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| Titel |
Water resource monitoring systems and the role of satellite observations |
| VerfasserIn |
A. I. J. M. Dijk, L. J. Renzullo |
| 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 ; 15, no. 1 ; Nr. 15, no. 1 (2011-01-04), S.39-55 |
| Datensatznummer |
250012584
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| Publikation (Nr.) |
 copernicus.org/hess-15-39-2011.pdf |
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| Zusammenfassung |
| Spatial water resource monitoring systems (SWRMS) can provide valuable
information in support of water management, but current operational systems
are few and provide only a subset of the information required. Necessary
innovations include the explicit description of water redistribution and
water use from river and groundwater systems, achieving greater spatial
detail (particularly in key features such as irrigated areas and wetlands),
and improving accuracy as assessed against hydrometric observations, as well
as assimilating those observations. The Australian water resources
assessment (AWRA) system aims to achieve this by coupling landscape models
with models describing surface water and groundwater dynamics and water use.
A review of operational and research applications demonstrates that
satellite observations can improve accuracy and spatial detail in
hydrological model estimation. All operational systems use dynamic forcing,
land cover classifications and a priori parameterisation of vegetation dynamics that
are partially or wholly derived from remote sensing. Satellite observations
are used to varying degrees in model evaluation and data assimilation. The
utility of satellite observations through data assimilation can vary as a
function of dominant hydrological processes. Opportunities for improvement
are identified, including the development of more accurate and higher
spatial and temporal resolution precipitation products, and the use of a
greater range of remote sensing products in a priori model parameter estimation,
model evaluation and data assimilation. Operational challenges include the
continuity of research satellite missions and data services, and the need to
find computationally-efficient data assimilation techniques. The successful
use of observations critically depends on the availability of detailed
information on observational error and understanding of the relationship
between remotely-sensed and model variables, as affected by conceptual
discrepancies and spatial and temporal scaling. |
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