Identifying and extracting a seasonal streamflow signal from remotely sensed snow cover in the Columbia River Basin

Benjamin Washington, Lynne Seymour, Thomas Mote, David Robinson, Thomas Estilow

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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).

Original languageEnglish (US)
Pages (from-to)207-223
Number of pages17
JournalRemote Sensing Applications: Society and Environment
Volume14
DOIs
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Computers in Earth Sciences

Keywords

  • Columbia River Basin
  • Discharge
  • Remote sensing
  • Snow cover
  • Streamflow

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