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| Titel |
Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model |
| VerfasserIn |
Y. Sun, Z. Hou, M. Huang, F. Tian, L. Ruby Leung |
| 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 ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-10), S.4995-5011 |
| Datensatznummer |
250086027
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| Publikation (Nr.) |
 copernicus.org/hess-17-4995-2013.pdf |
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| Zusammenfassung |
| This study demonstrates the possibility of inverting hydrologic parameters
using surface flux and runoff observations in version 4 of the Community
Land Model (CLM4). Previous studies showed that surface flux and runoff
calculations are sensitive to major hydrologic parameters in CLM4 over
different watersheds, and illustrated the necessity and possibility of
parameter calibration. Both deterministic least-square fitting and
stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are
evaluated by applying them to CLM4 at selected sites with different climate
and soil conditions. The unknowns to be estimated include surface and
subsurface runoff generation parameters and vadose zone soil water
parameters. We find that using model parameters calibrated by the
sampling-based stochastic inversion approaches provides significant
improvements in the model simulations compared to using default CLM4
parameter values, and that as more information comes in, the predictive
intervals (ranges of posterior distributions) of the calibrated parameters
become narrower. In general, parameters that are identified to be
significant through sensitivity analyses and statistical tests are better
calibrated than those with weak or nonlinear impacts on flux or runoff
observations. Temporal resolution of observations has larger impacts on the
results of inverse modeling using heat flux data than runoff data. Soil and
vegetation cover have important impacts on parameter sensitivities, leading
to different patterns of posterior distributions of parameters at different
sites. Overall, the MCMC-Bayesian inversion approach effectively and
reliably improves the simulation of CLM under different climates and
environmental conditions. Bayesian model averaging of the posterior
estimates with different reference acceptance probabilities can smooth the
posterior distribution and provide more reliable parameter estimates, but at
the expense of wider uncertainty bounds. |
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