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In-situ soil moisture data improve seasonal streamflow forecast accuracy in rainfall-dominated watersheds



Details

  • Journal Title:
    Journal of Hydrology
  • Personal Author:
  • NOAA Program & Office:
    NWS (National Weather Service)
  • Description:
    Seasonal streamflow forecasting methods are less skillful in rainfall-dominated catchments than snow-dominated catchments, where measurements of water storage in the snowpack enhance predictability. Recent research in snow-dominated catchments showed that forecasts can be further enhanced by also including soil moisture measurements, but the impact of soil moisture data on forecast performance in rainfall-dominated watersheds remains unknown. Our objective was to evaluate the potential improvements gained by including in-situ soil moisture data in seasonal streamflow forecasting models in rainfall-dominated watersheds. Precipitation and soil moisture data from four watersheds in the U.S. were incorporated into a modified principal components analysis and regression method to predict seasonal (4-month) streamflow totals at 0-, 1-, 2-, and 3-month lead times. Forecasts derived from antecedent precipitation alone were often statistically insignificant and explained less than 30% of the variance in seasonal streamflow, as indicated by the Nash-Sutcliffe efficiency coefficient. Conversely, forecast models that included soil moisture information explained up to 87% of seasonal streamflow variance at the 0-month lead time, up to 81% at the 1-month lead time, up to 71% at the 2-month lead time, and up to 52% at the 3-month lead time. The root mean square errors for forecasts which included soil moisture data were on average 55% lower than for those based on antecedent precipitation alone. The soil moisture-based forecasts for rainfall-dominated watersheds exhibited accuracies comparable to those previously reported in snow-dominated watersheds. This new forecast method shows strong potential for use in surface water management in rainfall-dominated regions.
  • Keywords:
  • Source:
    Journal of Hydrology, 590, 125404
  • DOI:
  • ISSN:
    0022-1694
  • Format:
    PDF
  • Publisher:
    Elsevier BV
  • Document Type:
    Journal Article
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:956ef880b278576788a39c13f89c0d19842b5f9767ffa262ba311d72449f7547407da1afcf8a1cfc4b3f1ce6a95f65f882b60055f709d57dcd1aeeb38f15cf31
  • Download URL:
  • File Type:
    Filetype[PDF - 1.54 MB ]
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