Droughts have pervasive societal impacts and remain difficult to characterize observationally, due to the limited number of droughts sampled in instrumental records. One approach to improving the statistical basis of drought occurrence probability estimation is to extend the observational record using proxy climatic archives, such as those based on tree-ring information. Additionally, since droughts are rare and characterized by multiannual durations and inter-arrival times, it is important to devise and apply statistical techniques that make full use of all of the available information so as to improve our ability to quantify the rarest droughts. We extract data from a publicly available tree-ring based Palmer Drought Severity Index (PDSI) dataset, the Old World Drought Atlas, for two sites in Italy where long rainfall and temperature observational time series are leveraged for a meaningful comparison. Drought events are defined in terms of drought deficit volumes below a threshold PDSI value, and are studied through the Metastatistical Extreme Value Distribution (MEVD) to quantify the occurrence probability of extreme drought events. The estimation uncertainty associated with a variety of possible assumptions in MEVD analysis is studied, in specific comparison with the performance obtained using the traditional Generalized Extreme Value distribution, through a cross-validation methodology. Results suggest that MEVD-based formulations are more robust and flexible with respect to traditional ones. The combination of paleoclimatic data and methodologies capable of using most of the existing information provide more reliable estimates of drought recurrence times, which may be used to design more effective drought risk management plans.

Because they are conceptually unable to consider events at the sub-annual scale, probabilistic flood analyses based on annual maxima (AM) underestimate the actual frequency of frequent floods (with return periods under 5 years), so that peaks-over-threshold (POT) approaches should be preferred. While this has been acknowledged for decades, frequent floods are still estimated too often using AM, probably because the procedure is simpler, and AM series are longer and easier to obtain. However, the negative bias incurred when performing flood frequency with AM can be severe. This affects fields such as river restoration, stream ecology, and fluvial geomorphology, which require a correct characterization of frequent floods. Using hundreds of U.S. watersheds with natural flow regimes, across different climatic and geomorphic conditions, we systematically study the variability in how AM frequency analyses underestimate frequent floods, finding clear spatial patterns. Exploiting the duality between the Generalized Extreme Value and the Generalized Pareto distributions (used for modeling AM and POT, respectively), we identify the drivers of frequent-flood underestimation, studying the influence of the distributions’ shapes. In turn, with the support of an optimal feature-selection technique, we determine the physical drivers explaining underestimation, from a wide spectrum of basin descriptors, investigating their linkages with the distributional characteristics that affect underestimation. A theoretical relationship is derived to infer the underestimation rate, allowing for post-hoc correction of AM-predicted frequent floods, without the need to perform POT frequency analyses. However, this approach underperforms at sites with mixed flood populations.