Climatic Change

, Volume 72, Issue 1–2, pp 17–36 | Cite as

Detection Time for Plausible Changes in Annual Precipitation, Evapotranspiration, and Streamflow in Three Mississippi River Sub-Basins

  • Alan D. Ziegler
  • Edwin P. Maurer
  • Justin Sheffield
  • Bart Nijssen
  • Eric F. Wood
  • Dennis P. Lettenmaier
Article

Abstract

We use diagnostic studies of off-line variable infiltration capacity (VIC) model simulations of terrestrial water budgets and 21st-century climate change simulations using the parallel climate model (PCM) to estimate the time required to detect predicted changes in annual precipitation (P), evapotranspiration (E), and discharge (Q) in three sub-basins of the Mississippi River Basin. Time series lengths on the order of 50–350 years are required to detect plausible P, E, and Q trends in the Missouri, Ohio, and Upper Mississippi River basins. Approximately 80–160, 50, and 140–350 years, respectively, are needed to detect the predicted P, E, and Q trends with a high degree of statistical confidence. These detection time estimates are based on conservative statistical criteria (α = 0.05 and β = 0.10) associated with low probability of both detecting a trend when it is not occurring (Type I error) and not detecting a trend when it is occurring (Type II error). The long detection times suggest that global-warming-induced changes in annual basin-wide hydro-climatic variables that may already be occurring in the three basins probably cannot yet be detected at this level of confidence. Furthermore, changes for some variables that may occur within the 21st century might not be detectable for many decades or until the following century – this may or may not be the case for individual recording station data. The long detection times for streamflow result from comparatively low signal-to-noise ratios in the annual time series. Finally, initial estimates suggest that faster detection of acceleration in the hydrological cycle may be possible using seasonal time series of appropriate hydro-climatic variables, rather than annual time series.

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Alan D. Ziegler
    • 1
  • Edwin P. Maurer
    • 2
  • Justin Sheffield
    • 1
  • Bart Nijssen
    • 3
  • Eric F. Wood
    • 1
  • Dennis P. Lettenmaier
    • 4
  1. 1.Environmental Engineering & Water ResourcesPrinceton UniversityPrincetonU.S.A.
  2. 2.Department of Civil EngineeringSanta Clara UniversitySanta ClaraU.S.A.
  3. 3.Departments of Civil Engineering & Engineering Mechanics and Department of Hydrology and Water ResourcesUniversity of ArizonaTucsonU.S.A.
  4. 4.Department of Civil EngineeringUniversity of WashingtonSeattleU.S.A.

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