DOI: 10.5061/dryad.rv15dv4dw
title: Data from: Can restoring tidal wetlands reduce nuisance flooding of coasts under future sea-level rise?

description: Wetland restoration is an increasingly popular nature-based method for
 reducing flooding impacts in coastal communities. In regions with steep
 topography such as Coos Bay, Oregon, wetland restoration reduces flooding
 by modulating the tidal signal through increased tidal prism. This study
 modeled the potential reductions in future flooding events, which will
 impact both transportation and physical infrastructure, in Coos Bay under
 a range of sea-level rise (SLR) and wetland restoration scenarios. These
 scenarios were co-developed through a stakeholder engagement climate
 adaptation planning effort which includes tidal wetland restoration. We
 found that restoration can reduce future peak water surface elevations
 (pWSE) by up to 10 cm dependent on the wetland location and future SLR
 scenario. Wetland restoration had minimal impact on reducing pWSE within 5
 km of the estuary mouth, but led to greater reduction in pWSE inland where
 wetland tidal storage volume constitutes a larger proportion of the tidal
 prism. Restoration was effective at reducing pWSE in all SLR scenarios
 through 2050 and reduced pWSE under the median SLR scenario by half. The
 reduction in pWSE under the lower SLR scenarios and higher wetland
 restoration scenarios can significantly reduce the number of hours that a
 major transportation artery is closed due to tidal flooding. By 2100, the
 benefit of restoration was reduced under the median SLR scenario and
 completely eliminated under the high SLR scenario because the increase in
 tidal prism overwhelms any potential increase in tidal storage volume
 provided by wetland restoration.
description: # Dataset for: Can restoring tidal wetlands reduce nuisance flooding of
 coasts under future sea-level rise? --- This dataset is raw output and
 post-processing MATLAB scripts for the upcoming paper: "Can restoring
 tidal wetlands reduce nuisance flooding of coasts under future sea-level
 rise?" The dataset provides the raw data and processing scripts used
 in model calibration and future projections of flooding in Coos Bay. ##
 Description of the data and file structure All model output is in NetCDF
 file format. Detailed information on the NetCDF file structure is here:
 [https://en.wikipedia.org/wiki/NetCDF](https://en.wikipedia.org/wiki/NetCDF). Each NetCDF file contains the following variables used in analysis: * station_x_coordinate - observation point x-coordinate * station_y_coordinate - observation point y-coordinate * station_id - observation point id * station_name - observation point name * waterlevel - observation point water level * cross_section_cumulative_discharge - discharge out of Coos Bay Note that all stations (observation points) are reported in the following coordinate system: NAD_1983_StatePlane_Oregon_South_FIPS_3602 (meters) Each folder and it's contents are described below: * Baseline - Contains the baseline model outputs, i.e. present day (2020) * Calibration - Contains all calibration datasets (summer and winter), model outputs, and scripts to compare the model output to data (water_level_post_processing_*.m). This folder also contains jpg outputs of model calibration for those without the MATLAB software. Each data subfolder is described below: * data_summer: Contains water depth data collected by Conroy et al., (2020) in .mat format for all the stations outlined in Figure 2 with the abbreviations matching the first three letters of the station. Each .mat file contains a structure with the sample time (t), measured depth (depth, meters) and salinity (sal, practical salinity units). The .mat file entiled charleston_noaa_measurement.mat is water level measurements taken from the NOAA tide gauge at Chareleston, Oregon. * data_winter: Contains 4 MATLAB variables: NERR_winter_depths.mat; contains 5 data structures of depths and salinities in South Slough NERR, with each structure containing a time (t), depth (d, meters) and salinity (sal, practical salinity units) variable. The .mat file noaa_WL_winter_NAVD88.mat contains both time and measured water level from the NOAA Charleston tide gauge and riv_Q.mat is the measured Coos River flow. Secondary_Stations_Winter.mat contains structures of measured water levels and times from Conroy et al., (2020) for the Catching Slough, Coos River, Isthmus Slough, Northpoint, and NOAA gauges. * summer_run: Delft3D output (NetCDF format) of water levels for the summer calibration time period. * winter_run: Delft3D output (NetCDF format) of water levels for the winter calibration time period. * output_2050/2100 - future model predictions of Coos Bay hydrodynamics for the years 2050 and 2100 for each scenario presented within the paper. SR stands for the sea-level rise (measured in cm) and R represents the restoration scenario (in percent land area restored). * tidal_prism_2050 - tidal prism calculations presented in the paper for year 2050 * tidal_prism_2100 - tidal prism calculations presented in the paper for year 2100 The script CB_post_processing.m takes future model output and generates the figures demonstrating the impact of wetland restoration on peak water surface elevations in Coos Bay. Please note the file structure requires CB_post_processing.m requires the output folders to be contained within the overarching file structure. Note that the tidal prism datasets (tidal_prism_2050 and tidal_prism_2100) to be combined in one folder called tidal_prism to work properly. ## Sharing/Access information Links to other publicly accessible locations of the data: * Measured datasets are also available at the following source: Conroy, T., Sutherland, D. A., & Ralston, D. K. (2020). Estuarine exchange flow variability in a seasonal, segmented estuary. Journal of Physical Oceanography, 50(3), 595-613. * This material is provided "as is", with absolutely no warranty expressed * or implied. Any use is at your own risk. ## Code/Software This dataset contains NetCDF files and MATLAB scripts which read and analyze the raw data. The raw data was generated using Delft3D-FM, an open source hydrodynamic model available here: [https://oss.deltares.nl/web/delft3dfm](https://oss.deltares.nl/web/delft3dfm)