1Institute for Environment and Sustainability, European Commission Joint Research Centre, Via E. Fermi, 2749, 21027 Ispra, VA, Italy
2European Centre for Medium-Range Weather Forecasts Shinfield Park Reading, Reading, RG2 9AX, UK
3Department of Geography, King's College London, London, UK
Received: 26 Sep 2011 – Discussion started: 17 Oct 2011
Abstract. The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.
Revised: 07 Feb 2012 – Accepted: 22 Mar 2012 – Published: 02 Apr 2012
Bogner, K., Pappenberger, F., and Cloke, H. L.: Technical Note: The normal quantile transformation and its application in a flood forecasting system, Hydrol. Earth Syst. Sci., 16, 1085-1094, doi:10.5194/hess-16-1085-2012, 2012.