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676 results for “hydrology”
Comparing vertical accretion, organic carbon (C) sequestration, and nitrogen burial between a natural, never diked tidal salt marsh and a hydrologically restored tidal salt marsh on Sapelo Island, Georgia.
Restoration of tidal marshes throughout the 20th century have attempted to bring back important functions of natural tidal systems. In this study, vertical accretion, organic carbon (C) sequestration, and nitrogen burial were compared between a natural, never diked tidal salt marsh and a hydrologically restored tidal salt marsh on Sapelo Island, Georgia to examine the impacts of restoration years later. On Sapelo Island there are two marshes near the University of Georgia Marine Institute, one of which is a natural marsh, and one of which is a restored marsh. The restored marsh had been diked in 1948, and the dike was breached, allowing for the marsh to be restored, in 1956. Soil cores were collected from both marshes, and the sediments were analysed for Nitrogen and Carbon concentrations and bulk density. This analysis was used to determine accretion rates for the two marshes as well as changes in the restored marsh since the dike was breached. Nitrogen burial, carbon sequestration, and soil accretion in the restored marsh as compared to the natural marsh were the focus of this study.
Soil Respiration Along a Hydrological Gradient at Harvard Forest EMS Tower 2003-2006
Soil respiration is an important component of the terrestrial carbon budget. Spatial variation of organic matter and plant cover induce temperature and moisture gradations that obscure direct response of soil respiration to radiative forcing. Separation of spatial patterns from microclimate and substrate availability is vital to predict the response to global change. We are measuring temporal and spatial patterns of soil CO2 flux across a hydrological gradient from wetland to upland soils. A system of 8 automated opaque chambers were installed in 2003 along a hydrological gradient on the northeast margin of the "Beaver Swamp" north of the EMS tower (+42.537755,-72.171478). Two additional clear chambers were installed in the wetland in 2006. Each automated chamber closes for a measurement every four hours. The resulting semi-continuous data has been providing a high-temporal density characterization of soil flux during the growing season since 2003. Soil temperature and moisture were recorded in the litter, organic, and mineral soils during 2004 at three locations along the slope. These structures will provide a database of soil respiration flux, temperature, and soil moisture with both high spatial and temporal resolution, across multiple cover types and ground water levels. The analysis will seek to determine the appropriate spatial scale of soil respiration measurement in eastern hardwood forests.
Prospect Hill Hydrological Stations at Harvard Forest since 2005
To better understand the critical role of headwater streams and wetlands in our forest ecosystem, long-term measurements were initiated in 2005 on two small watersheds in the Prospect Hill Tract of the Harvard Forest. On Nelson Brook, weirs were installed on outlet streams of an 11-ha spruce-hemlock wetland (watershed area = 44 ha). On Arthur Brook (formerly Bigelow Brook), pipes were installed to measure flow above (watershed = 24 ha) and below (watershed = 65 ha) a 3-ha shrub-dominated beaver swamp. The gaged watersheds, though adjacent and comparable in size, differ significantly in topography, soils, wetlands, stream chemistry, stream biota, land-use history, and forest vegetation, and provide an extraordinary opportunity to study the impacts of these factors on small watershed hydrology and ecology. Weekly manual measurements were initiated in April 2005. Continuous automated measurements were initiated at the stream gages in December 2007 and at the wetland gages in October 2008. Data for the current month are available online, updated every 15 minutes, with out-of-range values replaced by NA but values not otherwise checked. Earlier data are checked and archived monthly with missing, questionable, and estimated values flagged, following methods of the LTER ClimbDB project. A log of events affecting station measurements is also posted. For current data, please see: https://harvardforest.fas.harvard.edu/met-hydro-stations.
