Portal de Periódicos da CAPES

Evaluation of water resources in a high-mountain basin in Serra da Estrela, Central Portugal, using a semi-distributed hydrological model

2010; Springer Science+Business Media; Volume: 62; Issue: 6 Linguagem: Inglês

10.1007/s12665-010-0610-7

1866-6299

Jorge Espinha Marques, Javier Samper, Bruno Pisani, D. Alvares, José Martins Carvalho, Helder I. Chaminé, José M. Marques, Gonçalo Vieira, Carla Mora, Frederico Sodré Borges,

Groundwater and Isotope Geochemistry

High-mountain basins provide a source of valuable water resources. This paper presents hydrological models for the evaluation of water resources in the high-mountain Zêzere river basin in Serra da Estrela, Central Portugal. Models are solved with VISUAL BALAN v2.0, a code which performs daily water balances in the root zone, the unsaturated zone and the aquifer and requires a small number of parameters. A lumped hydrological model fails to fit measured stream flows. Its limitations are overcome by considering the dependence of the temperature and precipitation data with elevation and the spatial variability in hydrogeomorphological variables with nine sub-basins of uniform parameters. Model parameters are calibrated by fitting stream flow measurements in the Zêzere river. Computed stream flows are highly sensitive to soil thickness, whereas computed groundwater recharge is most sensitive to the interflow and percolation recession coefficients. Interflow is the main component of total runoff, ranging from 41 to 55% of annual precipitation. High interflows are favored by the steep relief of the basin, by the presence of a high permeability soil overlying the fractured low permeability granitic bedrock and by the extensive subhorizontal fracturing at shallow depths. Mean annual groundwater recharge ranges from 11 to 15% of annual precipitation. It has a significant uncertainty due to uncertainties in soil parameters. This methodology proves to be useful to handle the research difficulties regarding a complex mountain basin in a context of data scarcity.