Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

J Blanchet,J Touati,F Garavaglia,E Paquet,D Lawrence

doi:10.5194/nhess-15-2653-2015

Abstract

Abstract. Simulation methods for design flood analyses require estimates of extreme precipitation for simulating maximum discharges. This article evaluates the multi-exponential weather pattern (MEWP) model, a compound model based on weather pattern classification, seasonal splitting and exponential distributions, for its suitability for use in Norway. The MEWP model is the probabilistic rainfall model used in the SCHADEX method for extreme flood estimation. Regional scores of evaluation are used in a split sample framework to compare the MEWP distribution with more general heavy-tailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and weather pattern-based subsampling for extreme value estimation. The MEWP distribution is found to have an overall better performance as compared with the MGPWP, which tends to overfit the data and lacks robustness. Finally, we take advantage of the split sample framework to present evidence for an increase in extreme rainfall in the southwestern part of Norway during the period 1979–2009, relative to 1948–1978.

Highlights

Flood estimation is important for design and safety assessments, flood risk management and spatial planning
This article evaluates the multi-exponential weather pattern (MEWP) model, a compound model based on weather pattern classification, seasonal splitting and exponential distributions, for its suitability for use in Norway
This article evaluates a compound model based on weather pattern classification, seasonal splitting and exponential distributions, the so-called MEWP model, for its suitability for use in Norway

Summary

Introduction

Flood estimation is important for design and safety assessments, flood risk management and spatial planning It aims to assess the probability of occurrence of large events, e.g., discharges with return periods of 100 to 10 000 years. For example, return period estimations of 103 to 104 years are often used (Paquet et al, 2013) Methods for deriving such estimations can be classified into two main groups: statistical flood frequency analysis and precipitation–runoff modeling. Rainfall series are generally more abundant, often have longer periods of record, and they usually show stronger regional consistency This observation is one of the main motivations of the GRADEX method (Guillot, 1993) which uses the distribution of rainfall to extrapolate the distribution of discharge. Input rainfall may be either observed or synthetic events with an estimated probability of occurrence (event-based method) or, either historical or synthetic rainfall records for gener-

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