Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model
Highlights
► The first of its kind to provide evapotranspiration (E) with consideration of the soil water balance. ► Introducing a new leaf area index (Lai) based canopy conductance (Gc) model (Gc = gsmax × Rh × Lai). ► No calibration and validation at 19 flux sites indicates nice daily and monthly error statistics. ► Global gridded ARTS E agrees well with spatial pattern of the global E estimated by GSWP-2. ► The global annual ARTS E increased by 15.5 mm per decade from 1984 to 1998.
Introduction
As a crucial process in the terrestrial ecosystem connecting atmosphere, vegetation, and soil spheres, land evapotranspiration (E) is an important component of the water and energy cycles, and plant transpiration is driven by the same stomatal conductance term that governs carbon cycle. Global E consumes more than 50% of absorbed solar energy (Trenberth et al., 2009), and returns about 60% of annual land precipitation to the atmosphere (Oki & Kanae, 2006). Much evidence, mainly drawn from precipitation and runoff datasets, has confirmed the modification of the hydrologic cycle (Alkama et al., 2011, Huntington, 2006, Labat et al., 2004).
Direct observational evidence of this intensification of global land E is, unfortunately, lacking because there are only about 400 flux stations worldwide and their temporal records are very short (Huntington, 2006, Jung et al., 2010). However, large-scale E estimation is required for answering questions related to climate change. Climate change is expected to increase the global available renewable freshwater resources, but the increasing probability of drought and changes to regional precipitation patterns may offset this effect and lead to water stresses in many regions (Oki & Kanae, 2006). Since leaf stomata control the exchange of water and carbon between vegetation and atmosphere, and high stomatal conductance leads to higher transpiration and photosynthesis, an understanding of global E variation will help to elucidate the effects of climate change on biogeochemical cycling (Dang et al., 1997, Huntington, 2006, Jarvis, 1976, Kelliher et al., 1995, Nemani and Running, 1989, Shugart, 1998).
The surface energy balance partitions the available energy (Rn − G) between turbulent heat fluxes (λE and H):where λE is latent heat flux (λ is the latent heat of vaporization, and E is evapotranspiration), Rn is net radiation, G is ground heat flux, and H is sensible heat flux. E is mainly controlled by three factors: available water, available energy, and conductivity of the ecosystem to water vapor (Batra et al., 2006).
Satellite remote sensing can supply temporally and spatially continuous observations of key biophysical variables of the land surface, such as Lai, vegetation index (VI), albedo, land surface temperature, and emissivity. It has ushered in a new era for the development of land E models (Cleugh et al., 2007, Fisher et al., 2008, Leuning et al., 2008, Mu et al., 2007, Mu et al., 2011, Nagler et al., 2005, Su, 2002, Wang and Liang, 2008). There are two principal types of remote sensing E models: empirical and physical.
Section snippets
Empirical E models
These models often apply statistical regression to estimate E, using satellite VI and other meteorological data, such as air temperature and surface net radiation (Nagler et al., 2005, Wang and Liang, 2008). More recently, Jung et al. (2010) developed a model tree ensemble (MTE) approach that predicts global land E based on a set of explanatory variables (remote sensing-based fraction of absorbed photosynthetically active radiation data, and surface meteorological data), according to model
Physical E models
Physical E models use different biophysical metrics, derived from remote sensing. They can be further classified into two types:
- (1)
Energy balance E models. They estimate instantaneous E rates as a residual of the land surface energy balance using thermal infrared temperature as the most important input, combined with other data. Examples of this type include the Surface Energy Balance Algorithm for Land (SEBAL; Bastiaanssen et al., 1998), the Surface Energy Balance System (SEBS; Su, 2002), and the
Evapotranspiration algorithm
We propose a two-source E model to calculate actual E, in two steps. The first is to estimate plant transpiration and soil evaporation using respective equations, under the assumption of plentiful soil water. The second is to account for the effects of soil water stress, using a SWB model. The main improvements to the PM model in this study are explicit consideration of soil water stress impact on E.
Naturally, the available energy A is partitioned to two parts: the soil part (As) and canopy
Observation data for model evaluation
ET and meteorological data, measured at 19 AmeriFlux flux-tower sites (Table 2) by the eddy-covariance (EC) method, were used in model evaluation. The EC method is widely accepted for directly measuring heat fluxes (Paw et al., 2000) and is widely applied to global E measurements at flux tower sites in FLUXNET (Baldocchi et al., 2001). The AmeriFlux network is a core part of the global FLUXNET network. It includes sites from North, Central, and South America and continuously observes
Model evaluation at 19 flux sites
Statistics of model performance at the daily scale for all 19 sites (Table 4) show that the ARTS E goodness of fit and error varied from site to site. The slopes of the linear regression of estimated E vs. observed E ranged from 0.58 at Vaira to 1.32 at Bartlett, and the intercepts varied from − 0.08 mm d− 1 at Tonzi to 0.48 mm d− 1 at Donaldson. The E model had an average RMSE of 0.75 mm d− 1 for all sites, ranging from 0.45 mm d− 1 at Santa to 1.09 mm d− 1 at Donaldson. An average bias of − 0.11 mm d− 1 was
Discussion
The evaluation of ARTS E at flux sites was affected by measurement error of flux E data. The EC method has an energy imbalance problem, i.e., net radiation Rn minus ground heat flux G is greater than the sum of latent heat flux λE and sensible heat flux H at many eddy flux sites (Leuning et al., 2008, Wilson et al., 2002, Yan and Shugart, 2010). The ratio of λE + H to Rn – G is about 0.8 for global FLUXNET measurements (Wilson et al., 2002). Thus, a correction method, i.e., energy closure ratios
Conclusions
The ARTS E model uses remote sensing observations to predict the rates of E globally. It accounts for the impact of net radiation, air temperature, air moisture deficit, soil water deficit, and vegetation LAI, thereby adequately representing the principles of surface energy balance and water balance. The ARTS E model shows good agreement with observed E at 19 flux sites, at daily and monthly scales. The evaluation also indicates that the PM equation provides a practical framework with which to
Acknowledgments
The authors would like to thank the flux site investigators for allowing us to use their flux data through AmeriFlux program for the development of ARTS E model. This work was supported by National Natural Science Foundation of China (41171284, 40801129), Special Fund for Meteorological Research in the Public Interest (GYHY201106027, 200906022) and Chinese Academy of Sciences (XDA05050602-1). Flux observations at UMBS were supported by US DoE grant # DE-SC0006708. Finally the reviewers and Dr.
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