7/29/2019 Soria Apdiahr
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SENSITIVITY ANALYSIS OF DISTRIBUTED
RAINFALL-RUNOFF MODELS
Freddy SORIA1*
So KAZAMA2
Masaki SAWAMOTO1
1Graduate School of Engineering, Tohoku University, Sendai,
Japan, e-mail: [email protected]
[email protected]
Graduate School of Environmental Sciences, Tohoku University,
Sendai, Japan, e-mail:
[email protected]
Rainfall-runoff hydrological models are resources commonly used
in the analysis and
evaluation of natural processes in a watershed. After the
Stanford Watershed Model in the
1960s, a wide variety of conceptual models have been suggested.
As an important tool in the
modeling process, sensitivity analysis is used to assess the
evaluation of the confidence levelof the model and the
uncertainties associated, determining a subset of parameters
accounting
most of the output variance, and showing probable paths to
simplify or improve model
structure. Increase interest on distributed models have
introduced practical restrictions to
traditional sensitivity analysis methodologies since
dimensionality of the input space highly
increases, for instance a comparative evaluation among current
state of art methods becomes
necessary. This paper presents the application of three
sensitivity methods (Sobols variance
based method, variational methods, and a combination between
global sensitivity methods
and Generalized Likelihood Uncertainty Estimation technique
GLUE) in the evaluation of the
outputs of a distributed rainfall-runoff model (i.e. total
discharge rate), evaluating the
advantages and restrictions of each, as a way to contribute to
current knowledge in the field,
further reducing predictive uncertainty. The model is applied to
Natori basin (Northeast
Japan), a relatively homogeneous catchment selected in order to
avoid computational issues
that may come out in heterogeneous landscapes demanding the
consideration of different
structure conceptualizations. Sobols method has shown to be a
promising tool to further
evaluate model structure through the analysis of quantitative
indices. The reliability of the
method has been observed to be important during dry periods
since hydrological process have
no strong interactions; the main problem in the application of
the method was observed during
wet seasons where runoff processes are triggered, and the number
of samples to accomplish
acceptable convergence highly increases. GLUE technique is
somehow more complicated andt may introduce bias to the analysis if
likelihood decisions are not well taken, otherwise is
certainly useful. Variational methods are not commonly used in
rainfall-runoff modeling, and
are certainly promising although further development is still
required. Selecting a particular
methodology will always be made upon individual cases
conditions, but the combination of
techniques through empirical weighting could be perhaps an
interesting alternative. It is well
known the importance of sensitivity analysis in uncertainty
reduction, and the comparison
presented here aims to give an insight in currently considered
methodologies.
Key words: Uncertainty, Sobols method, variational methods,
Generalized Likelihood
Uncertainty Estimation.
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