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| Titel |
Flood and drought hydrologic monitoring: the role of model parameter uncertainty |
| VerfasserIn |
N. W. Chaney, J. D. Herman, P. M. Reed, E. F. Wood |
| 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 ; 19, no. 7 ; Nr. 19, no. 7 (2015-07-24), S.3239-3251 |
| Datensatznummer |
250120769
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| Publikation (Nr.) |
 copernicus.org/hess-19-3239-2015.pdf |
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| Zusammenfassung |
| Land surface modeling, in conjunction with numerical weather forecasting and
satellite remote sensing, is playing an increasing role in global monitoring
and prediction of extreme hydrologic events (i.e., floods and droughts).
However, uncertainties in the meteorological forcings, model structure, and
parameter identifiability limit the reliability of model predictions. This
study focuses on the latter by assessing two potential weaknesses that emerge
due to limitations in our global runoff observations: (1) the limits of
identifying model parameters at coarser timescales than those at which the
extreme events occur, and (2) the negative impacts of not properly accounting
for model parameter equifinality in the predictions of extreme events. To
address these challenges, petascale parallel computing is used to perform the
first global-scale, 10 000 member ensemble-based evaluation of plausible
model parameters using the VIC (Variable Infiltration Capacity) land surface
model, aiming to characterize the impact of parameter identifiability on the
uncertainty in flood and drought predictions. Additionally, VIC's
global-scale parametric sensitivities are assessed at the annual, monthly,
and daily timescales to determine whether coarse-timescale observations can
properly constrain extreme events. Global and climate type results indicate
that parameter uncertainty remains an important concern for predicting
extreme events even after applying monthly and annual constraints to the
ensemble, suggesting a need for improved prior distributions of the model
parameters as well as improved observations. This study contributes a
comprehensive evaluation of land surface modeling for global flood and
drought monitoring and suggests paths forward to overcome the challenges
posed by parameter uncertainty. |
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