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58 results for “water storage”
Xylem Embolism Formation, Refilling and Water Storage in Tree Trunks at Harvard Forest 2012
Trunks of large trees play an important role in whole-plant water balance but technical difficulties have limited most hydraulic research to small stems, leaves and roots. To investigate the dynamics of water-related processes in tree trunks, such as winter embolism refilling, xylem hydraulic vulnerability, and water storage, volumetric water content (VWC) in the main stem was monitored continuously using frequency domain moisture sensors in adult Betula papyrifera trees from early spring through the beginning of winter. An air injection technique was developed to estimate hydraulic vulnerability of the trunk xylem. Trunk VWC increased in early spring and again in autumn concurrent with root pressure during both seasons. Diurnal fluctuations and a gradual decrease in trunk VWC through the growing season were observed, which, in combination with VWC increase after significant rainfall events and depletion during periods of high water demand, indicate the importance of stem water storage in both short-and long-term water balance. Comparisons between the trunk air injection results and conventional branch hydraulic vulnerability curves showed no evidence of “vulnerability segmentation” between the main stem and small branches in B. papyrifera. Measurements of VWC following air injection, together with evidences from air injection and xylem dye perfusion, indicate that embolized vessels can be refilled by active root pressure but not in the absence of root pressure. The precise, continuous and non-destructive measurement of wood water content using frequency domain sensors provides an ideal way to probe many hydraulic processes in large tree trunks that are otherwise difficult to investigate.
GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
<p>Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional investigation of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent datasets such as the land-ocean mass budget, large-scale water balance in 341 large river basins, and streamflow measurements at 10,168 gauges. We find that the proposed approach performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the impact of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a broad range of applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.</p><p>Please cite the reference: <strong>Yin J, Slater L, Khouakhi A, et al. GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present. Earth System Science Data. 2023.</strong></p><p>For any inquiry about the dataset, welcome to contact Dr. Jiabo Yin (jboyn@whu.edu.cn).</p>
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
GRAiCE: Terrestrial water storage anomalies reconstructions
<p>Terrestrial Water Storage (TWS) is the total amount of freshwater stored on and below the Earth’s land surface, including surface water, groundwater, soil moisture, snow, and ice. As a result, TWS is a crucial variable of the global hydrologic cycle, representing an essential indicator of water availability.</p> <p>Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO) have been measuring temporal and spatial variations of TWS, namely the Terrrestrial Water Storage Anomalies (TWSA), enabling the monitoring of global hydrological changes over the last two decades. However, the lack of observations prior to 2002 along with the temporal gaps in GRACE/GRACE-FO time series limit our understanding of long-term variations of global freshwater availability.</p> <p>In this study, we use Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks and two sets of predictors to develop four global monthly reconstructions of TWSA from 1984 to 2021 at 0.5º spatial resolution (GR<em>Ai</em>CE). The first set of predictors is given by a combination of five fundamental meteorological forcings and data on vegetation dynamics, whereas the second set of predictors includes the five meteorological forcings only. Specifically, the meteorological predictors are monthly averaged data of total precipitation, snow depth water equivalent, surface net solar radiation, surface air temperature, and surface air relative humidity. We derive data on vegetation dynamics from a long-term reconstruction of solar-induced fluorescence (SIF), which represents a proxy for photosynthesis. Each model is trained with monthly TWSA data from the GRACE JPL mascon dataset. The GR<em>Ai</em>CE dataset accurately reproduces GRACE/GRACE-FO observations at the global scale and across different climatic regions. Moreover, we found that our models predict observed TWSA better than a previous reference reconstruction and produce reliable estimates of the water budget at the river basin scale. Beyond generating long-term continuous TWSA time series, our models allow us to detect and examine TWS changes due to climate variability/change.</p> <p>This repository contains the GR<em>Ai</em>CE dataset and includes four files in netCDF format. The dataset provides monthly TWSA estimates from 1984 to 2021 at a 0.5º spatial resolution. TWSA values are expressed in terms of cm of equivalent water thickness. GRAiCE_LSTM.nc and GRAiCE_BiLSTM.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with all predictors (i.e., including SIF data), respectively. GRAiCE_LSTMnoSIF.nc and GRAiCE_BiLSTMnoSIF.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with meteorological forcings only (i.e., without SIF data).</p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
