Abstract
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.
Highlights
In data sparse mountain regions it is challenging to derive areal precipitation estimates
This study indicates that spatial fields from downscaled reanalysis data can provide useful information for the spatial interpolation of precipitation data in regions where the spatial www.hydrol-earth-syst-sci.net/17/2415/2013/
While this assumption cannot be fully validated, plausibility tests, like (1) inspecting the simulated precipitation fields for any conspicuous features, (2) checking that the major orographic characteristics of the region are captured by the model orography and (3) the comparison of simulated and observed precipitation data at locations of available stations, were generally successful for the Karadarya catchment
Summary
In data sparse mountain regions it is challenging to derive areal precipitation estimates. Large uncertainties in areal precipitation estimates are generally due to measurement errors and the scale difference between the point measurements and the areal estimate. This is amplified in mountainous regions, where, despite the high spatial variability of precipitation, the gauge network often has a low density with an unequal distribution towards lower and less exposed locations (Frei and Schar, 1998). Despite complex relations between orography and precipitation, in general, these processes often result in an increase of precipitation with elevation, on windward slopes, and lower precipitation on the leeward side of a mountain range (rain shadow effect). For the spatial interpolation of precipitation in mountainous areas, methods which consider the orography are often advantageous over methods neglecting the relation with the terrain
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