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| Titel |
Multi-criteria parameter estimation for the Unified Land Model |
| VerfasserIn |
B. Livneh, D. P. Lettenmaier |
| 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 ; 16, no. 8 ; Nr. 16, no. 8 (2012-08-29), S.3029-3048 |
| Datensatznummer |
250013444
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| Publikation (Nr.) |
 copernicus.org/hess-16-3029-2012.pdf |
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| Zusammenfassung |
| We describe a parameter estimation framework for the Unified Land Model
(ULM) that utilizes multiple independent data sets over the continental
United States. These include a satellite-based evapotranspiration (ET)
product based on MODerate resolution Imaging Spectroradiometer (MODIS) and
Geostationary Operational Environmental Satellites (GOES) imagery, an
atmospheric-water balance based ET estimate that utilizes North American
Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage
content (TWSC) data from the Gravity Recovery and Climate Experiment
(GRACE), and streamflow (Q) primarily from the United States Geological
Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2)
tributary basins. ULM, which is essentially a merger of the Noah
Land Surface Model and Sacramento Soil Moisture Accounting Model, is the
basis for these experiments. Calibrations were made using each of the data
sets individually, in addition to combinations of multiple criteria, with
multi-criteria skill scores computed for all cases. At large scales,
calibration to Q resulted in the best overall performance, whereas certain
combinations of ET and TWSC calibrations lead to large errors in other
criteria. At small scales, about one-third of the basins had their highest
Q performance from multi-criteria calibrations (to Q and ET) suggesting that
traditional calibration to Q may benefit by supplementing observed Q with
remote sensing estimates of ET. Model streamflow errors using optimized
parameters were mostly due to over (under) estimation of low (high) flows.
Overall, uncertainties in remote-sensing data proved to be a limiting factor
in the utility of multi-criteria parameter estimation. |
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