Titlebook: Advances in the Statistical Sciences: Stochastic Hydrology; Volume IV Festschrif Ian B. MacNeill,Gary J. Umphrey,A. Ian McLeod Book 1987 D.

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Autoregressive Empirical Modelling of Multiple Precipitation Time Series,mum entropy method (MEM) of spectral estimation is equivalent to the AR representation of the data. The paper presents a new algorithm for spectral estimation based upon the MEM approach and is applied to multiple precipitation time series. The algorithm is similar to generalizations of the Burg met

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Simulation of Monthly Hydrological Time Series,ic autoregressive (PAR) models are reviewed. These techniques are then used for fitting PAR and deseasonalized ARMA models to three specific monthly riverflow time series. The Akaike information criterion (AIC) and Bayes information criterion (BIC) suggest the PAR model provides the best fit. Split-

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Solutions to Nonlinear Stochastic Differential Equations in Catchment Modelling,tual reservoirs of the Nash type. The particular stochastic differential equation studied herein is derived from the mass balance equation stated for a single reservoir by representing the input term, as well as the environmental (external) parameters within it, as white stationary stochastic proces

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Stochastic Partial Differential Equations in Hydrology,lems. Equations are treated as abstract stochastic evolution equations for elliptic partial differential operators in an appropriate functional Sobolev space. Explicit forms of solutions are obtained by using the strongly continuous semigroup of the partial differential operator. Application of the

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On the Outputs of the Stochasticized Nash-Dooge Linear Reservoir Cascade,tchments are formulated as simple linear stochastic dynamical systems within the formalism of stochastic differential equations [SDE’s]. The system driving process, rainfall and evapotranspiration losses (negative input) are modelled respectively as a compound Poisson process and a mean zero white G

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Nearest Neighbor Methods for Time Series, with Application to Rainfall/Runoff Prediction,vey of this method, with special attention to recent results extending the theory to dependent random sequences. The classical rainfall/runoff prediction problem serves as focus for this study, but the methodology offered potentially has much wider application.

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Statistical Approach to Floods,ribution to a set of observations of annual maxima. The choice of the probability distribution is often based on asymptotic results. We investigate this model selection criterion through evaluation of the errors in estimating of .(.) for a Markovian daily flow stochastic process..The design of spill

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