Abstract
CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 75:181-199 (2018) - DOI: https://doi.org/10.3354/cr01518 Statistical modelling of snow cover dynamics in the Central Himalaya Region, Nepal J. Weidinger1,*, L. Gerlitz2, B. Bechtel1, J. Böhner1 1CEN - Center for Earth System Research and Sustainability, Institute of Geography, University of Hamburg, 20146 Hamburg, Germany 2GFZ - German Research Centre for Geosciences, Helmholtz Centre Potsdam, 14473 Potsdam, Germany *Corresponding author: johannes.weidinger@uni-hamburg.de ABSTRACT: Snow cover modelling is primarily focussed on snow depletion in the context of hydrological research. Degree-day or temperature index models (TIMs) as well as energy balance models (EBMs) are conventional to quantify catchment runoff. Whereas the former exploit relationships between snow (and/or ice) melt and air temperatures, the latter rest upon quantifying melt as the deviation from heat balance equations. However, the 2 approaches contain distinct drawbacks. For example, increasing temporal resolution decreases the accuracy of TIMs, and no spatial variability is provided, whereas EBMs have large dataset requirements for climate, landscape and soil properties. Nevertheless, detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover is crucial for understanding hydrological systems, plant distribution and various other research interests. Therefore, we propose a statistical model based on a combination of high resolution spatio-temporal climate datasets and climate-related topographic data, which were obtained by meteorological network stations, remote sensing and GIS analysis. The main objectives were to identify suitable inputs and to develop a robust binary snow distribution model that enables the mapping of major physical processes controlling snow accumulation, melt and stagnation in a high mountain environment in the Gaurishankar Conservation Area in Nepal. We used the random forest technique, which represents a state of the art machine learning algorithm. The snow distribution was predicted very accurately with high spatio-temporal resolution (daily on 0.5 × 0.5 km), with hit rates of around 90% and an overall model accuracy of 90.8% compared to independent Moderate Resolution Imaging Spectroradiometer (MODIS) observations. KEY WORDS: Snow cover · Remote sensing · MODIS · Statistical modelling · Himalaya · Random forest Full text in pdf format NextCite this article as: Weidinger J, Gerlitz L, Bechtel B, Böhner J (2018) Statistical modelling of snow cover dynamics in the Central Himalaya Region, Nepal. 75:181-199. https://doi.org/10.3354/cr01518Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 75, No. 3. Online publication date: August 09, 2018 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2018 Inter-Research.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
