Streamflow Forecasting Based on Statistical Applications and Measurements Made with Rain Gage and Weather Radar
Techniques for streamflow forecasting are developed and tested for the Little Washita River in Oklahoma. The basic input for streamflow forecasts is rainfall. the rainfall amounts may be obtained from several sources; however, this study is concerned with the possibility of utilizing weather radar and probabilistic simulation to obtain the rainfall input. Also, the feasibility of a radar, raingage combination is examined. It is shown that quantitative estimates of runoff can be made from measurements taken with weather radar. In addition, accurate estimates of lag time can be made from radar observations. For a storm which is unevenly distributed over the watershed, it is demonstrated that a better estimation of lag time may be made from radar measurements than from measurements obtained from a sparse rain-gage network (1 gage/110 mi2). A technique for hydrograph synthesis which utllizes the Pearson type III function is developed. The use of the Pearson function for hydrograph synthesis constitutes a valuable tool for streamflow forecasting. Since this method of hydrograph synthesis is adaptable to the digital computer, the "time factor," which is so important for river forecasts, can be shortened. A stochastic model (which incorporates a sixth-order Markov chain) for rainfall-runoff simulation is developed. Monte Carlo techniques are coupled with the stochastic model to yield frequency histograms of hydrograph-peak discharges and corresponding lag times. A model such as the one developed in this study could be coupled with radar observations to provide a probabilistic forecast of streamflow-shortly after rainfall commencement.
Hudlow, M.D. (1967). Streamflow Forecasting Based on Statistical Applications and Measurements Made with Rain Gage and Weather Radar. Texas Water Resources Institute. Available electronically from