부산대학교 도서관

Drylands exert a strong influence over global interannual variability in carbon and water cycling due to their substantial heterogeneity over space and time. This variability in ecosystem fluxes presents challenges for understanding their primary drivers. Here we quantify the sensitivity of dryland gross primary productivity and evapotranspiration to various hydrometeorological drivers by synthesizing eddy covariance data, remote sensing products and land surface model output across the western United States. We find that gross primary productivity and evapotranspiration derived from eddy covariance are most sensitive to soil moisture fluctuations, with lesser sensitivity to vapour pressure deficit and little to no sensitivity to air temperature or light. We find that remote sensing data accurately capture the sensitivity of eddy covariance fluxes to soil moisture but largely over-predict sensitivity to atmospheric drivers. In contrast, land surface models underestimate sensitivity of gross primary productivity to soil moisture fluctuations by approximately 45%. Amid debates about the role of increasing vapour pressure deficit in a changing climate, we conclude that soil moisture is the primary driver of US dryland carbon–water fluxes. It is thus imperative to both improve model representation of soil water limitation and more realistically represent how atmospheric drivers affect dryland vegetation in remotely sensed flux products.
Soil moisture is the primary driver of variability in dryland carbon and water cycling, according to a synthesis of eddy covariance, remote sensing and land surface model data from the western United States.