http://www.cnr.it/ontology/cnr/individuo/prodotto/ID168181
Improving runoff prediction through the assimilation of the ASCAT soil moisture product. (Articolo in rivista)
- Type
- Label
- Improving runoff prediction through the assimilation of the ASCAT soil moisture product. (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.5194/hess-14-1881-2010 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Brocca L.1; Melone F.1; Moramarco T.1; Wagner W.2; Naeimi V.2,4; Bartalis Z.3; Hasenauer S.2 (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.hydrol-earth-syst-sci.net/14/1881/2010/hess-14-1881-2010.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- doi:10.5194/hess-14-1881-2010 doi:10.5194/hessd-7-1-2010 (literal)
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1 Research Institute for Geo-Hydrological Protection, National Research Council, Via Madonna Alta 126, 06128 Perugia, Italy
2 Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria
3 European Space Agency, Centre for Earth Observation (ESA/ESRIN), Via Galileo Galilei, 00044 Frascati, Italy
4 German Remote Sensing Data Centre, DFD, of the German Aerospace Centre, DLR, Wessling, Germany (literal)
- Titolo
- Improving runoff prediction through the assimilation of the ASCAT soil moisture product. (literal)
- Abstract
- The role and the importance of soil moisture for
meteorological, agricultural and hydrological applications is
widely known. Remote sensing offers the unique capability
to monitor soil moisture over large areas (catchment scale)
with, nowadays, a temporal resolution suitable for hydrological
purposes. However, the accuracy of the remotely sensed
soil moisture estimates has to be carefully checked. The validation
of these estimates with in-situ measurements is not
straightforward due the well-known problems related to the
spatial mismatch and the measurement accuracy. The analysis
of the effects deriving from assimilating remotely sensed
soil moisture data into hydrological or meteorological models
could represent a more valuable method to test their reliability.
In particular, the assimilation of satellite-derived
soil moisture estimates into rainfall-runoff models at different
scales and over different regions represents an important
scientific and operational issue.
In this study, the soil wetness index (SWI) product derived
from the Advanced SCATterometer (ASCAT) sensor
onboard of the Metop satellite was tested. The SWI was
firstly compared with the soil moisture temporal pattern derived
from a continuous rainfall-runoff model (MISDc) to
assess its relationship with modeled data. Then, by using a
simple data assimilation technique, the linearly rescaled SWI
that matches the range of variability of modelled data (denoted
as SWI?) was assimilated into MISDc and the model
performance on flood estimation was analyzed. Moreover,
three synthetic experiments considering errors on rainfall,
model parameters and initial soil wetness conditions were
carried out. These experiments allowed to further investigate
the SWI potential when uncertain conditions take place.
The most significant flood events, which occurred in the period
2000-2009 on five subcatchments of the Upper Tiber
River in central Italy, ranging in extension between 100 and
650 km2, were used as case studies. Results reveal that
the SWI derived from the ASCAT sensor can be conveniently
adopted to improve runoff prediction in the study
area, mainly if the initial soil wetness conditions are unknown. (literal)
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