Search for: All records

We use an idealized numerical model to investigate the dynamics and fate of a small river discharging into the surf zone. Our study reveals that the plume reaches a steady state, at which point the combined advective and diffusive freshwater fluxes from the surf zone to the inner shelf balance the river discharge. At a steady state, the surf zone is well-mixed vertically due to wave-enhanced vertical turbulent diffusion and has a strong cross-shore salinity gradient. The horizontal gradient drives a cross-shore buoyancy-driven circulation, directed offshore at the surface and onshore near the bottom, which opposes the wave-driven circulation. Using a scaling analysis based on momentum and freshwater budgets, we determine that the steady-state alongshore plume extent (Lp) and the fraction of river water trapped in the surf zone depend on the ratio of the near-field plume length to the surf zone width (Lnf/Lsz) across a wide range of discharge and wave conditions, and a limited set of tidal conditions. This scaling also allows us to predict the residence time and freshwater fraction (or dilution ratio) in the steady-state plume within the surf zone, which range from approximately 0.1 to 10 days and 0.1 to 0.3, respectively. Our findings establish the basic dynamics and scales of an idealized plume in the surf zone, as well as estimates of residence times and dilution rates that may provide guidance to coastal managers. # Data from: Dynamics and scaling of a small river discharging into the surf zone [https://doi.org/10.5061/dryad.2280gb608](https://doi.org/10.5061/dryad.2280gb608) The present dataset includes the [COAWST model](https://www.usgs.gov/centers/whcmsc/science/coawst-a-coupled-ocean-atmosphere-wave-sediment-transport-modeling-system) outputs used to describe the dynamics and scaling of a small river discharging into the surf zone. ## File structure The data are structured as follows: 1. plume_scale.mat - Data of plume scales of all the cases, where * Hs: significant wave height [m] * Q: river discharge [m^3 s^-1] * L_nf: near-field plume length [m] * L_p: alongshore plume extent [m] * h_sz: water depth at the surf zone edge [m] * x_sz: surf zone width [m] * S_in: inflow salinity [PSU] * g_p: reduced gravity at the river mouth [m s^-2] * g_p*_*0: reduced gravity at the river mouth calculated using the density difference between river inflow and ambient ocean water [m s^-2] * Eta_0: water surface elevation anomaly at the river mouth [m] * V_sz: total volume of freshwater trapped in the surf zone [m^3] * T: the time required for the plume to reach a steady state [day] * L_t: plume turning distance [m] * S_bar: averaged salinity in the plume [PSU] 2. DepthAveraged.mat - Depth-averaged flow fields. DepthAveraged_BaseCase.mat, DepthAveraged_Case1.mat, DepthAveraged_Case3.mat, DepthAveraged_Case4.mat, DepthAveraged_Case6.mat, DepthAveraged_Case7.mat, DepthAveraged_Case8.mat, DepthAveraged_Case9.mat, DepthAveraged_Case16.mat, DepthAveraged_Case17.mat, DepthAveraged_Case18.mat, DepthAveraged_Case19.mat includes the results of the base case, cases 1, 3, 4, 6-9, and 16-19, respectively. In these files: * Wetdry_mask: wet/dry mask on RHO-points [binary] * Wetdry_mask_u: wet/dry mask on U-points [binary] * Wetdry_mask_v: wet/dry mask on V-points [binary] * Z: free-surface [m] * S: surface salinity [PSU] * Hs: significant wave height [m] * U: vertically integrated u-momentum component [m s^-1] * U_st: vertically-integrated u-Stokes drift velocity [m s^-1] * V: vertically integrated v-momentum component [m s^-1] * V_st: vertically-integrated v-Stokes drift velocity [m s^-1] 3. FullField_BaseCase.mat - 3D flow fields for the base case, where * Z: free-surface [m] * S: salinity [PSU] * Hs: significant wave height [m] * Lw: mean wavelength [m] * U: u-momentum component [m s^-1] * U_st: u-Stokes drift velocity [m s^-1] * V: v-momentum component [m s^-1] * V_st: v-Stokes drift velocity [m s^-1] * W: