Articles | Volume 18, issue 6
Hydrol. Earth Syst. Sci., 18, 2089–2102, 2014

Special issue: Drought forecasting and warning

Hydrol. Earth Syst. Sci., 18, 2089–2102, 2014

Research article 04 Jun 2014

Research article | 04 Jun 2014

Statistical prediction of terrestrial water storage changes in the Amazon Basin using tropical Pacific and North Atlantic sea surface temperature anomalies

C. de Linage1, J. S. Famiglietti2,1, and J. T. Randerson1 C. de Linage et al.
  • 1Department of Earth System Science, University of California, Irvine, USA
  • 2UC Center for Hydrological Modeling, University of California, Irvine, USA

Abstract. Floods and droughts frequently affect the Amazon River basin, impacting transportation, agriculture, and ecosystem processes within several South American countries. Here we examine how sea surface temperature (SST) anomalies influence interannual variability of terrestrial water storage anomalies (TWSAs) in different regions within the Amazon Basin and propose a statistical modeling framework for TWSA prediction on seasonal timescales. Three simple semi-empirical models forced by a linear combination of lagged spatial averages of central Pacific and tropical North Atlantic climate indices (Niño 4 and TNAI) were calibrated against a decade-long record of 3°, monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Niño 4 was the primary external forcing in the northeastern region of the Amazon Basin, whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit significantly (p < 0.05) for at least 64% of the grid cells within the basin, compared to models forced solely with Niño 4 or TNAI. The combined model explained 66% of the observed variance in the northeastern region, 39% in the central and western region, and 43% for the Amazon Basin as a whole, with a 3-month lead time between the climate indices and the predicted TWSAs. Model performance varied seasonally: it was higher than average during the wet season in the northeastern Amazon and during the dry season in the central and western region. The predictive capability of the combined model was degraded with increasing lead times. Degradation rates were lower in the northeastern Amazon (where 49% of the variance was explained using an 8-month lead time versus 69% for a 1-month lead time) compared to the central and western Amazon (where 22% of the variance was explained at 8 months versus 43% at 1 month). These relationships may contribute to an improved understanding of the climate processes regulating the spatial patterns of flood and drought risk in South America.