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89 results for “river flooding”
Atchafalaya shelf velocity data from May 2011 during a Mississippi River flood period
<p>Current velocity measured over the Atchafalaya shelf by an ADCP mounted on from R/V Pelican during the Mississippi River flood period in May 2011.</p>
Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'
<p>Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to <em>Geophysical Review Letters.</em></p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
A flood inundation hindcast for Europe based on 26-year simulated river discharge
<p>This data set contains hydrodynamic simulations at 1 km for Europe between 1990-2016 at a daily time step for 298 basins.</p>
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta: supporting data - high water levels
<p>This dataset contains modelled high water levels in the Guayas delta supporting the paper 'Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta' published in HESS 2024.</p> <p>The dataset is organised through two folders:</p> <ul> <li>mangroves: all data from the scenarios with mangroves included in the domain</li> <li>no_mangroves: all data from the scenarios without mangroves included in the domain</li> </ul> <p>Each folder is further divided along 6 subfolders:</p> <ul> <li>I: El Niño Ocean & river</li> <li>II: El Niño Ocean</li> <li>III: El Niño river</li> <li>IV: Neutral</li> <li>I_50per: 50 % increase in seaward El Niño anomalies</li> <li>I_150per: 150 % increase in seaward El Niño anomalies</li> </ul> <p>Each folder contains two files:</p> <ul> <li>vars.csv - each row represents a mesh node and there are three columns: <ul> <li>X: x value of the model mesh node [m]</li> <li>Y: y value of the model mesh node [m]</li> <li>HIGH_WATER: high water level [m]</li> </ul> </li> </ul>
Deciphering the impacts of main inflowing rivers on dissolved organic matter in Lake Daye using isotopes, optical spectroscopy, and FT-ICR-MS during non-flood season
<p>The uploaded data include water quality data, isotopes, DOM fluorescence index and FT ICR MS data of Daye Lake and its inflowing rivers.</p>
Code of "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation"
<p>Code used to prepare a paper entitled "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation" which was submitted to Hydrology and Earth System Sciences.</p>
Adaptation required to preserve future high-end river flood risk at present levels
<p>Dataset accompanying the publication</p> <p>S.N. Willner, A. Levermann, F. Zhao, K. Frieler, Adaptation required to preserve future high-end river flood risk at present levels. Sci. Adv. 4, eaao1914 (2018).</p> <p>The dataset includes the increase in flood protection that is required to keep the observed high-end flood risk of the past constant in the next 25 years as well as the affected population in both periods used (1971-2004 and 2035-2044).</p>
Figure 3 in Behaviour of two predator fishes Esox lucius Linnaeus, 1758 and Silurus glanis Linnaeus, 1758 during two successive floods in the French Aisne River
Figure 3. –Boxplot characterizing the sizes ranges per behavioural group of northern pikes
Arno River flooding 1966 | WRF-ENS forecasts
<p>Contains WRF-ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. Files format is binary with auxiliary description file.</p>
Fig. 1 in Effects of extended absence of flooding on the fish assemblages of three floodplain lagoons in the middle São Francisco River, Brazil
Fig. 1. Study area showing the location of the marginal lagoons.
Analysis of Flood Mechanisms in the Delaware River Basin in a Warming Climate
<p>The dataset is used by a study focused on analyzing flood hazards in future climates and understanding the underlying mechanisms driving changes in floods across the Delaware River Basin. The dataset encompasses the following components:</p> <p>1. Historical Flood events (<em>AMF/hist/</em>): flood events for each HUC8 basin of the Delaware River Basin from water year (WY) 1980 to 2018. The flood events were simulated using the Distributed Hydrology Soil Vegetation Model (DHSVM), a process-based hydrological model. Each flood event is defined as the annual maximum daily flow within the respective water year, which spans from October 1 through September 30. The dataset is organized into two columns, where the first column represents the year, and the second column represents the discharge in cubic meters per second.</p> <p>2. Future flood events (<em>AMF/GCMs/</em>): DHSVM simulated flood events for each HUC8 basin of the Delaware River and each GCM for two distinct periods: the baseline period (WY 1970–1999) and the future period (WY 2070–2099). Future climates in the region are represented by 10 downscaled General Circulation Model (GCM) climate projections.The dataset is organized into two columns, where the first column represents the year, and the second column represents the discharge in cubic meters per second.</p> <p>3. Hydrometeorological Variables (<em>daily_states/</em>): daily-scale continuous simulations of various hydrometeorological variables for historical climate (<em>daily_states/hist) </em>and under GCM scenarios (<em>daily_states/GCMs)</em>. These data were used to analyze the generating mechanisms of each flood event within the HUC8 basins of the Delaware River Basin. The variables include soil moisture (SM), precipitation (PPT), snowfall, snow water equivalent (swq), and snowmelt (melt). These variables were derived from the simulation outputs of DHSVM and were aggregated from the grid cell levels to the HUC8 basin level by calculating daily averages over each basin.The files are stored in the .mat (Matlab data) format and consist of four dimensions, namely huc8 basins, GCMs, periods (baseline and future), and days. Specifically, the dimensions are 10x10x2x10957.</p>
Data for 'Rethinking variability in bedrock rivers: sensitivity of hillslope sediment supply to precipitation events modulates bedrock incision during floods'
