The metastatistical extreme value distribution for rainfall and flood frequency analysis with external drivers

An accurate estimation of hydrologic extremes is fundamental for its manyimplications on engineering design, flood quantification and mapping, insurance and re-insurance purposes, policy-making. Traditional methods,hinging on the Generalized Extreme Value (GEV) distribution, are founded on often-overlooked and untested assumptions, make an ineffective use of the available data, and are ill-suited for accounting for inter-annual variability. With the aim of improving the estimation accuracy of high return period extremes, this dissertation focuses on the Metastatistical Extreme Value Distribution (MEVD), an approach introduced to relax some of the limitations of the traditional Extreme Value Theory. The present work first analyzes the definition of the optimal MEVD formulation as a function of local climatic factors and of key statistical properties of rainfall at the daily scale. It concludes that the inter-annual variability of rainfall statistical properties plays an important role in the definition of the optimal time window to be used for parameter estimation. In the largest amount of cases examined, except for very dry climates, with few rainy days, the analysis window should be kept to the minimum of 1 year in order to resolve the time variability of the distributions. The use of short time windows also makes the MEVD a suitable approach to study extremes in a changing climate, as it contributes to its ability to resolve inter-annual variability. Up to now, the MEVD has been applied mainly to rainfall (at the daily and hourly scale). Here, for the first time, the MEVD is used to study streamflow data, developing a flood frequency analysis MEVD-based on series of flow peaks in the Continental United States. Moreover, the impact of El Niño Southern Oscillation (ENSO) on flood regimes is evaluated. In the comparison with the GEV, results show the outperformance of the MEVD in ~76% of the analyzed stations, with a significant reduction in the estimation error especially when considering return periods much higher than the size of the sample used to estimate the distributional parameters. Yet, a negligible improvement in the estimation of extreme floods was found when stratifying peaks according to ENSO phases. In the end, leveraging the appealing property of the MEVD to naturally include mixtures of distributions in its formulation, a MEVD that distinguishes between non-Tropical Cyclones (TCs) and Tropical Cyclones-induced rainfall is applied to several American metropolitan areas. The impact of TCs on rainfall is well distinguishable, and the use of a mixed MEVD approach resulted beneficial in several cases. Its advantage in the reduction of the estimation error when compared to the single-distribution MEVD was found to be more significant when considering cumulative values of rainfall over consecutive days, due to the prolonged impact TCs have on rainfall over time.