Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin. Najibi, N., Devineni, N., & Lu, M. Advances in Water Resources, 2017.
abstract   bibtex   
© 2016 Elsevier LtdA comprehensive framework is developed to assess the flood types, their spatiotemporal characteristics and causes based on the rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. The Missouri River Basin (MRB) is used as a case study for the application of the framework. Floods are defined using the multivariate characteristics of annual peak, volume, duration, and timing. The temporal clustering of flood durations is assessed using a hierarchical clustering analysis, and low-frequency modes are identified using wavelet decomposition. This is followed by an identification of the synoptic scale atmospheric processes and an analysis of storm tracks that entered the basin and their moisture releases. Atmospheric teleconnections are distinctively persistent and well developed for long duration flood events. Long duration floods are triggered by high antecedent flow conditions which are in turn caused by high moisture release from the tracks. For short duration floods, these are insignificant and appear to occur random across the MRB in the recent half-century. The relative importance of hydroclimatic drivers (rainfall duration, rainfall intensity and antecedent flow conditions) in explaining the variance in flood duration and volume is discussed using an empirical log-linear regression model. The implication of analyzing the duration and volume of the floods in the context of flood frequency analysis for dams is also presented. The results demonstrate that the existing notion of the flood risk assessment and consequent reservoir operations based on the instantaneous peak flow rate at a stream gage needs to be revisited, especially for those flood events caused by persistent rainfall events, high antecedent flow conditions and synoptic scale atmospheric teleconnections.
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 title = {Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin},
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 year = {2017},
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 abstract = {© 2016 Elsevier LtdA comprehensive framework is developed to assess the flood types, their spatiotemporal characteristics and causes based on the rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. The Missouri River Basin (MRB) is used as a case study for the application of the framework. Floods are defined using the multivariate characteristics of annual peak, volume, duration, and timing. The temporal clustering of flood durations is assessed using a hierarchical clustering analysis, and low-frequency modes are identified using wavelet decomposition. This is followed by an identification of the synoptic scale atmospheric processes and an analysis of storm tracks that entered the basin and their moisture releases. Atmospheric teleconnections are distinctively persistent and well developed for long duration flood events. Long duration floods are triggered by high antecedent flow conditions which are in turn caused by high moisture release from the tracks. For short duration floods, these are insignificant and appear to occur random across the MRB in the recent half-century. The relative importance of hydroclimatic drivers (rainfall duration, rainfall intensity and antecedent flow conditions) in explaining the variance in flood duration and volume is discussed using an empirical log-linear regression model. The implication of analyzing the duration and volume of the floods in the context of flood frequency analysis for dams is also presented. The results demonstrate that the existing notion of the flood risk assessment and consequent reservoir operations based on the instantaneous peak flow rate at a stream gage needs to be revisited, especially for those flood events caused by persistent rainfall events, high antecedent flow conditions and synoptic scale atmospheric teleconnections.},
 bibtype = {article},
 author = {Najibi, N. and Devineni, N. and Lu, M.},
 journal = {Advances in Water Resources}
}
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