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39 results for “Groundwater Level”
The groundwater-level anomalies observed by 446 monitoring wells in the North China Plain during 2003-2017
<p>The groundwater level anomalies observed by 446 monitoring wells in the North China Plain (NCP) during 2003-2017. The monthly GWL data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region . The GWL anomalies were calculated by subtracting the mean GWL values during 2003-2017 from the monthly time series values of each well. Unit: meter</p>
Sea level rise, groundwater rise, and contaminated sites in the San Francisco Bay Area, and Superfund Sites in the contiguous United States
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Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a monthly timescale.
Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a yearly timescale.
River Erpe (Berlin, Germany) groundwater levels, temperature and 222-radon data set, June 2019
<p>Time series of groundwater levels and temperature, chloride concentrations as well as 222-radon activities in piezometers P0 to P9 and P11 located close to Heidemühle at the River Erpe, Berlin, Germany, collected between June and November 2019. In "Rn_eql_incubations.csv", "mea" denotes the mean activity and "sdd" the associated standard deviation.</p>
The effect of the Hunga-Tonga volcanic eruption on groundwater levels in China
<p>"Barometric" folder - Barometric observed data</p> <p>"Water level" folder – Water level observed data</p> <p>pressSites.xlsx: basic information of observed station</p> <p>codes in code folder</p>
The data of precipitation, stream discharge, groundwater level, and DOC and DIC concentrations and DOM optical indices of water in the Hulugou catchemnt, upper reaches of Heihe River
<p>Here we provide the original data of the precipitation, stream discharge, air temperature, dissolved organic carbon concentrations and dissolved inorganic carbon concentrations and DOM optical indices of water at different locations in the Hulugou catchment, upper reaches of Heihe River, Northeastern Tibet Plateau, China.</p>
FrenchPiezo: the French mainland groundwater level multivariate time series
<p><strong>THIS REPOSITORY IS INCOMPLETE, THE CORRECT DATASET IS AT: <a href="https://zenodo.org/record/7193812#.Y3-ThhTMLic">FrenchPiezo| 7193812#.Y3-ThhTMLic</a> </strong></p> <p>This dataset is a multivariate time series of groundwater level (a.k.a piezometric level) measured by sensors in many cities in the French mainland from January 2015 to January 2021 (2,221 days). The dataset contains 1026 multivariate time series composed of three dimensions sampled daily:</p> <ul> <li><strong>p: </strong>groundwater level</li> <li><strong>tp: </strong>precipitation</li> <li><strong>e: </strong>evapotranspiration</li> </ul> <p>Each time series is identified by a <strong>bss code </strong> which is the identifier of the piezometer using to collect the associated groundwater level.</p> <p>The groundwater level is collected from <a href="https://hubeau.eaufrance.fr/">Hub'Eau</a>, the french service for accessing water data. The precipitation and evapotranspiration are collected from the Copernicus ERA5 climate database. </p> <p>The file <em>dataset_2015_2021.csv</em> contains the raw dataset with missing values</p> <p>The file <em>dataset_nomissing_linear.csv</em> is similar to the previous one, but the missing data have been imputed using linear interpolation.</p> <p> </p> <p>More details about this dataset with source code for collecting it can be found in our paper: </p> <p>```</p> <p>Mbouopda, Michael Franklin, et al. "Experimental study of time series forecasting methods for groundwater level prediction." <em>ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data</em>. 2022.</p> <p>```</p>
City-level virtual groundwater flows in northern China and the effect of agricultural relocation on alleviating groundwater scarcity
<p>This dataset included the nested multi-regional input output model (MRIO) used in our paper which including 13 cities in BTH region with other 28 provinces of China (30 sectors) of 2012 and the groundwater use inventory matched with MRIO. If you use this dataset, please kindly cite our papaer: Cai, B., Jiang, L., Liu, Y., Zhang, Z., Hu, X., & Zhang, W. (2023). City-level virtual groundwater flows in Northern China and the effect of agricultural relocation on alleviating groundwater scarcity. <em>Earth's Future</em>, 11, e2023EF003561. <a href="https://doi.org/10.1029/2023EF003561">https://doi.org/10.1029/2023EF003561</a>.</p>
Disentangling coastal groundwater level dynamics in a global dataset - data
