International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
coefficient & of 0.94 for Cordevole river basin and 0.96 for La
Vizza basin indicates the existence of a high degree of
corrclation between measured and computed discharges.
Figure 10 Comparison of measured and simulated discharges
lor La Vizza river basin.
[he model performance is further evaluated using the Nash-
Sutcliffe Coefficient (Nash and Sutcliffe 1970). The R^ value
of 0.89 for Cordevole river basin and 0.904 for La Vizza
catchment are found to be highly comparable with values
obtained for various test basins by World Meteorological
Organization, as given by Hall and Martinec (1985). The model
accuracy is also studied by deriving the percentage. volume
deviation (Seidel et al.. 1989). During the study period the
volume deviation between measured and simulated discharges
for the Cordevole river basin is 74.6?» and for La Vizza basin
+3.3%. These values arc in good agreement with the values of
catchments in the Swiss Alps.
8. CONCLUSIONS
The use of satellite optical remote sensing data for developing
hydrological forecasting models over Italian Alps has been
analyzed and the possible application of satellite remote
sensing data in conjunction with ground and meteorological
and hydrological data has been investigated over two
catchments in the eastern ltalian Alps. This study is important
for the management of water resources in the region. The snow
cover estimated by using supervised maximum likelihood
classification algorithm fits well into the present hydrological
model study, The results of the study demonstrate that optical
satellite remote sensing data can be used for snowmelt runoff
forecast in the high mountainous Italian Alps.. However, multi-
sensor data from various high spatial resolution optical remote
sensing sensors must be taken jointly into consideration to
solve the temporal coverage problem.
ACKNOWLEDGMENTS
One of the authors (A. Narayana Swamy) undertook this work
with the support of the ltalian Labs Program of the
International Center for Theoretical Physics, Trieste, Italy.
REFERENCES
Anderson. E.A.1973. The National Weather Service River
Forecast System — Snow accumulation and ablation model,
Tech. Memo, NWS-HYDRO-17 NOAA. Silver Spring, MD.
Beven, K. 1989, Changing idcas in hydrology
physically based models. J. Hvdrof., 105, 157-192,
- the case of
Dozier, J. 1984. Snow reflectance from Sandsat-4 Thematic
Mapper. [EEE Trans. Geaser. Remote Sens. 22. 323 - 328.
Ferris. J. S. and Congalton, R. G. 19890. Satellite and
geographic information system estimates of Colorado river
basin snowpack. Phatogramm. Engng. Remote Sens. 55, 1629
1635.
Goodison, B. E.. Glynn. J. E., Harvey, K. D. and Slater. J. E.
1987. Snow surveying in Canada: a perspective, Can War.
Resour. J, 12,27 - 42.
Haefher, H. 1980. Digital mapping of mountain snow cover
under European conditions, in Operational Application of
Satellite Snow cover Observation. NASA CP-2116. Greenbelt,
Maryland. P.73.
Hall, D. K. ande Martinec, J. 1985. Remote Sensing of lee and
Snow. Chapman and Hall. New York. P. 189.
Martinec. J. . Rango. A., and Major. E. 1983. The Snowmelt
Runoff Model (SRM) User's Manual, NASA RP-1100.
Greenbelt, Maryland. p. 110.
Martinec, J. , Rango. A. and Roberts. R. 1994. in
Baumgartner. M.F.(Ed.). Thc Snowmelt Runotf Model (SRM)
Users Manual. Publication No.P29, Dept. of Geography,
University of Bern. p.65.
Nash, J. E.
through conceptual models. Part I
J. Hydrol.. 10, 282 - 290.
and Sutcliffe, J. V. 1970. River flow forecasting
A discussion of principles,
Rot, H. 1987. Remote sensing of snow. In Goodison, B.E.
Barry, R.Ci, and Dozier, J (Eds), Large Scale Effects of
Seasonal Snowcover. lAHS Publ. No.l66. IAHS Press,
Wallingford, pp.279 .. 290.
Schwaiger, R.. Huber, R., Mayer, HL... A. 1996. Land. cover
classification of Landsat images using problem-adapted
arlificial neural networks, In Elisabetta Binaghi, Brivia, P.A,,
and Anna Rampini (Lids), Sofi computing in remote sensing
data analvsis. World Scientific, New Jersey. p.260.
Seidel, K., Ade, F., and Lichtenegger, J. 1983, Augmenting
Landsat MSS data with topographic information for enhanced
registration ande classification, [EEE Trans. Geosci Remote
Sens. 2h, 252-258,
Seidel, K.. Burkart, U.. Baumann. R.. and Martinec. J. 1989.
Snow cover monitoring by satellites and real time runoff
forecasts. Proceedings, International Symposium on Remote
Sensing: An Economic Tool for the Nineties. Geo-science and
Remote sensing Svmposium -. IGARSS'89, Vancouver, H.C.,
Canada, 10-14 July 1989, IEEE, pp.558 — 561
World Meteorological Organization, 1986, Inter-comparison of
models for snowmelt runofl’ modeling. Operational Hydrology
Report No.23, WMO. Geneva, Switzerland.
1226
