Webinar: Mapping and modeling annual probability of year-round streamflow, 2004-2016: A case study in the Pacific Northwest
Host: Great Northern LCC Rocky Mountain Partner Forum
Presenter: Roy Sando, USGS Wyoming-Montana Water Science Center
Co-authors: Kris Jaeger, USGS Washington Water Science Center; Ryan McShane, USGS Wyoming-Montana Water Science Center; Dave Hockman-Wert, USGS Forest and Rangeland Ecosystem Science Center; Jason Dunham, USGS Forest and Rangeland Ecosystem Science Center; Kyle Blasch, USGS Idaho Water Science Center; Theresa Olsen, USGS Washington Water Science Center; Tana Haluska, USGS Oregon Water Science Center; John Risley, USGS Oregon Water Science Center
To improve understanding of streamflow permanence in the Pacific Northwest, we have developed a method for predicting the annual probability of year-round streamflow at 30-meter intervals. The approach involves collecting and processing nearly 24,000 streamflow observations into “wet” or “dry” values, and synchronizing them with 291 predictor datasets that represent physical (one-time values) and climatic (monthly or annual values) conditions associated with the upstream area for each 30-meter point along streams in the Pacific Northwest. Both of these datasets are among the first of their kind and shed light on the scientific opportunities that ‘Big Data’ techniques allow for. The predictive models developed from this effort for the period 2004-2016 will provide insight into spatial and temporal patterns of year-round streamflow in a regionally-consistent manner, while retaining high enough spatial resolution to be locally relevant.
About the presenter:
Thomas "Roy" Sando is a physical scientist who specializes in remote sensing, GIS, and geostatistics. Roy works for the USGS Wyoming-Montana Water Science Center and is based out of Helena, Montana. His research interests interest include using GIS and remote sensing to advance our understanding of ecological drought, characterizing inter-annual variability of surface water availability, and using ‘Big Data’ techniques to improve accuracy and resolution of regional geospatial analyses.