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objective, a two-dimensional numerical flood model, the Flood in Two Dimensions–Graphics Processing Unit (Flood2D-GPU), is calibrated and used to simulate flood depths and velocities for various PMF simulations. These flood model outputs are compiled into quantifiable downstream flood hazard potential to population using existing flood depth–velocity hazard relationships. 2. Case study 2.1. Folsom Dam and American River watershed The study uses Folsom Dam and reservoir, which is located in the ARW near
objective, a two-dimensional numerical flood model, the Flood in Two Dimensions–Graphics Processing Unit (Flood2D-GPU), is calibrated and used to simulate flood depths and velocities for various PMF simulations. These flood model outputs are compiled into quantifiable downstream flood hazard potential to population using existing flood depth–velocity hazard relationships. 2. Case study 2.1. Folsom Dam and American River watershed The study uses Folsom Dam and reservoir, which is located in the ARW near
in Europe: 1950-2005 . Nat. Hazards , 42 , 125 – 148 , https://doi.org/10.1007/s11069-006-9065-2 . 10.1007/s11069-006-9065-2 Bates , P. D. , and A. P. J. De Roo , 2000 : A simple raster-based model for flood inundation simulation . J. Hydrol. , 236 , 54 – 77 , https://doi.org/10.1016/S0022-1694(00)00278-X . 10.1016/S0022-1694(00)00278-X Bates , P. D. , M. S. Horritt , and T. J. Fewtrell , 2010 : A simple inertial formulation of the shallow water equations for efficient two-dimensional
in Europe: 1950-2005 . Nat. Hazards , 42 , 125 – 148 , https://doi.org/10.1007/s11069-006-9065-2 . 10.1007/s11069-006-9065-2 Bates , P. D. , and A. P. J. De Roo , 2000 : A simple raster-based model for flood inundation simulation . J. Hydrol. , 236 , 54 – 77 , https://doi.org/10.1016/S0022-1694(00)00278-X . 10.1016/S0022-1694(00)00278-X Bates , P. D. , M. S. Horritt , and T. J. Fewtrell , 2010 : A simple inertial formulation of the shallow water equations for efficient two-dimensional
, there is a lack of understanding on how the variability of simulated future extreme floods is affected by various uncertainties in data and models in the chain from climate models to hydrological models. In assessment of future extreme floods, uncertainties from different sources must be taken into consideration, such as greenhouse gas emission scenarios, general circulation models (GCMs), downscaling techniques, and hydrological simulations. Emission scenarios, which are needed to drive GCMs, are
, there is a lack of understanding on how the variability of simulated future extreme floods is affected by various uncertainties in data and models in the chain from climate models to hydrological models. In assessment of future extreme floods, uncertainties from different sources must be taken into consideration, such as greenhouse gas emission scenarios, general circulation models (GCMs), downscaling techniques, and hydrological simulations. Emission scenarios, which are needed to drive GCMs, are
mixing over the entire water column” and the water column remained highly stratified. Isachsen and Pond suggested that mixing in the lee of the sill must be accounted for if the temporal dependence of the circulation near Second Narrows, and probably near First Narrows also, is to be accurately simulated. 3. The model The model used here is described in Stacey et al. (2002) and the references listed therein. It is two-dimensional (i.e., laterally integrated), nonlinear, and hydrostatic. Temperature
mixing over the entire water column” and the water column remained highly stratified. Isachsen and Pond suggested that mixing in the lee of the sill must be accounted for if the temporal dependence of the circulation near Second Narrows, and probably near First Narrows also, is to be accurately simulated. 3. The model The model used here is described in Stacey et al. (2002) and the references listed therein. It is two-dimensional (i.e., laterally integrated), nonlinear, and hydrostatic. Temperature
Hornberger 1978 ). In each hydrologic grid cell, there existed one major river channel conceptualized with a rectangular cross section, which was characterized by bank depth and width derived from a digital elevation model (DEM) dataset. With the predicted elevation of surface water and the groundwater table, water fluxes between the river, lake, and groundwater or the vadose zone are calculated using Darcy’s law. Surface water flow, including river and lake flow, is resolved as two-dimensional diffusion
Hornberger 1978 ). In each hydrologic grid cell, there existed one major river channel conceptualized with a rectangular cross section, which was characterized by bank depth and width derived from a digital elevation model (DEM) dataset. With the predicted elevation of surface water and the groundwater table, water fluxes between the river, lake, and groundwater or the vadose zone are calculated using Darcy’s law. Surface water flow, including river and lake flow, is resolved as two-dimensional diffusion
