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BAYES RISK ANALYSIS OF REGIONAL REGRESSION ESTIMATES OF FLOODSThis thesis defines a methodology for the evaluation of the worth of streamflow data using a Bayes risk approach. Using regional streamflow data in a regression analysis, the Bayes risk can be computed by considering the probability of the error in using the regionalized estimates of bridge or culvert design parameters. Cost curves for over- and underestimation of the design parameter can be generated based on the error of the estimate. The Bayes risk can then be computed by integrating the probability of estimation error over the cost curves. The methodology may then be used to analyze the regional data collection effort by considering the worth of data for a record site relative to the other sites contributing to the regression equations. The methodology is illustrated by using a set of actual streamflow data from Missouri. The cost curves for over- and underestimation of the streamflow design parameter for bridges and culverts are hypothesized so that the Bayes risk might be computed and the results of the analysis discussed. The results are discussed by demonstrating small sample bias that is introduced into the estimate of the design parameter for the construction of bridges and culverts. The conclusions are that the small sample bias in the estimation of large floods can be substantial and that the Bayes risk methodology can evaluate the relative worth of data when the data are used in regionalization.
RAINFALL-RUNOFF MODELING OF FLASH FLOODS IN SEMI-ARID WATERSHEDSFlash floods caused by localized thunderstorms are a natural hazard of the semi -arid Southwest, and many communities have responded by installing ALERT flood forecasting systems. This study explored a rainfall- runoff modeling approach thought to be appropriate for forecasting in such watersheds. The kinematic model KINEROS was evaluated because it is a distributed model developed specifically for desert regions, and can be applied to basins without historic data. This study examined the accuracy of KINEROS under data constraints that are typical of semi -arid ALERT watersheds. The model was validated at the 150 km2, semi -arid Walnut Gulch experimental watershed. Under the conditions examined, KINEROS provided poor simulations of runoff volume and peak flow, but good simulations of time to peak. For peak flows, the standard error of estimate was nearly 100% of the observed mean. Surprisingly, when model parameters were based only on measurable watershed properties, simulated peak flows were as accurate as when parameters were calibrated on some historic data. The accuracy of KINEROS was compared to that of the SCS model. When calibrated, a distributed SCS model with a simple channel loss component was as accurate as KINEROS. Reasons for poor simulations were investigated by examining a) rainfall sampling errors, b) model sensitivity and dynamics, and c) trends in simulation accuracy. The cause of poor simulations was divided between rainfall sampling errors and other problems. It was found that when raingage densities are on the order of 1/20 km2, rainfall sampling errors preclude the consistent and reliable simulation of runoff from localized thunderstorms. Even when rainfall errors were minimized, accuracy of simulations were still poor. Good results, however, have been obtained with KINEROS on small watersheds; the problem is not KINEROS itself but its application at larger scales. The study also examined the hydrology of thunderstorm -generated floods at Walnut Gulch. The space -time dynamics of rainfall and runoff were characterized and found to be of fundamental importance. Hillslope infiltration was found to exert a dominant control on runoff, although flow hydraulics, channel losses, and initial soil moisture are also important. Watershed response was found to be nonlinear.