Abstract

The Vapor Pressure Deficit (VPD) is one of the most relevant surface meteorological variables; with important implications in ecology, hydrology, and atmosphere. By understanding the processes involved in the variability and trend of the VPD, it is possible to assess the possible impacts and implications related to both physical and human environments, like plant function, water use efficiency, net ecosystem production, atmospheric CO2 growth rate, etc. This study analysed recent temporal variability and trends in VPD in Spain between 1980 and 2020 using a recently developed high-quality dataset. Also, the connection between VPD and soil moisture and other key climate variables (e.g. air temperature, precipitation, and relative humidity) was assessed on different time scales varying from weekly to annual. The objective was to determine if changes in land-atmosphere feedbacks connected with soil moisture and evapotranspiration anomalies have been relevant to assess the interannual variability and trends in VPD. Results demonstrate that VPD exhibited a clear seasonality and dominant positive trends on both the seasonal (mainly spring and summer) and annual scales. Rather, trends were statistically non-significant (p > 0.05) during winter and autumn. Spatially, VPD positive trends were more pronounced in southern and eastern of Spain. Also, results suggest that recent trends of VPD shows low contribution of variables that drive land-atmosphere feedbacks (e.g. evapotranspiration, and soil moisture) in comparison to the role of global warming processes. Notably, the variability of VPD seems to be less coupled with soil moisture variability during summertime, while it is better interrelated during winter, indicating that VPD variability would be mostly related to climate variability mechanisms that control temperature and relative humidity than to land-atmosohere feedbacks. Overall, our findings highlight the importance of assessing driving forces and physical mechanisms that control VPD variability using high-quality climate datasets, especially, in semiarid and sub-humid regions of the world.

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