A critical aspects of the uniqueness of coastal drought is the effects on salinity dynamics of creeks and rivers. The location of the freshwater-saltwater interface along the coast is an important factor in the ecological and socio-economic dynamics of coastal communities. Salinity is a critical response variable that integrates hydrologic and coastal dynamics including streamflow, precipitation, sea level, tidal cycles, winds, and tropical storms. The position of the interface determines the composition of freshwater and saltwater aquatic communities as well as the freshwater availability for water intakes. Many definitions of drought have been proposed, with most describing a decline in precipitation which has a negative impacts on water supply. Indices have been developed incorporating data such as rainfall, streamflow, soil moisture, groundwater levels, and snow pack. These water availability drought indices were developed for upland areas and may not be ideal for characterizing coastal drought. The availability of real-time and historical salinity datasets provides an opportunity for the development of a salinity-based coastal drought index. The challenge for the salinity data analysis is to characterize the salinity dynamics in response to drought while excluding responses attributable to the occasional and (or) periodic saltwater intrusion events. An approach similar to the Standardized Precipitation Index was modified and applied to salinity data obtained from sites in South Carolina and Georgia. Evaluation of the coastal drought index indicates that the index can be used for different estuary types, for regional comparison, and as an index for wet (high freshwater inflow) and drought conditions. This data release will provide all the supporting data for the journal article including salinity datasets (with estimated missing values) and the computed indices.

Salinity and variability of salinity in shallow waters shape living resources and habitat within Gulf of Mexico estuaries. The salinity gradient is widely recognized as foundational in maintaining biological diversity and productivity of estuaries. A clear understanding of the factors controlling salinity and variability of salinity in estuarine surface waters is essential for proper stewardship and for sustaining ecological structure and function. Salinity data are collected by numerous Federal, State, and local agencies and universities as part of routine data collection programs. We used online databases to compile salinity data in Gulf of Mexico estuaries. The primary criteria for inclusion in the compilation were a lengthy record of continuous collection with data sondes of at least hourly intervals. Stations that represented full estuarine gradients, from fresh to saline, were prioritized. Data were compiled in separate spreadsheets for each State using comma-delimited formatting. For each State, a second spreadsheet provides information on each station. A few stations started collecting salinity as early as the mid-1980s. More stations came on line by the mid- to late 1990s. Starting in the late 2000s many more stations came on line.

The presence of salinity in shallow waters influences living resources and habitats within Gulf of Mexico estuaries. The salinity gradient is widely recognized as foundational in maintaining biological diversity and productivity of estuaries. A clear understanding of the factors controlling salinity and its variability in estuarine surface waters is essential for proper stewardship and for sustaining ecological structure and function. Salinity data are collected by numerous Federal, State, and local agencies and universities as part of routine data-collection programs. The U.S. Geological Survey compiled salinity data from existing online databases – all water samples were collected in Gulf of Mexico estuaries. The primary criterion for data from a station to be included in the compilation was a lengthy record of continuous collection using a data sonde programmed to at least hourly intervals. Stations that represented full estuarine gradients, from fresh to saline, were prioritized. Data were compiled from salinity stations in the five Gulf states and combined into one .txt file and one .feather file. Continuous data collection of salinity concentrations began at a few stations in the mid-1980s, and the number of stations with data sondes has increased over time for a total of 532,076 observations at 92 stations provided in this data release.

This dataset includes water temperature and water salinity data from continuous hydrology loggers and spot checks from a handheld water quality meter at restoration and reference sites at Willapa National Wildlife Refuge, from March 2014 to August 2015.

This dataset includes water temperature and water salinity data from continuous hydrology loggers and spot checks from a handheld water quality meter at restoration and reference sites at Willapa National Wildlife Refuge, from March 2014 to August 2015.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, dataset variables, and data quality.

The polygon datasets were created to assist in visualizing the results of salinity modeling in Gulf of Mexico estuaries and bays. Statistical algorithms (Asquith and others, 2023) were developed to predict daily salinities for 91 salinity monitoring sites (Rodgers and Swarzenski, 2019) operated by 7 agencies in near coastal United States waters of the Gulf of Mexico. These monitoring sites are assigned to 15 salinity groups roughly corresponding to distinct bays and estuaries. The statistical algorithms facilitate the study of trends and drivers of salinity in near coastal waters. The groups polygon dataset consists of 15 polygons representing the outer boundary or hull of each of the 15 salinity groups. The site polygons dataset consists of 91 polygons—one polygon each per salinity monitoring site. The polygons were created using the Watershed Boundary Dataset, the National Hydrography Dataset, and aerial imagery. A detailed description of the polygon creation method is in the metadata processing steps. Creation of the polygons was motivated by a need to construct visual cues (maps and map animations) for testing the veracity of the statistical algorithms.

The polygon datasets were created to assist in visualizing the results of salinity modeling in Gulf of Mexico estuaries and bays. Statistical algorithms (Asquith and others, 2023) were developed to predict daily salinities for 91 salinity monitoring sites (Rodgers and Swarzenski, 2019) operated by 7 agencies in near coastal United States waters of the Gulf of Mexico. These monitoring sites are assigned to 15 salinity groups roughly corresponding to distinct bays and estuaries. The statistical algorithms facilitate the study of trends and drivers of salinity in near coastal waters. The groups polygon dataset consists of 15 polygons representing the outer boundary or hull of each of the 15 salinity groups. The site polygons dataset consists of 91 polygons—one polygon each per salinity monitoring site. The polygons were created using the Watershed Boundary Dataset, the National Hydrography Dataset, and aerial imagery. A detailed description of the polygon creation method is in the metadata processing steps. Creation of the polygons was motivated by a need to construct visual cues (maps and map animations) for testing the veracity of the statistical algorithms.

This dataset consists of volume discharges from the Mississippi and Atchafalaya rivers collected by Louisiana State University from September 1985 to December 1987 as part of the MMS/OCS development and potential coastal habitat alteration. The sampling area was coastal Louisiana. Parameters reported are salinity, temperature and water levels. This work was funded under MMS contract number 14-12-0001-30252. The documentation includes the data format and site identification. More information can be found in "Causes of Wetland Loss in the Coastal Central Gulf of Mexico", Vol II: Technical Narrative, OCS study MMS 87-0120, R.E. Turner and D.R. Calhoun (eds.), Louisiana State University, 1988. This dataset also includes historical water level data going back to 1933.

Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at various time scales relevant to ecology (e.g., daily, monthly, seasonally). This data release provides supporting data for the journal article titled, "Quantifying uncertainty in coastal salinity regime for biological application using quantile regression," by Yurek et al. (2022). A spatially-resolved model was developed that derives quantile distributions of salinity related to various landscape variables, such as tidal forcing, wind velocity and direction, and freshwater discharge into the Suwannee Sound estuary. The model also considers various time scales of freshwater streamflow, from daily to bi-weekly scales, which represent terrestrial watershed dynamics such as time-of-travel of overland flow from headwaters to the coast. This data release provides programming routines and supporting data for the model, including: (1) scripts used to run the model written in R programming language, (2) input data used to fit the model, and (3) model output predictions across the spatial extent of the Suwannee Sound estuary. The predictions of the model represent a method of quantifying uncertainty in predictions, and represent approximate ranges of salinity conditions. These predictions are intended for use in future ecological modeling studies and analyses of impacts of salinity uncertainty on estuarine biota. They are limited by the data set used here and are not intended to indicate exact levels for any given location or time.