TY - JOUR
T1 - Identifying and extracting a seasonal streamflow signal from remotely sensed snow cover in the Columbia River Basin
AU - Washington, Benjamin
AU - Seymour, Lynne
AU - Mote, Thomas
AU - Robinson, David
AU - Estilow, Thomas
N1 - Funding Information: This work was supported by the NOAA National Centers for Environmental Information (NOAA/NCEI) and Global Science and Technology, Inc. (GST) . We thank DeWayne Cecil, who had the insightful vision that spawned this study. Our team included investigators Philip Mote and Kathie Dello (Oregon State University) in addition to the authors of this study, and we thank our Oregon State colleagues for their contributions. We are also indebted to individuals from several agencies who contributed their valuable time and tremendous expertise to this project. Those individuals include Taylor Dixon from the National Weather Service Northwest River Forecast Center in Portland, Oregon, Kyle Dittmer from the Columbia River Inter-Tribal Fish Commission, and especially Rashawn Tama from the U.S. Department of Agriculture Natural Resources Conservation Service. Many of the figures in this paper were created with the R-package 'Ggplot2' ( Wickham, 2009 ). Funding Information: This work was supported by the NOAA National Centers for Environmental Information (NOAA/NCEI) and Global Science and Technology, Inc. (GST). We thank DeWayne Cecil, who had the insightful vision that spawned this study. Our team included investigators Philip Mote and Kathie Dello (Oregon State University) in addition to the authors of this study, and we thank our Oregon State colleagues for their contributions. We are also indebted to individuals from several agencies who contributed their valuable time and tremendous expertise to this project. Those individuals include Taylor Dixon from the National Weather Service Northwest River Forecast Center in Portland, Oregon, Kyle Dittmer from the Columbia River Inter-Tribal Fish Commission, and especially Rashawn Tama from the U.S. Department of Agriculture Natural Resources Conservation Service. Many of the figures in this paper were created with the R-package ?Ggplot2? (Wickham, 2009). Publisher Copyright: © 2018 Elsevier B.V.
PY - 2019/4
Y1 - 2019/4
N2 - In the western United States, meltwater from mountain snowpacks serves as the dominant water supply for many communities. Efficient distribution and use of this renewable, yet temporally and spatially variable resource relies critically on accurate forecasting of future water availability. Here we report on initial efforts to use Interactive Multisensor Snow and Ice Mapping System (IMS) data on snow coverage to forecast flow in six selected watersheds within the Columbia River Basin. Little research has been done on identifying the relationship between seasonal discharge volume and these satellite-derived snow cover data. In the Yakima watershed within the Columbia River Basin, we could explain 52% of the spring discharge (April – July total streamflow volume) variance by selecting specific 24-km grid cells that exhibit both strong correlation with historical flows as well as high inter-annual variation. This approach yielded reasonable success in other watersheds. Of the six Columbia River subbasins examined in this paper, five of them give statistically significant predictors of April – July streamflow volume at the α = 0.05 level. When comparing this optimized specific-cell technique to the overall average across the entire watershed of interest, we observe improvements in each of our six subbasins, although in some regions, improvements were minimal. Clearly, this optimization technique is inherently limited by the role of snow cover variation in determining streamflow discharges in different subbasins. For both mountainous regions with extensive and stable snow cover as well as low-elevation regions with consistently minimal snow, the snow cover variation only accounts for a small inter-annual streamflow discharge variance. Our methodology shows that the IMS provides remotely-sensed data that are ready to “plug and play” into existing streamflow forecast models such as the Natural Resources Conservation Service's (NRCS) Visual Interactive Prediction and Estimation Routines (VIPER).
AB - In the western United States, meltwater from mountain snowpacks serves as the dominant water supply for many communities. Efficient distribution and use of this renewable, yet temporally and spatially variable resource relies critically on accurate forecasting of future water availability. Here we report on initial efforts to use Interactive Multisensor Snow and Ice Mapping System (IMS) data on snow coverage to forecast flow in six selected watersheds within the Columbia River Basin. Little research has been done on identifying the relationship between seasonal discharge volume and these satellite-derived snow cover data. In the Yakima watershed within the Columbia River Basin, we could explain 52% of the spring discharge (April – July total streamflow volume) variance by selecting specific 24-km grid cells that exhibit both strong correlation with historical flows as well as high inter-annual variation. This approach yielded reasonable success in other watersheds. Of the six Columbia River subbasins examined in this paper, five of them give statistically significant predictors of April – July streamflow volume at the α = 0.05 level. When comparing this optimized specific-cell technique to the overall average across the entire watershed of interest, we observe improvements in each of our six subbasins, although in some regions, improvements were minimal. Clearly, this optimization technique is inherently limited by the role of snow cover variation in determining streamflow discharges in different subbasins. For both mountainous regions with extensive and stable snow cover as well as low-elevation regions with consistently minimal snow, the snow cover variation only accounts for a small inter-annual streamflow discharge variance. Our methodology shows that the IMS provides remotely-sensed data that are ready to “plug and play” into existing streamflow forecast models such as the Natural Resources Conservation Service's (NRCS) Visual Interactive Prediction and Estimation Routines (VIPER).
KW - Columbia River Basin
KW - Discharge
KW - Remote sensing
KW - Snow cover
KW - Streamflow
UR - http://www.scopus.com/inward/record.url?scp=85044544321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044544321&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.rsase.2018.03.003
DO - https://doi.org/10.1016/j.rsase.2018.03.003
M3 - Article
VL - 14
SP - 207
EP - 223
JO - Remote Sensing Applications: Society and Environment
JF - Remote Sensing Applications: Society and Environment
SN - 2352-9385
ER -