Under the NSW DPIE-EES Environmental Water Management Program the distribution and extent of inundation is monitored in large inland floodplain wetland assets which are targeted for environmental flow delivery and located in the NSW portion of the Murray-Darling Basin: Gwydir wetlands, Lowbidgee floodplain, Lower Lachlan wetlands, Macquarie Marshes, and Barmah-Millewa Forest. Inundation maps are derived from image observations sourced from the satellite data sources of Landsat (30m pixel) and Sentinel-2 (10m pixel) for the period July 2014-June 2019. Image observations are automatically downloaded by NSW DPIE from the USGS (Unites State Geological Survey’s Earth Explorer website (http://earthexplorer.usgs.gov ) and the Copernicus Sentinel Open Access Hub (https://scihub.copernicus.eu/dhus/#/home ) as orthorectified images. NSW DPIE process these images to standardised surface reflectance (Flood et al. 2013). Image observations with high cloud coverage (>50%) are not considered because they cannot be processed. The inundation mapping procedure is a modified version of Thomas et. al (2015) which is a method to map inundation in vegetated floodplain wetlands using an integrated spectral response to water and vigorous vegetation. From each satellite image observation NSW DPIE-EES automatically generates a water index (Fisher et al. 2016) and the NDVI vegetation index. These indices are used to allocate inundated pixels to classes of open water, mixed water and vegetation, and dense vegetation cover that was inundated (Thomas et al. 2015). A process of pixel recoding is conducted to produce each inundation map. First all inundation classes are merged and allocated a value of one (1) whilst all other pixels are allocated a value of zero (0). Second, ancillary data is then used to identify irrigation infrastructure to do two things: locate inundated pixels within off-river storages (ORS) by recoding to a value of (2) and to remove cropped areas that have similar spectral properties to wetland vegetation by coding the pixels to a value of zero (0). Third, for observation dates affected by cloud shadow, which is often incorrectly detected as water, pixels are manually reclassified as cloud shadow by recoding them to a value of three (3). The final inundation classes are inundated (1), off-river storages with water (ors) (2), cloud shadow (3), and not inundated (0). Final inundation maps are clipped to the inland floodplain wetland boundaries. The naming format of the files are: Wetland_date _sensor_inundation1_ors2_cloud3.tif or Wetland_path_date _sensor_inundation1_ors2_cloud3.tif Wetland: bm = Barmah Millewa floodplain gw = Gwydir floodplain lachlan = Lachlan floodplain lo = Lowbidgee floodplain mm = Macquarie Marshes floodplain Path: Specific to the Lachlan Date: Satellite image date processed Sensor: Sensor type- l7 (Landsat7; l8 (Landsat 8); s2 (Sentinel2) Inundation1: Inundated ors2: Off-River Storage with water cloud3: Cloud shadow (in filename if present) References: Fisher, A., Flood, N. and Danaher, T. (2016). Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment, 175, 167-182. Flood, N., Danaher, T., Gill, T., & Gillingham, S. (2013). An operational scheme for deriving standardised surface reflectance from Landsat TM/ETM+ and SPOT HRG imagery for eastern Australia. Remote Sensing, 5, 83–109. Thomas, R. F., Kingsford, R. T., Lu, Y., Cox, S. J., Sims, N. C. and Hunter, S. J., (2015). Mapping inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper. Journal of Hydrology 524, 194-213.

This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.

Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1 kilometer) to sufficiently characterize water surface profiles along an approximately 800 kilometer stretch upstream from the confluence with the Mississippi River over an 80-year record at a daily time step. To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. We completed these calculations for an 800 kilometer stretch of the Missouri River, spanning from Rulo, Nebraska to the river's confluence with the Mississippi River. This approach resulted in a 29,000+ day time series of inundation depths across the floodplain using grid cells with 30 meter spatial resolution. This dataset presents 17 metrics for each of two scenarios, one using a baseline timeseries of stages from the HEC-RAS simulation and one using a timeseries of stages adjusted to account for changes in discharge under one possible climate change scenario. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water depths across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. Lastly, we include the base elevation layer that we generated to calculate depth of inundation from interpolated water-surface elevations.

Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1 kilometer) to sufficiently characterize water surface profiles along an approximately 800 kilometer stretch upstream from the confluence with the Mississippi River over an 80-year record at a daily time step. To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. We completed these calculations for an 800 kilometer stretch of the Missouri River, spanning from Rulo, Nebraska to the river's confluence with the Mississippi River. This approach resulted in a 29,000+ day time series of inundation depths across the floodplain using grid cells with 30 meter spatial resolution. This dataset presents 17 metrics for each of two scenarios, one using a baseline timeseries of stages from the HEC-RAS simulation and one using a timeseries of stages adjusted to account for changes in discharge under one possible climate change scenario. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water depths across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. Lastly, we include the base elevation layer that we generated to calculate depth of inundation from interpolated water-surface elevations.

Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1 kilometer) to sufficiently characterize water surface profiles along an approximately 800 kilometer stretch upstream from the confluence with the Mississippi River over an 80-year record at a daily time step. To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. We completed these calculations for an 800 kilometer stretch of the Missouri River, spanning from Rulo, Nebraska to the river's confluence with the Mississippi River. This approach resulted in a 29,000+ day time series of inundation depths across the floodplain using grid cells with 30 meter spatial resolution. This dataset presents 17 metrics for each of two scenarios, one using a baseline timeseries of stages from the HEC-RAS simulation and one using a timeseries of stages adjusted to account for changes in discharge under one possible climate change scenario. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water depths across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. Lastly, we include the base elevation layer that we generated to calculate depth of inundation from interpolated water-surface elevations.

Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1 kilometer) to sufficiently characterize water surface profiles along an approximately 800 kilometer stretch upstream from the confluence with the Mississippi River over an 80-year record at a daily time step. To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. We completed these calculations for an 800 kilometer stretch of the Missouri River, spanning from Rulo, Nebraska to the river's confluence with the Mississippi River. This approach resulted in a 29,000+ day time series of inundation depths across the floodplain using grid cells with 30 meter spatial resolution. This dataset presents 17 metrics for each of two scenarios, one using a baseline timeseries of stages from the HEC-RAS simulation and one using a timeseries of stages adjusted to account for changes in discharge under one possible climate change scenario. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water depths across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. Lastly, we include the base elevation layer that we generated to calculate depth of inundation from interpolated water-surface elevations.

This raster dataset covers all of NSW and is a raw count of inundated pixel observations from all available Landsat acquisitions from mid 1984 to mid 2016. The dataset was produced by applying a water index to each Landsat scene using the technique developed by Fisher and Danaher (2016). Water indexed images were classified into inundated and not inundated classes using a threshold value of -10. Masking of cloud, cloud shadow and other erroneous pixels resulting from sensor anomalies was undertaken using the F-mask technique (Zhu and Woodcock 2012) and these pixels were allocated a 'no data' value . The classified images (with pixels allocated to 'inundated' or 'not inundated' or 'no data' classes) were then stacked and the number of inundated observations were counted for each pixel in available Landsat scenes. Known commission errors include areas of terrain shadow, building shadow especially in urban areas, and tall dense forest such as some pine plantations. Known omission errors include areas of greater vegetation cover. Potential users should note that inundated observations are only recorded for cloud free observation times and locations, thus inundation events on cloudy days may not have been detected.

The basis for these features is U.S. Geological Survey Scientific Investigation Report 2017-5024 Flood Inundation Mapping Data for Johnson Creek near Sycamore, Oregon. The domain of the HEC-RAS hydraulic model is a 12.9 mile reach of Johnson Creek from just upstream of SE 174th Avenue in Portland, Oregon to its confluence with the Willamette River. Some of the hydraulics used in the model were taken from Federal Emergency Management Agency, 2010, Flood Insurance Study, City of Portland, Oregon, Multnomah, Clackamas and Washington Counties, Volume 1 of 3, November 26, 2010. The Digital Elevation Model (DEM) utilized for the project was developed from LiDAR data flown in 2015 and provided by the Oregon Department of Geology and Mineral Industries. Bridge decks are generally removed from DEMs as standard practice. Therefore, these features may be shown as inundated when they are not. Judgement should be used when estimating the usefulness of a bridge during flood flow. Comparing the bridge to the surrounding ground can be more informative in this respect than simply looking at the bridge itself. Two model plans were used in the creation of the flood layers. The first is a stable model plan using unsteady flow in which the maximum streamflow is held in place for a long period of time (a number of days) in order to replicate a steady model using an unsteady plan. The stable model plan produced the areas of uncertainty contained in the sycor_breach.shp shapefile. The second is an unstable model plan that uses unsteady flow in which the full hydrograph (rising and falling limb) is represented based on the hydrograph shape of the December 2015 peak annual flood. The unstable model plan produced the flood extent polygons contained in the sycor.shp shapefile and the depth rasters and represents the best estimate of flood inundation for the given streamflow at U.S. Geological Survey streamgage 14211500.

The basis for these features is U.S. Geological Survey Scientific Investigation Report 2017-5024 Flood Inundation Mapping Data for Johnson Creek near Sycamore, Oregon. The domain of the HEC-RAS hydraulic model is a 12.9 mile reach of Johnson Creek from just upstream of SE 174th Avenue in Portland, Oregon to its confluence with the Willamette River. Some of the hydraulics used in the model were taken from Federal Emergency Management Agency, 2010, Flood Insurance Study, City of Portland, Oregon, Multnomah, Clackamas and Washington Counties, Volume 1 of 3, November 26, 2010. The Digital Elevation Model (DEM) utilized for the project was developed from LiDAR data flown in 2015 and provided by the Oregon Department of Geology and Mineral Industries. Bridge decks are generally removed from DEMs as standard practice. Therefore, these features may be shown as inundated when they are not. Judgement should be used when estimating the usefulness of a bridge during flood flow. Comparing the bridge to the surrounding ground can be more informative in this respect than simply looking at the bridge itself. Two model plans were used in the creation of the flood layers. The first is a stable model plan using unsteady flow in which the maximum streamflow is held in place for a long period of time (a number of days) in order to replicate a steady model using an unsteady plan. The stable model plan produced the areas of uncertainty contained in the sycor_breach.shp shapefile. The second is an unstable model plan that uses unsteady flow in which the full hydrograph (rising and falling limb) is represented based on the hydrograph shape of the December 2015 peak annual flood. The unstable model plan produced the flood extent polygons contained in the sycor.shp shapefile and the depth rasters and represents the best estimate of flood inundation for the given streamflow at U.S. Geological Survey streamgage 14211500.

Floodplain inundation is believed to be the dominant physical driver of an array of ecosystem patterns and processes in the Upper Mississippi River System (UMRS). Here, we present the relative elevation of a slope-detrended floodplain terrain surface and river mile location used to map surface water depths derived from gaging locations along UMRS, as described in Van Appledorn et al. (2021; doi: 10.1002/rra.3628). We excluded areas permanently wetted (aquatic areas), surfaces in agricultural production, roads, and developed areas. The data are intended for use in geospatial analyses of UMRS floodplain ecosystem patterns and processes.