Open AccessPHEV! The PHysically-based Extreme Value distribution of river flows
Author(s) -
Stefano Basso,
Gianluca Botter,
Ralf Merz,
Arianna Miniussi
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/ac3d59
Subject(s) - environmental science , magnitude (astronomy) , flood myth , flooding (psychology) , generalized extreme value distribution , calibration , range (aeronautics) , streamflow , extreme value theory , empirical distribution function , hydrology (agriculture) , econometrics , drainage basin , statistics , geography , mathematics , geology , engineering , cartography , psychology , physics , archaeology , geotechnical engineering , astronomy , aerospace engineering , psychotherapist
Magnitude and frequency are prominent features of river floods informing design of engineering structures, insurance premiums and adaptation strategies. Recent advances yielding a formal characterization of these variables from a joint description of soil moisture and daily runoff dynamics in river basins are here systematized to highlight their chief outcome: the PHysically-based Extreme Value (PHEV) distribution of river flows. This is a physically-based alternative to empirical estimates and purely statistical methods hitherto used to characterize extremes of hydro-meteorological variables. Capabilities of PHEV for predicting flood magnitude and frequency are benchmarked against a standard distribution and the latest statistical approach for extreme estimation, by using both an extensive observational dataset and long synthetic series of streamflow generated for river basins from contrasting hydro-climatic regions. The analyses outline the domain of applicability of PHEV and reveal its fairly unbiased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration in a wide range of case studies. The results also emphasize reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point. These features, arising from the mechanistic understanding embedded in the novel distribution of the largest river flows, are key for a reliable assessment of the actual flooding hazard associated to poorly sampled rare events, especially when lacking long observational records.