Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements
The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly Kc for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly Kc data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), Kc values had large seasonal variation across all land covers. The spatial variability of Kc was well explained by latitude, suggesting site factors are a major control on Kc. Seasonally, Kc increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly Kc in all land covers, except in EBF. During the peak growing season, forests had the highest Kc values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for Kc by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The Kc models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.