<p>Data to reproduce the research study "Disentangling coastal groundwater level dynamics in a global dataset" by Nolte et al. containing:</p> <ol> <li>cluster_indices.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 45 index values</li> <li>cluster_attributes.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 28 attribute values</li> </ol> <p>These data enable cluster analysis and random forest modeling. However, please note that the repository does not include raw groundwater level time series data.</p> <p>The code to reproduce the cluster analysis and random forest modeling as conducted in the study can be made available upon request from the first author.</p>
Continuous data of unconfined groundwater level recorded at Isawa Fan (Iwate Prefecture, northern Japan) from January 2008 to July 2021
<p>We present the continuous data of unconfined groundwater level recorded at Mizusawa Campus, National Astronomical Observatory of Japan (hereafter referred to as NAOJ Mizusawa), located in the central part of Isawa Fan, Iwate Prefecture, northern Japan. In 2008, we installed a water pressure gauge (Mini-Diver DI501, Van Essen Instruments B.V.) at each of four shallow wells located around the gravity observation building of NAOJ Mizusawa, to monitor temporal variations in the water level at NAOJ Mizusawa. We also installed a barometer (Baro-Diver DI500, Van Essen Instruments B.V.) above the water level at the northern well, to subtract the effect of air pressure changes from the water pressure changes observed in the groundwater. The unconfined groundwater levels have been continuously measured with these instruments at 5-minute intervals, and the water level data have been used in some studies (Kazama et al., 2012; Tamura et al., 2023) to model gravity changes due to spatiotemporal variations in underground water such as soil water and groundwater.</p> <p>PNG files in map.zip shows the location of NAOJ Mizusawa with the aerial photographs obtained from Google Earth Pro. The red pin denoted by SG indicates the gravity observation building, in which a superconducting gravimeter have been recording gravity continuously (Kazama et al., 2012; Tamura et al., 2023). Yellow pins around the gravity observation building indicate the shallow wells to monitor unconfined groundwater levels.</p> <p>JPG files in photo.zip are the photographs taken on October 3, 2008, when the water pressure gauges were installed at the four wells in NAOJ Mizusawa. A water pressure gauge was connected to the top of a well by a metal wire of about 5 m in length, and installed into the groundwater in the well.</p> <p>data.zip contains the time series of unconfined groundwater level recorded at NAOJ Mizusawa from January 2008 to July 2021. The time series from 2008 to 2010 indicates the average water level of the four water wells (see Kazama et al., 2012 in detail). In and after 2011, however, we were unable to obtain the average water level, because some of the four water gauges failed. Alternatively, we here used the water level data observed at the eastern well from 2011 to 2013, assuming that the variation of the water level at each well is consistent with that of the average water level. We also used the water level data observed at the northern well from 2014 to 2021. Note that we corrected for artificial steps in the water level caused by the switching of the water gauges to be used.</p> <p>Each file of 20xx.txt in data.zip stores the times series of water level in the year of 20xx (xx = 08, 09, ..., 21). The water level was recorded every 5 minutes and time-stamped in Japan Standard Time (JST). Each column of 20xx.txt means as follows: ($1) year, ($2) month, ($3) day, ($4) day of year, ($5) hour, ($6) minute, ($7) time [day] from 00:00 in January 1, ($8) water level [cm] of unconfined groundwater relative to ground surface. For example, the value of -51.88 for the eighth column means that the water table is located at a depth of 51.88 cm below the ground surface.</p> <p>Note that the file of 2011.txt stores incorrect water level values from March 28 to April 25, 2011, because we were unable to collect the data during this period due to the 2011 Tohoku earthquake. Therefore, we additionally provide a file of 2011_modified.txt, in which synthetic water level values are stored for the period from March 28 to April 25. The synthetic water level was calculated by assuming a linear response to precipitation of 16.5 cm/cm and a constant decrease in water level of -3 cm/day. These parameters were determined from the water level and precipitation data observed at NAOJ Mizusawa in March 2011.</p> <p>Each file of 20xx.png in figure.zip shows the time variation of unconfined groundwater level at NAOJ Mizusawa in the year of 20xx. The water level rises rapidly due to rainfall and decreases exponentially after rainfall. The water level typically changes in the range of 1-3 m below the ground surface, but it becomes shallower than 1 m during heavy rainfall events. It also becomes deeper than 3 m below the ground surface during extreme dry periods such as in August 2011 and September 2012.</p>
