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| Titel |
Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC |
| VerfasserIn |
J. Yang, F. Castelli, Y. Chen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 10 ; Nr. 18, no. 10 (2014-10-15), S.4101-4112 |
| Datensatznummer |
250120499
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| Publikation (Nr.) |
 copernicus.org/hess-18-4101-2014.pdf |
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| Zusammenfassung |
| Calibration of distributed hydrologic models usually involves how
to deal with the large number of distributed parameters and optimization
problems with multiple but often conflicting objectives that arise in a
natural fashion. This study presents a multiobjective sensitivity and
optimization approach to handle these problems for the MOBIDIC (MOdello di
Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which
combines two sensitivity analysis techniques (the Morris method and the
state-dependent parameter (SDP) method) with multiobjective optimization
(MOO) approach ε-NSGAII (Non-dominated Sorting Genetic
Algorithm-II). This approach was implemented to calibrate MOBIDIC with its
application to the Davidson watershed, North Carolina, with three objective
functions, i.e., the standardized root mean square error (SRMSE) of
logarithmic transformed discharge, the water balance index, and the mean
absolute error of the logarithmic transformed flow duration curve, and its
results were compared with those of a single objective optimization (SOO)
with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking
the objective function as the Euclidean norm of these three objectives.
Results show that (1) the two sensitivity analysis techniques are effective
and efficient for determining the sensitive processes and insensitive
parameters: surface runoff and evaporation are very sensitive processes to
all three objective functions, while groundwater recession and soil hydraulic
conductivity are not sensitive and were excluded in the optimization.
(2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the
average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in
the validation period. (3) Evaporation and surface runoff show similar
importance for watershed water balance, while the contribution of baseflow
can be ignored. (4) Compared to SOO, which was dependent on the initial
starting location, MOO provides more insight into parameter sensitivity and
the conflicting characteristics of these objective functions. Multiobjective
sensitivity analysis and optimization provide an alternative way for future
MOBIDIC modeling. |
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