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.

Despite recent advances in sensors, hydro-meteorological data remain scarce in urban watersheds. In the current, reactive approach to stormwater management, whether or not an urban flood event is documented, as well as where and how it actually occurred, is highly dependent on the level of monitoring. Even though there are many methods for observing flooding extent and predicting flooding vulnerability, issues with data availability and accuracy persist at the local level. Urban watersheds are spatially and temporally complex and flash floods, while of particular interest and importance to both hydrologists and communities, are hard to characterize, given that they are rare, spatially localized, short-lived, and often occur in locations without formal monitoring. On the other hand, identifying vulnerable areas in a large city using hydraulic/hydrological modeling would be very difficult, either because models have parameters that need to be calibrated against mostly non-existent data (in the case of conceptual models), or else we do not know all of the actual physical processes at work, or how to quantify them (in the case of physically-based models). Community-based monitoring activities can support the characterization of urban watersheds, as well as stormwater management, because they provide valuable spatial and temporal knowledge about the behavior of water flow and other related issues at a local level. An urban watershed study was conducted to demonstrate the value of community-based observations for understanding and characterizing urban pluvial flash flooding, by addressing the following questions: (i) How can communities feasibly monitor their local watershed using low-cost, straightforward approaches? (ii) How can local knowledge help us to better define and characterize urban pluvial flooding vulnerability? (iii) Are community-based data reliable and meaningful to urban watershed management and the decision-making process? To pursue these questions, participatory research was applied in a low-resourced community in the City of Paternò, in Sicily, Italy. We collected as unbiased as possible information about flash flooding events through 300 surveys. The locally-gathered data were compared to the results of advanced hydrodynamic models on smaller scales.