Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed

Abstract

Evaluating the interactions between above- and below-ground processes is important to understand and quantify how ecosystems respond differently to atmospheric forcings and/or perturbations and how this depends on their intrinsic characteristics and heterogeneity. Improving such understanding is particularly needed in snow-impacted mountainous systems where the complexity in water and carbon storage and release arises from strong heterogeneity in meteorological forcing and terrain, vegetation and soil characteristics. This study investigates spatial and temporal interactions between terrain, soil moisture, and plant seasonal dynamics at the intra- and inter-annual scale along a 160 m long mountainous, non-forested hillslope-to-floodplain system in the upper East River Watershed in the upper Colorado River Basin. To this end, repeated UAV-based multi-spectral aerial imaging, ground-based soil electrical resistivity imaging, and soil moisture sensors were used to quantify the interactions between above and below-ground compartments. Results reveal significant soil-plant co-dynamics. The spatial variation and dynamics of soil water content and electrical conductivity, driven by topographic and soil intrinsic characteristics, correspond to distinct plant types, with highest plant productivity in convergent areas. Plant productivity in heavy snow years benefited from more water infiltration as well as a shallow groundwater table depth. In comparison, low snowpack years with an early first bare-ground date, which are linked to an early increase in plant greenness, imply a short period of saturated conditions that leads to lower average and maximum greenness values during the growing season. Overall, these results emphasize the strong impact of snowpack dynamics, and terrain and subsurface characteristics on the heterogeneity in plant type and seasonal dynamics.