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87 results for “inundation”
Spartina alterniflora above- and belowground biomass predictions and inundation intensity as estimated by the Belowground Ecosystem Resiliency Model for U.S. Georgia marshes from 2014 to 2023.
We applied the Belowground Ecosystem Resiliency Model (BERM) to estimate monthly aboveground biomass (AGB) and belowground biomass (BGB) in U.S. Georgia Spartina alterniflora marshes from 2014 to 2023 at 30 m scale. This application involved BERM version 2.0 (https://doi.org/10.5281/zenodo.13306821), which was built using data in the PLT-GCET-2308 dataset (https://dx.doi.org/10.6073/pasta/4a0b715104849d98320fcc34e7cd63a4). Data sources for BERM application included Landsat-8/9, NOAA CO-OPS Station ID: 8670870, Daymet, and USGS 3DEP 2018 DEM. Download and processing steps are described in the BERM code and in metadata methods section. Specific descriptions of data processing are available in model code: https://doi.org/10.5281/zenodo.13306821. Data provided here include model output of AGB estimates, BGB estimates, and calculated inundation intensity. See "Data reporting" method in the metadata for description of data files. For logisitical purposes here we present only select data from the model input and output. All model input data sources as listed in the abstract are publicly available. Model calibration data and code are published as well. Additional predictions not published here include foliar chlorophyll, foliar nitrogen, and leaf area index.
Estimated Inundation Periods in the Yolo Bypass, California, 1998 – 2024
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. The YBFMP operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. The YBFMP serves to fill information gaps regarding environmental conditions in the bypass that trigger migrations and enhanced survival and growth of native fishes, as well as provide data for IEP synthesis efforts. YBFMP staff also conduct analyses of YBFMP monitoring data to address pertinent management related questions as identified by IEP. The Yolo Bypass has been identified as a high restoration priority by the National Marine Fisheries Service and US Fish and Wildlife Service Biological Opinions for Delta Smelt, Winter and Spring-run Chinook salmon and by California EcoRestore. The YBFMP informs the restoration actions that are mandated or recommended in these plans and provides critical baseline data on the ecology of the bypass and how it interacts with the broader San Francisco Estuary. YBFMP’s data is often accompanied by information on whether the Yolo Bypass is inundated, as water quality, and species composition and abundance can be greatly altered during inundation. This dataset was created to consistently estimate inundation over time. Estimating
Discrete water temperature, flow, solar radiation, chlorophyll-a and inundation, Sacramento-San Joaquin Delta, CA, 1999-2019
The objective of our study is to better understand the factors affecting chlorophyll-a production within a floodplain and its transport downstream to determine how lateral connectivity influences longitudinal connectivity. The Yolo Bypass is an engineered floodplain of the Sacramento River that inundates during periods of high outflow via overtopping weirs. Water traveling through the Yolo Bypass flows parallel to the Sacramento River and re-connects to the mainstem at the southern extent of the floodplain. Several monitoring programs in the Sacramento San-Joaquin Delta and Yolo Bypass collect discrete and continuous water quality data, including chlorophyll measurements. For this study, we synthesized available flow, water temperature, chlorophyll and inundation data between March 1999 to December 2019 and modeled the effects of environmental variables and inundation on chlorophyll-a production in the floodplain, the mainstem, and downstream of the floodplain/mainstem.
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
Modeled daily Yolo Bypass inundation
Hydrology is one of the major disturbance regimes thought to shape aquatic habitat. However, the San Francisco Estuary (SFE) has been altered by urban and agricultural development. Approximately 95% of the estuary’s wetlands have been diked, channelization is pervasive, and the biological community and water quality have been changed by exotic species introductions, sediment inputs from mining, and pollution from agricultural and urban chemicals. The hydrography has also been altered by upstream dams, which reduce the magnitude of both winter precipitation pulses and spring snow melt pulses when filling reservoirs. In addition to reservoir storage, 35% to 65% of tributary inflow is diverted by large water diversions, as well as thousands of smaller agricultural pumps and siphons. Nevertheless, the SFE retains a substantial area of seasonal off-channel habitat in the Sacramento River, the Yolo Bypass. The Yolo Bypass is hydraulically dynamic and is strongly influenced by tides during low discharge periods and dominated by fluvial river dynamics during flood events. This river floodplain-tidal slough complex is, therefore, much more hydrodynamically variable than the adjacent mainstem Sacramento River channel and benefits from additional metrics than those typically measured for river systems. These data are the modeled duration of inundation in the Yolo Bypass, which was approximated as the number of days in which Yolo Bypass flow was greater than 113.27 m3/s following an inundation event from the Sacramento River (e.g., when the stage height of the Sacramento River exceeded the height of the Fremont Weir, 10.2 meters). Inundation in the Yolo Bypass creates a complex transition zone between the river floodplain and tidal slough habitat, which likely represents the historically dominant habitat in the North Delta. Therefore, inundation brings many advantages for native fish, such as increased growth opportunities on the floodplain, an alternative route into the estuar
Data Source: Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation
Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Data were compiled for 152 wetlands in west-central Florida over 14 years to investigate the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Further methods are defined in the Methods section of the journal article associated with this dataset (Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation, published in the Journal of Environmental Management, 2023).
