Abstract
Abstract. Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time–space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990–2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.
Highlights
Precipitation is inevitably a crucial variable in any water resources assessment
The resulting parameter values are quite similar for both methods and generally fall within the expected ranges in Table 2, except for the root depth for the model based on the historic mean monthly (HMM) correction method
The main difference between the parameters when the two different correction methods are applied is on the root depth, where the HMM correction results in very large values in contrast to the time–space variable (TSV) method, which results in values well within the expected ranges
Summary
Precipitation is inevitably a crucial variable in any water resources assessment. Without accurate measurements or estimates of precipitation, water balance studies and modelling becomes meaningless (Larson and Peck, 1974).Numerous studies have investigated the impact of precipitation data quality on hydrological model predictions. Precipitation is inevitably a crucial variable in any water resources assessment. Without accurate measurements or estimates of precipitation, water balance studies and modelling becomes meaningless (Larson and Peck, 1974). Numerous studies have investigated the impact of precipitation data quality on hydrological model predictions. It is clear that precipitation input errors should always be minimized and corrected prior to model calibration (Leimer et al, 2011). Mizukami and Smith (2012) analysed the effect of inconsistencies in multiannual precipitation records through a modelling exercise, but focussed on gauge relocation and changes in data processing, not on changes caused by actual climatic variations, which could affect the precipitation correction.
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