Creating a gridded climate database in Sierra Nevada, in the southern Iberian Peninsula

Mountainous regions are especially vulnerable to the effects of climate change. Therefore, it is essential to study these areas, which contain an invaluable diversity of species and resources. Meanwhile, the southern part of the Iberian Peninsula (IP), as a semi-arid region, is particularly susceptible to climate change. Consequently, the mountainous area of the IP, Sierra Nevada (SN), is an area of interest that needs more research.

Currently, there are different sources of meteorological, observational data for SN. There are several stations scattered across the area. However, certain difficult-to-access areas, located in the highest peaks of the IP, exhibit a lack of data. This issue can be solved by applying spatial interpolation techniques, such as the Regionalisierung der Niederschlagshöhen (RegNie) method. The result of this method is a gridded dataset for the complete region of study. Nevertheless, the coherence of the available observational data is not always guaranteed, so that the statistical analysis of the RegNie results may show low quality gridded data. There is an ongoing effort to create a curated database for the area, known as Climanevada, which is used in this research.

The aim of this study is to create a gridded meteorological database using the RegNie method, containing at least precipitation and temperature data, for SN and its surrounding area. To achieve this goal, the available data for the period 1990-2020 has been deeply analyzed, searching for errors or discardable data. Once this step was complete, the most adequate grid size and resolution were chosen, considering the specific physical characteristics of the area. The whole process is described in this study, together with the description of the features of the final gridded dataset.

Keywords: Sierra Nevada, precipitation, temperature, gridded data, RegNie.

ACKNOWLEDGEMENTS
This research was financed by the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AIe-Services University of Granada-SierraNevada”(LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program2014-2020 (POPE), LifeWatch-ERIC action line; the project P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades; and by the project PID2021-126401OB-I00 funded by MCIN/AEI/ 10.13039/501100011033/FEDER Una manera de hacer Europa.