Interactive effects of UV radiation and water deficit on production characteristics in upland grassland and their estimation by proximity sensing

Abstract

An increase in extreme weather and changes in other conditions associated with ongoing climate change are exposing ecosystems to a very wide range of environmental drivers that interact in ways which are not sufficiently understood. Such uncertainties in how ecosystems respond to multifactorial change make it difficult to predict the impacts of environmental change on ecosystems and their functions. Since water deficit (WD) and ultraviolet radiation (UV) trigger similar protective mechanisms in plants, we tested the hypothesis that UV modulates grassland acclimation to WD, mainly through changes in the root/shoot (R/S) ratio, and thus enhances the ability of grassland to acquire water from the soil and hence maintain its productivity. We also tested the potential of spectral reflectance and thermal imaging for monitoring the impacts of WD and UV on grassland production parameters. The experimental plots were manipulated by lamellar shelters allowing precipitation to pass through or to be excluded. The lamellas were either transmitting or blocking the UV. The results show that WD resulted in a significant decrease in aboveground biomass (AB). In contrast, belowground biomass (BB), R/S ratio, and total biomass (TB) increased significantly in response to WD, especially in UV exclusion treatment. UV exposure had a significant effect on AB and BB, but only in the last year of the experiment. The differences in the effect of WD between years show that the effect of precipitation removal is largely influenced by the potential evapotranspiration (PET) in a given year and hence mainly by air temperatures, while the resulting effect on production parameters is best correlated with the water balance given by the difference between precipitation and PET. Canopy temperature and selected spectral reflectance indices showed a significant response to WD and also significant relationships with morphological (AB, R/S) and biochemical (C/N ratio) parameters. In particular, the vegetation indices NDVI and RDVI provided the best correlations of biomass changes caused by WD and thus the highest potential to remotely sense drought effects on terrestrial vegetation.