Hydrology and Earth System Sciences
www.hydrol-earth-syst-sci.net
1027-5606
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11
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2007
10.5194/hess-11-1249-2007
http://www.hydrol-earth-syst-sci.net/11/1249/2007/
http://www.hydrol-earth-syst-sci.net/11/1249/2007/hess-11-1249-2007.html
http://www.hydrol-earth-syst-sci.net/11/1249/2007/hess-11-1249-2007.pdf
1249
1266
2007-05-03
Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology
M. Ratto
P. C. Young
R. Romanowicz
F. Pappenberger
A. Saltelli
A. Pagano
Joint Research Centre, European Commission, Ispra, Italy
Centre for Research on Environmental Systems and Statistic, Lancaster University, Lancaster, UK
Centre for Resource and Environmental Studies, Australian National University, Canberra, Australia
European Centre for Medium-Range Weather Forecasts, Reading, UK
In this paper, we discuss a joint approach to calibration and
uncertainty estimation for hydrologic systems that combines a
top-down, data-based mechanistic (DBM) modelling methodology; and
a bottom-up, reductionist modelling methodology. The combined
approach is applied to the modelling of the River Hodder catchment
in North-West England. The top-down DBM model provides a well identified,
statistically sound yet physically meaningful description of the
rainfall-flow data, revealing important characteristics of the
catchment-scale response, such as the nature of the effective
rainfall nonlinearity and the partitioning of the effective
rainfall into different flow pathways. These characteristics are
defined inductively from the data without prior assumptions about
the model structure, other than it is within the generic class of
nonlinear differential-delay equations. The bottom-up modelling is
developed using the TOPMODEL, whose structure is assumed a
priori and is evaluated by global sensitivity analysis (GSA) in
order to specify the most sensitive and important parameters. The
subsequent exercises in calibration and validation, performed with
Generalized Likelihood Uncertainty Estimation (GLUE), are carried
out in the light of the GSA and DBM analyses. This allows for the
<i>pre-calibration</i> of the the priors used for GLUE, in order to
eliminate dynamical features of the TOPMODEL that have little
effect on the model output and would be rejected at the structure
identification phase of the DBM modelling analysis. In this way,
the elements of meaningful subjectivity in the GLUE approach,
which allow the modeler to interact in the modelling process by
constraining the model to have a specific form prior to
calibration, are combined with other more objective, data-based
benchmarks for the final uncertainty estimation. GSA plays a major
role in building a bridge between the hypothetico-deductive
(bottom-up) and inductive (top-down) approaches and helps to
improve the calibration of mechanistic hydrological models, making
their properties more transparent. It also helps to highlight
possible mis-specification problems, if these are identified. The
results of the exercise show that the two modelling methodologies
have good synergy; combining well to produce a complete joint
modelling approach that has the kinds of checks-and-balances
required in practical data-based modelling of rainfall-flow
systems. Such a combined approach also produces models that are
suitable for different kinds of application. As such, the DBM
model considered in the paper is developed specifically as a
vehicle for flow and flood forecasting (although the generality of
DBM modelling means that a simulation version of the model could
be developed if required); while TOPMODEL, suitably calibrated
(and perhaps modified) in the light of the DBM and GSA results,
immediately provides a simulation model with a variety of
potential applications, in areas such as catchment management and
planning.
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