Abstract
Abstract. A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000–2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3 mm/month, RMSE value of 36.9 mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.
Highlights
The energy balance method, e.g. SEBS (Su 2002), is structured around estimating the turbulent sensible heat flux (H) based on a parameterization method of the aerodynamic resistance
Where Rn [W/m2] is monthly mean net radiation derived from ERA-Interim and MODIS monthly mean LST data; G is the monthly mean ground heat flux [W/m2], which is taken as zero at the monthly time resolution; H is monthly mean sensible heat flux [W/m2], calculated with MODIS monthly mean remote sensed LST and ERA-Interim monthly mean air temperature; and LE is the monthly mean latent heat flux [W/m2], which is computed as the residual of equation 1
Our result shows that the energy balance has a high promise to produce a global ET product which take into account irrigation impacts on the global water cycle
Summary
The energy balance method, e.g. SEBS (Su 2002), is structured around estimating the turbulent sensible heat flux (H) based on a parameterization method of the aerodynamic resistance. Remote sensing energy balance model needs an estimate of roughness length to characterize the momentum and heat turbulent exchange between the surface and atmosphere. An accurate simulation of the sensible heat flux (H) over vegetation from thermal remote sensing requires an a priori estimate of roughness length and the excess resistance parameter. Chen et al (2019) reported that sensible heat is significantly underestimated by SEBS at forest sites due to a high value of excess resistance (kB ). The enhanced SEBS energy balance (EB) model has been verified to provide accurate simulation over different canopy structures
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