NRCS-USFS Soil Moisture Measurements - Coweeta Hydrologic Laboratory, NC, 2022-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 12 soil pedons distributed across Watersheds 32 and 7 at the Coweeta Hydrologic Laboratory from March 2022 to April 2025. This work is a part of a larger partnership between the U.S. Forest Service (USFS) and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Associated data packages from both the Fernow and Hubbard Brook Experimental Forests can be found on the EDI Data Portal. Dataset contributors: Project planning led by Carlos Quintero (USFS, ORISE), with help from Amos Stead (NRCS) and Tiffany Allen (NRCS) in site selection. Scientific and logistical support from Chris Oishi (USFS), Amanda Pennino (NRCS), and Erin Rooney (NRCS). Seth Strickland (USFS), Amos Stead (NRCS), Ann Tan (NRCS), and Tiffany Allen (NRCS) assisted with site installation. Site visits, data downloading, and logger maintenance was by Seth Strickland (USFS). The dataset was curated by Emily Piché (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS)
Model simulated hydrological estimates for the North Slope drainage basin, Alaska, 1980-2010
Estimates of runoff, river discharge, snow water equivalent (SWE), subsurface runoff, and soil temperatures are drawn from the Permafrost Water Balance Model (PWBM). The simulation and derived data span the period 1980-2010. The model was forced with daily gridded meteorological data obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis (version 5.2.0). The estimates of total runoff (daily), soil temperature (daily), subsurface runoff (monthly), and SWE (monthly) are expressed on a spatial grid (N=312; 25x25 km EASE-Grid version 1, Northern Hemisphere) over the North Slope drainage basin, with the coastline extending from Utqiagvik (formerly Barrow) to just west of the Mackenzie River delta. River discharge, calculated as a volume flux of runoff at each grid cell, was routed through the river network defined on a simulated topological network (STN). Archived files contain discharge flux through the grid cell representing the outlet of each of forty-two basins defined across the region on the 25 km resolution EASE-Grid. Details of the PWBM, forcing variables, model validation and results of analysis are described in Rawlins et al. (2019).
Macrophyte and microbial mat biomass co-variation along a hydrologic gradient and response to a removal experiment in temporary wetlands Everglades, FL, USA, February 2003 – November 2006
This data package encompasses hydrologic variables, soil depth, hydrologically-regulated macrophyte community types, macrophyte biomass and community structure, and microbial mat biomass that was collected in two observational surveys and one in-situ experimental manipulation in six temporary wetland regions located in the Everglades, FL, USA. The goal of this project was to examine the co-variation in macrophyte and microbial mat biomass along the hydrologic gradient present across wetland regions and to determine the type and strength of interactions occurring between the two communities, which was tested using a biomass (macrophyte or microbial mat) removal experiment. The census observational survey took place at 140 sites from 2003-04-09 to 2004-05-26, which were randomly distributed across the hydrologic gradient present across the six temporary wetland regions. The transect observational survey occurred along six transects and each was deliberately established along the present hydrologic gradient within each region; a total of 254 sites were sampled from 2003-02-19 to 2005-03-04. The experiment took place at three temporary wetland sites with contrasting hydroperiods (3 – 6 months), and four transects were established per site with 24 pairs of control and treatment plots per transect. The removal treatment occurred one year before data collection, and data collection occurred from 2004-06-20 to 2006-11-25. The package includes six datasets, one R code file, and two shape files associated with the R code. Data collection for all datasets is complete. FCE1274_Census_Survey includes hydrologically-regulated macrophyte community type classifications, macrophyte biomass, microbial mat ash-free dry mass, mean soil depth, water depth, mean annual hydroperiod, and vegetation-inferred hydroperiod; each site was sampled once during the survey period and a subset of sites were sampled each year. FCE1274_Transect_Survey includes macrophyte community type classifications
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
Urban Residential Surface and Subsurface Hydrology: Synergistic Effects of Low-Impact Features at the Parcel Scale
Accurately predicting the hydrologic effects of urbanization requires an understanding of how hydrologic processes are affected by low‐impact development practices. In this study, we explored how growing season surface runoff, deep drainage, and evapotranspiration on a residential parcel are affected by several low‐impact interventions, including three "impervious‐centric" interventions (disconnecting downspouts, disconnecting sidewalks, and adding a transverse slope to the driveway and front walk), two "pervious‐centric" interventions (decompacting soil and adding microtopography), and all possible "holistic" combinations. Results were compared to both a highly and moderately compacted baseline parcel under an average and a dry weather scenario for a temperate climate. We find that under reasonable assumptions for highly compacted soil, pervious areas are a major source of runoff and disconnecting impervious surfaces may be relatively less effective without improving soil conditions. Under both highly and moderately compacted soil conditions, combining efforts to decompact soil with impervious disconnection has a synergistic effect on reducing surface runoff and increasing deep drainage and evapotranspiration. All combinations of interventions enhance infiltration, but the partitioning of additional root zone water between deep drainage and evapotranspiration depends on the weather scenario. Importantly, when all low‐impact interventions are applied together, growing season deep drainage is higher than that from a vacant lot with no impervious surfaces. We infer that ecohydrologic interfaces between impervious and pervious areas are strong controls on urban hydrologic fluxes and that high‐resolution, process‐based models can be used to account for these interfaces and thereby improve predictions of the hydrologic effects of low‐impact interventions.
