Articles | Volume 23, issue 5
https://doi.org/10.5194/hess-23-2439-2019
https://doi.org/10.5194/hess-23-2439-2019
Research article
 | 
21 May 2019
Research article |  | 21 May 2019

Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Katrina E. Bennett, Jessica E. Cherry, Ben Balk, and Scott Lindsey

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (08 Oct 2018) by Shraddhanand Shukla
AR by Katrina Bennett on behalf of the Authors (03 Dec 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (20 Dec 2018) by Shraddhanand Shukla
RR by Stephen Déry (26 Dec 2018)
RR by Anonymous Referee #2 (16 Jan 2019)
ED: Publish subject to minor revisions (review by editor) (16 Jan 2019) by Shraddhanand Shukla
AR by Katrina Bennett on behalf of the Authors (07 Feb 2019)  Author's response   Manuscript 
ED: Publish as is (12 Mar 2019) by Shraddhanand Shukla
AR by Katrina Bennett on behalf of the Authors (24 Mar 2019)  Manuscript 
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Short summary
Remotely sensed snow observations may improve operational streamflow forecasting in remote regions, such as Alaska. In this study, we insert remotely sensed observations of snow extent into the operational framework employed by the US National Weather Service’s Alaska Pacific River Forecast Center. Our work indicates that the snow observations can improve snow estimates and streamflow forecasting. This work provides direction for forecasters to implement remote sensing in their operations.