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4 results for “Coastal inundation”
The dataset and model code pertinent to the Everglades Peat Elevation Model (EvPEM): The salinity and inundation mesocosm experiment in freshwater and brackish water sawgrass wetlands in Florida Coastal Everglades (2015-2017).
This is an assembled data and Everglades Peat Elevation Model (EvPEMv1.0) Stella code used to estimate and simulate net ecosystem carbon balance (NECB) and peat elevation change in response to saltwater intrusion and level of inundations. Data from several studies were combined for the estimation of NECB, model parameterization, and calibration (Wilson, 2018; Wilson et al., 2018, 2019; Charles et al., 2019; Servais et al., 2020). The reported data includes aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP), peat elevation change, and decomposition rates that were collected from outdoor laboratory mesocosm experiments conducted at the Florida Bay Interagency Science Center in Key Largo, Florida during 2015-17. The plant-soil monoliths were obtained from a freshwater peat and a brackish water peat marsh located within the Florida Coastal Everglades and transported to the Key Largo facility for the experimental manipulations. In experiments focused on the brackish water marsh, three experiments were carried out reflecting the combined effect of salinity, inundation, and peat exposure to air. The brackish water experiments characterized submerged (SUB), exposed (EXP), and extended depth of exposure of peat surface (EXTEXP) conditions, as we varied water depth relative to the peat surface. Each experiment was subjected to two salinity manipulations: (1) ambient (~10 ppt) porewater salinity (AMB) and (2) elevated (~20 ppt) salinity (SALT). The experimental design included six (2 X 3) treatments: (1) submerged ambient salinity (AMB.SUB), (2) submerged elevated salinity (SALT.SUB.), (3) exposed ambient salinity (AMB.EXP), (4) exposed elevated salinity (SALT.EXP), (5) exposed with extended exposure/dry-down ambient salinity (AMB.EXTEXP), and (6) exposed with extended exposure/dry-down elevated salinity (SALT.EXTEXP). The water level was kept 4 cm above the peat surface for the brackish water SUB treatments. Exposure for the EXP treatment
High-resolution inundation dataset for coastal India and Bangladesh
<p>This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh.</p> <p><strong>Input data:</strong></p> <p>These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH.</p> <p><strong>Methods:</strong></p> <p>We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water.</p> <p>We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions.</p> <p><strong>Data set format:</strong></p> <p>The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Data set for download:</strong></p> <p>A. Three data layers for Odisha, India:</p> <ol> <li>OD_pre_binary.tif</li> <li>OD_post_binary.tif</li> <li>OD_inundation.tif</li> </ol> <p>These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.</p> <p>B. Three data layers for West Bengal, India:</p> <ol> <li>WB_pre_binary.tif</li> <li>WB_post_binary.tif</li> <li>WB_inundation.tif</li> </ol> <p>These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.</p> <p>C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region.</p> <ol> <li>BNG_L_pre_binary.tif</li> <li>BNG_L_post_binary.tif</li> <li>BNG_L_inundation.tif</li> <li>BNG_U_pre_binary.tif</li> <li>BNG_U_post_binary.tif</li> <li>BNG_U_inundation.tif</li> </ol> <p>The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.</p> <p>The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.</p>
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
<p>Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies are poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of <em>Enterococcus spp</em>. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean spring tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, but also site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.</p>
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
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