Development of Regional Regression Equations in Connecticut

Science Center Objects

Knowledge of the magnitude and frequency of floods is needed for the effective and safe design of bridges, culverts, and other structures.  This information is also important for flood-plain planning and management. Periodic examination of flood-frequency characteristics is essential to ensure the best estimates of flood magnitudes for a given annual exceedance probabilities (AEP).

Two recent improvements in computational methods for flood-frequency analysis Expected Moments Algorithm and the multiple Grubbs-Beck test. The Expected Moments Algorithm (EMA) (Cohn and others, 1997, 2001) permits treating flood-peak data as intervals rather than just a point value, thereby improving the ability to incorporate information about uncertainty in the peak-flow values. EMA also provides improvements with respect to treatment of periods of missing record by incorporating non-exceedance information in the form of observation thresholds.The multiple Grubbs-Beck (MGB) test (Cohn and others, 2013) provides improvements in the detection of low outliers. Additional (15+) years of peak-flow data obtained since the last comprehensive study of peak flows in Connecticut,along with new computational methods described in Bulletin 17C for computing as-site estimates and new regionalization estimates at ungaged locations will ensure the best estimates of flood magnitudes for a given AEP.



  • Calculate magnitude of peak flows for 50-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent Annual Exceedance Probabilities (AEP) at streamgages in Connecticut and at selected streamgages in neighboring states using newer (EMA and MGB) methods and data collected through at least the 2015 water year
  • Produce new regional regression equations for estimating the 50-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP flows at ungaged sites in Connecticut;
  • Document the methods and findings in a USGS publication, and make the findings available in USGS StreamStats, a “user friendly” WEB-based program; and,
  • Perform exploratory data analysis on peaks flows in Connecticut to examine trends and determine extent of changes to peak flows over time.