Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

AbstractTropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty. Here we formulate a mixed‐population Metastatistical Extreme Value Distribution explicitly incorporating non‐TC and TC‐induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC‐ and non‐TC‐related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered

Identifying discontinuities of flood frequency curves

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.publishedVersio

Extreme flooding controlled by stream network organization and flow regime

River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly, it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.publishedVersio

Shifts in flood generation processes exacerbate regional flood anomalies in Europe

Anomalies in the frequency of river floods, i.e., flood-rich or -poor periods, cause biases in flood risk estimates and thus make climate adaptation measures less efficient. While observations have recently confirmed the presence of flood anomalies in Europe, their exact causes are not clear. Here we analyse streamflow and climate observations during 1960-2010 to show that shifts in flood generation processes contribute more to the occurrence of regional flood anomalies than changes in extreme rainfall. A shift from rain on dry soil to rain on wet soil events by 5% increased the frequency of flood-rich periods in the Atlantic region, and an opposite shift in the Mediterranean region increased the frequency of flood-poor periods, but will likely make singular extreme floods occur more often. Flood anomalies driven by changing flood generation processes in Europe may further intensify in a warming climate and should be considered in flood estimation and management.publishedVersio

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

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

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