and inverse distance weighting methods were practically as accurate as MICE (RMSE 1.87–1.89 versus 1.85 mm; differences below 2%) while preserving dry days and producing no negative values. Method complexity therefore mattered for air temperature but not for precipitation. The propagation of reconstruction errors into simulated runoff was not assessed and is the subject of ongoing research.