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首页> 外文期刊>Journal of hydrologic engineering >Developing Rainfall Intensity-Duration-Frequency Curves for Alabama under Future Climate Scenarios Using Artificial Neural Networks
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Developing Rainfall Intensity-Duration-Frequency Curves for Alabama under Future Climate Scenarios Using Artificial Neural Networks

机译：使用人工神经网络开发未来气候情景下阿拉巴马州的降雨强度-持续时间-频率曲线

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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'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 <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.
机译：水资源管理基础设施的水文设计是基于特定的设计风暴，这些风暴来自以强度-持续时间-频率（IDF）曲线形式出现的历史降雨事件。但是，由于地球大气中温室气体浓度的增加，预计未来极端降雨的频率和强度将发生变化。这项研究评估了在预计的未来气候情景下阿拉巴马州当前IDF曲线的潜在变化。使用人工神经网络（ANN）在时间上缩减了由全球和区域气候模型的五种组合模拟的三小时降水数据。建立了前馈，后向传播模型以估计最大15分钟，30分钟，45分钟，60分钟和120分钟的降水量。将结果与使用随机方法得出的分类降雨进行了比较。两种方法的比较表明，人工神经网络模型在估计最大降雨深度方面提供了卓越的性能，而随机方法往往会低估最大降雨深度。发达的IDF曲线表明，与使用ANN模型的当前事件相比，持续时间<2 h的事件的未来降雨强度预计将下降33-74％，而较长时间的预计降雨强度存在较大不确定性。持续时间事件。该结果与所使用的时间缩减方法无关。

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