Geoadditive modeling for extreme rainfall data

Bocci, Chiara; Caporali, Enrica; Petrucci, Alessandra

doi:10.1007/s10182-012-0192-7

Cited by 9 publications

(11 citation statements)

References 16 publications

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“…1 could potentially reveal additional structure. If a more detailed characterization of the decadal variations in the return level were of interest at this site, the use of a spline basis function for the trend in the location parameter may be appropriate, as shown by Bocci et al (2012), Padoan and Wand (2008), Nasri et al (2013) and Yousfi and El Adlouni (2016).…”

Section: Bonnafous Et Al: a Water Risk Index For Portfolio Exposumentioning

confidence: 99%

A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

Bonnafous

Lall

Siegel

2017

Hydrol. Earth Syst. Sci.

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Abstract. Corporations, industries and non-governmental organizations have become increasingly concerned with growing water risks in many parts of the world. Most of the focus has been on water scarcity and competition for the resource between agriculture, urban users, ecology and industry. However, water risks are multi-dimensional. Waterrelated hazards include flooding due to extreme rainfall, persistent drought and pollution, either due to industrial operations themselves, or to the failure of infrastructure. Most companies have risk management plans at each operational location to address these risks to a certain design level. The residual risk may or may not be managed, and is typically not quantified at a portfolio scale, i.e. across many sites. Given that climate is the driver of many of these extreme events, and there is evidence of quasi-periodic climate regimes at inter-annual and decadal timescales, it is possible that a portfolio is subject to persistent, multi-year exceedances of the design level. In other words, for a multi-national corporation, it is possible that there is correlation in the climateinduced portfolio water risk across its operational sites as multiple sites may experience a hazard beyond the design level in a given year. Therefore, from an investor's perspective, a need exists for a water risk index that allows for an exploration of the possible space and/or time clustering in exposure across many sites contained in a portfolio. This paper represents a first attempt to develop an index for financial exposure of a geographically diversified, global portfolio to the time-varying risk of climatic extremes using long daily global rainfall datasets derived from climate re-analysis models. Focusing on extreme daily rainfall amounts and using examples from major mining companies, we illustrate how the index can be developed. We discuss how companies can use it to explore their corporate exposure, and what they may need to disclose to investors and regulators to promote transparency as to risk exposure and mitigation efforts. For the examples of mining companies provided, we note that the actual exposure is substantially higher than would be expected in the absence of space and time correlation of risk as is usually tacitly assumed. We also find evidence for the increasing exposure to climate-induced risk, and for decadal variability in exposure. The relative vulnerability of different portfolios to multiple extreme events in a given year is also demonstrated.

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Section: Bonnafous Et Al: a Water Risk Index For Portfolio Exposumentioning

confidence: 99%

A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

Bonnafous

Lall

Siegel

2017

Hydrol. Earth Syst. Sci.

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“…The effective degrees of freedom estimated for such a model term is usually a small proportion of m so that a low‐rank approximation is desired. A simple choice is to predefine k knot locations for the second term in (10) with k much smaller than m (e.g., Bocci et al , ). Usually, optimality cannot be guaranteed for such choices.…”

Section: Methodsmentioning

confidence: 99%

Bayesian geoadditive modelling of climate extremes with nonparametric spatially varying temporal effects

Yang

2016

Intl Journal of Climatology

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Non-stationary modelling of climate extremes has attracted significant attention in recent years. Generalized extreme value (GEV) distribution is the standard approach for modelling block extremes. The non-stationary form of GEV distribution with location and scale parameters linearly regressing to time has been widely used for single-site time series of climate extremes. In the present paper, such a model is extended to be geoadditive for regional climate extremes with nonparametric spatially varying temporal effects. The model is implemented through the Bayesian hierarchical approach and is applied to the annual minimum surface air temperature in Mainland China as a demonstration. The five bivariate functions representing the regional regression parameters in the model are approximated by low-rank thin plate regression splines. All the necessary software tools for producing a thin plate regression spline basis and Markov Chain Monte Carlo sampling can be seamlessly integrated and called through the R software environment. Results show that the model can capture the spatial inhomogeneity and temporal non-stationarity features of regional climate extremes. Expressions for return levels with different return periods and their trends are also derived accordingly. Trends in different return levels, especially those in the 'extreme state' (e.g., 50-year return level) that cannot be inferred by usual observational trend analysis, can also be derived through samples drawn from posterior predictive distributions. The regional trend in a 50-year return level is quite different to that in a 2-year return level. More attention should be paid to trends in the extreme state of climate under climate change.

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“…The nonstationary extreme value analysis has been widely used to study extreme events in hydrology, hydroclimatology, as well as in environmental, anthropogenic and geophysical processes. Particularly, it has been used to study the long-term risks in rainfall [21], winds [22], heat waves [16] and earthquakes [23,24]. In these studies, it can be seen that the trend of extreme values has been adjusted using several approaches.…”

Section: Introductionmentioning

confidence: 99%

On the Smoothing of the Generalized Extreme Value Distribution Parameters Using Penalized Maximum Likelihood: A Case Study on UVB Radiation Maxima in the Mexico City Metropolitan Area

Aguirre-Salado

Alvarado

et al. 2020

Mathematics

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This paper concerns the use and implementation of penalized maximum likelihood procedures to fitting smoothing functions of the generalized extreme value distribution parameters to analyze spatial extreme values of ultraviolet B (UVB) radiation across the Mexico City metropolitan area in the period 2000–2018. The model was fitted using a flexible semi-parametric approach and the parameters were estimated by the penalized maximum likelihood (PML) method. In order to investigate the performance of the model as well as the estimation method in the analysis of complex nonlinear trends for UVB radiation maxima, a simulation study was conducted. The results of the simulation study showed that penalized maximum likelihood yields better regularization to the model than the maximum likelihood estimates. We estimated return levels of extreme UVB radiation events through a nonstationary extreme value model using measurements of ozone (O3), nitrogen oxides (NOx), particles of 10 μm or less in diameter (PM10), carbon monoxide (CO), relative humidity (RH) and sulfur dioxide (SO2). The deviance statistics indicated that the nonstationary generalized extreme value (GEV) model adjusted was statistically better compared to the stationary model. The estimated smoothing functions of the location parameter of the GEV distribution on the spatial plane for different periods of time reveal the existence of well-defined trends in the maxima. In the temporal plane, a presence of temporal cyclic components oscillating over a weak linear component with a negative slope is noticed, while in the spatial plane, a weak nonlinear local trend is present on a plane with a positive slope towards the west, covering the entire study area. An explicit spatial estimate of the 25-year return period revealed that the more extreme risk levels are located in the western region of the study area.

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Geoadditive modeling for extreme rainfall data

Cited by 9 publications

References 16 publications

A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

Bayesian geoadditive modelling of climate extremes with nonparametric spatially varying temporal effects

On the Smoothing of the Generalized Extreme Value Distribution Parameters Using Penalized Maximum Likelihood: A Case Study on UVB Radiation Maxima in the Mexico City Metropolitan Area

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