Delft3D Modeling Inputs & Outputs in the Houston-Galveston Bay Area
Funded By:
National Academies of Sciences Gulf Research Program
Funding Cycle:
Healthy Ecosystems 4
Research Group:
Development of Gulf Coast Resiliency Management Plan Using Sentinel Species and Natural Infrastructure
Gioia Kennedy
Environmental Defense Fund
gkennedy@edf.org
Flooding, Nature based features, Contaminant transport, Numerical model, Hurricane, Surge, Residence time, Water level, Waves, Winds, Tides
Abstract:
These data include input and output files for the Delft3D modeling tool used in the Development of Gulf Coast Resiliency Management Plan Using Sentinel Species and Natural Infrastructure project which aims to characterize flooding caused by the combined effects of stormwater and storm surge in the Houston-Galveston Bay area, as well as how climate change may amplify storms and flooding in the future. Inputs for the model include water depth (bathymetry), land elevation (topography), wind speed, wind direction, tidal elevation, and daily-averaged discharge inflows. Outputs from the model consist of wave heights, water levels, water depths, depth-averaged velocities, and other ancillary flow and wave information. These were provided at the spatial resolution of the model grid, and at a temporal resolution specified in the model input (either one or two hours). In addition, locations of drogues were also output by the model.
Suggested Citation:
Kaihatu, Jim. Delft3D Modeling Inputs & Outputs in the Houston-Galveston Bay Area. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/p7g8rjx0
Purpose:
This model was run to characterize flooding caused by the combined effects of stormwater and storm surge, as well as how climate change may amplify storms and flooding in the future. The modeling results show where and how stormwater and flooding may affect facilities and move contaminants into vulnerable communities and ecosystems.
Data Parameters and Units:
Boundaries: time [minutes], free surface elevation relative to mean water surface (south, east, west) [m], boundary type [dimensionless] Grid: grid coordinates in x (east-west) [decimal degrees], grid coordinates in y (south-north) [decimal degrees], water depth and topography (referenced to NGVD) [m], enclosures defining grid boundaries via grid indices [dimensionless] Wind: time [minutes], wind velocity component in east-west direction at each grid point for each time step, at 10 m elevation [m/s], wind velocity component in south-north direction at each grid point for each time step, at 10 m elevation [m/s], atmospheric pressure at each grid point for each time step [millibars] Drogue: time [minutes], latitude position of drogue for each time step [decimal degrees], longitude position of drogue for each time step [decimal degrees]
Methods:
Model inputs: Data on the water depth (bathymetry) and land elevation (topography) were obtained from the National Center for Environmental Information (NCEI) via the Delft Dashboard model setup software. The NCEI database contains various topographical and bathymetric databases which were melded to create the terrain data for the model. For the greater Gulf of Mexico region, the General Bathymetric Chart of the Oceans (GEBCO) data set was used. This global data set is comprised of water depth data at a resolution of 15 arc-seconds (roughly 450 meters). Near to the coast, the Coastal Relief Model was used for the bathymetry and topography. This data has 1 arc-second resolution (about 30 meters). Finally, the Continuously Updated Digital Elevation Model (CUDEM) database for Texas was used where it was available. This data set has a resolution of 1/9 arc seconds (about 3 m), and was a valuable source of information for resolving the geography of creeks and waterways, as well as the Houston Ship Channel. Winds for the year-long simulations were obtained from the Climate Forecast System Reanalysis (CFSR) database maintained by the National Center for Environmental Prediction (NCEP), NOAA. This database is comprised of a combination of modeled wind fields and satellite observations. The horizontal resolution of the data is approximately 0.5 degree latitude and longitude (about 55 km north to south and about 48 km east to west), with a one-hour temporal resolution. For rapidly varying winds, such as those of a hurricane, the spatial resolution of the CFSR windfield may not be sufficient. This is particularly true for the Hurricane Ike case study, since the hazard for this event was primarily wind driven surge. In this event, we used the HURDAT2 database from the National Hurricane Center (NHC) for hurricane winds. This is a reduced data set which provides general parameters regarding the hurricane (radius to maximum winds, central pressure, latitude and longitude of the center along the hurricane track), as well as the distance from the center to the location of the 34 kt (17.5 m/s); 50 kt (26 m/s) and 64 kt (33 m/s) wind contours along the eight compass directions (N, S, E, W, NW, NE, SW, and SE). These are provided once every six hours, starting from when the storm is first recognized by the NHC to when the center ceases tracking it. The parameterization of Holland1 was used to fill in the wind field from these parameters. Time series of tidal