Abstract
This study focuses on the reconstruction of incomplete and spatially sparse air temperature data for the purpose of estimating evaporation from Lake Most – a large artificial reservoir in the Czech Republic with no natural inflow. The primary objective is to generate daily spatial temperature fields using spatio-temporal kriging and subsequently compute evaporation using a calibrated Hargreaves-Samani (HS) model. We utilize daily data from the years 2020-2022, collected from six low-cost microstations installed around the lake and from a nearby professional meteorological station (Kopisty, operated by the Czech Hydrometeorological Institute). Due to frequent outages, data coverage from the microstations ranges from 5% to 38%. To fill in missing values and estimate temperature over the lake surface, we apply a Gneiting covariance model. All computations are carried out in MATLAB using a in-house implementation. The reconstructed temperature fields exhibit realistic spatial structure and seasonal variability. Based on the interpolated daily mean, maximum, and minimum air temperatures, we compute daily and cumulative evaporation from the lake surface. The results show that even a sparse and unreliable sensor network can yield physically consistent inputs for evaporation estimation when combined with statistical interpolation. The proposed method is readily applicable to other reservoirs under limited measurement conditions and may support hydrological modeling and water balance analysis.