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Validation of and comparison between a semidistributed rainfall–runoff hydrological model (PREVAH) and a spatially distributed snow‐evolution model (SnowModel) for snow cover prediction in mountain ecosystems
Author(s) -
Randin Christophe F.,
Dedieu JeanPierre,
Zappa Massimiliano,
Long Li,
Dullinger Stefan
Publication year - 2015
Publication title -
ecohydrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.982
H-Index - 54
eISSN - 1936-0592
pISSN - 1936-0584
DOI - 10.1002/eco.1570
Subject(s) - snow , environmental science , snow cover , surface runoff , climate change , physical geography , snow field , ecosystem , period (music) , hydrology (agriculture) , atmospheric sciences , climatology , ecology , geology , meteorology , geography , geotechnical engineering , acoustics , biology , physics
Snow cover is an important control in mountain environments and a shift of the snow‐free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT‐HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long‐lasting snow cover and evaluating whether they might survive under climate change. Copyright © 2014 John Wiley & Sons, Ltd.