A non‐Gaussian multicomponent model for river flow

Vandewiele, Georges; Dom, A.

doi:10.1029/wr025i003p00397

Cited by 7 publications

(2 citation statements)

References 7 publications

(1 reference statement)

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“…They remain simple empirical models of complex natural processes, and there are many other ways of modeling streamflows. Some [Vandewiele and Dom, 1989;Cowpertwait and O'Connell, 1992], for example, have a more intuitive physical structure. Even so, they inevitably involve many simplifications and do not necessarily give better predictions of flood risk.…”

Section: Discussionmentioning

confidence: 99%

Estimation of seasonal flood risk using a two‐stage transformation

Atan¹,

Metcalfe²

1994

Water Resources Research

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Hydrological time series are often asymmetric in time, insomuch as rises are more rapid than recessions, as well as having highly skewed marginal distributions. A two‐stage transformation is proposed for deseasonalized series. Rises are stretched, and recessions are squashed until the series is symmetric over time. An autoregressive moving average (ARMA) model is then fitted to the natural logarithms of this new series. The residuals from the ARMA model are represented by a double mixture of Weibull and exponential distributions. The method is demonstrated with 24 years of daily flows from the River Cherwell in the south of England, and a 40‐year record from the upper reaches of the Thames. Seasonal estimates of flood risk are given, and these can be conditioned on catchment wetness at the time of prediction.

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Section: Discussionmentioning

confidence: 99%

Estimation of seasonal flood risk using a two‐stage transformation

Atan¹,

Metcalfe²

1994

Water Resources Research

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“…In a rough classification of the literature on the subject one can distinguish: a. models in which the response function derives, as in Weiss [1973], from a linear conceptual scheme of the watershed [e.g. Pegram, 1980;Hino and ttasebe, 1981;Vandewiele and Dom, 1989]; b. non-linear or non-parametric models [Treiber and Plate, 1977;Yakowitz, 1979]. Some of these models use Markov processes as input [Treiber and Plate, 1977;Yakowitz, 1979;Vandewiele and Dora, 1989] and often the input process is reconstructed by inverse estimation [Treiber and Plate, 1977;Hino and Hasebe, 1981;Battaglia, 1986;Kron et al, 1990;Wang and Vandewiele, 1994] as opposed to Weiss' approach, in which parameters of the input model are directly estimated from runoff through the moments method.…”

Section: Introductionmentioning

confidence: 99%

Conceptually-based shot noise modeling of streamflows at short time interval

Murrone

Rossi

Claps

1997

Stochastic Hydrol Hydraul

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"A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.

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Stochastic simulation of streamflow with short time interval

Wang¹,

Vandewiele²

1994

Coping With Floods

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A non‐Gaussian multicomponent model for river flow

Cited by 7 publications

References 7 publications

Estimation of seasonal flood risk using a two‐stage transformation

Estimation of seasonal flood risk using a two‐stage transformation

Conceptually-based shot noise modeling of streamflows at short time interval

Stochastic simulation of streamflow with short time interval

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