Articles | Volume 23, issue 5
https://doi.org/10.5194/hess-23-2439-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2439-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 May 2019
Research article |
| 21 May 2019
| 21 May 2019
Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska
International Arctic Research Center, University of Alaska Fairbanks,
Fairbanks, Alaska, 99775, USA
Water and Environmental Research Center, University of Alaska
Fairbanks, Fairbanks, Alaska, 99775, USA
current address: Earth and Environmental Sciences, Los Alamos
National Lab, Los Alamos, NM, 87545, USA
Jessica E. Cherry
International Arctic Research Center, University of Alaska Fairbanks,
Fairbanks, Alaska, 99775, USA
Water and Environmental Research Center, University of Alaska
Fairbanks, Fairbanks, Alaska, 99775, USA
Alaska Pacific River Forecast Center, Anchorage, Alaska, 99502, USA
Ben Balk
Deltares USA, Silver Spring, Maryland, 20910, USA
Scott Lindsey
Alaska Pacific River Forecast Center, Anchorage, Alaska, 99502, USA
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Cited
21 citations as recorded by crossref.
- Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine E. Sthapit et al. 10.3390/w14142145
- Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning A. Bennett et al. 10.3390/w16141949
- Recent advances in integrated hydrologic models: Integration of new domains A. Brookfield et al. 10.1016/j.jhydrol.2023.129515
- Assessment of soil erosion risk using RUSLE model, SATEEC system, remote sensing, and GIS techniques: a case study of Navroud watershed M. Fallah et al. 10.1007/s12665-023-11053-4
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow R. Koster et al. 10.1038/s41467-023-39318-3
- Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al. 10.1002/hyp.15075
- Incorporating rain-on-snow into the SWAT model results in more accurate simulations of hydrologic extremes D. Myers et al. 10.1016/j.jhydrol.2021.126972
- A Decade of Hydrological Drought in Central-Western Argentina J. Rivera et al. 10.3389/frwa.2021.640544
- The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas S. Taia et al. 10.3390/s23031246
- Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths E. Thaler et al. 10.1016/j.rse.2022.113403
- On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River M. Xiao et al. 10.5194/hess-26-5627-2022
- Behind the scenes of streamflow model performance L. Bouaziz et al. 10.5194/hess-25-1069-2021
- Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska T. Douglas & C. Zhang 10.1088/1748-9326/ac04d8
- A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States I. Garousi‐Nejad & D. Tarboton 10.1002/hyp.14469
- Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS D. Gyawali & A. Bárdossy 10.5194/hess-26-3055-2022
- Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products K. Schneider & T. Hogue 10.1016/j.ejrh.2021.100979
- Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes A. Muhuri et al. 10.1109/JSTARS.2021.3089655
- The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0 W. Hansen et al. 10.5194/gmd-16-2011-2023
- Impacts of snow surface aerodynamic resistance on snow water equivalent simulations in forested regions X. Tang et al. 10.1002/hyp.14985
- Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds R. Adnan et al. 10.3390/atmos15121407
21 citations as recorded by crossref.
- Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine E. Sthapit et al. 10.3390/w14142145
- Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning A. Bennett et al. 10.3390/w16141949
- Recent advances in integrated hydrologic models: Integration of new domains A. Brookfield et al. 10.1016/j.jhydrol.2023.129515
- Assessment of soil erosion risk using RUSLE model, SATEEC system, remote sensing, and GIS techniques: a case study of Navroud watershed M. Fallah et al. 10.1007/s12665-023-11053-4
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow R. Koster et al. 10.1038/s41467-023-39318-3
- Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al. 10.1002/hyp.15075
- Incorporating rain-on-snow into the SWAT model results in more accurate simulations of hydrologic extremes D. Myers et al. 10.1016/j.jhydrol.2021.126972
- A Decade of Hydrological Drought in Central-Western Argentina J. Rivera et al. 10.3389/frwa.2021.640544
- The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas S. Taia et al. 10.3390/s23031246
- Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths E. Thaler et al. 10.1016/j.rse.2022.113403
- On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River M. Xiao et al. 10.5194/hess-26-5627-2022
- Behind the scenes of streamflow model performance L. Bouaziz et al. 10.5194/hess-25-1069-2021
- Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska T. Douglas & C. Zhang 10.1088/1748-9326/ac04d8
- A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States I. Garousi‐Nejad & D. Tarboton 10.1002/hyp.14469
- Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS D. Gyawali & A. Bárdossy 10.5194/hess-26-3055-2022
- Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products K. Schneider & T. Hogue 10.1016/j.ejrh.2021.100979
- Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes A. Muhuri et al. 10.1109/JSTARS.2021.3089655
- The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0 W. Hansen et al. 10.5194/gmd-16-2011-2023
- Impacts of snow surface aerodynamic resistance on snow water equivalent simulations in forested regions X. Tang et al. 10.1002/hyp.14985
- Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds R. Adnan et al. 10.3390/atmos15121407
Latest update: 14 Dec 2024
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.
Remotely sensed snow observations may improve operational streamflow forecasting in remote...