Soil Lake Inundation Moat Experiment (SLIME): Physical, chemical, and biological measurements from planktonic water columns, McMurdo Dry Valleys, Antarctica (2018-2020)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the margins of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. To study these habitats, sampling transects were established on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. This data package includes three seasons of physical, chemical, and biological measurements from the planktonic water columns of these lakes, collected from SLIME transects between January 2018 and January 2020. Parameters include water temperature, conductivity, ion and nutrient concentrations, chlorophyll-a concentrations, as well as fluorescence and photochemical efficiencies for major algal classes. NCBI accession numbers are also provided for the microbial sequence data associated with each sample.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore East Lake Bonney (NELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore East Lake Bonney SLIME transect is located approximately 2000 m west of the Priscu Stream inflow to the East Lobe of Lake Bonney. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore Lake Fryxell (NFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore Lake Fryxell SLIME transect is located approximately 500 m west of the Lake Fryxell Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore East Lake Bonney (SELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2017-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore Lake Fryxell (SFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The South Shore Lake Fryxell SLIME transect is located approximately 1500 m west of the F6 Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
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>
Data from: Synergistic effects of grass competition and insect herbivory on the weed Rumex obtusifolius in an inundative biocontrol approach
<p>Data are from a field experiment to test for synergistic interactions between grass competition and herbivory on <i>Rumex obtusifolius</i>, a prominent weed in temperate grasslands worldwide.</p><p><i>Rumex obtusifolius</i> was grown in the presence and absence of competition from the grass <i>Lolium perenne</i> and subjected to herbivory through targeted inoculation with root-boring <i>Pyropteron</i> spp.</p><p>To explore whether the interactive effects of competition and herbivory were size-dependent, <i>R. obtusifolius</i> was planted covering a large range of plant sizes found in managed grasslands.</p><p>The experimental layout followed a split-split plot design. Main-level factor was <i>L. perenne</i> competition, split-level factor was herbivory application, split-split-level factor was initial root mass of <i>R. obtusifolius</i>. Main-plots were arranged according to a randomized complete block design on the site (8 blocks, each containing a <i>L. perenne</i> competition and a no competition treatment).</p>
Data supporting tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)
<p>The workbook contains time series of wetlandscape properties, climate variables, and climate oscillation indices for 1984–2020, and land cover statistics for 1992–2020 in the Lake Winnipeg Watershed. The wetlandsacpe properties were generated as part of a study by Fendereski, Ma, Mohammady, Spence, Trick, and Creed ("Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)") submitted<span> </span>to the International Journal of Applied Earth Observation and Geoinformation. The use of the data is subject to citing the paper.</p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Inundation maps of Donana for 23 dates within the period 2015/12/19 to 2017/08/20 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2015/12/19 to 2017/08/20 were generated for Donana based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Donana_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Danube Delta for 10 dates within the period 2016/10/05 to 2017/08/01 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/10/05 to 2017/08/01 were generated for Danube Delta based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Danube_Delta_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. The regions, which are manually denoted as affected by clouds, are denoted with 2. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Camargue for 47 dates within the period 2016/02/09 to 2018/06/19 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/02/09 to 2018/06/19 were generated for Camargue based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Camargue_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
Modelling of inundation scenario under defended hypothesis for RP100 years in Rimini (2050)
<p>This video shows the output of ANUGA hydrodynamic model simulating the total water level generated by a synthetic storm surge scenario corresponding to RP 100 years in Rimini. The period considered is 2050, that means the simulation accounts for changes in Mean Sea Level due to Sea Level Rise and vertical land movements.</p> <p>ANUGA is a 2D hydrodynamic model suitable for the simulation of flooding events resulting from riverine peak flows and storm surges. Being a 2D hydrodynamic model, ANUGA does not resolve vertical convection, waves breaking or 3D turbulence (e.g. vorticity), thus it not accounting for the swash component of wave runup. The fluid dynamics in ANUGA is based on a finite-volume method for solving the shallow water wave equations, thus being based on continuity and simplified momentum equation.<br> The case study area is represented by an irregular triangular mesh in which water level, water depth and horizontal momentum are computed. The size of the triangles is variable within the mesh, varying from higher resolution areas (16 m²) for canals and coastal defence structures, to lower resolution (900 m²) for sea areas.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.