Evaluation of Evapotranspiration Models Using Different LAI and Meteorological Forcing Data from 1982 to 2017

Abstract

We evaluated the performance of three global evapotranspiration (ET) models at local, regional, and global scales using the multiple sets of leaf area index (LAI) and meteorological data from 1982 to 2017 and investigated the uncertainty in ET simulations from the model structure and forcing data. The three ET models were the Simple Terrestrial Hydrosphere model (SiTH) developed by our team, the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), and the MODerate Resolution Imaging Spectroradiometer (MODIS) ET algorithm (MOD16). Comparing the observed with simulated monthly ET by the three models over 43 Fluxnet sites, we found that SiTH overestimated ET for forests with mean slope from 1.25 to 1.67, but it performed better than the other two models over short vegetation. MOD16 and PT-JPL models simulated well for forests but poorly in dryland biomes (slope = 0.25~0.55; R2 = 0.02~0.46). At the catchment scale, all models performed well, except for some tropical and high latitudinal catchments, with NSE values lower than 0 and RMSE and MAE values far beyond their mean values. At the global scale, SiTH highly overestimated ET in tropics, while PT-JPL slightly underestimated ET between 30°N and 60°N and MOD16 underestimated ET between 15°S and 30°S. Generally, the PT-JPL provided the better performance than SiTH and MOD16 models. This study also revealed that the estimated ET by SiTH and especially PT-JPL model were influenced by the uncertainty in meteorological data, and the estimated ET was performed better using MERRA-2 datasets for PT-JPL and using ERA5 datasets for SiTH. While the estimated ET by MOD16 were relatively sensitive to LAI data. In addition, our results suggested that the GLOBMAP and GIMMS datasets were more suitable for long-term ET simulations than the GLASS dataset.

Correlation Analysis of Evapotranspiration, Emissivity Contrast and Water Deficit Indices: A Case Study in Four Eddy Covariance Sites in Italy with Different Environmental Habitats

“…The models reproduce physical and plant physiological mechanisms that regulate ET, such as stomata processes, radiation absorption and water interception [14]. Different remote sensing approaches use land surface characteristics such as the leaf area index (LAI) and the albedo to estimate ET via surface energy balance or within-scene scaling [19,20]. Remote sensing thermal infrared measurements have also been largely used for the retrieval of ET information [21,22].…”

On the Sensitivity of the Potential Evapotranspiration of Egypt to Different Initial Conditions of the Soil Moisture Using a High-resolution Regional Climate Model

“…ERA5 is available in 0.1 º grid spacing in the period of 1981 to 2022 over a hierarchy of time scales (hourly, daily sum and monthly mean/sum). It is important to highlight that ERA5 was used as the observational dataset over other available products such as CRU, MODIS16 (based on the Moderate Resolution Imaging Spectroradiometer MODIS; [49]) and Global Land Evaporation Amsterdam Model (GLEAM; [50]) because MOD16 and GLEAM compute the PET over vegetated land units only. On the other hand, CRU is available only on a monthly time scale.…”

Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model

“…However, it is difficult to obtain reliable total LAI globally, either by RS or empirical methods [34]. In practice, RS LAI data are widely used for allocating energy availability to the canopy and soil surface [35].…”

“…Both strategies have their advantages and shortcomings. The first strategy can make full use of multi-source RS vegetation information, but the uncertainties of RS vegetation variables from different sources may also be propagated into ET simulations [35]. The second strategy simplifies the model's vegetation inputs but may introduce the uncertainty of empirical equation(s) into ET simulations [64].…”