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Closing the scale gap in measuring snow water equivalent—the added value of Cosmic-Ray Neutron Sensing for regional snow modelling

Author:
Schattan, Paul, Baroni, Gabriele, Oswald, Sascha, Fey, Christine, Schöber, Johannes, Achleitner, Stefan
Source:
Österreichische Wasser- und Abfallwirtschaft 2018 v.70 no.9-10 pp. 497-506
ISSN:
0945-358X
Subject:
Landsat, basins, energy balance, hydrologic models, monitoring, neutrons, remote sensing, runoff, snow, snowpack, uncertainty, winter
Abstract:
This work presents Cosmic-Ray Neutron Sensing (CRNS) for measuring snow water equivalent (SWE) in mountain regions. The contactless and low-maintenance method allows for continuous and non-destructive monitoring of the snow pack in a footprint of several hectares. A field campaign with the subsequent modelling of a gauged headwater basin was set up to close the gap between point-scale snow observations and the spatial resolution of the snow hydrological model. The spatial and the temporal development of the snow pack was observed over three winter seasons. During this period, (i) continuous conventional SWE measurements, (ii) a total of 17 field campaigns (snow pits and terrestrial laser scanning), and (iii) continuous CRNS measurements were conducted. Comparing laserscan based SWE values with CRNS data confirms its potential for continuous SWE measurements in high alpine areas with deep snow packs. In contrast to conventional point-scale SWE measurements, the transferability between winter seasons with differing snow patters is very good. The high potential of the CRNS data is also proven in the subsequent calibration experiment. Therein, an energy balance based snow hydrological model was calibrated with regard to (i) runoff observations (ii) remote sensing (Landsat-8 and Sentinel-2A data) based snow covered area maps and (iii) in-situ snow measurements. The in-situ data refer to either conventional, or CRNS based SWE data. Due to the absence of a scale gap between measurements and model resolution, using CRNS based SWE data improves the modelling results and reduces uncertainties in snow pack modelling.
Agid:
6158889