Modelling precipitation in Sweden using multiple step markov chains and a composite model

Lennartsson, Jan; Baxevani, Anastassia; Chen, Deliang

doi:10.1016/j.jhydrol.2008.10.003

Cited by 54 publications

(49 citation statements)

References 36 publications

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“…This selection is consistent with previous works (e.g. Wilks [10], Chen et al [21], Lennartsson et al [39], Allard et al [40]). Chen et al [21] stated that generating precipitation quantity using the gamma distribution performed reliably better than the exponential distribution.…”

Section: Climate Statistics Space (Css)supporting

confidence: 93%

The Realism of Stochastic Weather Generators in Risk Discovery

Alodah¹,

Seidou²

2017

WIT Transactions on Ecology and the Environment

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Weather generators reproduce artificial climate time series that are commonly used for hydrological modeling and climate adaptation studies. To examine the representativeness of a stochastically generated climate time series, a novel stochastic method is suggested where these time series are projected in two spaces (the Climate Statistics Space -CSS; and the Risk and Performance Indicators Space -RPIS). A visual inspection as well as the Mahalanobis distance are used to assess the two spaces relative position and their proximity to the points representing the observations. The dimensions of the CSS are a subset of climate statistics, while the dimensions of the RPIS are a set of risk and performance indicators calculated using streamflow time series. A rainfall-runoff model is used to convert all climate time series from the CSS into streamflow time series in the RPIS. Three stochastic weather generators were used in this study: The Weather Generator École de Technologie Supérieure (WeaGETS), the Multisite Stochastic Weather Generator (MulGETS) using two different generation algorithms, and a k-nearest neighbour weather generator. Each generator was used to construct precipitation, maximum and minimum temperatures time series representing the historical period. The suggested approach was tested on a 41-years-long climate and flow time series from South Nation watershed in Eastern Ontario, Canada. The MulGETS model was able to perform well where the point representing the observations was centered inside the cloud of points representing the synthetic time series in some CSS.

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Section: Climate Statistics Space (Css)supporting

confidence: 93%

The Realism of Stochastic Weather Generators in Risk Discovery

Alodah¹,

Seidou²

2017

WIT Transactions on Ecology and the Environment

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“…In that respect, implementing copulas in stochastic rainfall models could be beneficial in the modeling of the temporal dependence of the rainfall process, including the internal storm structure. Some useful contributions in this direction are provided by Salvadori and De Michele [2006], Grimaldi and Serinaldi [2006b], Kao and Govindaraju [2008], Lennartsson et al [2008], Sun [2008], Bárdossy and Pegram [2009], and Serinaldi [2009] and will be the subject of future research.…”

Section: Resultsmentioning

confidence: 99%

A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall

Vandenberghe

Verhoest

Onof

et al. 2011

Water Resources Research

114

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[1] Because of a lack of historical rainfall time series of considerable length, one often has to rely on simulated rainfall time series, e.g., in the design of hydraulic structures. One way to simulate such time series is by means of stochastic point process rainfall models, such as the Bartlett-Lewis type of model. For the evaluation of model performance, with a focus on the reproduction of extreme rainfall events, often a univariate extreme value analysis is performed. Recently developed concepts in statistical hydrology now offer other means of evaluating the overall performance of such models. In this study, a copula-based frequency analysis of storms is proposed as a tool to evaluate differences between the return periods of several types of observed and modeled storms. First, this study performs an analysis of several storm variables, which indicates a problem with the modeling of the temporal structure of rainfall by the models. Thereafter, the bivariate frequency analysis of storms, defined by their duration and volume, illustrates the underestimation and overestimation of the return period of the storms simulated by the models, which is partially explained by a large difference in the marginal distribution functions of the storm duration and storm volume, the difference in the degree of association between the latter, and a different mean storm interarrival time. The proposed methodology allows for the identification of some problems with the rainfall simulations from which recommendations for possible improvements to rainfall models can be made.

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“…We consider as a second marginal model a mixture of Log-Normal distributions although some authors recommend to model rainfall with a hybrid distribution. Such a hybrid distribution combines a parametric (Carreau and Bengio 2009;Li et al 2012) or non-parametric model (Lennartsson et al 2008) for the bulk of the distribution with the Generalized Pareto distribution (GPD) in the upper tail. Univariate extreme value theory (EVT) provides an asymptotic justification for the GPD to be an appropriate model for the distribution of values exceeding a suitably chosen high threshold (Pickands 1975).…”

Section: Marginal Distributionsmentioning

confidence: 99%

Multivariate density model comparison for multi-site flood-risk rainfall in the French Mediterranean area

Carreau

Bouvier

2015

Stoch Environ Res Risk Assess

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The French Mediterranean area is subject to intense rainfall events which might cause flash floods, the main natural hazard in the area. Flood-risk rainfall is defined as rainfall with a high spatial average and encompasses rainfall which might lead to flash floods. We aim to compare eight multivariate density models for multi-site flood-risk rainfall. In particular, an accurate characterization of the spatial variability of flood-risk rainfall is crucial to help understand flash flood processes. Daily data from eight rain gauge stations at the Gardon at Anduze, a small Mediterranean catchment, are used in this work. Each multivariate density model is made of a combination of a marginal model and a dependence structure. Two marginal models are considered: the Gamma distribution (parametric) and the Log-Normal mixture (nonparametric). Four dependence structures are included in the comparison: Gaussian, Student t, Skew Normal and Skew t in increasing order of complexity. They possess a representative set of theoretical properties (symmetry/asymmetry and asymptotic dependence/independence). The multivariate models are compared in terms of three types of criteria: (1) separate evaluation of the goodness-of-fit of the margins and of the dependence structures, (2) model selection with a leave-one-out evaluation of the AndersonDarling and Cramer-Von Mises statistics and (3) comparison in terms of two hydrologically interpretable quantities (return periods of the spatial average and conditional probabilities of exceedances). The key outcome of the comparison is that the Skew Normal with the Log-Normal mixture margins outperform significantly the other models. The asymmetry introduced by the Skew Normal is an added-value with respect to the Gaussian. Therefore, the Gaussian dependence structure, although widely used in the literature, is not recommended for the data in this study. In contrast, the asymptotically dependent models did not provide a significant improvement over the asymptotically independent ones.

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Modelling precipitation in Sweden using multiple step markov chains and a composite model

Cited by 54 publications

References 36 publications

The Realism of Stochastic Weather Generators in Risk Discovery

The Realism of Stochastic Weather Generators in Risk Discovery

A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall

Multivariate density model comparison for multi-site flood-risk rainfall in the French Mediterranean area

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