Influence of plant traits on water cycle processes in the Amazon Basin
Abstract. Plants play a key role in the soil-plant-atmosphere-climate hydrological continuum. Plants depend on the water cycle and, in return, several hydrological processes could be impacted via vegetation-induced mechanisms. Changes in plant composition are known to affect this relationship, however, detailed understanding on how plant characteristics, i.e., their traits, are seldom included in observational and modeling studies. Here we examine the effect of plant traits on water cycle processes in the Amazon Basin. We used remotely-sensed estimates of four plant traits, namely Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC), Leaf Nitrogen Content (LNC), and two vegetation indices, the Normalised Difference Vegetation Index (NDVI), and Leaf Area Index (LAI), for 10 years between 2001 and 2010. We examined the relationship between plant traits and six parameters relevant for water cycle processes, namely Evapotranspiration (ET), Potential Evapotranspiration (PET), Vapour Pressure Deficit (VPD), Land Surface Temperature (LST) Day/Night and Soil Moisture (SM). We used multivariate and quantile regressions to analyse how plant traits explain the average and standard deviation in water cycle process parameters. We find that SLA, NDVI, and LAI exert the strongest effects across the whole of Amazon basin and the sub-basins, most important for the regulation of atmospheric water content and of land surface temperature, but little effect on the regulation of soil moisture content. These effects are exacerbated at extreme values of water process parameters, where plant traits exert an even stronger effect at low values of ET and PET and high values of VPD and LST. Leaf gas exchange traits are most important in comparison to the other traits, and these results also highlight that if water cycle process parameters achieve extreme values, plant traits are key to the persistence of hydrological processes fundamental to the resilience of the Amazon.
Viewed (geographical distribution)