Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta-analysis approach

Abstract

A meta-analysis data-driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02–0.56, a sand fraction range of 0.05–0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (θ) and two resistance parameters rss;ref and θefolding. The data-driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut-off soil moisture value θ1/2 at which SEE=0.5, and first derivative of SEE at θ1/2, named ∆θ-11/2. An analytical relationship between (rss;ref ; θefolding and (θ1/2; ∆θ-11/2) is first built by running a soil energy balance model for two extreme conditions with rss = 0 and rss ~ ∞ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair (θ1/2; ∆θ-11/2) either from the time series of SEE and θ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear SEE(θ) relationships and potentially large random deviations of observed SEE from the mean observed SEE(θ). The second method parameterizes θ1/2 as a multi-linear regression of clay and sand percentages, and sets ∆θ-11/2 to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol-Biosphère-Atmosphère), H-TESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land-surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.