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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 11
Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010
https://doi.org/10.5194/hess-14-2193-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010
https://doi.org/10.5194/hess-14-2193-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Nov 2010

Research article | 04 Nov 2010

The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments

J. L. Peña-Arancibia3,1,2, A. I. J. M. van Dijk3, M. Mulligan1, and L. A. Bruijnzeel2 J. L. Peña-Arancibia et al.
  • 1CSIRO Land and Water, GPO 1666, Black Mountain ACT, Australia
  • 2Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, Strand, London WC2R 2LS, UK
  • 3Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1085-1087, 1081 HV, Amsterdam, The Netherlands

Abstract. The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Daily streamflow data (m3 s–1) from the Global River Discharge Center (GRDC) and a linear reservoir model were used to obtain baseflow recession coefficients (kbf) for these catchments. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients. Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of kbf. The rest of climatic indices and the terrain indices average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a potential alternative for kbf parameterisation in ungauged catchments.

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