elevations were provided along the grid boundaries bisecting the Florida and Yucatan Straits, comprising the southeast corner of the Gulf of Mexico grid. The time series were provided by the Oregon State Inverse Tidal Model2, which assimilates satellite observations of sea surface height into a tidal model for accurate tidal elevations. The tidal elevations along these boundaries were then allowed to propagate into the Gulf of Mexico. Riverine and rainfall-induced flooding were provided by the SWAT model; see GRIIDC submission H4.x952.000:0001. This input was in the form of daily-averaged discharge inflows to each of the six subdomains at locations corresponding to the boundaries of the SWAT subdomains that occur inside the six Delft3D subdomains. All Delft3D model inputs, with the exception of CFSR winds and SWAT discharges, were accessed from the various databases by the Delft Dashboard model setup software. Model outputs: Outputs from the model consisted of wave heights, water levels, water depths, depth-averaged velocities, and other ancillary flow and wave information. These were provided at the spatial resolution of the model grid, and at a temporal resolution specified in the model input (either one or two hours). In addition, locations of drogues were also output by the model. Additional post-processing was required to make the output useful for inclusion in the vulnerability assessment as facility and community level vulnerability indicators. Spatial maps of water depths (water level above datum minus the local land elevation) were output by the model at the pre-set time intervals. Ensuing analysis required a metric for water depth that typified an entire year or event, so the output was analyzed to determine the maximum water depth at every grid cell over the course of a year or event. It was also necessary to eliminate cells which were filled with water under normal conditions (e.g., portions of the Ship Channel or Buffalo Bayou, rivers) from the maximum flood depth calculation. Since it requires some time for the discharge from SWAT to propagate through the Delft3D subdomain along the watercourses, each subdomain was inspected to determine the point in time when an equilibrium in water depth appeared to be reached. Areas filled with water at that time were assigned NaN (not a number) and thus eliminated as a cell for which maximum flood elevation was calculated. Another important flood characterization parameter is residence time – the length of time that flood water remains in an area. Since it is difficult for the model to go to zero water depth after flooding, we instead looked at the slope of the water depth with time. A positive slope (increasing water depth) indicates that the area is filling with water, while a negative slope (decreasing water depth) denotes drainage out of the area. For any given cell, we tracked the time from the point where the water depth slope over time went from positive, to the point where a negative slope was quickly followed by a slightly positive slope or a zero slope. The difference between those times was used as the residence time for that specific flooding event in that grid cell, which may have been one of several over the course of a year. The maximum, minimum, and average retention time over the course of a year or event was then determined for each cell. Drogues were placed in the model at the locations of facilities, at the beginning of the simulation. These locations are always dry until reached by flood waters. The specific scenario replicated would be the case of a spill occurring prior to flooding, with flood water then transporting the spilled material through the domain. Most information from the model and used in the vulnerability assessment and other tools were processed using MATLAB codes developed within the project. Translation between the native NEutral FIle System (NEFIS) format (developed by Deltares) and MATLAB binary formats was performed using MATLAB routines developed by Deltares. In-house MATLAB codes were then developed to perform the required analyses from the model results. Maps of residence time and maximum flood depths were output in TIFF format. Drogue tracks were written into ASCII files identified by facility ID. Each drogue file consisted of three columns: time (in minutes) from the start time of the simulation, and latitude and longitude of the drogue. Residence time and maximum flood depth outputs in the form of TIFF files were processed for the vulnerability map using R code (01_toxpi_inputs_Delft3D.R) developed internally for the project. For the community-scale flood depth and flood duration indicators, the data are transformed and aggregated to a 100x100m spatial grid where each cell has a unique ID. For the facility-scale flood depth and flood duration indicators, the data are transformed and aggregated onto the spatial footprint of each petrochemical facility where each facility has a unique ID.
Provenance and Historical References:
[1] Holland, G., “An analytic model of the wind and pressure profiles in hurricanes.” Monthly Weather Review, v. 136, p. 1212. [2] Egbert, G., and Erofeeva, S., “Efficient inverse modeling of barotropic ocean tides.” Journal of Atmospheric and Oceanic Technology, v. 19, p. 183, Feb. 2002