2025/08/07 by Jaime J. Carrera-Hernández, Gilles Lévresse, Antonio Hernández‐Espriú · 1 voice
Earth and Planetary Sciences · Environmental Science · #Precipitation Measurement and Analysis #Soil Geostatistics and Mapping #Soil Moisture and Remote Sensing
paper · pdf · doi:10.1080/02626667.2025.2534192
openalex publication_date 2025/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/14
Precipitation is a highly heterogeneous variable – even on a yearly basis, because its spatial distribution over large areas is affected by topography and nearby large water bodies. This study compares the performance of different kriging variants in order to take into consideration the factors that affect the spatial distribution of yearly precipitation in the country. The results obtained in this work show that kriging with external drift (KEDl) provides the best estimates of precipitation because KEDl is able to interpolate precipitation values that vary from 800 to 3500 mm at locations that are only 110 km apart and because it reflects the effect of topography on precipitation. Finally, the spatial variation of precipitation obtained with KEDl is similar to that obtained with satellite-estimated precipitation, with the advantage that KEDl can be used to generate spatially distributed climatological data with higher resolution and larger temporal extent.