Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate...
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ftmdpi:oai:mdpi.com:/2072-4292/9/3/256/ 2023-08-20T04:07:54+02:00 Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations Shusen Wang Fuqun Zhou Hazen Russell agris 2017-03-10 application/pdf https://doi.org/10.3390/rs9030256 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs9030256 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 9; Issue 3; Pages: 256 flood GRACE satellites snow river flow cold region Mackenzie River basin model Text 2017 ftmdpi https://doi.org/10.3390/rs9030256 2023-07-31T21:04:14Z Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s−1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data. Text Mackenzie river MDPI Open Access Publishing Mackenzie River Canada Snow River ENVELOPE(-102.368,-102.368,62.817,62.817) Remote Sensing 9 3 256 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
flood GRACE satellites snow river flow cold region Mackenzie River basin model |
spellingShingle |
flood GRACE satellites snow river flow cold region Mackenzie River basin model Shusen Wang Fuqun Zhou Hazen Russell Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
topic_facet |
flood GRACE satellites snow river flow cold region Mackenzie River basin model |
description |
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s−1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data. |
format |
Text |
author |
Shusen Wang Fuqun Zhou Hazen Russell |
author_facet |
Shusen Wang Fuqun Zhou Hazen Russell |
author_sort |
Shusen Wang |
title |
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
title_short |
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
title_full |
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
title_fullStr |
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
title_full_unstemmed |
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations |
title_sort |
estimating snow mass and peak river flows for the mackenzie river basin using grace satellite observations |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2017 |
url |
https://doi.org/10.3390/rs9030256 |
op_coverage |
agris |
long_lat |
ENVELOPE(-102.368,-102.368,62.817,62.817) |
geographic |
Mackenzie River Canada Snow River |
geographic_facet |
Mackenzie River Canada Snow River |
genre |
Mackenzie river |
genre_facet |
Mackenzie river |
op_source |
Remote Sensing; Volume 9; Issue 3; Pages: 256 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs9030256 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs9030256 |
container_title |
Remote Sensing |
container_volume |
9 |
container_issue |
3 |
container_start_page |
256 |
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1774719869496852480 |