HR: 1340h
AN: A53A-0859    [Abstracts]
TI: Regional Climate Prediction and Water Resources Management: Hydropower Applications
AU: * Martins, E S
EM: martins@funceme.br
AF: FUNCEME, Av. Rui Barbosa 1246, Fortaleza, CE 60150170 Brazil
AU: Costa, A A
EM: alexandre.costa@yale.edu
AF: Yale University, PO BOX 208109, New Haven, CT 06520-8109 United States
AB: The great concern due to a recent energy crisis in Brazil (in which hydropower accounts for the major part of power generated) brought to authorities' attention the need for better monitoring and predicting both precipitation and reservoir inflows. The possibility of streamflow prediction several months ahead can play a major role in regional economies highly dependent on hydropower generation and irrigation for crop yield. The streamflow variability is very connected to climatic signals which occurs in the tropical zone of the globe. In this context, the use of atmospheric numerical models for climate prediction is becoming widely used, as a tool for many applications, including management of water resources. As mesoscale models are able to better represent atmospheric circulations induced by local forcing (such as topography, land-water and land use contrasts, etc.), they are used nowadays for climate prediction at the regional scales, usually with the input of data from general circulation models (GCMs). In this work, a mesoscale model (the Regional Atmospheric Modeling System, RAMS), forced by data from the ECHAM model, is used to simulate the interannual variability over a major river basin in Brazil (Sao Francisco basin). Emphasis is given in the analysis of the precipitation field, to allow predictions of river discharge and reservoir levels, especially critical in hydropower reservoirs at the Sao Francisco basin. An 80x80 horizontal grid is used, with a 40 km grid-spacing in both directions, along with a vertically-stretched, 41-level vertical grid. A 30-year climatology for one of the ECHAM members was generated, and the mesoscale model sensitivity to the large-scale model forcing was explored, in order to achieve the best representation of the interannual variability. Along with seasonal prediction, the use of regional atmospheric model for short term streamflow forecast and simulation of scenarios for long term hydropower planning are also future goals in this project.
DE: 3329 Mesoscale meteorology
DE: 3337 Numerical modeling and data assimilation
DE: 3300 METEOROLOGY AND ATMOSPHERIC DYNAMICS
DE: 1800 HYDROLOGY
DE: 1857 Reservoirs (surface)
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting