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dc.contributor.authorDi Marco N
dc.contributor.authorRighetti M
dc.contributor.authorAvesani D
dc.contributor.authorZaramella M
dc.contributor.authorNotarnicola C
dc.contributor.authorBorga M
dc.date.accessioned2020-04-01T16:40:31Z
dc.date.available2020-04-01T16:40:31Z
dc.date.issued2019
dc.identifier.urihttps://bia.unibz.it/handle/10863/13511
dc.description.abstractThis work aims to analyze the benefits of using different types of MODIS snow cover products in snowmelt runoff modelling by means of TOPMELT. With TOPMELT, pixels with similar clear sky radiation and air temperatureare identified by subdividing basin elevation bands into a number of radiation index classes. Then, the snowpack modelling is carried out for each class of radiation index and for each elevation band. This ensures to achieve the significant computational efficiency, which characterizes the temperature index models, allowing at the sametime the higher accuracy of enhanced temperature index models and the possibility of generating maps of snowwater equivalent and of snow cover area. This is a potentially significant advantage when parameter sensitivity anduncertainty estimation procedures are carried out. In this work, TOPMELT is integrated within a lumped conceptual hydrological model to simulate hourly discharges at basin outlet and snow cover maps. The Generalized LikelihoodUncertainty Estimation (GLUE) is used to condition model parameters by using different set of data, includingdischarges and MODIS snow cover products. Given the well-known uncertainties in remote snow detectabilityunder forest canopies, criteria based on overlapping solar radiation and land cover maps are introduced to identifyareas where MODIS snow detection is likely to be more accurate. The use of these criteria gives raise to differenttypes of MODIS snow cover products. The analysis is carried out by applying this integrated framework to the Passirio river catchment (400 km2) in theUpper Adige river basin (Eastern Italian Alps). Elevation ranges from 360 to 3500 m and the averaged total annualprecipitation is about 1200 mm (40% of snow). Results show a positive impact of MODIS snow cover data onrunoff prediction, since they lead to a sharper uncertainty range around the observed values. Moreover, the work is able to single out the effect of using MODIS snow cover products corrected for the effect of forested area onimproving snowmelt flow simulation.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relationEGU General Assembly 2019 ; Vienna : 7.4.2019 - 12.4.2019
dc.rights
dc.titleEvaluating the potential benefit from use of different types of MODIS-based snow cover products on snowmelt runoff modelling using TOPMELTen_US
dc.typeBook chapteren_US
dc.date.updated2020-04-01T16:30:07Z
dc.publication.title21st EGU General Assembly, EGU2019, Proceedings from the conference held 7-12 April, 2019 in Vienna, Austria
dc.language.isiEN-GB
dc.description.fulltextnoneen_US


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