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| Titel |
Estimating water discharge from large radar altimetry datasets |
| VerfasserIn |
A. C. V. Getirana, C. Peters-Lidard |
| 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. 3 ; Nr. 17, no. 3 (2013-03-04), S.923-933 |
| Datensatznummer |
250018813
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| Publikation (Nr.) |
 copernicus.org/hess-17-923-2013.pdf |
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| Zusammenfassung |
| The objective of this study is to evaluate the potential of large altimetry
datasets as a complementary gauging network capable of providing water
discharge in ungauged regions. A rating curve-based methodology is adopted
to derive water discharge from altimetric data provided by the Envisat
satellite at 475 virtual stations (VS) within the Amazon basin. From a
global-scale perspective, the stage–discharge relations at VS are built
based on radar altimetry and outputs from a modeling system composed of a
land surface model and a global river routing scheme. In order to quantify
the impact of model uncertainties on rating-curve based discharges, a second
experiment is performed using outputs from a simulation where daily observed
discharges at 135 gauging stations are introduced in the modeling system.
Discharge estimates at 90 VS are evaluated against observations during the
curve fitting calibration (2002–2005) and evaluation (2006–2008) periods,
resulting in mean normalized RMS errors as high as 39 and 15% for
experiments without and with direct insertion of data, respectively. Without
direct insertion, uncertainty of discharge estimates can be mostly
attributed to forcing errors at smaller scales, generating a positive
correlation between performance and drainage area. Mean relative streamflow
volume errors (RE) of altimetry-based discharges varied from 15 to 84%
for large and small drainage areas, respectively. Rating curves produced a
mean RE of 51% versus 68% from model outputs. Inserting discharge data
into the modeling system decreases the mean RE from 51 to 18%, and
mean NRMSE from 24 to 9%. These results demonstrate the feasibility
of applying the proposed methodology to the continental or global scales. |
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