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...
Published in: | Environmental Research Letters |
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Language: | English |
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SMHI, Forskningsavdelningen
2021
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-6150 https://doi.org/10.1088/1748-9326/abfe8d |
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ftsmhi:oai:DiVA.org:smhi-6150 2023-05-15T17:44:51+02:00 Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach Zhang, Jie Pohjola, Veijo A. Pettersson, Rickard Norell, Bjorn Marchand, Wolf-Dietrich Clemenzi, Ilaria Gustafsson, David 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-6150 https://doi.org/10.1088/1748-9326/abfe8d eng eng SMHI, Forskningsavdelningen Environmental Research Letters, 1748-9326, 2021, 16:8, http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-6150 doi:10.1088/1748-9326/abfe8d ISI:000678345800001 info:eu-repo/semantics/openAccess Oceanography Hydrology and Water Resources Oceanografi hydrologi och vattenresurser Article in journal info:eu-repo/semantics/article text 2021 ftsmhi https://doi.org/10.1088/1748-9326/abfe8d 2022-12-09T10:06:17Z 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 SMHI (Swedish Meteorological and Hydrological Institute): Vetenskapliga Publikationer (DiVA) Environmental Research Letters 16 8 084007 |
institution |
Open Polar |
collection |
SMHI (Swedish Meteorological and Hydrological Institute): Vetenskapliga Publikationer (DiVA) |
op_collection_id |
ftsmhi |
language |
English |
topic |
Oceanography Hydrology and Water Resources Oceanografi hydrologi och vattenresurser |
spellingShingle |
Oceanography Hydrology and Water Resources Oceanografi hydrologi och vattenresurser Zhang, Jie Pohjola, Veijo A. Pettersson, Rickard Norell, Bjorn Marchand, Wolf-Dietrich Clemenzi, Ilaria Gustafsson, David Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach |
topic_facet |
Oceanography Hydrology and Water Resources Oceanografi hydrologi och vattenresurser |
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 A. Pettersson, Rickard Norell, Bjorn Marchand, Wolf-Dietrich Clemenzi, Ilaria Gustafsson, David |
author_facet |
Zhang, Jie Pohjola, Veijo A. Pettersson, Rickard Norell, Bjorn Marchand, Wolf-Dietrich 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 |
SMHI, Forskningsavdelningen |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-6150 https://doi.org/10.1088/1748-9326/abfe8d |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
Environmental Research Letters, 1748-9326, 2021, 16:8, http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-6150 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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1766147142872727552 |