Data-based mechanistic modelling and forecasting of hydrological systems

Lees, Matthew

doi:10.2166/hydro.2000.0003

Cited by 47 publications

(43 citation statements)

References 35 publications

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“…Another poorly identifiable parameter was the fast flow residence time K f . This was partly because it had been restricted a priori on the basis of the range of values previously found for comparable catchments [Young and Beven, 1994;Lees, 2000;McIntyre and Marshall, 2010] and may have also been because of the trade-off between fitting the smaller and larger flow peaks, as discussed. If a more spatially discretized catchment representation had been used [see, e.g., Bulygina et al, 2009], then surface topography, in particular the channel network characteristics, may have been used to add some physicsbased constraints to this parameter.…”

Section: Discussionmentioning

confidence: 99%

“…Parameter K f is restricted to vary between 1 and 10 h because of the low fast runoff residence times typically found for small, steep catchments in this region [e.g., Lees, 2000;Young and Beven, 1994;McIntyre and Marshall, 2010]. Other parameter ranges are defined within broad bounds based on UK catchment experience [Wagener et al, 2004].…”

Section: Model Descriptionmentioning

confidence: 99%

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Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bulygina¹,

McIntyre²,

Ballard³

et al. 2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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[1] A novel method is presented for conditioning rainfall-runoff models for ungauged catchment and land use impact applications. The method conditions the model on information from multiple regionalized response indices using a formal Bayesian approach. Two indices that hold information about soil type and land use effects are the base flow index from the Hydrology of Soil Type (HOST) classification and curve number from the U.S. Department of Agriculture's Soil Conservation Service soil and land use classification. These indices are used to constrain a five-parameter probability distributed moisture model for subcatchments of the Wye (grazed grassland) and Severn (mainly afforested) catchments in the United Kingdom. The base flow index and curve number constrain only two of the five model parameters, indicating that ideally, other sources of information would be sought. Nevertheless, the procedure significantly reduces the prior uncertainty in runoff prediction and gives predictions close to those of the calibrated models. For the case study, the introduction of the curve number in addition to the base flow index has only a small effect on model performance and uncertainty; however, it allows a distinction between the effects of soil type and land management for the purpose of scenario analysis. The principal assumptions used in the method are the applicability of the curve number classification system and its mapping to UK soil types and the likelihood function used for Bayesian conditioning.

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Section: Discussionmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bulygina¹,

McIntyre²,

Ballard³

et al. 2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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“…Nonlinearity in natural systems has been predominantly studied in the past in the form of 'input nonlinearities' as it enables reliable estimation of the model parameters (e.g. Lees (2000) and Young (2000)), and for the same reason this approach was adopted in this study. Furthermore, we consider the results obtained using this approach satisfactory because (as discussed in detail on pages 12-13 of the revised manuscript), the estimated SDP gain does in fact effectively describe variation in the speed of the profile's volume response as well as the direction of volume change (i.e.…”

Section: C1 (B) " …We Do Not Know At This Point What the Form Of Thementioning

confidence: 99%

“…An example of this includes, estimation and prediction of the nonlinearity in rainfall-flow processes, which results in similar rainfall rates producing different rates of river flow due a dependence on the preceding catchment conditions (e.g. wetness) (Young and Beven, 1994;Fawcett, 1999;Lees, 2000;Young, 2000;2003).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Gunawardena

Ilic

Pinkerton

et al. 2009

Coastal Engineering

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This paper presents a simple nonlinear data-based modelling approach for predicting the beach profile volume at Duck, North Carolina, USA. The state-dependent parameter form of the general transfer function (SDP TF) model is used to describe nonlinearity influencing these morphological data in two case examples. Case 1 investigates the nonlinearity associated with the dependency of wave forcing on the preceding beach volume. Case 2 investigates the ability to model the variables within the well known diffusion equation for beach volume using this data-based approach. The results of this study show that the SDP TF approach can be used successfully to develop statistically robust models for describing nonlinearity in beach morphological systems. Furthermore, these models are shown to predict the beach volumes over both short (1 month ahead) and long (2 years ahead) time periods, and thus show great potential for practical applications in coastal zone management and engineering. We thank you and the two reviewers for their constructive comments and suggestions.We have fully taken on board all suggested revisions, we believe these will further improve the manuscript. The following addresses all comments made by the two reviewers in detail. Please note that the revised figures are enclosed within the manuscript as these were generated using Matlab TM , and hence could not be saved separately as image files. Best regards yohamaComments from "Reviewer 1" Specific Comments C1. "Clarify position of profiles along coast (including position of Pier)."We welcome the suggestion of the reviewer. This has been clarified by including a location map (Figure 1) of the beach profiles (Profiles 58, 62, 188 and 190) and the FRF pier at Duck. C2. "Clear explanation of the 'volume' discussed in the paper"We agree with the reviewer and apologise for the confusion. We have clarified that the beach profile volume per unit metre of shoreline were computed by integrating the beach profile data between 75 m and 700 m, which extend well beyond the average position of the depth of closure at Duck (latter is at the 4 m depth at ~410 m crossshore) to the 7 m depth (page 10). C3. "Applying Eq.(6) and a straight and parallel coastline is present, no interaction between the profiles should occur. That calls for equal 'volumes' in the 3 profiles; but the 'volume' depends [see also ii)] e.g. on the position of the reference line. Please clarify this point."Detailed Response to Reviewers 2 Here, is our explanation for this comment, which is summarised in the revised paper (page 10). Although the bathymetric contours at Duck are parallel at times, this is not always the case (Miller et al., 1983;Plant et al., 1996;Miller and Dean, 2007) because of longshore differences in the morphology of profiles on either side of the pier. As seen in Figure 2 of the revised manuscript, alongshore volume differences between northerly and southerly profiles occur, particularly post 1991. Reversed migration directions of the nearshore sand bars on either si...

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“…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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Data-based mechanistic modelling and forecasting of hydrological systems

Cited by 47 publications

References 35 publications

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems

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