학술논문

Estimating Watershed Evapotranspiration with PASS. : Part II: Moisture Budgets during Drydown Periods
Document Type
research-article
Source
Journal of Hydrometeorology, 2000 Oct 01. 1(5), 462-473.
Subject
Language
English
ISSN
1525755X
15257541
Abstract
The second part of the parameterization of subgrid-scale surface fluxes model (PASS2) has been developed to estimate long-term evapotranspiration rates over extended areas at a high spatial resolution by using satellite remote sensing data and limited, but continuous, surface meteorological measurements. Other required inputs include data on initial root-zone available moisture (RAM) content computed by PASS1 for each pixel at the time of clear-sky satellite overpasses, normalized difference vegetation index (NDVI) from the overpasses, and databases on available water capacity and land-use classes. Site-specific PASS2 parameterizations evaluate surface albedo, roughness length, and ground heat flux for each pixel, and special functions distribute areally representative observations of wind speed, temperature, and water vapor pressure to individual pixels. The surface temperature for each pixel and each time increment is computed with an approximation involving the surface energy budget, and the evapotranspiration rates are computed via a bulk aerodynamic formulation. Results from PASS2 were compared with observations made during the 1997 Cooperative Atmosphere–Surface Exchange Study field campaign in Kansas. The modeled diurnal variation of RAM content, latent heat flux, and daily evapotranspiration rate were realistic in comparison to measurements at eight surface sites. With the limited resolution of the NDVI data, however, model results deviated from the observations at locations where the measurement sites were in fields with surface vegetative conditions notably different than surrounding fields. Comparisons with aircraft-based flux measurements suggested that the evapotranspiration rates over distances of tens of kilometers were modeled without significant bias.