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UNC-Chapel Hill GEOG 801 - A biophysical process-based estimate of global land surface evaporation using satellite and ancillary data

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Journal of Hydrology ELSEVIER Journal of Hydrology 205 (1998) 164-185 A biophysical process-based estimate of global land surface evaporation using satellite and ancillary data I. Model description and comparison with observations Bhaskar J. Choudhury a'*, Nicolo E. DiGirolamo b aCode 974, Hydrological Sciences Branch Laboratory for Hydrospheric Processes NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA bScience, Systems and Applications, Inc., 5900 Princes Garden Parkway, Lanham, MD 20706, USA Received 30 May 1996; revised 28 July 1997; accepted 20 November 1997 Abstract A biophysical process-based model is used to estimate transpiration, soil evaporation and interception over the global land surface for a 24-month period (January 1987 to December 1988). The model parameters are determined from published records, and their geographical distribution has been prescribed according to land use and land cover data. Satellite obser- vations are used to obtain fractional vegetation cover, isothermal net and photosynthetically active radiation, air temperature and vapor pressure deficit. Precipitation and friction velocity are derived as blended products (disaggregated and assimilated data). The calculated seasonal and geographical variations of evaporation, net radiation and soil moisture are in good agreement with field observations, catchment water balance data, and atmospheric water budget analysis; explained variances being greater than 75%. Uncertainties in the estimated evaporation are about 15 and 20%, respectively, for annual and monthly values. © 1998 Elsevier Science B.V. All rights reserved. Keywords: Evaporation; Biophysical model; Satellite data I. Introduction Partitioning of precipitation into evaporation and runoff, and partitioning of available energy into latent and sensible heat fluxes are fundamental aspects of hydrology and meteorology. In hot arid environments, long-term average annual total evaporation (E) is essentially equal to annual precipitation (P), while evaporation in humid tropical areas is essentially determined by the available energy or potential eva- poration (Eo). To bridge these two limiting situations, Pike (1964) proposed the following empirical * Corresponding author. 0022-1694/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved. PH S0022-1694(97)00147-9 interpolation equation for estimating E: E=P/{ 1 + (P/Eo)2} °'5 (1) Other similar interpolation equations can be found in Budyko (1974), who considered one such equation (referred to in Budyko as "the equation of relation- ship") for calculating global land surface evapora- tion. The dependence of E on soil and vegetation char- acteristics is not apparent in Eq. (1), although there is much evidence that changes in land surface character- istics can affect partitioning of precipitation and avail- able energy (Bruijnzeel, 1990; Bastable et al., 1993; Calder, 1993). A process-based understanding of total evaporation is needed to assess evaporation for theB.J. Choudhury, N.E. DiGirolamo/Journal of Hydrology 205 (1998) 164-185 165 current state of the land surface and to quantify likely changes in evaporation due to land surface change. It is important to quantify total evaporation in terms of its components (transpiration, soil evaporation, and evaporation of intercepted water) because different biophysical and environmental characteristics exert dominant control on these components, and changes in land use and land cover directly affect these com- ponents. Previous estimates of global land surface evaporation (Brutsaert, 1982) did not quantify these components, and these estimates provide climatologic values (average of many years) rather than the values for any specific year. The magnitudes of uncertainties in these estimates are also not well documented. The objective of this paper is to describe and evaluate a biophysical process-based model of total evaporation. The model parameters have been determined from published literature and their geo- graphical distribution has been prescribed according to land use and land cover. The model has been run using spatially representative satellite and assimilated data providing fractional cloud cover, surface albedo, solar and photosynthetically active radiation incident on the surface, air temperature and vapor pressure, precipitation, friction velocity, and fractional vegeta- tion cover for a 24-month period (January 1987 to December 1988). All data used in this study are in monthly time step; the precipitation data have been disaggregated into daily values. The spatial resolution of these data is variable (largely determined by the instruments providing these measurements and data processing algorithms); the highest resolution being that for fractional vegetation cover (0.25 ° x 0.25°; latitude × longitude cell size) and the lowest for albedo, radiation and vapor pressure (2.5 ° × 2.5°). The model has been run at 0.25 ° x 0.25 ° resolution; all other data have been put into this resolution by duplicating their values within their own resolution. Thus, while the calculations have been done at a daily time step and 0.25 ° × 0.25 ° spatial resolution, the results can be considered to be realistic at a monthly time step and 2.5 ° × 2.5 ° resolution. 2. Model description The model used in this study follows several previous process-based models (cf. Ritchie, 1972; McMurtrie et al., 1992). It is a deterministic, lumped-parameter model, which has been run at a daily time step and at a spatial resolution of 0.25 ° x 0.25 ° (latitude x longitude cell). Water balance fluxes have been calculated for each cell separately (i.e. there are no cell-to-cell transfer of fluxes). Precipitation has been considered to provide all water available for evaporation and runoff (i.e. no irrigation or extraction of ground water). Water balance calculations have been done for the root zone, treated as a single layer, whose maximum capacity does not change with time. The daily change of soil moisture has been calculated using the follow- ing equation: W(j + 1) = W(j) + P(j) -I- Sm (j) - I(j) - Qs0) -O(j)


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UNC-Chapel Hill GEOG 801 - A biophysical process-based estimate of global land surface evaporation using satellite and ancillary data

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