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ROMFORSK-Program for romforskning

SNOWDEPTH - Global snow depths from spaceborne remote sensing for permafrost, high-elevation precipitation, and climate reanalyses

Alternative title: SNOWDEPTH - Globale snødybdemålinger fra satellittdata for permafrost, nedbør i høyfjellet og klima-reanalyser

Awarded: NOK 8.0 mill.

Project Number:


Project Period:

2021 - 2026

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SNOWDEPTH combines measurements from the laser satellite ICESat-2 with other satellite data, climate reanalyses, elevation data, and statistical methods to determine how much snow there is on the ground. The final product are time series with snow depth maps for places across the globe. Currently, no comparable data exists, as there are no efficient ways to measure snow depth in remote areas: Mountain snow is the source for drinking water, hydropower, irrigation, but also a source of floods for the majority of the population. Snow depth measurements such as from weather stations mainly exist in easily accessible places in wealthy regions of the planet. SNOWDEPTH will provide currently unavailable and much sought-after global snow data. This data will be useful for many related research fields and applications, in particular also for less developed countries and areas with few field measurements. During the first part of the project, we will develop the methods to retrieve global snow depths by means of ensemble-based data assimilation, similar to the methods used within climate reanalyses. The second part of the project includes three application areas where our global snow depths have especially great potential to improve our knowledge, also in the light of climate change: 1) permafrost: a thick snow cover isolates the ground from cold winter air and keeps it warm, and accurate snow depth data is thus one of the key factors to correctly model permafrost distribution; 2) climate reanalyses: as these are based on field observations, climate reanalyses do not represent the past weather and climate very well in areas with few measurements. The snow depth measurements of SNOWDEPTH can thus improve climate reanalyses; 3) high-elevation precipitation: precipitation processes at higher elevations are poorly understood due to the lack of direct measurements in mountain areas, and satellite-based snow depth measurements will thus fill a knowledge gap.

The SNOWDEPTH project will, as the first in the world, directly measure snow depths globally at high spatial resolution from freely available ICESat-2 spaceborne laser altimetry data available since autumn 2018. To generate global monthly snow depth maps, including for mountainous and forested areas, we will combine the ICESat-2-derived snow depths with Sentinel snow cover/depth data in an ensemble-based data assimilation (DA) framework. This global snow depth data will fill a large data and knowledge gap within hydrology and cryosphere/climate sciences and is directly relevant for the three application cases within the project: permafrost, high-elevation precipitation and climate reanalysis. The project has two parts and is supported by field activities for ground reference. In phase 1, we will develop algorithms to derive snow depths at two complementary scales: A) local snow depths from ICESat-2 profiles that capture the high spatial variability in areas with small-scale topography, and B) global snow depth maps with monthly temporal resolution, using DA methods. In phase 2, we will use the derived snow depths within three application fields where they directly benefit to advance the state of the art: i) Permafrost: include snow depths in an existing model framework to greatly improve modelling of the ground thermal regime, both locally at targeted field sites and at global scale. The current lack of snow depth data is a key bottleneck for permafrost modelling. ii) High-elevation precipitation: analyse how snow depths vary across orographic barriers to increase understanding of high-altitude precipitation processes. These are currently largely unconstrained due to lack of measurements. iii) Climate reanalysis: verify and improve operational and climate reanalysis products through cross-comparison and improved process understanding. In data-sparse areas, reanalysis products are less accurate and largely model-driven given the lack of observations.

Funding scheme:

ROMFORSK-Program for romforskning