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60 results for “water balance”
Data from: Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale
<p><span>Abstract</span></p> <p><span>The uploaded data are relative to the investigation around (i) the carbon source/sink nature and, further, (ii) the water and carbon balances, of a drip-irrigated and mulched watermelon. The crop was cultivated under the semi-arid climate of the Apulia region, in south Italy.</span></p> <p><span>The used mulching films were biodegradable as indicate by the producer; plants and some non-standard fruits were left on the soil as green manure after harvesting, thus, the experiment spanned from planting to the subsequent crop (6 months of continuous measurement from June to November 2023). </span></p> <p><span>The results detailed in the original publication indicate that mulching films contribute to carbon sequestration in the soil (+19.3 gC m<sup>−2</sup>). However, this mulched watermelon represents a net carbon source, with a net biome exchange, as loss from ecosystems, equal to +230 gC m<sup>−2</sup>. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 gC m<sup>−2</sup>). </span></p> <p><span> </span></p> <p><span>Methods</span></p> <p><span>Site and crop</span></p> <p><span>The field site was at the CREA-AA Research Unit experimental farm located in southern Italy (Rutigliano–Bari, 41 01’ N, 17°01’ E, altitude 147 m a.s.l.)., characterized by a Mediterranean semi-arid climate (average annual rainfall of 535 mm). The soil is classified as Lithic Rhodoxeralf, with a clay texture, stable structure, shallow profile (0.6–1.1 m) and rapid drainage due to an underlying cracked limestone subsoil. The SOC content averages around 12.0 g kg<sup>−1</sup>. The field capacity and the permanent wilting point volumetric water contents are 0.36 and 0.21 m<sup>3</sup> m<sup>−3</sup>, respectively; with a bulk density of 1.15 Mg m<sup>−3</sup>, the available soil water ranges from 80 to 140 mm.</span></p> <p><span>The studied watermelon crop (seedless var. Lion king), followed a broccoli cabbage crop harvested in April and partially incorporated (0.81 kg m<sup>−2</sup> of fresh biomass in a soil layer depth of 0.30 m, corresponding to 0.69 kgH2O m<sup>−2</sup>) as green manure on 25 May 2023. Main tillage at medium depth ploughing (0.30 m) and seedbed preparation were performed between 25 and 30 May 2023; the biodegradable film mulch (model PC 100 d8, BASF, Italy, 1 m width) was applied on 1 June 2023. On the same day, driplines (2.1 Lh<sup>−1</sup> emitters, 0.60 m apart) and the main organic fertilization (Orga-Kem 6.11.8 + 11CaO, 300 kg ha<sup>−1</sup>) were also applied. The watermelon plants were transplanted on 9 June at a spacing of 2.70 m between rows and 1 m between plants, covering an area of about 4.0 ha, with a density of approximately 3200 plants ha<sup>−1</sup>. Every 6 rows, the inter-row distance was 5 m to facilitate machinery passage. The first irrigation was performed the day before planting. Crop management adhered to the usual treatments in the area including mechanical weed removal every 4 weeks, irrigation around three times per week to maintain optimal soil water conditions and monthly fertigation (ammonium sulphate 50 kg ha<sup>−1</sup>, magnesium nitrate 30 kg ha<sup>−1</sup>, calcium nitrate 60 kg ha<sup>−1</sup>, mycorrhizae 20 kg ha<sup>−1</sup>). The scalar harvest of marketable fruits occurred between 28 and 31 August 2023. After harvesting, on 25 September 2023, the fresh plant residues (0.6 kg m<sup>−2</sup> of fresh biomass, corresponding to 0.49 kgH2O m<sup>−2</sup>), unharvested fruits (4.0 kg m<sup>−2</sup> of fresh material, corresponding to 3.7 kgH2O m<sup>−2</sup>) and the mulching film were chopped by a tractor shredder and ploughed in two steps, on 2 and 13 October 2023, to a soil depth of 0.30 m. Measurements concluded at the end of November 2023, when tillage for the new winter crop commenced.