Estimation of extreme daily precipitation return levels at-site and in ungauged locations using the simplified MEV approach

Abstract

Estimating extreme precipitation return levels at ungauged locations is key for hydrological applications and risk management, and demands improved techniques to decrease the large uncertainty of traditional methods. Here, we leverage the perks of the simplified metastatistical extreme value (SMEV) approach with a twofold aim: we show how it can be effectively used in situations in which the ordinary daily precipitation events cannot be fully described using a two-parameter distribution, and we examine the performance of different interpolation techniques for the estimation of return levels in ungauged locations. SMEV proved adequate at representing at-site extremes for a set of 4000+ stations in Germany, with a general tendency to underestimate the probability of the largest annual maxima. At the same time SMEV tends to overestimate with respect to the design return levels currently adopted in the country, suggesting that these might actually underestimate the distribution tail. Among the investigated methods, the inverse distance weighted interpolation of SMEV parameters provides the most accurate estimates of extreme return levels for ungauged locations, with typical standard errors of 0.79 (0.83) for rain gauge densities of 1/500 km−2 (1/1000 km−2). Albeit only less than 10% of the variance in estimation errors is explained by elevation, the correlation between SMEV parameters and orography (up to 43% explained variance) suggests that future applications should test the inclusion of such information in spatial estimates.