scholarly journals PHEV! The PHysically-based Extreme Value distribution of river flows

2021 ◽  
Author(s):  
Stefano Basso ◽  
Gianluca Botter ◽  
Ralf Merz ◽  
Arianna Miniussi
Keyword(s):  
Extreme Value Distribution ◽  
River Basins ◽  
Extreme Value ◽  
Engineering Structures ◽  
River Flows ◽  
Empirical Estimates ◽  
Flooding Hazard ◽  
Wide Range ◽  
Physically Based ◽  
Flood Magnitude

Abstract 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.

PHEV! The PHysically-based Extreme Value distribution of river flows

2021 ◽  
Author(s):  
Stefano Basso ◽  
Gianluca Botter ◽  
Ralf Merz ◽  
Arianna Miniussi
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
River Flows ◽  
Physically Based

Detecting hazardous rivers: the physically-based extreme value distribution (PHEV!)

2020 ◽  
Author(s):  
Stefano Basso ◽  
Andrea Domin ◽  
Ralf Merz ◽  
Gianluca Botter ◽  
Arianna Miniussi
Keyword(s):  
Time Series ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Alternative Methods ◽  
Frequency Curve ◽  
Engineering Structures ◽  
Empirical Observation ◽  
Physically Based

<p>Flood frequency curves are the basis to design ordinary engineering structures and devise strategies aimed at mitigating an increasing flood risk. Moreover, they are a crucial tool of risk assessment for insurance and reinsurance purposes. This work is concerned with the presence of abrupt increases of the flood frequency curve (i.e., sudden increments of streamflow magnitudes for a certain return period, named step changes), and investigate their occurrence by means of the physically-based extreme value distribution (PHEV!) of streamflow. This is an analytic probability distribution of extremes, which emerges from a lumped mechanistic-stochastic description of runoff generation and rainfall, soil moisture and discharge dynamics.</p><p>In the study, long synthetic time series of streamflow for river catchments exhibiting step changes have been generated and randomly resampled to construct sub-series of decreasing length. These shorter series are then used to test the performance of the PHEV!, of standard purely statistical distributions of the extremes, and of empirical observation-based estimates of the flood frequency curve in detecting the existence of a step change in the long time series from scarce data. Findings show that the PHEV! robustly detects the occurrence of step changes also when only short time series (e.g., 10 years) are used for parameter estimation. Conversely, the alternative methods tested mostly fails in this objective. These results indicate that the PHEV! might be a reliable tool for detecting the propensity of rivers to generate extreme floods in regions lacking long series of discharge observations.</p>

scholarly journals Spatial dependences among precipitation maxima over Belgium

2007 ◽  
Vol 14 (5) ◽  
pp. 621-630 ◽  
Author(s):  
S. Vannitsem ◽  
P. Naveau
Keyword(s):  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Wide Range ◽  
Fundamental Quantity ◽  
Full Independence ◽  
Weather Stations ◽  
Flood Planning ◽  
The Impact

Abstract. For a wide range of applications in hydrology, the probability distribution of precipitation maxima represents a fundamental quantity to build dykes, propose flood planning policies, or more generally, to mitigate the impact of precipitation extremes. Classical Extreme Value Theory (EVT) has been applied in this context by usually assuming that precipitation maxima can be considered as Independent and Identically Distributed (IID) events, which approximately follow a Generalized Extreme Value distribution (GEV) at each recording site. In practice, weather stations records can not be considered as independent in space. Assessing the spatial dependences among precipitation maxima provided by two Belgium measurement networks is the main goal of this work. The pairwise dependences are estimated by a variogram of order one, also called madogram, that is specially tailored to be in compliance with spatial EVT and to capture EVT bivariate structures. Our analysis of Belgium precipitation maxima indicates that the degree of dependence varies greatly according to three factors: the distance between two stations, the season (summer or winter) and the precipitation accumulation duration (hourly, daily, monthly, etc.). Increasing the duration (from one hour to 20 days) strengthens the spatial dependence. The full independence is reached after about 50 km (100 km) for summer (winter) for a duration of one hour, while for long durations only after a few hundred kilometers. In addition this dependence is always larger in winter than in summer whatever is the duration. An explanation of these properties in terms of the dynamical processes dominating during the two seasons is advanced.

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

scholarly journals Multi-stable composite twisting structure for morphing applications

2012 ◽  
Vol 468 (2141) ◽  
pp. 1230-1251 ◽  
Author(s):  
X. Lachenal ◽  
P. M. Weaver ◽  
S. Daynes
Keyword(s):  
Analytical Model ◽  
Material Properties ◽  
Degrees Of Freedom ◽  
Design Parameters ◽  
Engineering Structures ◽  
The Past ◽  
Wide Range ◽  
Morphing Structure ◽  
Low Mass ◽  
Unstable States

Conventional shape-changing engineering structures use discrete parts articulated around a number of linkages. Each part carries the loads, and the articulations provide the degrees of freedom of the system, leading to heavy and complex mechanisms. Consequently, there has been increased interest in morphing structures over the past decade owing to their potential to combine the conflicting requirements of strength, flexibility and low mass. This article presents a novel type of morphing structure capable of large deformations, simply consisting of two pre-stressed flanges joined to introduce two stable configurations. The bistability is analysed through a simple analytical model, predicting the positions of the stable and unstable states for different design parameters and material properties. Good correlation is found between experimental results, finite-element modelling and predictions from the analytical model for one particular example. A wide range of design parameters and material properties is also analytically investigated, yielding a remarkable structure with zero stiffness along the twisting axis.

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