A Comparison of GLDAS Soil Moisture Anomalies against Standardized Precipitation Index and Multisatellite Estimations over South America.

Publicado en Journal of Hydrometeorology, v. 16(1):158–171
Autores

Spennemann, P.C., Rivera, J.A., Saulo, A.C. and Penalba, O.C.
Año de publicación 2014
DOI https://doi.org/10.1175/JHM-D-13-0190.1
Afiliaciones
  • Centro de Investigaciones del Mar y la Atmósfera, Consejo Nacional de Investigaciones Científicas y Técnicas&ndashUniversidad de Buenos Aires, UMI&ndashInstituto Franco&ndashArgentino sobre Estudios de Clima y sus Impactos/CNRS, Buenos Aires, Argentina
  • Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (CCT-Mendoza/CONICET), Parque General San Martín, Mendoza, Argentina
  • Centro de Investigaciones del Mar y la Atmósfera, Consejo Nacional de Investigaciones Científicas y Técnicas&ndashUniversidad de Buenos Aires, UMI&ndashInstituto Franco&ndashArgentino sobre Estudios de Clima y sus Impactos/CNRS, Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, and Servicio Meteorológico Nacional, Buenos Aires, Argentina
  • Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and Consejo de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

 
Programa

CRN3
Proyecto CRN3035
Keywords
South America
Hydrometeorology
Soil moisture
Land surface model

Abstract

This study aims to compare simulated soil moisture anomalies derived from different versions of the Global Land Data Assimilation System (GLDAS), the standardized precipitation index (SPI), and a new multisatellite surface soil moisture product over southern South America. The main motivation is the need for assessing the reliability of GLDAS variables to be used in the characterization of soil state and its variability at the regional scale. The focus is on the southeastern part of South America (SESA), which is part of the La Plata basin, one of the largest basins of the world, where agriculture is the main source of income. The results show that GLDAS data capture soil moisture anomalies and their variability, taking into account regional and seasonal dependencies and showing correspondence with other proxies used to characterize soil states. Over large portions of the domain, and particularly over SESA, the correlation with the SPI is very high, with the second version of GLDAS, version 2 (GLDAS-2 v2), exhibiting the highest values regardless of the season. Similar results were obtained by comparing the surface soil moisture anomalies from the GLDAS land surface model (LSM) against the satellite estimations for a shorter period of time. This work documents that the precipitation dataset used to force each LSM and the choice of the LSM are of major relevance for representing soil conditions in an adequate manner. The results are considered to support the use of GLDAS as an indicator of soil moisture states and for developing new soil moisture&ndashmonitoring indices that can be applied, for example, in the context of agricultural production management.