Regional mapping of climate variables in Galicia from point samples

Galicia's (NW Spain) diverse climate, driven by various factors, exhibits significant spatial and temporal variability, directly affecting the region's economy and society. Taking this into account, it is of great importance to have climate observation networks that cover most of the territory, and in the case of not having them, to have tools that allow the estimation of climate parameters. The main tools used for the estimation of parameters in unmeasured locations are climate database and the interpolation of data from climate stations, with kriging being one of the most widely used interpolation methods. The evaluation of climatic database is very important to establish how well they estimate the parameters and to ensure that they result in a homogeneous data series. For this purpose, in the present study a comparison was made of point data from the MeteoGalicia weather station network with data extracted from the SIMPA, AEMET and ERA5 databases and with data resulting from interpolation using regression kriging methods. The climatic parameters for which data were used for the study are: mean temperature, precipitation and reference potential evapotranspiration (Eto). To extract the data from SIMPA, ERA5 and AEMET and to interpolate point data from MeteoGalicia, we worked with the statistical programming environment R. After extracting the data or interpolating them, they were compared with the data measured in the MeteoGalicia network of climatological stations using the Nash-Sutcliffe index. The results of this study seek to conclude which method is more effective for estimating each of the parameters studied. In general, the predictions based on interpolation (AEMET and regression kriging) presented the best goodness of fit, while SIMPA also presented good results for some parameters. In general, the parameters with the best estimation results were mean temperature, followed by precipitation and finally potential evapotranspiration.