부산대학교 도서관

Abstract Estimation of probable maximum flood (PMF) is a crucial process in water resources management and in the design of large hydraulic structures. However, there are uncertainties in the estimation of hydrologic conditions that contribute to extreme floods. In particular, this is the case in snow‐dominated regions, as surface air temperature and wind speed are understood to have a substantial effect on the magnitude of a flood during a storm event. Motivated by the development of a new approach to investigate and estimate reliable PMF values and in an attempt to resolve the uncertainty issues, this study introduces a physically based modeling approach. For the case study, seven watersheds located in the Sierra‐Nevada mountain range of California, including Cosumnes, Mokelumne, Stanislaus, Tuolumne, Merced, Upper San Joaquin, and Upper Kings were selected. The hydroclimate model was first implemented over the physical boundaries of the study region, and then utilized to simulate possible maximum flood conditions with input from 10 extreme precipitation scenarios. The study results provide evidence of a nonlinear atmospheric–hydrologic system; the extreme 72‐h basin‐averaged precipitation depth was found not to be linearly proportional to 72‐h flow volume equivalent depth. It can also be concluded that a large precipitation depth may not be the sole reason for a large flood event. Temperature and other atmospheric variables also contribute significantly to the production of snowfall and liquid water available for runoff, and to the resulting hydrologic response, such as the flood peak discharge and volume.