Proceedings of the 1st International Congress on Hydroclimatology pages:14 p.
International Congress on Hydroclimatology location:Cochamamba, Bolivia date:24-28 August 2009
Information on the reference evapotranspiration (ET0), or the consumptive water use, is very important and significant for water resources planning and management. The FAO Penman-Monteith (FAO56) equation is considered as the reference methodology for computing ET0. Nevertheless, in some developing countries as in the case of Bolivia, the only available data at some weather stations are the maximum and minimum or mean temperatures; therefore the need for lower data demanding methods is preponderant.
The study area considered for this paper is part of the Pirai River basin, which is a tributary of the Amazon River.
The available data consisted of 3 weather stations with complete climatic data (CWS). Complete means in this case
that daily series are available for nubosity, maximum and minimum temperature, humidity and wind speed, since 1950 to 2000. This data has been collected from the SENAMHI-Santa Cruz I. The data set also consist of another five weather stations with only mean temperature records (TWS) collected from SEARPI II. Maximum and minimum temperature values for these stations could be obtained after extrapolation from the CWS. This extrapolation, however, introduced extra uncertainty, taking into account that the spatial distribution and the range of altitudes of the stations is quite high (373 m.a.s.l. to 1350 m.a.s.l.). The FAO56 method has been applied as a reference method with the objective to calibrate the less data demanding methods to local conditions: Hargreaves-Samani, Thornthwaite and pan evaporation. It has been shown that the Hargreaves- Samani method produces better results than the Thornthwaite method at the CWS stations. It has also been found, as in literature, that the method of Thornthwaite underestimates
evapotranspiration in humid areas. Therefore, a local correction factor has been calculated at yearly and monthly
basis in order to eliminate the bias, improving the predicting power of the low data demanding formulas under local conditions.