</span></p> <p><span> </span></p> <p><span>Measurements of H<sub>2</sub>O and CO<sub>2</sub> fluxes; partitioning in evaporation, transpiration, photosynthesis and respiration</span></p> <p><span>The eddy covariance technique was employed to monitor water vapor (H<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes. The equipment comprised a three-dimensional sonic anemometer (uSonic 3 Scientific, Metek GmbH, 25337 Elmshorn, Germany) and a fast response open-path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The three wind components, sonic temperature and atmospheric concentrations of CO<sub>2</sub> and H<sub>2</sub>O were continuously measured at 1.5 m above the crop canopy, with the sensor height adjusted to follow crop growth, reaching a maximum of 1.75 m. </span></p> <p><span>Data were recorded at a frequency of 10 Hz on a dedicated computer using the MeteoFlux software (Servizi Territorio, S.n.c., Cinisello Balsamo, Italy) and were stored on an hourly scale. Post-processing and computation of hourly fluxes of H<sub>2</sub>O (mmol m<sup>−2</sup> s<sup>−1</sup>) and CO<sub>2</sub> (</span>μ<span>mol m<sup>−2</sup> s<sup>−1</sup>) were conducted using EddyPro software, v7.0.9 (</span><a href="http://www.licor.com/eddypro"><span>http://www.licor.com/eddypro</span></a><span>), applying 60 min block averaging, double coordinate rotation, the statistical test, the maximum cross-covariance method, and the WPL density correction.</span></p> <p><span>H<sub>2</sub>O and CO<sub>2</sub> fluxes were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method. This method utilizes the Monin–Obukhov similarity theory to separate stomatal (photosynthesis, Fp, and transpiration, Ft) from non-stomatal (respiration, Fr, and evaporation, Fe) processes (Palatella et al., 2014). the H<sub>2</sub>O and CO<sub>2</sub> EC fluxes were partitioned using an adaptation of the code in Phyton provided by (Skaggs et al., 2018) and downloaded from <span> </span></span><a href="https://github.com/usda-arsussl/fluxpart"><span>https://github.com/usda-arsussl/fluxpart</span></a><span> (V0.2.10).</span></p>
Monthly water balance data for southern Taylor Slough Watershed (FCE LTER) from January 2001 to December 2011
The following abstract is from Sandoval (2013). The purpose of this research was to investigate the water balance, flushing time, and water chemistry of Taylor Slough; one of the main natural waterways of the coastal Everglades, during its early stages of restoration. Watershed flushing times were estimated as the surface water volume divided by the total water outputs. Both the water balance and water residence times were calculated on monthly from 2001 – 2011. Flushing times varied between 3 and 78 days, with the highest values occurring in December and the lowest in May. Flushing times were negatively correlated with evapotranspiration (ET), but were longer when surface water volume exceeded ET and shorter when ET exceeded water volume.
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Water Balance Modeling Project at the Sevilleta National Wildlife Refuge, New Mexico: Vegetation Plot Data (1995-1998)
The water balance vegetation plots were part of a larger water balance monitoring project at the Sevilleta LTER. The plots were designed to measure the percent cover of photosynthetic/transpiring (green) plant species at specific sites where time domain reflectometry (TDR) probes and weather stations were already installed. In 1995, there were three sites (Field Station, Deep Well and Rio Salado). A 30m x 30m plot was installed at each site, and collection of vegetation data commenced in July 1995. Percent cover (green) and species identities were recorded monthly at a representative sample of 1m square quadrats within each plot.