w-momentum component [m s^-1] * W_st: w-Stokes drift velocity [m s^-1] * Aks: salinity vertical diffusion coefficient [m^2 s^-1] * Akv: vertical viscosity coefficient [m^2 s^-1] * Cs_r: S-coordinate stretching curves at RHO-points [-] * Cs_w: S-coordinate stretching curves at W-points [-] 4. FreshwaterTrace_BaseCase.mat - Time series of freshwater volume and fluxes for the base case, where * i_sz: XI-index of the location of the surf zone edge [-] * i_shore: XI-index of the location of the shoreline [-] * Vsz: volume of freshwater in the plume in the surf zone [m^3] * Vis: volume of freshwater in the plume in the inner shelf [m^3] * Vsz_total: total volume of freshwater in the surf zone [m^3] * Vis_total: total volume of freshwater in the inner shelf [m^3] * R2SZ_flux: freshwater flux discharging into the surf zone [m^3 s^-1] * Vchannel: volume of freshwater in the plume in the river channel [m^3] * Vchannel_total: volume of freshwater in the river channel [m^3] * SBoundary_flux_SZ: the freshwater fluxes through the southern domain boundaries of the surf zone [m^3 s^-1] * SBoundary_flux_IS: the freshwater fluxes through the southern domain boundaries of the inner shelf [m^3 s^-1] * NBoundary_flux_SZ: the freshwater fluxes through the northern domain boundaries of the surf zone [m^3 s^-1] * NBoundary_flux_IS: the freshwater fluxes through the northern domain boundaries of the inner shelf [m^3 s^-1] * WBoundary_flux: the freshwater fluxes through the westhern domain boundary [m^3 s^-1] 5. DepthAveraged_XDiagnostic.mat - Depth-averaged diagnostic output of cross-shore momentum terms. DepthAveraged_XDiagnostic_BaseCase.mat includes the results of the base case at the steady state, and DepthAveraged_XDiagnostic_0day_1mWave.mat includes those at the start of river flow. In these files: * ubar_xadv: time-averaged 2D u-momentum, horizontal XI-advection term [m s^-2] * ubar_yadv: time-averaged 2D u-momentum, horizontal ETA-advection term [m s^-2] * ubar_xvisc: time-averaged 2D u-momentum, horizontal XI-viscosity term [m s^-2] * ubar_yvisc: time-averaged 2D u-momentum, horizontal ETA-viscosity term [m s^-2] * ubar_prsgrd: time-averaged 2D u-momentum, pressure gradient term [m s^-2] * ubar_zqsp: time-averaged 2D u-momentum, quasi-static pressure [m s^-2] * ubar_zbeh: time-averaged 2D u-momentum, Bernoulli head [m s^-2] * ubar_bstr: time-averaged 2D u-momentum, bottom stress term [m s^-2] * ubar_wbrk: time-averaged 2D u-momentum, wave breaking term [m s^-2] 6. DepthAveraged_YDiagnostic_BaseCase.mat - Depth-averaged diagnostic output of alongshore momentum terms, where * vbar_xadv: time-averaged 2D v-momentum, horizontal XI-advection term [m s^-2] * vbar_yadv: time-averaged 2D v-momentum, horizontal ETA-advection term [m s^-2] * vbar_xvisc: time-averaged 2D v-momentum, horizontal XI-viscosity term [m s^-2] * vbar_yvisc: time-averaged 2D v-momentum, horizontal ETA-viscosity term [m s^-2] * vbar_prsgrd: time-averaged 2D v-momentum, pressure gradient term [m s^-2] * vbar_zqsp: time-averaged 2D v-momentum, quasi-static pressure [m s^-2] * vbar_zbeh: time-averaged 2D v-momentum, Bernoulli head [m s^-2] * vbar_bstr: time-averaged 2D v-momentum, bottom stress term [m s^-2] * vbar_wbrk: time-averaged 2D v-momentum, wave breaking term [m s^-2] 7. grid.zip - Model grid file. * This grid file is designed for use with [ROMS](https://www.myroms.org/index.php), the hydrodynamic module of the COAWST modeling system. A diagram illustrating how the variables are placed on the grid and where the boundaries lie relative to the grid is available on [WikiROMS](https://www.myroms.org/wiki/Grid_Generation). * This grid file is in NetCDF format, which can be opened and used by a wide range of application software such as MATLAB, Python, and Panoply. For more detailed information, please refer to its [official website](https://www.unidata.ucar.edu/software/netcdf/). ## Code/Software All the post-processing scripts and data are prepared by MATLAB. 