, is seldom analyzed, and the performance of the land–atmosphere coupled with both precipitation and evaporation is seldom investigated as well. Therefore, we looked into the possible influence of 4D-Var assimilation with the satellite-based precipitation on the performance of the coupled land–atmosphere model, through detailed analysis of its impacts on the two connecting elements (precipitation and evaporation) and the two hydrological variables (soil moisture and discharge) in one flood process
, is seldom analyzed, and the performance of the land–atmosphere coupled with both precipitation and evaporation is seldom investigated as well. Therefore, we looked into the possible influence of 4D-Var assimilation with the satellite-based precipitation on the performance of the coupled land–atmosphere model, through detailed analysis of its impacts on the two connecting elements (precipitation and evaporation) and the two hydrological variables (soil moisture and discharge) in one flood process
1. Introduction The unanticipated variability of monsoon rainfall in the current and future climate can have devastating consequences for the developing world, where societies are centered on agrarian pursuits ( Webster and Jian 2011 ). Ensembles of extended-range weather forecasts are now being used as forcing data for hydrological model simulations to determine the risk posed by potential weather extremes ( Cloke and Pappenberger 2009 ). For example, hydrological forecasts and flood warnings
1. Introduction The unanticipated variability of monsoon rainfall in the current and future climate can have devastating consequences for the developing world, where societies are centered on agrarian pursuits ( Webster and Jian 2011 ). Ensembles of extended-range weather forecasts are now being used as forcing data for hydrological model simulations to determine the risk posed by potential weather extremes ( Cloke and Pappenberger 2009 ). For example, hydrological forecasts and flood warnings
next steps to be taken. 2. Theoretical background As noted above, MR09 and MR10 have recently reported on a series of three-dimensional numerical simulations of conditionally unstable flows impinging on a two-dimensional mesoscale mountain ridge. The simulations were performed with a three-dimensional explicitly cloud-resolving model [Cloud Model 1 (CM1), release 8; Bryan and Fritsch 2002 ] in order to investigate the statistically stationary (along-ridge averaged) features of the solution
next steps to be taken. 2. Theoretical background As noted above, MR09 and MR10 have recently reported on a series of three-dimensional numerical simulations of conditionally unstable flows impinging on a two-dimensional mesoscale mountain ridge. The simulations were performed with a three-dimensional explicitly cloud-resolving model [Cloud Model 1 (CM1), release 8; Bryan and Fritsch 2002 ] in order to investigate the statistically stationary (along-ridge averaged) features of the solution
simulation time is shown above each panel, and Q refers to the two-dimensional tidal flux (m 2 s −1 ) Fig . 13. (a) Time series of volume flux in Fig. 12. (b) Time series of the form drag showing an almost symmetrical response between flood and ebb. The drag at rest has been removed from the signal. Dashed line is pseudodrag [ Eq. (2) ] Fig . 14. Numerical model run similar to Fig. 12 except with a density contrast and a salty pool similar to that observed in the data. Every second layer is
simulation time is shown above each panel, and Q refers to the two-dimensional tidal flux (m 2 s −1 ) Fig . 13. (a) Time series of volume flux in Fig. 12. (b) Time series of the form drag showing an almost symmetrical response between flood and ebb. The drag at rest has been removed from the signal. Dashed line is pseudodrag [ Eq. (2) ] Fig . 14. Numerical model run similar to Fig. 12 except with a density contrast and a salty pool similar to that observed in the data. Every second layer is
the ten ebb tides, and was underestimated bythe present model. Errors in wind-wave estimates alsoappear to be due to two-dimensional effects; lateralinteraction scales typical of the model at moderatelylarge incident angles are significantly larger than thelateral length scale characterizing the river entrance.The net result is that wind waves tend to be overestimated on the ebb and underestimated on the flood. Our conclusions are that wave-current interactionsare the dominant physical
the ten ebb tides, and was underestimated bythe present model. Errors in wind-wave estimates alsoappear to be due to two-dimensional effects; lateralinteraction scales typical of the model at moderatelylarge incident angles are significantly larger than thelateral length scale characterizing the river entrance.The net result is that wind waves tend to be overestimated on the ebb and underestimated on the flood. Our conclusions are that wave-current interactionsare the dominant physical
