Your browser doesn't support javascript.
loading
Estimating snow water equivalent from GPS vertical site-position observations in the western United States.
Ouellette, Karli J; de Linage, Caroline; Famiglietti, James S.
Afiliação
  • Ouellette KJ; Department of Earth System Science, University of California Irvine, Irvine, California, USA.
Water Resour Res ; 49(5): 2508-2518, 2013 May.
Article em En | MEDLINE | ID: mdl-24223442
ABSTRACT
[1] Accurate estimation of the characteristics of the winter snowpack is crucial for prediction of available water supply, flooding, and climate feedbacks. Remote sensing of snow has been most successful for quantifying the spatial extent of the snowpack, although satellite estimation of snow water equivalent (SWE), fractional snow covered area, and snow depth is improving. Here we show that GPS observations of vertical land surface loading reveal seasonal responses of the land surface to the total weight of snow, providing information about the stored SWE. We demonstrate that the seasonal signal in Scripps Orbit and Permanent Array Center (SOPAC) GPS vertical land surface position time series at six locations in the western United States is driven by elastic loading of the crust by the snowpack. GPS observations of land surface deformation are then used to predict the water load as a function of time at each location of interest and compared for validation to nearby Snowpack Telemetry observations of SWE. Estimates of soil moisture are included in the analysis and result in considerable improvement in the prediction of SWE. Citation Ouellette, K. J., C. de Linage, and J. S. Famiglietti (2013), Estimating snow water equivalent from GPS vertical site-position observations in the western United States, Water Resour. Res., 49, 2508-2518, doi10.1002/wrcr.20173.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Water Resour Res Ano de publicação: 2013 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Water Resour Res Ano de publicação: 2013 Tipo de documento: Article