Articles | Volume 17, issue 7
Hydrol. Earth Syst. Sci., 17, 2415–2434, 2013
https://doi.org/10.5194/hess-17-2415-2013
Hydrol. Earth Syst. Sci., 17, 2415–2434, 2013
https://doi.org/10.5194/hess-17-2415-2013

Research article 02 Jul 2013

Research article | 02 Jul 2013

Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

D. Duethmann1, J. Zimmer1,*, A. Gafurov1, A. Güntner1, D. Kriegel1, B. Merz1, and S. Vorogushyn1 D. Duethmann et al.
  • 1GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
  • *now at: Engineering Consultancy Load and Energy Management, Leipzig, Germany

Abstract. In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

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