Stand-level variation drives canopy water storage by non-vascular epiphytes across a temperate-boreal ecotone
<p>Epiphytes, including bryophytes and lichens, can significantly change the water interception and storage capacities of forest canopies. However, despite some understanding of this role, empirical evaluations of canopy and bole community water storage capacity by epiphytes are still quite limited. Epiphyte communities are shaped by both microclimate and host plant identity, and so the canopy and bole community storage capacity might also be expected to vary across similar spatial scales. We estimated canopy and bole community cover and biomass of bryophytes and lichens from ground-based surveys across a temperate-boreal ecotone in continental North America (Minnesota). Multiple forest types were studied at each site, to separate stand level and latitudinal effects. Biomass was converted into potential canopy and bole community storage on the basis of water-holding capacity measurements of dominant taxa. Bole biomass and potential water storage was a much larger contributor than outer canopy. Biomass and water storage capacity varied greatly, ranging from 9 to &gt;900kg ha<sup>–1</sup> and 0.003 to 0.38 mm, respectively. These values are lower than most reported results for temperate forests, which have emphasized coastal and old-growth forests. Variation was greatest within sites and appeared to reflect the strong effects of host tree identity on epiphyte communities, with conifer-dominated plots hosting more lichen-dominated epiphyte communities with lower potential water storage capacity. These results point to the challenges of estimating and incorporating epiphyte contributions to canopy hydrology from stand metrics. Further work is also needed to improve estimates of canopy epiphytes, including crustose lichens.</p>
Global expansion of sustainable irrigation limited by water storage (Schmitt, Rosa, Daily - PNAS 2022)
<p>Global input and output data for "Global expansion of sustainable irrigation limited by water storage" by Rafael J. P. Schmitt, Lorenzo Rosa and Gretchen C. Daily, PNAS, 2022. See included Metadata file for more details. </p> <p></p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Global terrestrial water storage reconstruction using cyclostationary empirical orthogonal functions (1979 - present)
<p>The dataset consists of a 1979-present reconstruction of Terrestrial Water Storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) using common modes of variability between TWS and terrestrial precipitation and land surface temperatures. </p>
Supplementary data to: Climate change threatens terrestrial water storage over the Tibetan Plateau
<p>This data archive includes the boundary of the Tibetan Plateau (TP) and river basins, and projected changes in terrestrial water storage by the mid-21<sup>st</sup> century (up to 2060). The boundary of the TP and river basins is in the shapefile (.shp) format, and all other processed data are in the geotiff (.tif) format. Please see Readme for more data information. For calculation details please see the publication.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Data used for the article "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".
<p>This is the data used in the HESS paper "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".</p>
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River
Total water storage anomalies (TWSA) and streamflow in the Mississippi River Basin for 01/2003 to 12/2016
<p>This dataset includes observations and simulations of total water storage anomalies (TWSA) as well as streamflow that were used in the paper Schulze et al. (2024). The data covers the time period from 01/2003 to 12/2016. The TWSA [in millimeters] are basin averages, while the streamflow data [in cubic meters per second] refer to 68 gauge stations distributed over the Mississippi River Basin. </p> <p>The TWSA observations are derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The streamflow observations are provided by the Global Runoff Database (GRDC).</p> <p>The simulations are uncalibrated model runs of the WaterGAP Global Hydrology Model (WGHM; based on WaterGAP version 2.2d) as well as an Open Loop Simulation and simulations by assimilating GRACE-derived TWSA and/or streamflow observations. The TWSA observations are assimilated on a 4°x4° grid. The streamflow observations are assimilated at (1) all gauge stations with mean observed streamflow above 500 cubic meters per second, (2) all gauge stations with mean observed streamflow between 10 and 100 cubic meters per second, and (3) at randomly selected gauge stations.</p> <p>All files are packaged into a single ZIP archive for easy access and distribution.</p> <p>More details about the dataset and the assimilation are provided in:<br>Schulze, K., Kusche, J., Gerdener, H., Döll, P., Müller Schmied, H. (2024): Benefits and Pitfalls of GRACE and Streamflow Assimilation for Improving the Streamflow Simulations of the WaterGAP Global Hydrology Model. Submitted to Journal of Advances in Modeling Earth Systems (JAMES).</p>
Storage experiment for the analysis of dissolved organic matter in surface water and groundwater
