Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps
Abstract Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR035019 |
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| author | I. Brangers H. Lievens A. Getirana G. J. M. De Lannoy |
| author_facet | I. Brangers H. Lievens A. Getirana G. J. M. De Lannoy |
| author_sort | I. Brangers |
| collection | DOAJ |
| description | Abstract Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth (SD) at sub‐kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah‐Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA‐2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel‐1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel‐1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general. |
| format | Article |
| id | doaj-art-aafaa22da3a64a63b51b253ca7c0f3e7 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-aafaa22da3a64a63b51b253ca7c0f3e72025-08-23T13:05:51ZengWileyWater Resources Research0043-13971944-79732024-11-016011n/an/a10.1029/2023WR035019Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European AlpsI. Brangers0H. Lievens1A. Getirana2G. J. M. De Lannoy3Department of Earth and Environmental Sciences KU Leuven Heverlee BelgiumDepartment of Earth and Environmental Sciences KU Leuven Heverlee BelgiumHydrological Sciences Laboratory NASA Goddard Space Flight Center Greenbelt MD USADepartment of Earth and Environmental Sciences KU Leuven Heverlee BelgiumAbstract Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth (SD) at sub‐kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah‐Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA‐2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel‐1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel‐1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general.https://doi.org/10.1029/2023WR035019sentinel‐1data assimilationsnow depthmountain hydrology |
| spellingShingle | I. Brangers H. Lievens A. Getirana G. J. M. De Lannoy Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps Water Resources Research sentinel‐1 data assimilation snow depth mountain hydrology |
| title | Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps |
| title_full | Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps |
| title_fullStr | Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps |
| title_full_unstemmed | Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps |
| title_short | Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps |
| title_sort | sentinel 1 snow depth assimilation to improve river discharge estimates in the western european alps |
| topic | sentinel‐1 data assimilation snow depth mountain hydrology |
| url | https://doi.org/10.1029/2023WR035019 |
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