The Topographic Control on Land Surface Energy Fluxes: A Statistical Approach to Bias Correction

Subsurface hydrodynamics are an important component of the hydrological cycle and a key factor in the partitioning of land surface water and energy fluxes. Because of computational reasons they are often neglected, or strongly simplified, in numerical weather prediction and climate models. Particularly in regions where the water table is shallow, soil moisture acts as a link between groundwater, land and atmosphere. Because of its long-term memory, groundwater represents a buffer for the effects of climate variability. To dynamically model this system we study the outputs of a variably saturated groundwater flow model (ParFlow) coupled with a surface model (CLM) and propose an empirical approach for the statistical correction of the bias of the energy fluxes in a highly-parametrized simulation. Corrections are based on a comparison of the simple scheme with the fully-coupled subsurface-land-atmosphere simulations. This simple dynamical scheme computes the potential latent heat flux in case of near saturation, while the fully-coupled simulations approximate the reality. Our statistical model examines the evapotranspiration surplus, i.e., the difference in latent heat flux between the two runs, and aims to correct the bias in the latent heat flux of the simple scheme, based on the characteristics of each point of the domain. In particular, we focus on the ability of topography-related indices, such as the topographic wetness index and the depth-to-water index, to provide information on the availability of water for evapotranspiration. Our results confirm that topographic indices are good predictors for moisture availability. While small-scale structures cannot be accurately reproduced by our model, large-scale biases of latent heat flux over the domain are effectively removed. Moreover, the bias corrected fluxes show a better agreement with the fluxes of the full modelling system than commonly used free-drainage simulations. Thus, the proposed approach can be useful in approximating the effect of groundwater on land surface water and energy fluxes in, e.g., regional climate models.