Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water...

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Published in:Environmental Research Letters
Main Authors: Zhang, Jie, Pohjola, Veijo, Pettersson, Rickard, Norell, Björn, Wolf-Dietrich, Marchand, Clemenzi, Ilaria, Gustafsson, David
Format: Article in Journal/Newspaper
Language:English
Published: Uppsala universitet, Luft-, vatten- och landskapslära 2021
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035
https://doi.org/10.1088/1748-9326/abfe8d
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spelling ftuppsalauniv:oai:DiVA.org:uu-452035 2024-02-11T10:07:11+01:00 Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach Zhang, Jie Pohjola, Veijo Pettersson, Rickard Norell, Björn Wolf-Dietrich, Marchand Clemenzi, Ilaria Gustafsson, David 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 https://doi.org/10.1088/1748-9326/abfe8d eng eng Uppsala universitet, Luft-, vatten- och landskapslära Vattenregleringsforetagen, Östersund, Sweden. Sweco Norge AS, Trondheim, Norway. Swedish Meteorol & Hydrol Inst, Norrköping, Sweden. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden. Environmental Research Letters, 2021, 16:8, orcid:0000-0001-6851-1673 orcid:0000-0002-6961-0128 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035 doi:10.1088/1748-9326/abfe8d ISI:000678345800001 info:eu-repo/semantics/openAccess Physical Geography Naturgeografi Article in journal info:eu-repo/semantics/article text 2021 ftuppsalauniv https://doi.org/10.1088/1748-9326/abfe8d 2024-01-17T23:33:35Z Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas. Article in Journal/Newspaper Northern Sweden Uppsala University: Publications (DiVA) Environmental Research Letters 16 8 084007
institution Open Polar
collection Uppsala University: Publications (DiVA)
op_collection_id ftuppsalauniv
language English
topic Physical Geography
Naturgeografi
spellingShingle Physical Geography
Naturgeografi
Zhang, Jie
Pohjola, Veijo
Pettersson, Rickard
Norell, Björn
Wolf-Dietrich, Marchand
Clemenzi, Ilaria
Gustafsson, David
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
topic_facet Physical Geography
Naturgeografi
description Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.
format Article in Journal/Newspaper
author Zhang, Jie
Pohjola, Veijo
Pettersson, Rickard
Norell, Björn
Wolf-Dietrich, Marchand
Clemenzi, Ilaria
Gustafsson, David
author_facet Zhang, Jie
Pohjola, Veijo
Pettersson, Rickard
Norell, Björn
Wolf-Dietrich, Marchand
Clemenzi, Ilaria
Gustafsson, David
author_sort Zhang, Jie
title Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
title_short Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
title_full Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
title_fullStr Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
title_full_unstemmed Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
title_sort improving the snowpack monitoring in the mountainous areas of sweden from space : a machine learning approach
publisher Uppsala universitet, Luft-, vatten- och landskapslära
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035
https://doi.org/10.1088/1748-9326/abfe8d
genre Northern Sweden
genre_facet Northern Sweden
op_relation Environmental Research Letters, 2021, 16:8,
orcid:0000-0001-6851-1673
orcid:0000-0002-6961-0128
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452035
doi:10.1088/1748-9326/abfe8d
ISI:000678345800001
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.1088/1748-9326/abfe8d
container_title Environmental Research Letters
container_volume 16
container_issue 8
container_start_page 084007
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