Improving the accuracy of tipping-bucket rain records using disaggregation techniques

We present a methodology able to infer the influence of rainfall measurement errors on the reliability of extreme rainfall statistics. We especially focus on systematic mechanical errors affecting the most popular rain intensity measurement instrument, namely the tipping-bucket rain-gauge (TBR). Such uncertainty strongly depends on the measured rainfall intensity (RI) with systematic underestimation of high RIs, leading to a biased estimation of extreme rain rates statistics. Furthermore, since intense rain-rates are usually recorded over short intervals in time, any possible correction strongly depends on the time resolution of the recorded data sets. We propose a simple procedure for the correction of low resolution data series after disaggregation at a suitable scale, so that the assessment of the influence of systematic errors on rainfall statistics become possible. The disaggregation procedure is applied to a 40-year long rain-depth dataset recorded at hourly resolution by using the IRP (Iterated Random Pulse) algorithm. A set of extreme statistics, commonly used in urban hydrology practice, have been extracted from simulated data and compared with the ones obtained after direct correction of a 12-year high resolution (1 min) RI series. In particular, the depth–duration–frequency curves derived from the original and corrected data sets have been compared in order to quantify the impact of non-corrected rain intensity measurements on design rainfall and the related statistical parameters. Preliminary results suggest that the IRP model, due to its skill in reproducing extreme rainfall intensities at fine resolution in time, is well suited in supporting rainfall intensity correction techniques.