<p>This dataset contains the data resulting from a storage experiment, whereby samples from surface water (<em>n</em>=3) and groundwater (<em>n</em>=1) were stored cooled (4°C) or frozen (-20°C) and analysed after 1, 2, 3, 7, 14, 30 and 90 days using fluorescence spectroscopy (Aqualog, Horiba Scientific, Edison, NJ, USA). For each sample, the absorbance spectrum (240–500 nm) and a fluorescence excitation-emission matrix (EEM) were obtained. EEMs were obtained for excitation wavelengths 240–500 nm (3 nm interval) and emission wavelengths 248–825 nm (~4.6 nm interval) with an integration time of 1 second. Five absorbance indicators and eight fluorescence indicators were calculated. The DOC concentration was estimated using the two-wavelength approach of Cook et al. (2017).</p> <p>The dataset consists of four files:</p> <ul> <li>Aqualog data processing and index calculation.R: R script with the code to process the Aqualog data and to calculate the indices.</li> <li>Aqualog.zip: Raw data obtained from the Aqualog, divided into subfolders corresponding to the day of measurement. For each sample, the absorbance spectrum (*ABS.dat) and EEMs of the sample (*SEM.dat) and corresponding blank measurement (*BEM.dat) are provided.</li> <li>Indices.xlsx: Table with index values for all samples.</li> <li>Samples.xlsx: Table with information about all samples (e.g. sampling site, storage duration, storage method)</li> </ul> <p>The geolocations of the sampling sites are:</p> <ul> <li>Site 102 - 50° 29' 6.09''N - 8° 33' 33.57''E</li> <li>Site 134 - 50° 28' 36.55''N - 8° 32' 39.56''E</li> <li>Site 175 - 50° 28' 34.05''N - 8° 32' 42.89''E</li> <li>Site 187 - 50° 28' 49.98''N - 8° 32' 35.12''E</li> </ul>
A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
<p><strong>1. Summary</strong></p> <p>The Congo basin’s Surface Water Storage (SWS) datasets are generated by Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, Sly Wongchuig in the article entitled "A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015", <strong><em>Earth System Science Data (submitted)</em></strong>.</p> <p>The dataset was generated using two methods, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of surface water extent (SWE) from the Global Inundation Extent from Multi-satellite (GIEMS-2) and satellite-derived surface water height (SWH) from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from GIEMS-2 dataset and hypsometric curves obtained from various digital elevation models (DEMs) (i.e., ASTER, ALOS, MERIT, and FABDEM). Both methods estimate monthly spatio-temporal variations of SWS changes across the entire Congo River basin.</p> <p><strong>2. Name Description</strong></p> <ul> <li>HYPSO_XX: hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>HYPSO_CORR_XX: corrected hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>AREA_STOR_XX: hypsometric curve providing the surface water extent area-storage relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>SWS_XX: monthly surface water storage variations from XX (where XX stands for ASTER, ALOS, MERIT, FABDEM DEMs, and Multi-satellite approach).</li> </ul> <p><strong>3. File Description </strong></p> <p>The SWS estimates from the multi-satellite approach (1995-2015), as well as the hypsometric curves providing the surface water extent area-elevation relationship from the four DEMs (before and after the corrections), the surface water extent area-storage relationship, along with the four SWS estimates (1992-2005).</p> <p>The dataset is gridded on equal-area of 0.25° spatial resolution at the equator, each pixel covers almost 773 km². The reference point for calculating the volume variation is the minimum of surface water extent for each pixel.</p> <p>The files are organized in matrix:</p> <ul> <li><strong>First column</strong> represents the latitude in degree.</li> <li><strong>Second column</strong> represents the longitude in degree.</li> <li><strong>From the third column</strong>: data. In case of hypsometric curve, the data represents elevation in meter on 101 columns representing the increment of 1% flooding in each 773 km<sup>2</sup> pixel from GIEMS-2. In case of SWS data (in km³), there are 288 (respectively 252) columns representing each month of the period over 1992-2015 (respectively 1995-2015) from hypsometric curve approach (respectively multi-satellite approach).</li> </ul>
Data from: Recent decreases in snow water storage in western North America
<p>We present long-term average and annual Snow Storage Index values over mountainous western North America, intended to represent the snow water storage capacity (and associated change) across the region. The Snow Storage Index represents the temporal phase difference between daily precipitation and surface water inputs – the sum of rainfall and snowmelt into the terrestrial systems – weighted by relative magnitudes. Different from snow water equivalent or snow fraction, the Snow Storage Index represents the degree to which the snowpack delays the timing and magnitude of surface water inputs relative to preciptiation, a fundamental component of how snow water storage influences the hydrologic cycle. Our analysis of a 64-year modeled dataset and 34-year observation dataset shows that the Snow Storage Index has decreased significantly across mountainous, western North America, due primarily to substantially earlier snowmelt and rainfall in spring months, with additional declines in winter precipitation. The Snow Storage Index and associated trends offer a new perspective on hydrologic sensitivity to climate change which have broad implications for water resources and ecosystems.</p>
Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"
<p>"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model. </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.