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The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental...

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Autores principales: Cho, Eunsang, Jacobs, Jennifer M., Vuyovich, Carrie M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375042/
https://www.ncbi.nlm.nih.gov/pubmed/32713970
http://dx.doi.org/10.1029/2019WR025813
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author Cho, Eunsang
Jacobs, Jennifer M.
Vuyovich, Carrie M.
author_facet Cho, Eunsang
Jacobs, Jennifer M.
Vuyovich, Carrie M.
author_sort Cho, Eunsang
collection PubMed
description Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.
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spelling pubmed-73750422020-07-23 The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications Cho, Eunsang Jacobs, Jennifer M. Vuyovich, Carrie M. Water Resour Res Research Articles Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions. John Wiley and Sons Inc. 2020-01-15 2020-01 /pmc/articles/PMC7375042/ /pubmed/32713970 http://dx.doi.org/10.1029/2019WR025813 Text en ©2019 The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Cho, Eunsang
Jacobs, Jennifer M.
Vuyovich, Carrie M.
The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title_full The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title_fullStr The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title_full_unstemmed The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title_short The Value of Long‐Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation‐Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications
title_sort value of long‐term (40 years) airborne gamma radiation swe record for evaluating three observation‐based gridded swe data sets by seasonal snow and land cover classifications
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375042/
https://www.ncbi.nlm.nih.gov/pubmed/32713970
http://dx.doi.org/10.1029/2019WR025813
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