| description abstract | Many hydraulic structures are designed based on intensity-duration-frequency (IDF) curves. A design based on an inaccurate design storm can cause problems, such as malfunction of the infrastructure, excessive cost, or loss of life. In previous studies the authors developed IDF curves under future climate scenarios for Alabama using six different North American Regional Climate Change Assessment Program (NARCCAP)-based projections. Results demonstrated that these models do not project identical results, and there is uncertainty regarding future rainfall intensities projected by these six climate models. Understanding the uncertainties associated with climate model outputs can help decision makers to explain the impacts of climate change with more confidence. Therefore, the objective of this study was to develop probability-based IDF curves incorporating climate projections from six different climate models using a kernel density estimator. IDF curves were previously created using two different temporal disaggregation methods: a stochastic method and an artificial neural network (ANN) model. A kernel density estimator was applied to the resulting estimated rainfall intensities from both methods and probability-based IDFs were developed. In addition to the probability-based IDFs, typical IDF curves as a resultant of incorporating all models were also developed. A comparison of the results with the current (historical) IDF curves for the city of Auburn, Alabama, indicated that, when the stochastic method was used for rainfall disaggregation, future rainfall intensities are expected to decrease by 29 to 39% for durations less than 6 to 8 h, and to increase by 14 to 19% for longer durations. Analysis of the results of the ANN model for durations of less than 2 h indicates that the precipitation pattern for Alabama veers toward less intense rainfalls for the investigated durations and for all the return periods. This decrease is expected to be between 48 and 52% for the city of Auburn. | |
