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58 results for “water storage”
Inversion of GNSS-based 3D Displacement for Daily Terrestrial Water Storage Changes under the 2023 Beijing-Tianjin-Hebei Heavy Rain in China
<p>The data and code for "Inversion of GNSS-based 3D Displacement for Daily TerrestriaI Water Storage Changes under the 2023 Beijing-Tianjin-Hebei Heavy Rain in China".</p> <p>Including:</p> <p>I. the GNSS and water level dataset;</p> <p>II, the codes for Inversion of GNSS for mass loads.</p>
Survival under conditions of variable food availability: Resource utilization and storage in the cold‐water coral Lophelia pertusa
<p>Abstract</p> <p>Cold-water coral (CWC) reefs are hotspots of biodiversity and productivity in the deep sea, but their distribution is limited by the availability of food, which undergoes complex local and temporal variability. We studied the resource utilization, metabolism, and tissue storage of CWC Lophelia pertusa during an experimentally simulated 3-day food pulse, of 13C15N-enriched phytodetritus, followed by a 4-week food deprivation. Oxygen consumption (0.145 μmol O2 [mmol organic carbon {OC}]−1 h−1), release of particulate organic matter (0.029 μmol particulate organic carbon [POC] [mmol OC]−1 h−1 and 0.005 μmol particulate organic nitrogen [mmol OC]−1 h−1), ammonium excretion (0.004 μmol NH4 + [mmol OC]−1 h−1), tissue C and N content, and fatty acid (FA) and amino acid composition did not change significantly during the experiment. Metabolization of the labeled phytodetritus, however, underwent distinct temporal dynamics. Initially, L. pertusa preferentially used phytodetritus-derived C for respiration (2.2 0.36 nmol C [mmol OC]−1 h−1) and mucus production (0.94 0.52 nmol C [mmol OC]−1 h−1), but those tracer fluxes declined exponentially to <20% within 2 weeks after feeding and then remained stable, indicating that the remainder of the incorporated phytodetritus had entered a tissue pool with lower turnover. Analysis of 13C in individual FAs revealed a mismatch between the FAs incorporated from phytodetritus and the FA requirements of the coral.We suggest that feeding on other resources, such as lipid-rich zooplankton, could fill this deficiency. A release of 10% of their total OC as respired C and POC during the 4-week food deprivation underlines the importance of regular food pulses for CWC reefs. </p>
Terrestrial water storage deficits during the GRACE and GRACE-FO gap
<p>This datasets provide predictions of the terrestrial water storage anomalies (TWSAs) during the 11-month gap (July 2017-May 2018) between the GRACE satellite and its follow-on GRACE-FO. The predictions were obtained by a hydroclimatic data-driven Bayesian convolutional neural network driven. The training period is April 2002-March 2014. Based on the predicted TWSAs, the terrestrial water storage deficit (WSD) dataset and its standardized version, the WSD index (WSDI) dataset, are also provided. </p> <p>The GIF animation depicts the GRACE and BCNN TWSAs in the 70 testing months (April 2014-June 2017 and June 2018-December 2020) and the 11-month gap (July 2017-May 2018).</p>
Global reconstructions of the GRACE-like terrestrial water storage anomalies
<p>These datasets provide global (excluding Antarctica) reconstructions of the GRACE-like terrestrial water storage anomalies (TWSA) from April 2002 to July 2021 with a spatial resolution of 1 degree.</p> <p>These reconstructed TWSAs were generated by a two-step linear model. The driving data include the ERA5-Land model-extracted precipitation and air temperature data, as well as the Noah model-simulated TWSA. The training period is from April 2002 to April 2013, and the testing period is from May 2013 to July 2021.</p> <p>‘REC-JPL-M’ means the reconstructed TWSA based on the JPL mascon solution; ‘REC-JPL-SH’ means the reconstructed TWSA based on the JPL spherical harmonic solution.</p>
Data from: Seasonal and spatial dynamics of gas ebullition in a temperate water-storage reservoir
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Data from: Pushing the limits to tree height: could foliar water storage compensate for hydraulic constraints in Sequoia sempervirens
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Data from: Long-term (1979-present) total water storage anomalies over the global land derived by reconstructing GRACE data
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Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States
<p>These research data are associated with the manuscript entitled "Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States" (<a href="https://doi.org/10.1016/j.jhydrol.2020.125053" title="Persistent link using digital object identifier"><span>https://doi.org/10.1016/j.jhydrol.2020.125053</span></a>). The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. This study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. The performance evaluation of the model with a lag signals consideration shows 0.5 ≤ R<sup>2</sup><span><span><span><span><span><span><span><span> ≤ 0.8 for 41.2% of the CONUS. However, the model's predictive power is unevenly distributed. The model could be useful for predicting and monitoring freshwater resources anomalies for the locations with high model performances.</span></span></span></span></span></span></span></span> The processed data used as inputs in the study are here provided including the GIS files of the different maps reported.</p>
Data and code for 'Coal mine closure substantially increases terrestrial water storage in China'
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Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States
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ISLSCP II Total Plant-Available Soil Water Storage Capacity of the Rooting Zone
This data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone ("rooting zone water storage size") on a 1.0 degree global grid. Two inverse modeling methods were used. The first modeling approach (optimization) was based on the assumption that vegetation has adapted to the environment such that it makes optimum use of water (Kleidon and Heimann 1998). The second method (assimilation) was based on the assumption that green vegetation indicates sufficient available water for transpiration (Knorr 1997). The data set was developed to provide alternative means to describe rooting characteristics of the global vegetation cover for land surface and climate models in support of the ISLSCP Initiative II data collection. There are three files in this data set.
Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks
<p>Abstract</p> <p>Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users.</p> <p>A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale.</p> <p>The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network.</p> <p> </p> <p>Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China)</p> <p> </p> <p>Citations</p> <p>If you find our code to be useful, please cite the following papers:</p> <p>Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020).</p> <p>Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021).</p> <p>Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).</p>
Trial of Safe Water Storage Among People Living With HIV
ClinicalTrials.gov study NCT01376336. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Exploring Water-free Sodium Storage
ClinicalTrials.gov study NCT06872645. IPD Sharing: NO. Countries: 1. Publications: 0.
CDDIS MEaSUREs SESES products Change in Water Storage Time Series
Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are grids of changes in total water storage over the continental U.S.; continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.
Redox-independent low water solubility in quartz and implications for water storage in the crust
<p>The data for quartz samples in the work.</p>
CDDIS SESES MEaSUREs products total water storage grids
Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are grids of changes in total water storage over the continental U.S.; continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S.
Global estimates of the storage and transit time of water through vegetation
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