The Spatio-Temporal Dynamics of Water Resources (Rainfall and Snow) in the Sierra Nevada Mountain Range (Southern Spain)

Eulogio Pardo-Igúzquiza (), Sergio Martos-Rosillo, Jorge Jódar and Peter A. Dowd
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Eulogio Pardo-Igúzquiza: Instituto de Geociencias (CSIC-UCM), Severo Ochoa, Planta 4, 28040 Madrid, Spain
Sergio Martos-Rosillo: Instituto Geológico y Minero de España (IGME, CSIC), Unidad de Granada, Urbanización Alcázar del Genil, 4. Edificio Zulema, 18006 Granada, Spain
Jorge Jódar: IGME, CSIC, Unidad de Zaragoza, Manuel Lasala 44, 9B, 50006 Zaragoza, Spain
Peter A. Dowd: Faculty of Sciences, Engineering and Technology, The University of Adelaide, Adelaide 5005, Australia

Resources, 2024, vol. 13, issue 3, 1-25

Abstract: This paper describes the use of a unique spatio-temporally resolved precipitation and temperature dataset to assess the spatio-temporal dynamics of water resources over a period of almost seven decades across the Sierra Nevada mountain range, which is the most southern Alpine environment in Europe. The altitude and geographical location of this isolated alpine environment makes it a good detector of climate change. The data were generated by applying geostatistical co-kriging to significant instrumental precipitation and temperature (minimum, maximum and mean) datasets. The correlation between precipitation and altitude was not particularly high and the statistical analysis yielded some surprising results in the form of mean annual precipitation maps and yearly precipitation time series. These results confirm the importance of orographic precipitation in the Sierra Nevada mountain range and show a decrease in mean annual precipitation of 33 mm per decade. Seasonality, however, has remained constant throughout the period of the study. The results show that previous studies have overestimated the altitudinal precipitation gradient in the Sierra Nevada and reveal its complex spatial variability. In addition, the results show a clear correspondence between the mean annual precipitation and the NAO index and, to a much lesser extent, the WeMO index. With respect to temperature, there is a high correlation between minimum temperature and altitude (coefficient of correlation = −0.84) and between maximum temperature and altitude (coefficient of correlation = −0.9). Thus, our spatial temperature maps were very similar to topographic maps, but the temporal trend was complex, with negative (decreasing) and positive (increasing) trends. A dynamic model of snowfall can be obtained by using the degree-day methodology. These results should be considered when checking the local performance of climatological models.

Keywords: high mountain climatology; orographic rainfall; alpine environment; temperatures; snow; NAO index; WeMO index; rain gauges; co-kriging (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2024
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