Developing Rainfall Intensity-Duration-Frequency Curves for Alabama under Future Climate Scenarios Using Artificial Neural Networks

作者：Mirhosseini Golbahar^*; Srivastava Puneet; Fang Xing

来源：Journal of Hydrologic Engineering, 2014, 19(11): 04014022.

DOI：10.1061/(ASCE)HE.1943-5584.0000962

• Intensity-duration-frequency (IDF) curve
• Climate change
• Artificial neural network (ANN)
• Temporal downscaling
• General circulation models (GCMs)

Hydrologic design of water management infrastructures is on the basis of specific design storms derived from historical rainfall events available in the form of intensity-duration-frequency (IDF) curves. However, it is expected that the frequency and magnitude of future extreme rainfalls will change due to the increase in greenhouse gas concentrations in Earth%26apos;s atmosphere. This study evaluated potential changes in current IDF curves for Alabama under projected future climate scenarios. Three-hour precipitation data simulated by five combinations of global and regional climate models were temporally downscaled using artificial neural networks (ANNs). A feed-forward, back-propagation model was developed to estimate maximum 15-, 30-, 45-, 60-, and 120-min precipitation. The results were compared with disaggregated rainfall derived using a stochastic method. Comparison of these two methods indicates that the ANN model provides superior performance in estimating maximum rainfall depths, whereas the stochastic method tends to under-predict maximum rainfall depths. Developed IDF curves indicate that future rainfall intensities for the events with duration %26lt;2 h are expected to decrease by 33-74% compared with those of current events when the ANN model is used, whereas large uncertainty exists in the projected rainfall intensities of longer-duration events. This result was independent of the temporal downscaling method used.

• 出版日期2014-11