Hydrological modelling of snowmelt in the Italian Alps using visible and infrared remote sensing (Articolo in rivista)

Type
Label
  • Hydrological modelling of snowmelt in the Italian Alps using visible and infrared remote sensing (Articolo in rivista) (literal)
Anno
  • 1996-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1080/01431169608949137 (literal)
Alternative label
  • Swamy A. N., P.A. Brivio (1996)
    Hydrological modelling of snowmelt in the Italian Alps using visible and infrared remote sensing
    in International journal of remote sensing (Print); Taylor and Francis, Abingdon (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Swamy A. N., P.A. Brivio (literal)
Pagina inizio
  • 3169 (literal)
Pagina fine
  • 3188 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 17 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 16 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Swamy A. N.: University of Visakhapatnam, India; Brivio P.A.: CNR-IGL, Milano (literal)
Titolo
  • Hydrological modelling of snowmelt in the Italian Alps using visible and infrared remote sensing (literal)
Abstract
  • The capabilities of optical and microwave satellite remote sensing for snow cover monitoring and forecasting snowmelt run-off are critically evaluated in this paper in order to improve the utilization of water resources from high mountainous catchments in the Italian Alps. Integration of snow cover data derived from visible and infrared satellite sensors is made with ground meteorological and hydrological information for three elevation zones of the Cordevole river basin of Alpine arch in eastern part of Italy. Digital image processing of nine sets of Landsat Multi-spectral Scanner System and Thematic Mapper computer compatible tapes (CCTs) representing a hydrological year have been made. Digital elevation model, slope and hill shading maps were generated and used in the study. The snow cover estimated using supervised maximum likelihood classification algorithm was observed to fit well for the Cordevole river basin model study when compared to the parallelepiped and minimum distance methods of classification. The elevation zones were classified into snow, mixed snow and aper. Daily snow cover depletion has been obtained with an approximation of second order polynomial fit to the satellite derived snow cover data. A deterministic, semi-distributed, temperature index model has been used to simulate the daily streamflow hydrograph for the snowmelt season. The simulated run-off satisfactorily approximated the measured run-off both in terms of time distribution and volume. Model performance evaluation using correlation coefficient, Nash-Sutcliffe coefficient and percentage volume deviation has indicated good results when compared to the test basins of World Meteorological Organization. (literal)
Editore
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Autore CNR di
Prodotto
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Editore di
Insieme di parole chiave di
data.CNR.it