A data-based mechanistic modelling (DBM) approach to understanding dynamic sediment transmission through Wyresdale Park Reservoir, Lancashire, UK

Price, Laura; Goodwill, P.; Young, Peter C.; Rowan, John S.

doi:10.1002/(sici)1099-1085(200001)14:1<63::aid-hyp910>3.0.co;2-h

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…The flowingwater layer is based upon a river dispersion (mixing) model (e.g. Wallis et al 1989;Price et al 2000). For a simple one-dimensional model such as that presented here, an important simplifying assumption is that the water is completely mixed across the channel crosssection (Henderson-Sellers et al 1988).…”

Section: Riparian Pasture Inputmentioning

confidence: 99%

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Wilkinson

McKergow

Davies-Colley

et al. 2011

New Zealand Journal of Marine and Freshwater Research

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Storm-induced Escherichia coli pulses in the Motueka River (2074 km 2 ) and the Sherry River (78.4 km 2 ) are modelled. The model focuses on the catchment outlets, representing key processes, including E. coli transfer to and from the river bed, with account taken of the hysteresis in, and non-linear, non-stationary, response of E. coli concentrations to river stormflows. The model fits the Motueka River observations well, but less well in the Sherry River. A greatly simplified description of headwater and riparian inputs is satisfactory at the larger catchment scale where near-field, in-channel processes dominate the response. Spatial heterogeneity in rainfall-run-off and faecal sources probably contribute to the poorer fit in the smaller catchment. Despite using a relatively small number of driving variables and parameters, the model has the potential to predict real-time E. coli input to Tasman Bay in river plumes causing shellfish and bathing beach contamination.

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Section: Riparian Pasture Inputmentioning

confidence: 99%

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Wilkinson

McKergow

Davies-Colley

et al. 2011

New Zealand Journal of Marine and Freshwater Research

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“…A third category of model identification procedures is formed by procedures that use physical insight into the system, in contrast to the above mentioned methods that are based only on the data itself. In the data‐based mechanistic modeling methodology [e.g., Young and Beven , 1994; Price et al , 2000] physical analysis is combined with statistical model identification. TFN models are identified directly from the data but are only accepted as a reasonable representation of the system if they have a valid physical interpretation.…”

Section: Introductionmentioning

confidence: 99%

Transfer function‐noise modeling in continuous time using predefined impulse response functions

Asmuth

Bierkens

Maas

2002

Water Resources Research

108

175

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[1] A method of transfer function-noise (TFN) modeling is presented that operates in continuous time and uses a predefined family of impulse response (IR) functions. The resulting class of models is referred to as predefined IR function in continuous time (PIRFICT). It provides a useful tool for standardized analysis of time series, as it can be calibrated using irregularly spaced data and does not require a model identification phase prior to calibration. In section 2, the discrete Box-Jenkins (BJ) model is presented and transformed into continuous time to obtain the PIRFICT model. The discrete transfer function of a BJ model, which is made up of a variable number of parameters, is replaced by a simple analytical expression that defines the IR function. From the IR function, block response functions are derived that enable the model to handle irregularly spaced data. In the example application, the parameter estimates and performance of the BJ and PIRFICT model are compared using a data set of 15 piezometers and a simulated series. It was found that the estimated transfer and BR functions of both models follow the same general pattern, although the BJ transfer functions are partly irregular. The performance of both models proves to be highly comparable for all piezometers.

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Understanding complex environmental systems: a dual approach

Lin

Beck

2006

Environmetrics

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SUMMARYAn approach to interpreting field data exploiting the duality of data-and theory-based models, and their associated methods of system identification, is presented. This approach seeks to overcome the respective limitations of the two branches of the duality: that theory-based models are not unambiguously identifiable from the observations, while a well-identified data-based model may not be capable of a satisfactory theoretical interpretation. The purpose of the approach is thereby to gain a deeper understanding of complex, poorly defined environmental systems. Recursive methods of time-series analysis are used to identify the data-based models (as transfer functions) and companion recursive methods, specifically the recursive prediction error (RPE) algorithm, are employed for structure identification and parameter estimation of the theory-based models (in ordinary differential equation forms). The results of these identification exercises for the two classes of models can be compared in terms of the macroparameters of the studied system's time constant and steady-state gain. Two case studies are presented to illustrate the overall performance of the dual-thrust approach, on an activated sludge system and an aquaculture pond. It is found that: (1) as opposed to the exclusive use of either approach alone, more is to be gained through the joint application of the two classes of models, which historically have tended to reflect quite separate, unconnected approaches to interpreting environmental systems data; (2) to some extent, identifying the structure and estimating the parameters of one type of model can be readily improved by recourse to the corresponding results for the other; and (3) reconciliation of the results from identifying the two classes of model in the parameter space-of the time constant and steady-state gain-has significant advantages over the more familiar process of evaluating a model's performance in the terms of its (observed) state-space features.

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A data-based mechanistic modelling (DBM) approach to understanding dynamic sediment transmission through Wyresdale Park Reservoir, Lancashire, UK

Cited by 9 publications

References 36 publications

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Transfer function‐noise modeling in continuous time using predefined impulse response functions

Understanding complex environmental systems: a dual approach

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