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Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow

Nature Geoscience volume 3, pages 613–616 (2010)Cite this article

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Abstract

Seasonal predictions of streamflow can benefit from knowledge of the amounts of snow and other water present in a basin when the forecast is issued1,2,3,4,5. In the American west, operational forecasts for spring–summer streamflow rely heavily on snow-water storage and are often issued at the time of maximum snow accumulation. However, forecasts issued earlier can also show skill, particularly if proxy information for soil moisture, such as antecedent rainfall, is also used as a predictor1,4. Studies using multiple regression approaches and/or model-produced streamflows6,7,8,9 indeed suggest that information on soil moisture—a relatively underappreciated predictor—can improve streamflow predictions. Here, we quantify the relative contributions of early-season snow and soil moisture information to the skill of streamflow forecasts more directly and comprehensively: in a suite of land-modelling systems, we use the snow and soil moisture information both together and separately to derive seasonal forecasts. Our skill analysis reveals that early-season snow-water storage generally contributes most to skill, but the contribution of early-season soil moisture is often significant. In addition, we conclude that present-generation macroscale land-surface models forced with large-scale meteorological data can produce estimates of water storage in soils and as snow that are useful for basin-scale prediction.

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Figure 1: Streamflow skill levels in the Western United States achieved in the simulation experiments, plotted by basin.

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Acknowledgements

We thank J. Verdin for discussions at the onset of this study and T. Pagano for an insightful review. We also thank the providers of the naturalized streamflow data: US Army Corps of Engineers, Omaha NB office, for the Missouri River basin; US Army Corps of Engineers, Tulsa OK office, for the Arkansas–Red River basin; Columbia River Basin Climate Change Scenarios Database for the Columbia River basin; California Data Exchange Commission for the California basins; US Bureau of Reclamation, Lower Colorado Region, for the Colorado River basin.

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Authors and Affiliations

  1. Global Modeling and Assimilation Office, Code 610.1, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

    Randal D. Koster, Sarith P. P. Mahanama & Rolf H. Reichle

  2. Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, Baltimore, Maryland 21250, USA

    Sarith P. P. Mahanama

  3. Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, USA

    Ben Livneh & Dennis P. Lettenmaier

Authors
  1. Randal D. Koster
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  2. Sarith P. P. Mahanama
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  3. Ben Livneh
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  4. Dennis P. Lettenmaier
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  5. Rolf H. Reichle
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Contributions

R.D.K. oversaw the study and wrote the paper. S.P.P.M. and B.L. carried out the Catchment and Noah/VIC/Sac integrations, respectively, and S.P.P.M. carried out extensive data post-processing. All authors (R.D.K., S.P.P.M., B.L., D.P.L., R.H.R.) contributed to the analysis and interpretation of the results.

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Correspondence to Randal D. Koster.

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The authors declare no competing financial interests.

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Koster, R., Mahanama, S., Livneh, B. et al. Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nature Geosci 3, 613–616 (2010). https://doi.org/10.1038/ngeo944

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