Improving Seasonal Streamflow Forecasts in the Western U.S.
(NEW YORK, NY – October 11, 2013) – A recently published study demonstrates that statistical seasonal streamflow forecasts in the Western United States, which primarily depend on observations of snowpack conditions for accuracy, can be improved mainly through estimates of soil moisture, which have not traditionally been used in these forecasts.
The paper, published in the Journal of Hydrometeorology, was written by Hazen and Sawyer’s Eric Rosenberg, and co-authored by Andrew Wood and Anne Steinemann.
Findings indicate that, except for basins with sparse existing observation networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where observation stations are traditionally situated.
The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the snow accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.
For a copy of the full paper, please contact the author at email@example.com.
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