Dataset: The SPIKE II experiment - Tracing the water balance
<p>This repository holds data collected during the “SPIKE II” tracer experiment. The experiment was carried out on a large vegetated lysimeter (2.5 m<sup>3</sup>) planted with two willow trees (clones) (<em>Salix viminalis</em>) within the EPFL campus (CH), in Switzerland. SPIKE II took place from May 10 to June 29 in 2018. This composite dataset contain stable isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of more than 900 water samples of precipitation, soil water, bulk soil collected at different depths in the soil profile, xylem from willow, and leakage flow in the bottom of the lysimeter. The dataset comprises environmental conditions and water fluxes recorded during the experiment. This includes: meteorological conditions, soil moisture and tension, evapotranspiration in the lysimeters, and tree transpiration recorded at high resolution. Finally, the repository holds tree hydraulic and growth measurements and root traits.</p> <p>Specifically, this dataset contains six files:</p> <ul> <li>“METADATA_spikeII.txt” contains specific information about each recorded variable and data point collected throughout the experiment.</li> <li>“spike.hydrometric.II.csv” contains information about meteorological and soil conditions, evapotranspiration fluxes, and tree stem radius, including growth and tree water deficit.</li> <li>"spike.isotopes.II.csv” contains stable isotope data.</li> <li>“fineroots_spike.II.csv” contains root traits information.</li> <li>“events_chronology.csv” summarizes the main events that occurred during SPIKE II.</li> <li>“Figure1_SpikeII_Aerial_Image.PNG” illustrates the location and spatial display of the experiment at the EPFL campus.</li> </ul> <p>This data repository was used in the following SPIKE II publications:</p> <p>Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., & McDonnell, J. J. (2021). Tree water deficit and dynamic source water partitioning. <em>Hydrological Processes</em>, <em>35</em>(1), e14004. doi:10.1002/hyp.14004</p> <p>Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., & McDonnell, J. J. (2021). On the use of leaf water to determine plant water source: A proof of concept. <em>Hydrological Processes</em>, <em>35</em>(3), e14073. doi:10.1002/hyp.14073</p> <p>Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2021). Tracing and closing the water balance in a vegetated lysimeter. <em>Water Resources Research</em>, 57, e2020WR029049. doi:org/10.1029/2020WR029049</p> <p>For any further inquiry, please contact Magali Nehemy or Paolo Benettin.</p> <p>We thank Kim Janzen for assistance with laser and mass spec analysis. We thank the Laboratory of Ecohydrology at EPFL (ECHO/IIE/ENAC/EPFL) for assistance throughout the experiment. We also thank Pierre Queloz and Scott Allen for precious help, Gabriel Cotte and Torsten Vennemann from University of Lausanne (CH) for the collection and analysis of atmospheric vapor samples. This research was supported by the American Geophysical – Horton Research Grant 2019 awarded to MFN, an NSERC CREATE in Water Security and an NSERC Discovery Grant to JJM, AR and PB thank ENAC school at EPFL for financial support and acknowledge the Swiss National Science Foundation grant number CRSII5\_186422.</p> <p> </p>
The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components
<p>The excel file <em>Attributes_manuscript.csv</em> contains the mean annual variables and the catchments' attributes used in the manuscript: "<strong>The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components</strong>". Details of each attributes are indicated in the .txt file <em>Attributes_description.txt</em>.</p>
A low-dimensional, mechanistic water balance model for piñon pine-juniper woodlands in southern Nevada, USA
<p>This is a low-dimensional water balance model developed for pinon pine-juniper woodlands in southern Nevada. It may require extensive revision for use in other similar or dissimilar woodland ecosystems. The model will require parameterization before use in any capacity.</p> <p>This model is mechanistic, but is driven from randomized precipitation. This framework allows the user to estimate the mean and standard deviation of water balance variables by simulating the same average meteorological year 1000s of times, each with randomized precipitation. This technique approximates a normal distribution of precipitation, and will need to be modified for locations/sites that have a non-normal precipitation distribution. It is not recommended to use the model in any other way without first testing and validating its output.</p> <p>I have provided documentation throughout the wrapper, model and parameter files to assist with your use of the model. You are encouraged to learn from, build on, and improve this model. You will need to set your own pathways and build your own meteorological inputs and site parameterizations. You may need to update, revise and add/remove different submodules depending on your intended use.</p>
Great Lakes monthly water balance components from the Large Lakes Statistical Water Balance Model (L2SWBM)
<p>**Note that an updated version of the data (v3.0) was uploaded on October 2, 2024, which supersedes earlier versions**</p> <p>These data sets are the results of leveraging bi-national data and the Large Lakes Statistical Water Balance Model (L2SWBM) specifically tailored for the Laurentian Great Lakes to produce value-added time series of water supply components, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. </p> <p>The model serves as a new cornerstone for bi-national coordination of hydrologic data throughout this international transboundary basin, providing an improved means of capturing data patterns, revealing seasonal variabilities, as well as short-term and long-term trends.</p> <p>This repository includes monthly output from the L2SWBM. Output datasets include over-lake precipitation, over-lake evaporation, lateral tributary inflow (runoff), connecting channel flow (cms and also included in mm normalized to lake area), diversion flow (cms and also included in mm normalized to lake area), and component net basin supply. Data is included for lakes Superior, Michigan-Huron, Erie, and Ontario.</p> <p>This version contains data from 1950 to 2022.</p>
