Multi-site evaluation of terrestrial evaporation models using FLUXNET data.

Ershadi, A., M.F. McCabe, J.P. Evans N.W. Chaney and E.F. Wood
Agricultural and Forest Meteorology, 187, 46-61, doi: 10.1016/j.agrformet.2013.11.008, 2014.


We evaluated the performance of four commonly applied land surface evaporation models using a high-quality dataset of selected FLUXNET towers. The models that were examined include an energy balance approach (Surface Energy Balance System; SEBS), a combination-type technique (single-source Penman–Monteith; PM), a complementary method (advection-aridity; AA) and a radiation based approach (modified Priestley–Taylor; PT-JPL). Twenty FLUXNET towers were selected based upon sat- isfying stringent forcing data requirements and representing a wide range of biomes. These towers encompassed a number of grassland, cropland, shrubland, evergreen needleleaf forest and deciduous broadleaf forest sites. Based on the mean value of the Nash–Sutcliffe efficiency (NSE) and the root mean squared difference (RMSD), the order of overall performance of the models from best to worst were: ensemble mean of models (0.61, 64), PT-JPL (0.59, 66), SEBS (0.42, 84), PM (0.26, 105) and AA (0.18, 105) [statistics stated as (NSE, RMSD in W m−2 )]. Although PT-JPL uses a relatively simple and largely empirical formulation of the evaporative process, the technique showed improved performance com- pared to PM, possibly due to its partitioning of total evaporation (canopy transpiration, soil evaporation, wet canopy evaporation) and lower uncertainties in the required forcing data. The SEBS model showed low performance over tall and heterogeneous canopies, which was likely a consequence of the effects of the roughness sub-layer parameterization employed in this scheme. However, SEBS performed well overall. Relative to PT-JPL and SEBS, the PM and AA showed low performance over the majority of sites, due to their sensitivity to the parameterization of resistances. Importantly, it should be noted that no single model was consistently best across all biomes. Indeed, this outcome highlights the need for further evaluation of each model’s structure and parameterizations to identify sensitivities and their appropriate application to different surface types and conditions. It is expected that the results of this study can be used to inform decisions regarding model choice for water resources and agricultural management, as well as providing insight into model selection for global flux monitoring efforts.

Key Figure

Fig. 4. Mean per-month value of the Nash–Sutcliff efficiency calculated for each of the four studied models at each of the 20 tower locations. The x-axis represents month of the year, while each point on the graph represents the temporally averaged per-month NSE calculated for all available tower record years (see Table 1 for details on individual tower data length). Note that the per-month NSE values are for half-hourly or hourly scale E data, not in monthly scale. fE is normalized fraction of monthly observed E. EM is for ensemble mean of the models.

UNSW    This page is maintaind by Jason Evans | Last updated 29 November 2013