Regional Characteristics of Unit Hydrographs
The unit hydrograph is defined as a direct runoff hydrograph resulting from a unit pulse of excess rainfall generated uniformly over the watershed at a constant rate for an effective duration. The unit hydrograph method is a well-known hydrologic-engineering technique for estimation of the runoff hydrograph given an excess rainfall hyetograph. Four separate approaches are used to extract unit hydrographs from the database on a per watershed basis. A large database of more than 1,600 storms with both rainfall and runoff data for 93 watersheds in Texas is used for four unit hydrograph investigation approaches. One approach is based on 1-minute Rayleigh distribution hydrographs; the other three approaches are based on 5-minute gamma-distribution hydrographs. The unit hydrographs by watershed from the approaches are represented by shape and time to peak parameters. Weighted least-squares regression equations to estimate the two unit hydrograph parameters for ungaged watersheds are provided on the basis of the watershed characteristics of main channel length, dimensionless main channel slope, and a binary watershed development classification. The range of watershed area is approximately 0.32 to 167 square miles. The range of main channel length is approximately 1.2 to 49 miles. The range of dimensionless main channel slope is approximately 0.002 to 0.020. The equations provide a framework by which hydrologic engineers can estimate shape and time to peak of the unit hydrograph, and hence the associated peak discharge. Assessment of equation applicability and uncertainty for a given watershed also is provided. The authors explicitly do not identify a preferable approach and hence equations for unit hydrograph estimation. Each equation is associated with a specific analytical approach. Each approach represents the optimal unit hydrograph solution on the basis of the details of approach implementation including unit hydrograph model, unit hydrograph duration, objective functions, loss model assumptions, and other factors.