# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
ET is also coupled with vegetation photosynthesis through canopy stomatal conductance [1; 5]. These linkages among water, energy, and carbon cycle make ET a crucial parameter in short-term numerical weather forecasts as well as long-term climate projections. Thus, it is important to estimate terrestrial ET accurately for advancing our understanding of the Earth's climate system. Global terrestrial ET was firstly simulated with the land surface model, of which ET simulation was coupled with the carbon processes [6]. Various estimates of the magnitude of global terrestrial ET in different units have been reported: (1) 524.9 mm \(\,\,\mathrm{yr}^{-1}\) (58.4 \(\times\) 10\({}^{3}\) km\({}^{3}\) yr\({}^{-1}\)) [7]; (2) 272.0-441.5 mm \(\,\mathrm{yr}^{-1}\)[8]; (3) 604 mm \(\,\mathrm{yr}^{-1}\)[9]; and (4) 564.4 mm \(\,\mathrm{yr}^{-1}\) (62.8 \(\times\) 10\({}^{3}\) km\({}^{3}\) yr\({}^{-1}\)) [10]. These estimates differ considerably due to different datasets and different methods used for the estimation of global terrestrial ET. [PERSON] et al. [11] compared ET simulations of nine flux tower sites using different leaf-to-canopy upscaling schemes with the same data inputs. They found that the big-leaf scheme underestimated ET across all sites and this underestimation was more significant at the sites with higher leaf area index (LAI) values. It indicated that methods of canopy-scale ET simulation would be a major source of uncertainties in global ET simulation. Based on the pioneering works of [PERSON] [12] and [PERSON] [13], ET can be calculated by the widely used Penman-Monteith (PM) equation [13]. Although the PM equation has a solid physical foundation, its application to large scales still has several challenges, one of which is the upscaling methodology for canopy ET estimation [14]. The big-leaf model treating the canopy as a big leaf over the soil is a widely used leaf-to-canopy upscaling methodology for ET estimation [13; 15; 16] and it has several limitations. One is that many parameters of the big-leaf model can only be determined by tuning the model using eddy covariance (EC) flux measurements and do not represent any measurable biochemical or biophysical variables [17]. Another shortcoming of the big-leaf model is the effects of ignoring micrometeorological gradients and intermittent turbulence on the estimation of EC fluxes [17]. The big-leaf model would cause biases in the simulation of canopy gross primary productivity (GPP) and ET [11; 18; 19]. The two-leaf model, which divides the canopy into sunlit and shaded components has been proved to perform better in estimating canopy GPP and ET than the big-leaf model [11; 18; 19]. Sunlit-shaded leaf separation needs an accurate description of canopy structure using two canopy structural parameters [14]. One is LAI which is defined as half of the total surface area of green leaves per unit ground surface area [20]. The other one is CI, which quantifies the extent to which leaves deviate from the random distribution [21; 22]. There are four reasons why CI is required for ET simulation: (1) CI is required for deriving true LAI from effective LAI (L\({}_{\mathrm{e}}\)) that can be measured directly using ground-based optical instruments (i.e., LAI-2200) or derived from satellite sensors; (2) CI is needed in the two-leaf model for calculating sunlit and shaded LAI; (3) CI is used in the calculation of canopy gap fraction for accurately simulating radiation transfer inside the canopy [18; 22; 23]; and (4) CI is also used in estimating the rate of intercepted rain or snow by the canopy [24]. [PERSON] et al. (2016) assessed the impacts of leaf clumping on ET estimation at eight forest flux tower sites using the BEPS model and found that the annual ET could be underestimated by 11.5% on average if Le was used ignoring leaf clumping. Also, under the same modeling scheme, the bias of ET estimation was larger in a more clumped canopy [14]. Moreover, the knowledge of experimental LAI is crucial in ecohydraulic research [25; 26].