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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-505
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-505
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Oct 2018

Research article | 29 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Comparison of approaches to interpolating climate observations in steep terrains with low-density gauging networks

Juan Ossa-Moreno1, Greg Keir1, Neil McIntyre1, Michela Cameletti2, and Diego Rivera3 Juan Ossa-Moreno et al.
  • 1Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Australia
  • 2Department of Management, Economics and Quantitative Methods, Universita degli Studi di Bergamo, Bergamo, Italy
  • 3School of Agricultural Engineering, Water Research Centre for Agriculture and Mining (WARCAM), Universidad de Concepcion, Concepcion, Chile

Abstract. The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges, coupled with complex spatial variations in climate. Increasingly, spatial data sets (i.e. satellite and gridded products) and new computational tools are used to address this problem, by assisting with the spatial interpolation of ground observations. This paper presents a comparison of approaches of different complexity to spatially interpolate precipitation and temperature time-series in the upper Aconcagua catchment in central Chile. A Generalised Linear Mixed Model whose parameters are estimated through approximate Bayesian inference is compared with three simpler alternatives: Inverse Distance Weighting, Lapse Rates and a method based on WorldClim data. The assessment is based on a leave-one-out cross validation, with the Root Mean Squared Error being the primary performance criterion for both climate variables, while Probability of Detection and False Alarm Ratio are also used for precipitation. Results show that for spatial interpolation of the expected values of temperature and precipitation, the WorldClim approach may be recommended as being the more accurate, easy to apply and relatively more robust to tested reductions in the number of estimation gauges, particularly for temperature. The Generalised Linear Mixed Model has comparable performance when all gauges were included, but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

Juan Ossa-Moreno et al.
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Juan Ossa-Moreno et al.
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Short summary
Water management in mountains is challenging when there is no climate data of good quality. This can be addressed by using statistical methods, or by including alternative sources of information. This project tests a relatively complex statistical method and compares it with simpler alternatives, while including satellite data. It was found that the simple alternative may behave as well as the complex one, and it could also be established how good the alternative sources of information are.
Water management in mountains is challenging when there is no climate data of good quality. This...
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