<p>This is the repository for model code, sample data, and plotting scripts for the OTTER model for Python.</p> <p>The file OTTERPy.py contains the actual model code</p> <p>The file OTTERPlottingFuncs.py contains a few scripts for plotting model results.</p> <p>The four sample data files in the repository are results from two long (1.5million model year) runs at different rock uplift rates which took several days to run on the IU Bloomington Quartz supercomputing cluster. This data should be viewed and plotted using the OTTERPlottingFuncs.py script. See that code for information on how to select which model to plot.</p> <p> </p> <pre><code>Glossary of variables - this is pretty close to exhaustive, there are a probably a couple of throwaway variables not listed here. a: Sternberg's law multiplier Ah: drainage area from Hack's Law (m^2) beta: fraction of sediment transported as bedload from 0 to 1 D: grain size at each node (m) dA: change in drainage area from node to node (m^2) depth_thresh: sediment depth at which bedrock erosion can occur Do: initial grain size Do: grain size at headwaters (m) dQs: change in sediment supply from node to node dt: time step (years) dx: space step (m) dz_b: bedrock erosion at each node dz_s: change in sed depth dz_b_t: bedrock erosion saved through time dz_b_store: bedrock erosion stored dz_s_store: sed depth change stored eQ: exponent for scaling Qw from Ah eroded_sedsup: sediment created by bedrock erosion eW: exponent for scaling W from Qw F: fractional bedrock exposure g: gravitational acceleration (M/s^2) H: water depth Hc: Hack's Law constant He: Hack's law Exponent initial_sedsup: Sediment supply in first 10k yr (equal to uplift) kf: bedrock erodibility kQ: constant for scaling Qw from Ah kv: parameter controlling discharge variability - Crave & Davy, 2001. Lower kv is more variable kw: lateral bedrock erosion constant kWid: constant for scaling W from Qw last_z: elevation at channel mouth lmbda: sediment porosity n: manning's roughness coefficient onlyonce: changes from 0 to 1 to make sure the uplift pulse only happens one time onoff: turns sediment on and off entirely onoffsedvar: turns stochastic sediment on and off Qs: sediment supply along the length of the river Qs_down: downstream sediment flux at each node Qt: sediment transport capacity Qw: water discharge Qwo: initial water discharge QwScale: array of scalar multipliers for Qw, from the chosen distribution R: effective density of submerged sediment rho_w: density of water (kg/m^3) rho_s: density of sediment (kg/m^3) savesteps: at what time should we save outputs? sed_depth: depth of seidment at each node (m) SedStore: storing snapshots of sediment thickness through time slope: river gradient (negative usually) SlopeStore: storing snapshots of channel slope through time tarray: array of model time tau_b: basal shear stress tau_c: critical shields criterion for initiation of sediment motion time: model duration (years) topo: channel elevation (bedrock + sediment) upinc: size up uplift pulse, as a multiplier of initial uplift rate upinctime: when should the pulse happen? (years) uplift: uplift field across the entire domain uprate: initial uplift rate, if using constant uplift across domain W: channel width WidthStore: storing snapshots of channel width through time Wo: initial channel width yr2sec: number of seconds in a year z: bedrock channel elevation ZStore: storing snapshots of bedrock elevation through time</code></pre>
Floods connect tropical river-floodplain food webs but shrink fish community isotopic trophic niches
Open the record for dataset details and reuse information.
Data from: Quantifying flooding regime in floodplain forests to guide river restoration
Determining the flooding regime needed to support distinctive floodplain forests is essential for effective river conservation under the ubiquitous human alteration of river flows characteristic of the Anthropocene Era. At over 100 sites throughout the Connecticut River basin, the largest river system in New England, we characterized species composition, valley and channel morphology, and hydrologic regime to define conditions promoting distinct floodplain forest assemblages. Species assemblages were dominated by floodplain-associated trees on surfaces experiencing flood durations between 4.5 and 91 days/year, which were generally well below the stage of the two-year recurrence interval flood, a widely-used benchmark for floodplain restoration. These tree species rarely occurred on surfaces that flooded less than 1 day/year. By contrast abundance of most woody invasive species decreased with flooding. Such flood-prone surfaces were jointly determined by characteristics of the hydrograph (high discharges of long duration) and topography (low gradient and reduced valley constraint), resulting in increased availability of floodplain habitat with increasing watershed area and/or decreasing stream gradient. Downstream mainstem reaches provided the most floodplain habitat, largely associated with low-energy features such as back swamps and point bars, and were dominated by silver maple (Acer saccharinum). However, we were able to identify a number of suitable sites in the upper part of the basin and in large tributaries, often associated with in-channel islands and bars and frequently dominated by sycamore (Platanus occidentalis) and flood disturbance-dependent species. Our results imply that restoring flows by modifying dam operations to benefit floodplain forests on existing surfaces need not conflict with flood protection in some regional settings. These results underscore the need to understand how flow, geomorphology, and species traits interact to produce characteristic patterns of floodplain vegetation, and that these interactions should form the basis of effective river restoration and conservation.
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008). in Muridae
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008).
Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin
<p>This archive contains masked data over the Susquehanna River Basin and associated Python code to reproduce the findings of the manuscript "Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin." Additional water year analysis not included in the manuscript are also tarred in this archive.</p>
flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau
Open the record for dataset details and reuse information.
Arno River flooding 1966 | ENS forecasts | MARS ID experiment b09d
<p>Contains ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. The dataset is also available at the ECMWF MARS Catalogue, experiment ID b09d. The data format is GriB version 1.</p>
A sand spit under the 2018 flood event: case study at Arakawa River mouth in Niigata, Japan
<p>The datasets are the results of a numerical simulation study of a sand spit at the Arakawa river mouth in Niigata, Japan under the 2018 flood event. The datasets include observed data of tides, river discharge, and waves, and numerical results of SWAN and XBeach.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.