气候变化领域集成服务门户(CCPortal): The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling

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DOI	10.1016/j.rse.2019.111603
	The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling
	Schattan P.; Schwaizer G.; Schöber J.; Achleitner S.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	A combined snow modelling approach integrating remote sensing data, in-situ data, and an improved hydrological model is presented. Complementary information sources are evaluated in terms of its value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation. The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site. The available remote sensing datasets include Terra MODIS based medium resolution and Landsat-7/8 and Sentinel-2A based high resolution fractional snow covered area maps. Recently, Sentinel-1 based wet snow covered area maps have become increasingly available. To the knowledge of the authors the first evaluation of their value for snow-hydrological modelling is presented. Besides conventional small footprint station data, in-situ time-series of snow water equivalent (SWE) of a Cosmic-Ray Neutron Sensor (CRNS) having a footprint of several hectares is additionally used. For including these data the model now provides respective outputs such as fractional snow cover, wet/dry snow surface and SWE areal means equivalent to the CRNS sensor footprint. By means of 40,000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS-SWE data. Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel-1 backscatter. The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes. © 2019 Elsevier Inc.
英文关键词	Cosmic-Ray Neutron Sensing; High resolution optical snow cover fraction; Sentinel-1 wet snow cover maps; Snow hydrological modelling
语种	英语
scopus关键词	Air navigation; Catchments; Cosmic rays; Cosmology; Neutrons; Remote sensing; Runoff; Cosmic ray neutron; Fractional snow covers; Hydrological modelling; Remote sensing information; Snow covers; Snow water equivalent; Water resources management; Wet snow; Information management; cosmic ray; data assimilation; detection method; hydrological modeling; Landsat; MODIS; remote sensing; river basin; runoff; satellite data; Sentinel; snow cover; snow water equivalent; Terra (satellite); Alps
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179466
作者单位	Institute of Geography, University of Innsbruck, Innrain 52f, Innsbruck, Austria; alpS GmbH, Grabenweg 68, Innsbruck, Austria; ENVEO IT GmbH, Fürstenweg 176, Innsbruck, Austria; TIWAG Tiroler Wasserkraft AG, Eduard-Wallnöfer-Platz 2, Innsbruck, Austria; Unit of Hydraulic Engineering, University of Innsbruck, Technikerstraße 13, Innsbruck, Austria
推荐引用方式 GB/T 7714	Schattan P.,Schwaizer G.,Schöber J.,et al. The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling[J],2020,239.
APA	Schattan P.,Schwaizer G.,Schöber J.,&Achleitner S..(2020).The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling.Remote Sensing of Environment,239.
MLA	Schattan P.,et al."The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling".Remote Sensing of Environment 239(2020).