Analysis of diurnal Evaporative Fraction behavior

Estimation of the land surface energy balance, linking energy and mass transfer, is required in numerous applications in hydrology and meteorology. The only currently available way to obtain a regional or global coverage of the land surface heat fluxes is to use remote sensing data, which has the advantage of continuous spatial coverage and good temporal frequency. However, remotely sensed measurements such as radiative temperature are only indirectly related to the state of the land surface and the corresponding heat fluxes. Estimation of Evaporative Fraction (EF), the ratio of latent heat flux over available energy (EF = λE/(Rn-G) = λE/(H+λE)), is one of the most promising methods to use remote sensing data for evaluating land surface fluxes and in particular Evapotranspiration (ET). Usually it has been assumed that EF was a daily constant. However, just a few experimental and theoretical studies on EF diurnal behavior are currently available. Generally, those studies have stated that EF is almost constant during daytime and fair weather conditions.

This presentation will first discuss the diurnal shape of EF. Both an experimental flux dataset and a dual-source, diffusive soil, SVAT model (ICARE-SVAT) were used for more than 100 continuous days of data, covering a wide range of phenological and climatic conditions of a wheat field in a semi-arid region. It will be shown that EF exhibits a typical concave-up shape during clear days. Moreover, under some restricted soil moisture and atmospheric conditions, EF is nearly constant during the main part of the day. Yet for most of the remaining conditions, EF cannot be considered as a diurnal constant, and ET estimation errors will result from this assumption. The EF diurnal shape will be explained through the phase difference of the daily maximum of both the sensible and latent heat fluxes, respectively H and λE. The mean diurnal EF dependency on the top 5cm soil moisture and Leaf Area Index (LAI) is examined. To do so, different SVAT simulations have been carried out with different LAIs and surface soil wetness conditions. Those simulations show that EF is strongly related to the soil moisture state, but is only a weak function of the LAI. Then, we will try to describe the partitioning of the canopy and soil evaporative fraction, respectively EFc and EFs, and their typical diurnal shape. This will be of greater importance for sparse vegetation ET estimation using remote sensing data.

Second, the constancy of the diurnal EF and the representativity of the instantaneously measured EF are discussed. They depend on both the soil moisture and the climatic conditions. The time chosen to estimate EF is critical to have the best cumulative ET estimation. Indeed, assuming a constant diurnal EF, ET can be estimated each day by multiplying an instantaneously measured EF by the daily available energy. Hence, a measurement strategy is developed, leading to the best ET estimation, depending on the hour of the day when EF is evaluated. The daily latent heat flux error due to the use of the mean daily EF value is also investigated.

Then, the correlations between EF and the most important climatic forcing parameters are calculated, for different soil moisture and LAI conditions. The results are really promising as, for example, EF is only weakly correlated to the solar incoming radiation, and strongly correlated to the radiative temperature for most soil moisture and vegetation cover conditions, contrary to H and λE. These results are extremely important for remote sensing applications, indicating EF as one of the main unknown parameters to estimate ET.