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A Bayesian Spatial Assimilation Scheme for Snow Coverage Observations in a Gridded Snow Model : Volume 2, Issue 4 (25/07/2005)

By Kolberg, S.

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Book Id: WPLBN0004012475
Format Type: PDF Article :
File Size: Pages 35
Reproduction Date: 2015

Title: A Bayesian Spatial Assimilation Scheme for Snow Coverage Observations in a Gridded Snow Model : Volume 2, Issue 4 (25/07/2005)  
Author: Kolberg, S.
Volume: Vol. 2, Issue 4
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2005
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Rue, H., Kolberg, S., & Gottschalk, L. (2005). A Bayesian Spatial Assimilation Scheme for Snow Coverage Observations in a Gridded Snow Model : Volume 2, Issue 4 (25/07/2005). Retrieved from http://hawaiilibrary.net/


Description
Description: SINTEF Energy Research, Sem Sælands vei 11, 7465 Trondheim, Norway. A spatial probability distribution of the variables in a parametric snow depletion curve (SDC) is tailored to the assimilation of satellite snow cover data into a gridded hydrological model. The assimilation is based on Bayes' theorem, in which the proposed distribution represents the a priori information about the SDC variables. From the prior gridded maps of snow storage and accumulated melt depth, the elevation gradients and the degree-day factor are separated out, creating elevation-normalised surfaces of snow storage and degree-day sum. Because the small-scale variability linked to elevation is removed, these surfaces can be described by prior distribution models with a strong spatial dependency structure. This reduction of spatial uniqueness in the prior distribution greatly increases the informational value of the remotely sensed snow coverage data.

The assimilation is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E), based on two Landsat 7 ETM+ images evaluated at 1 km2 scale. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not yet started. Caution is therefore required when using early images.


Summary
A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

 

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