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
A Complete Water Balance of a Rain Garden
<p>A bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a five-minute timeseries resolution and with instrumentation that would enable a water balance calculation. This twenty-year dataset allows for an in-depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real-time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed.</p> <p>The water balance data (totals of individual water balance components) of all storms used in assessing the rain garden’s performance in the study are provided in this dataset. This dataset is associated with the forthcoming publication: <em>A Complete Water Balance of a Rain Garden</em> (McGauley et al. 2023). </p>
ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins
<p>This is the readme file for the ET-WB dataset described in the ESSD paper "ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins" from Xiong et al. (2023)<br> ET-WB dataset-The monthly water balance data from May 2002-December 2021 for the 168 river basins and global land from 23 precipitation, 29 runoff, and 7 terrestrial water storage changes datasets.<br> The five dimensions (236*169*23*7*29) of the matrix represent the time, regions, precipitation, terrestrial water storage changes, and runoff datasets used respectively.<br> ET-WB is distributed in three kind of formats: Mat (ET-WB.mat), NetCDF (ET_WB.nc), and Shapefile (ET_WB.shp) (only for the ensemble median value). All the formats share the same definitions of dimensions (as below), except for the ArcGIS shapefile that is provided for individual regions (168 river basins and global land excluding Antarctic and Greenland).<br> File shapefile.rar is the geospatial database of the study area that can be opened in ArcGIS software. </p> <p>Please find more details in the Readme file.</p>
Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance
<p>Dataset to reproduce Figure 9 in Morin et. al, 2023, Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance, Hydro. Earth. Syst. Sci.</p>
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
Open the record for dataset details and reuse information.
Fire Events with Water Balance Shift for Western US during 2014-2020
<p>This dataset comprises fire events in the Western US during 2014-2020, with respective fire characteristics, centroid locations, and variables necessary to calculate water balance shift during pre- and post-fire years, as described in the manuscript (Ahmad et al., 2023)</p>
Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling
<p>This dataset and the scripts accompany the manuscript "<strong>Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling</strong>". The manuscript is published in the Journal Climatic Change.</p>
Regional-scale assessment of groundwater recharge and the water balance for Austria
<p><strong>Version 1.0 - This version is the final revised one. <br></strong></p> <p>This dataset accompanies the paper: Zeitfogel et al. (2025), Regional-scale assessmentof groundwater recharge and the water balance for Austria, published at Journal of Hydrology: Regional Studies.</p> <p>The dataset includes spatially distributed maps of groundwater recharge rates, precipitation, runoff, and actual evapotranspiration for Austria, resulting from an Austria-wide implementation of the rainfall-runoff model COSERO. By downloading these datasets, you acknowledge that neither the authors nor the providers of the source datasets can be held liable for the accuracy or completeness of the data.</p> <p>The model parameterization of soil information was based on regionalized soil hydraulic property maps.</p> <p>The basins_info file provides spatial information about the delinated model basins, the corresponding gauges, and the calibration stages. </p> <p>This study was funded by the Austrian Academy of Science (Project RechAUT), the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), and by the Austrian Climate Research Program (ACRP) - 14th call, under grant agreement No. KR21KB0K00001 (HyMELT-CC).</p>
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
Convection-permitting projections of future changes in water balance and groundwater-surface water interactions
<p>This dataset consists of processed data in the manuscript of "Convection-permitting projections of future changes in water balance and groundwater-surface water interactions".</p>
Model output data to "Land surface modeling in the Himalayas: on the importance of evaporative fluxes for the water balance of a high elevation catchment"
<p>We provide i) gridded initial conditions (.tif), ii) modeled gridded monthly outputs (.tif), and iii) modeled hourly outputs at the station locations (.txt) for the hydrological year 2019. Information about the variables and units can be found in the figures (.png) associated to each dataset. Details about the datasets can be found in the original publication by Buri and others (2023).</p><p> </p><p>Buri, P., Fatichi, S., Shaw, T. E., Miles, E. S., McCarthy, M. J., Fyffe, C. L., ... & Pellicciotti, F. (2023). Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment. <i>Water Resources Research</i>, <i>59</i>(10), e2022WR033841. DOI: <a href="https://doi.org/10.1029/2022WR033841"><strong>10.1029/2022WR033841</strong></a></p>
ScienceDex guides
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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.