Prediction of temperature variation within a snowpack in open areas and under different canopy covers
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Dosyalar
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley-Blackwell
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Snow 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.
Açıklama
WOS: 000312548800007
Anahtar Kelimeler
temperature variation, snowpack, multilayer perceptron, prediction, leaf area index
Kaynak
Hydrological Processes
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
26
Sayı
26