Automation of DEM Cutting for Hydrologic/Hydraulic Modeling, TR-631, 2015

Hydrologic analysis is a critical part of transportation design because it helps ensure that hydraulic structures are able to accommodate the flow regimes they are likely to see. This analysis is currently conducted using computer simulations of water flow patterns, and continuing developments in elevation survey techniques result in higher and higher resolution surveys. Current survey techniques now resolve many natural and anthropogenic features that were not practical to map and, thus, require new methods for dealing with depressions and flow discontinuities. A method for depressional analysis is proposed that uses the fact that most anthropogenically constructed embankments are roughly more symmetrical with greater slopes than natural depressions. An enforcement method for draining depressions is then analyzed on those depressions that should be drained. This procedure has been evaluated on a small watershed in central Iowa, Walnut Creek of the South Skunk River, HUC12 # 070801050901, and was found to accurately identify 88 of 92 drained depressions and place enforcements within two pixels, although the method often tries to drain prairie pothole depressions that are bisected by anthropogenic features.

Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.