Abstract

Prior research confirmed the substantial bias from using precipitation-based intensity-duration-frequency curves (PREC-IDF) in design flood estimates and proposed next-generation IDF curves (NG-IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG-I... Show morePrior research confirmed the substantial bias from using precipitation-based intensity-duration-frequency curves (PREC-IDF) in design flood estimates and proposed next-generation IDF curves (NG-IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG-IDF technology for a snow-dominated test basin in the Sierra Nevada. A well-validated physics-based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG-IDF technology. We find that, for the studied small snow-dominated basin, the use of standard rainfall hyetographs in the NG-IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG-IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell-shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections. Show less