Time variable and state dependent modelling of non-stationary and nonlinear time series

Young, Paul Thomas

doi:10.1007/978-1-4899-4515-0_26

Cited by 72 publications

(79 citation statements)

References 37 publications

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“…As it is unrealistic to simulate the discharge of these two catchments by simple conceptual models, the Data-Based Mechanistic (DBM) modelling approach proposed by Young and Beven (1994) has been tested. In this approach, the most parsimonious model structure is inferred statistically from the available time series, using the time variable parameter estimation method called Fixed Interval Smoothing (FIS) presented by Young (1984) and Young (1993). For the Aare and Jura catchments, the DBM modelling approach highlighted the predominating effect of the lagged runoff, resulting in an autoregressive model where the rainfall as driving forces is negligible.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Hingray¹,

Mouhous²,

Mezghani³

et al. 2007

Hydrol. Earth Syst. Sci.

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A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-ofthe-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) cannot, however, be used to simulate such a large number of scenarios. This paper presents a methodology for obtaining future climate scenarios through a simple scaling methodology. The projections of several key meteorological variables obtained from a few regional climate model runs are scaled, based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (19611990) and a future period (20702099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The results obtained show that future climate conditions will have a significant influence on the performance of the system and that the uncertainty induced by the inter-RCM variability will contribute to much of the uncertainty of the prediction of the total impact. This research has been conducted within the 20012004 EU funded project SWURVE.

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Section: Hydrological Modellingmentioning

confidence: 99%

Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Hingray¹,

Mouhous²,

Mezghani³

et al. 2007

Hydrol. Earth Syst. Sci.

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“…Kirkby, 1976;Hornberger et al, 1985;Jakeman & Hornberger, 1993;Young, 1993Young, , 1998bYoung et al, 1997a,b;Ye et al, 1998) suggest that a typical set of rainfall-runoff observations contain only sufficient information to estimate up to a maximum of six parameters within simple, nonlinear dynamic models of dynamic order three or less. In the rainfall-runoff example discussed later, for instance, there is clear evidence in the data of only two dominant modes between the effective rainfall input and the flow response (as described by a second order transfer function model with only four parameters): a 'quick' mode with a residence time (time constant) of a few hours; and a 'slow' mode, with a residence time of many hours.…”

Section: Rainfall-flow Modellingmentioning

confidence: 99%

Advances in real–time flood forecasting

Young

2002

Philosophical Transactions of the Royal Society of London. Seri

210

167

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A shorter version of this report has been published (Young, 2002a)The report discusses the modelling of rainfall-flow (rainfall-runoff) and flow routing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parameterized Data-Based Mechanistic (DBM) models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive data assimilation and forecasting system based on recursive state space estimation (an adaptive version of the stochastic Kalman Filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in NW England, demonstrates the utility of this methodology and illustrates the advantages of incorporating real-time state and parameter adaption.

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“…As far as the form of DBM models is concerned, the standard DBM modeling philosophy allows for any generic model type to be used in the initial data-based analysis. However, the main generic model form used so far is a multi-input, multi-output (MIMO), nonlinear differential equation or its discretetime equivalent, characterized by state-dependent parameter (SDP) nonlinearities [see e.g., Young, 1993Young, , 2001a. Here, the SDP model is one in which the model parameters are not assumed constant over the observation interval but, if the observational data support it, can be a priori unknown functions of one or more ''state variables'' of the system; functions that are identified and estimated as part of the statistical analysis.…”

Section: Dbm Modelingmentioning

confidence: 99%

“…[7] Interestingly, one of the first applications that led to the idea of DBM modeling was to the modeling of rainfallflow processes [Young, 1974;Whitehead et al, 1976]; and this was generalized later in an example considered first in Young [1993] and then in Young and Beven [1994]. This led to numerous examples that have demonstrated the utility of DBM modeling applied to rainfall-flow processes [see e.g., Young, 1998;Lees, 2000;Young, 2001aYoung, , 2003Ratto et al, 2007;Chappell et al, 2006;Ochieng and Otieno, 2009;Young, 2010aYoung, , 2010bBeven et al, 2012;McIntyre et al, 2011, and references therein], including catchments affected by snow melt, where the nonlinearities are more complex .…”

Section: Introductionmentioning

confidence: 99%

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

Young

2013

Water Resources Research

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[1] The paper introduces a logical extension to data-based mechanistic (DBM) modeling, which provides hypothetico-inductive (HI-DBM) bridge between conceptual models, derived in a hypothetico-deductive manner, and the DBM model identified inductively from the same time-series data. The approach is illustrated by a quite detailed example of HI-DBM analysis applied to the well-known Leaf River data set and the associated HyMOD conceptual model. The HI-DBM model significantly improves the explanation of the Leaf River data and enhances the performance of the original DBM model. However, on the basis of various diagnostic tests, including recursive time-variable and state-dependent parameter estimation, it is suggested that the model should be capable of further improvement, particularly as regards the conceptual effective rainfall mechanism, which is based on the probability distributed model hypothesis. In order to verify the efficacy of the HI-DBM analysis in a situation where the actual model generating the data is completely known, the analysis is also applied to a stochastic simulation model based on a modified HyMOD model.

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Time variable and state dependent modelling of non-stationary and nonlinear time series

Cited by 72 publications

References 37 publications

Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Advances in real–time flood forecasting

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

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