The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Jobos Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six mangrove forest locations in Jobos Bay National Estuarine Research Reserve (JBNERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Maria (September 2017) were identified and selected for hydrologic monitoring coincident with sites sampled for structural metrics in 2022. One reference site, defined as a site that was observed to be recovering following the hurricane, was selected in black mangrove forest. Two of the six sampling locations were selected to monitor effects of human encroachment on the western boundary of the reserve. These two sites were not coincident with structural sampling plots established in 2022. This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Rookery Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six black mangrove forest locations in Rookery Bay National Esturarine Research Reserve (NERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Irma (September 2017) were identified and selected for hydrologic monitoring. The design consists of three sites in mainland/interior black mangroves and three sites on ocean-facing islands, all of which are located on the east side of Hurricane Irma eyewall. In each group, two of the sites selected were considered sites of minimal recovery whereas one site was selected as a reference (location of recovering mangroves). This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
ClimHyrdoDB Archive: Meteorologic and hydrologic observations from LTER and USFS sites, 2001-2020 - orignal database format
This dataset is an archive of the ClimHydroDB database, which was actively used from early 2001 to mid 2020. The database contained contributions from 62 contributors (primarily from the LTER Network and US Forest Service) and 672 research sites. Data records total approximately 16 million (raw) or 1.6 million (aggregated) for 22 meteorologic or hydrologic variables. This archive contains the 23 core tables of the ClimHydroDB database as text tables of comma separated values, plus the database entity relationship diagram (ERD), User Guide, database table descriptions (DDL, SQL script), and a zip file of related documents and presentations. Database design: At last upgrade, the database was implemented in Microsoft SQL Server 2008 (see DDL for more information). Database tables are primarily in a key-value pair arrangement, with controlled input for many fields, and extensive cross referencing. This design allows many types of descriptors to be assigned, e.g., for the types of activities taking place at research stations, or for physical parameters to describe a research area itself. The EML metadata for tables holding controlled vocabularies are described using the string “List of …”. Cross reference tables are described in metadata as such, including the parent table names. Database history: To facilitate intersite research within the LTER network, site data managers developed a system to provide climatic summaries dynamically, called ClimDB. Later funding from the U. S. Forest Service allowed the original database to be expanded to include hydrologic variables, and the combined database was renamed ClimHydroDB in 2003. The database also harvested real-time streamflow data from USGS gauging stations, using code developed by the Georgia Coastal Ecosystem LTER. As of 2021, the ClimHydroDB content is available as data packages from individual contributing sites, each containing identically formatted text tables in the ODM 1.1 format, for integration with CUAHSI tools
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight tributary monitoring stations around the lake collecting data on water quality, soil quality and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Urban Riparian Wetland Hydrology Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p>This is the initial release of a <strong>hydrology</strong> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (<em>Castor canadensis</em>)</strong>. This dataset contains data specific to the hydrology of Walnut Creek, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January 22, 2023 through January 30, 2024</strong>. </p> <p>The core of the dataset is water stage measured in five surface pond sites and six groundwater monitoring wells within Walnut Creek Wetland Park. This data was collected using synchronized Solinst Levelogger pressure transducer sensors at 15-minute intervals, compensated with corrections for barometric pressure measured locally using a Solinst Barologger sensor. In addition to this data collected by the authors, this dataset also includes publicly available stream stage and precipitation data obtained from the <strong>US Geological Survey,</strong> and weather and soils data from the <strong>North Carolina State Climate Office</strong>. In total, this dataset aims to provide a comprehensive view of surface and subsurface hydrology in the studied wetlands as it connects with precipitation events, antecedent moisture conditions, directed stormwater flows and overbank flood events. </p> <p>This hydrology dataset is intended to accompany the <u>separate</u> <strong>water quality dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10888463">https://doi.org/10.5281/zenodo.10888463</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</p> <p>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </p> <p>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</p> <p>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</p>
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Hydrological, physicochemical and metabolic activity data for streams in the Japanese Alps
<p>A series of files with hydrological, physicochemical and metabolic activity data from a study investigating the environmental dynamics of six stream systems in the Japanese Alps.</p>
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.