ALBERT

Description: A method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid distributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC), which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E), based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. 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%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. 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 started and the snow coverage is close to unity. Caution is therefore required when using early images.

Description: 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.

Description: The Norwegian Water Resources and Energy Directorate (NVE) operates the Norwegian Snow Avalanche, Lake Ice, Landslide, and Flood Warning Services and has been using Copernicus satellite data since 2014 to improve these services, enhance safety management, and support research. The NVE Copernicus Services project, launched in 2022, aims to provide operational monitoring of snow avalanches, snow cover, lake ice, glaciers, floods, and other potential hazards using Sentinel data. In addition, the services make a baseline for future studies quantifying the climatic effects on snow avalanches, snow, and glaciers.New neural network-based detection algorithms have been implemented to improve monitoring of snow avalanches and snow cover. In addition, the project seeks to enhance monitoring of glacier lake outburst floods, snow and firn lines on glaciers, lake ice phenology, and flooded areas among others. The project also explores new applications for the Copernicus data, such as mapping and monitoring landslides and slushflows and using soil moisture data for hydrological models. Furthermore, the project investigates the potential use of Sentinel data for energy purposes. To ensure the efficient and safe delivery of these services, the project focuses on upgrading the IT infrastructure at NVE. The project not only generates new products from satellite data but also combines satellite data with modelled data and will use in situ data from the "Varsom" mobile app for validation. Visualization of products derived from optical and radar satellite images will be published in NVEs warning and preparedness tools xGeo and seNorge, among others.