Analysis of Hydrologic Droughts in Amazon Watersheds

Wednesday, 17 December 2014: 11:50 AM
Alan V Lopes, Sally E Thompson and John A Dracup, University of California Berkeley, Civil & Environmental Engineering, Berkeley, CA, United States
The Amazon river basin is vulnerable to severe seasonal droughts that cause significant ecological and economic impacts, as happened in 2005 and 2010. Although drought analysis tools have been largely developed and applied in arid and semi-arid environments, challenges remain in characterizing droughts and related hydrologic mechanisms in tropical regions with large storage capacity and densely forested, such as in the Amazon. In this study, we analyze the hydrologic behavior of the unsaturated zone of typical Amazon watersheds during droughts using a coupled soil-vegetation model. That model uses a one-dimensional, multiple wetting front representation of a 12 meters-deep unsaturated zone, and was calibrated with existing field data such as soil moisture and evaporation from flux towers. That model was forced with rainfall and potential evaporation synthetic time series generated with a stochastic model developed to represent different climate patterns. Such stochastic simulation of the soil-vegetation system produces the probability distributions of water balance components and allows for the analysis of the sensitivity of the hydrologic fluxes to varying rainfall properties. The results show a contrasting behavior of hydrologic components: as rainfall is reduced, interception decreases but transpiration increases due to less cloudiness. The increase in transpiration seems to be supported by larger changes in soil water storages. Moreover, periods of reduced rainfall are also followed by enhanced deep percolation underneath the deep root zones. Thus, dry periods are associated with higher rates of soil water loss to transpiration and deep percolation. Those results advance the existing knowledge of the response of tropical, forested environments to droughts and can potentially contribute to better prediction of impacts of hydrologic droughts in the Amazon.