Threshold modelling of spatially dependent non‐stationary extremes with application to hurricane‐induced wave heights

  title={Threshold modelling of spatially dependent non‐stationary extremes with application to hurricane‐induced wave heights},
  author={Paul Northrop and Philip Jonathan},
In environmental applications it is common for the extremes of a variable to be non‐stationary, varying systematically in space, time or with the values of covariates. Multi‐site datasets are common, and in such cases there is likely to be non‐negligible inter‐site dependence. We consider applications in which multi‐site data are used to infer the marginal behaviour of the extremes at individual sites, while adjusting for inter‐site dependence. For reasons of statistical efficiency, it is… 
A model for space-time threshold exceedances with an application to extreme rainfall
In extreme value studies, models for observations exceeding a fixed high threshold have the advantage of exploiting the available extremal information while avoiding bias from low values. In the
Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model
  • E. Eastoe
  • Environmental Science
  • 2019
Under the influence of local‐ and large‐scale climatological processes, extreme river flow events often show long‐term trends, seasonality, interyear variability, and other characteristics of
A nonstationary peaks-over-threshold approach for modelling daily precipitation with covariate-dependent thresholds
The estimation of extreme precipitation events is a topic of growing interest and concern, particularly in highly urbanized areas. The Fifth Assessment Report by the Intergovernmental Panel on
Cross‐validatory extreme value threshold selection and uncertainty with application to ocean storm severity
Bayesian cross‐validation is used to address the trade‐off by comparing thresholds based on predictive ability at extreme levels by using Bayesian model averaging to combine inferences from many thresholds, thereby reducing sensitivity to the choice of a single threshold.
Threshold modeling of extreme spatial rainfall
We propose an approach to spatial modeling of extreme rainfall, based on max‐stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models
Spatial regression models for extreme precipitation in Belgium
[1] Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the T year return level. Extreme precipitation depths in Belgium are
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts
  • B. Youngman
  • Mathematics
    Journal of the American Statistical Association
  • 2019
ABSTRACT Generalized additive model (GAM) forms offer a flexible approach to capturing marginal variation. Such forms are used here to represent distributional variation in extreme values and
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles
This work estimates a high non-stationary threshold using a gamma distribution for precipitation intensities that incorporates spatial and temporal random effects and develops a penalized complexity (PC) prior specification for the tail index that shrinks the GP model towards the exponential distribution, thus preventing unrealistically heavy tails.
Assessing the performance of the independence method in modeling spatial extreme rainfall
Spatial statistical methods are often employed to improve precision when estimating marginal distributions of extreme rainfall. Methods such as max‐stable and copula models parameterize the spatial


Modelling non‐stationary extremes with application to surface level ozone
It is suggested that the preprocessing method gives a model that better incorporates the underlying mechanisms that generate the process, produces a simpler and more efficient fit and allows easier computation.
Spatial Regression Models for Extremes
Meteorological data are often recorded at a number of spatial locations. This gives rise to the possibility of pooling data through a spatial model to overcome some of the limitations imposed on an
A Spatiodirectional Model for Extreme Waves in the Gulf of Mexico
The characteristics of extreme waves in hurricane dominated regions vary systematically with a number of covariates, including location and storm direction. Reliable estimation of design criteria
Bayesian Spatial Modeling of Extreme Precipitation Return Levels
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of
Improved estimation for temporally clustered extremes
Through a simulation study, it is demonstrated that the common practice of analysing peaks over thresholds (POT) is liable to incur serious bias in the estimation of parameters, as well as the return levels used as design specifications when building to withstand extremes of wind or rain, or river or sea level.
Regional Estimation from Spatially Dependent Data
  • R. Smith
  • Environmental Science, Mathematics
  • 2005
Regional estimation methods are used in such fields as hydrology and meteorology, for estimating parameters such as return values (or quantiles) of a distribution, when data are available at many
Trend estimation in extremes of synthetic North Sea surges
Summary.  Mechanistic models for complex atmospheric and hydrological processes are often used to simulate extreme natural events, usually to quantify the risks that are associated with these events.
Generalized additive modelling of sample extremes
Summary.  We describe smooth non‐stationary generalized additive modelling for sample extremes, in which spline smoothers are incorporated into models for exceedances over high thresholds. Fitting is
Models for exceedances over high thresholds
We discuss the analysis of the extremes of data by modelling the sizes and occurrence of exceedances over high thresholds. The natural distribution for such exceedances, the generalized Pareto