Salt intrusion poses a global threat to estuaries and deltas, exacerbated by climate change, drought, and sea level rise. This observational study investigates the impact of river discharge, wind, and tidal variations on salt intrusion in a branching river delta during drought. The complexity and spatial extent of deltas make comprehensive measurements challenging and rare. In this paper, we present a 17‐week data set of a historic drought in the Rhine‐Meuse Delta, capturing dynamics in a multiple‐channel system in a wide range of conditions. Key characteristics of this low‐lying delta are its branching channel network and complicated, human‐controlled discharge. Despite the system's complexity, we found that the subtidal salt intrusion length, defined by the 2 PSU isohaline , follows a power law relationship with Rhine River discharge . Subtidal water level variations contribute to short‐term variations in intrusion length, shifting the limit of salt intrusion upstream and downstream with a distance similar to the tidal excursion length. This can be attributed to the up‐estuary transport of seawater, caused by the estuary adjusting to variations in water levels at its mouth. However, spring‐neap variation in the tidal range does not alter the subtidal salt intrusion length. Side branches exhibit distinct dynamics from the main river, and their most important control is the downstream salinity. We show that treating the side branches separately is crucial to incorporate the highly variable downstream boundary condition, and may apply in other deltas or complex estuaries. 

Abstract We use an idealized numerical model to investigate the dynamics and fate of a small river discharging into the surf zone. Our study reveals that the plume reaches a steady state, at which point the combined advective and diffusive freshwater fluxes from the surf zone to the inner shelf balance the river discharge. At a steady state, the surf zone is well mixed vertically due to wave-enhanced vertical turbulent diffusion and has a strong cross-shore salinity gradient. The horizontal gradient drives a cross-shore buoyancy-driven circulation, directed offshore at the surface and onshore near the bottom, which opposes the wave-driven circulation. Using a scaling analysis based on momentum and freshwater budgets, we determine that the steady-state alongshore plume extent (L_p) and the fraction of river water trapped in the surf zone depend on the ratio of the near-field plume length to the surf-zone width (L_nf/L_sz) across a wide range of discharge and wave conditions and a limited set of tidal conditions. This scaling also allows us to predict the residence time and freshwater fraction (or dilution ratio) in the steady-state plume within the surf zone, which ranges from approximately 0.1 to 10 days and from 0.1 to 0.3, respectively. Our findings establish the basic dynamics and scales of an idealized plume in the surf zone, as well as estimates of residence times and dilution rates that may provide guidance to coastal managers. Significance StatementSmall rivers and estuaries often carry pollutants, sediments, and larvae into the coastal ocean, where wave action in the surf zone can trap them near the shore. This process can play an important role in the flux of material into and out of the nearshore ecosystem and presents a potential risk to swimmers when materials are harmful. The present study uses a numerical model to investigate the fate of freshwater discharged from small rivers into the surf zone and the processes through which trapped riverine freshwater escapes from the surf zone. These results establish a basis for predicting the fate of river-borne materials from coastal rivers and understanding the exchange between the surf zone and the inner shelf. Additionally, this work provides a theoretical framework for predicting the residence time and concentration of river-borne material trapped in the surf zone. 

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies. 

Subaqueous vortex ripples in equilibrium are characterized by their unique geometry and dimensions. Motivated by the recent direct numerical simulation study of oscillatory turbulent flow over a wavy bottom by Önder & Yuan ( J. Fluid Mech. , vol. 858, 2019, pp. 264–314), the objective of this study is to further investigate the fluid dynamical controls that determine the distinctive equilibrium dimensions of vortex ripples. We use direct numerical simulations to investigate the differences in flow kinetics between sinusoidal oscillatory flow over equilibrium and out-of-equilibrium vortex ripples. In comparison with the equilibrium case, the spanwise coherent vortices, the averaged bottom shear stress on overlying flow and the shear stress distribution on the ripple surface are identified as the key fluid dynamical controls on equilibrium dimensions. Based on these controls, we propose mechanisms in the selection of vortex ripple dimensions. We observe that the flow adjusts in such a way that the interaction between overlying flow and vortex ripples tends to generate the strongest coherent vortices while the ripple surface (or overlying flow) experiences the smallest shear stress averaged over ripple wavelength during the selection process. Through a triple decomposition of the flow, the component of the ripple-induced fluctuation is found to dictate these fluid dynamical controls, which implies that this component plays an important role in the evolution of vortex ripples. 

Abstract Changes in the severity and likelihood of flooding events are typically associated with changes in the intensity and frequency of streamflows, but temporal adjustments in a river's conveyance capacity can also contribute to shifts in flood hazard. To assess the relative importance of channel conveyance to flood hazard, we compare variations in channel conveyance to variations in the flow magnitude of moderate (1.2 years) floods at 50 river gauges in western Washington State between 1930 and 2020. In unregulated rivers, moderate floods have increased across the region, but in regulated rivers this trend is suppressed and in some cases reversed. Variations in channel conveyance are ubiquitous, but the magnitude and timing of adjustments are not regionally uniform. At 40% of gages, conveyance changes steadily and gradually. More often, however, conveyance variability is nonlinear, consisting of multidecadal oscillations (36% of gages), rapid changes due to unusually large sediment‐supply events (14% of gages), and increases or decreases to conveyance following flow regulation (10% of gages). The relative importance of conveyance variability for flood risk depends on the mode of adjustment; in certain locations with historic landslides, extreme floods, and flow regulation, the influence of conveyance changes on flood risk matches or exceeds that of streamflow at the same site. Flood hazard management would benefit from incorporating historic long‐term and short‐term conveyance changes in predictions of future flood hazard variability.