Prediction of temperature variation within a snowpack in open areas and under different canopy covers

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dc.contributor.authorAltunkaynak, Abdüsselam
dc.contributor.authorAydın, Abdurrahim
dc.date.accessioned2020-04-30T23:21:18Z
dc.date.available2020-04-30T23:21:18Z
dc.date.issued2012
dc.departmentDÜ, Orman Fakültesi, Orman Mühendisliği Bölümüen_US
dc.descriptionWOS: 000312548800007en_US
dc.description.abstractSnow temperature is a major component of many physical processes in a snowpack. The temperature and the change in temperature across a layer have a dominant effect on physical properties of snow grains as well as its hardness, strength, and failure resistance. In this study, temperature and snow cover thickness were measured during the snow season of 20072008 in 11 elevation classes and in three different sampling locations, one in an open area and two under different forest canopy covers for each class along Kartalkaya road, Bolu. Each sampling site was visited 44 times to collect data including snow depth, snow surface temperature, ground temperature, and temperature within snowpack at 20-cm intervals. Seven different models are developed to determine snowpack temperature variations under forest canopy covers and in an open area with different leaf area index values. All models were performed using a multilayer perceptron (MP) method for the BoluKartalkaya area, Turkey. MP approach constitutes a standard form of neural network modeling and can modify two-layer linear perceptron methods using three and more layers. The ability of MP is to handle complex nonlinear interactions, which ease the natural process of modeling. This method can overcome complex computations using neuron networks, and they can easily nonlinearly link input and output variables. The predictive errors are determined on the basis of mean absolute error and mean square error criteria. The NashSutcliffe sufficiency score showing compliance between observed and predicted values is also calculated. According to the mean absolute error, the mean square error, and the NashSutcliffe sufficiency score criteria, the predictive errors are within reasonable error intervals, justifying the use of the developed MP models for engineering applications. Copyright (c) 2012 John Wiley & Sons, Ltd.en_US
dc.description.sponsorshipWestern Black Sea Forestry Research Instituteen_US
dc.description.sponsorshipThe authors gratefully acknowledge the support of The Western Black Sea Forestry Research Institute. They thank Ahmet Duyar for assistance in the field.en_US
dc.identifier.doi10.1002/hyp.9203en_US
dc.identifier.endpage4028en_US
dc.identifier.issn1099-1085
dc.identifier.issue26en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4015en_US
dc.identifier.urihttps://doi.org/10.1002/hyp.9203
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4172
dc.identifier.volume26en_US
dc.identifier.wosWOS:000312548800007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofHydrological Processesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecttemperature variationen_US
dc.subjectsnowpacken_US
dc.subjectmultilayer perceptronen_US
dc.subjectpredictionen_US
dc.subjectleaf area indexen_US
dc.titlePrediction of temperature variation within a snowpack in open areas and under different canopy coversen_US
dc.typeArticleen_US

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