Data from: Seasonal variability of groundwater level effects on the growth of Carex cinerascens in lake wetlands
Groundwater level is crucial for wetland plant growth and reproduction, but the extent of its effect on plant growth can vary along with changed precipitation and temperature at different seasons. In this context, we investigated the effect of two groundwater levels (10 cm vs. 20 cm depth) on growth and reproductive parameters of Carex cinerascens, a dominant plant species in the Poyang Lake wetland, during three seasons (spring, summer and autumn) and during two consecutive years (2015 and 2016). Carex cinerascens showed low stem number, height, individual and population biomass in summer compared to spring and autumn. Lower groundwater depth was overall more suitable for plant growth resulting in higher stem height and biomass. However, the interactive effect between groundwater level and season clearly demonstrated that the effect of groundwater level on plant growth occurred mainly in autumn. After the withering of the plant population in summer, we observed that C. cinerascens growth recovered in autumn to similar values observed in spring only with 10 cm groundwater level. By consequence, we could deduce that lowering groundwater level in the studied Poyang Lake wetland will negatively impact C. cinerascens regeneration and growth particularly during the second growth cycle occurring in autumn. Additionally, our results also showed that, independently of the season and groundwater level, population biomass of C. cinerascens was lower during drier year. Altogether, our findings suggest that water limitation due to both reduction in precipitation and decreased groundwater level during the year can strongly impact plant communities.
Global impact of sea level rise on coastal fresh groundwater resources
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Anticipatory Drainage Base Management for Groundwater Level Optimization [Data Set]
<p>This dataset is related to https://doi.org/10.1029/2021WR029623. The dataset includes both source code (in R) and data underlying the related publication, which is used to make analytical and numerical approximations of the groundwater level midway between the drains in a tile-drained field under controlled drainage. Both steady and transient simulations can be made, and active management of the drainage base is possible. </p> <p>When using the data, cite it as follows: van de Craats, D., van der Zee, S.E.A.T.M., & Leijnse, A. (2021). Anticipatory drainage base management for groundwater level optimization [Data Set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4428869">https://doi.org/10.5281/zenodo.4428869</a><br> Please also cite the related publication if this data or source code is used.</p>
Data from: Monitoring groundwater levels in Oasis Valley, NV, and evaluating potential drawdown during the North Bullfrog aquifer test
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Data from: Seasonal variability of groundwater level effects on the growth of Carex cinerascens in lake wetlands
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LBA-ECO LC-02 Groundwater Levels, Catuaba Experimental Farm, Acre, Brazil: 1999-2004
This data set reports bi-weekly or monthly depth-to-water measurements for three wells located in a ~1,500 ha forest fragment on the Catuaba Experimental Farm, which is the property of the Federal University of Acre, Brazil. Data were collected between February 1999 and December 2004. There is one comma-delimited ASCII data file with this data set.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS: The depth-to-water measurements for the three wells lack ground surface elevation reference points, therefore, the groundwater table elevation for the site cannot be determined. The depth-to-water measurements are of limited use unless paired with other site data for precipitation, tree growth, etc.
Preliminary groundwater level data from work in progress - 01
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Preliminary groundwater level data from work in progress - 02
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