This dataset relates to the analysis of the mechanical properties, the fibre structural properties and the mathematical models. The analyses were computed using four MS Excel spreadsheets:ANALYSIS Mech Prop ...xls: analysis of mechanical properties from each specimenANALYSIS Fibre Diam...xls: analysis of fibre diameterANALYSIS Pore Diam...xls: analysis of pore sizeanalysis_dib...xls: analysis of the mathematical models used in this paper to predict stiffness (E), fracture strength (sU) and water flux

Scientific and related management challenges in the water domain require synthesis of data from multiple domains. Many data analysis tasks are difficult because datasets are large and complex; standard formats for data types are not always agreed upon nor mapped to an efficient structure for analysis; water scientists may lack training in methods needed to efficiently tackle large and complex datasets; and available tools can make it difficult to share, collaborate around, and reproduce scientific work. Overcoming these barriers to accessing, organizing, and preparing datasets for analyses will be an enabler for transforming scientific inquiries. Building on the HydroShare repository’s established cyberinfrastructure, we have advanced two packages for the Python language that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS), loading of data into performant structures keyed to specific scientific data types and that integrate with existing visualization, analysis, and data science capabilities available in Python, and then writing analysis results back to HydroShare for sharing and eventual publication. These capabilities reduce the technical burden for scientists associated with creating a computational environment for executing analyses by installing and maintaining the packages within CUAHSI’s HydroShare-linked JupyterHub server. HydroShare users can leverage these tools to build, share, and publish more reproducible scientific workflows. The HydroShare Python Client and USGS NWIS Data Retrieval packages can be installed within a Python environment on any computer running Microsoft Windows, Apple MacOS, or Linux from the Python Package Index using the PIP utility. They can also be used online via the CUAHSI JupyterHub server (https://jupyterhub.cuahsi.org/) or other Python notebook environments like Google Collaboratory (https://colab.research.google.com/). Source code, documentation, and examples for the software are freely available in GitHub at https://github.com/hydroshare/hsclient/ and https://github.com/USGS-python/dataretrieval.

This presentation was delivered as part of the Hawai'i Data Science Institute's regular seminar series: https://datascience.hawaii.edu/event/data-science-and-analytics-for-water/

The Water Quality analysis simulation Program, an enhancement of the original WASP. This model helps users interpret and predict water quality responses to natural phenomena and man-made pollution for variious pollution management decisions.

The datasets in this data release contain the results of an analysis of the U.S. Geological Survey's historical water-use data from 1985 to 2015. Data were assessed to determine the top category of water use by volume. Data from groundwater, surface water, and total water (groundwater plus surface water) use were parsed by water type, and the top category of use by county or the geographic region or local government equivalent to a county (for example, parishes in Louisiana) was determined. There are two sets of results provided, one for the "Priority" categories of water use and the second for all categories of water use. "Priority" categories are irrigation, public supply, and thermoelectric power and comprise 90 percent of all water use nationwide. In addition to the priority categories, the remaining categories of water use are as follows: aquaculture, domestic, industrial, livestock, and mining. Water-use data historically have been compiled at the county level every 5 years as part of the U.S. Geological Survey's National Water Use Science Project. In 2020 the U.S. Geological Survey began transitioning the collection of water-use data from every 5 years to an annual collection, from county level to hydrologic unit code (HUC) 12, and to a model-based approach. To assist in the transition, an assessment of the current (2022) historical water-use data was done by the Water-Use Gap Analysis Project.

The development and adoption of technologies by the water science community to improve our ability to openly collaborate and share workflows will have a transformative impact on how we address the challenges associated with collaborative and reproducible scientific research. Jupyter notebooks offer one solution by providing an open-source platform for creating metadata-rich toolchains for modeling and data analysis applications. Adoption of this technology within the water sciences, coupled with publicly available datasets from agencies such as USGS, NASA, and EPA enables researchers to easily prototype and execute data intensive toolchains. Moreover, implementing this software stack in a cloud-based environment extends its native functionality to provide researchers a mechanism to build and execute toolchains that are too large or computationally demanding for typical desktop computers. Additionally, this cloud-based solution enables scientists to disseminate data processing routines alongside journal publications in an effort to support reproducibility. For example, these data collection and analysis toolchains can be shared, archived, and published using the HydroShare platform or downloaded and executed locally to reproduce scientific analysis. This work presents the design and implementation of a cloud-based Jupyter environment and its application for collecting, aggregating, and munging various datasets in a transparent, sharable, and self-documented manner. The goals of this work are to establish a free and open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This presentation will discuss recent efforts towards achieving these goals, and describe the architectural design of the notebook server in an effort to support collaborative and reproducible science

This was presented as an EPoster at the 2017 American Geophysical Union and can be found at: https://agu2017fallmeeting-agu.ipostersessions.com/default.aspx?s=2B-C4-70-3C-B8-A0-0D-77-35-04-7C-F2-A4-1B-36-10

Provide data analysis for environmental element monitoring Air Water resources and marine environ

Abstract This dataset is a combined Excel spreadsheet dataset that integrates all available groundwater and surface water chemistry historical records. It includes field quality parameters, methane …Show full descriptionAbstract This dataset is a combined Excel spreadsheet dataset that integrates all available groundwater and surface water chemistry historical records. It includes field quality parameters, methane concentrations and major and minor ion concentrations. It is based on the following data sources: GA compiled hydrochemistry datasets: Surface water data from the Queensland water monitoring information portal (https://water-monitoring.information.qld.gov.au/), accessed and downloaded in January 2019; Data from EHS Support (2014) Water baseline assessment (ATP1087) prepared for Armour Energy. Attribution Geological and Bioregional Assessment Program History A hierarchical cluster analysis was conducted on groundwater and surface water datasets from the Isa GBA region. For this purpose, nine variables (Ca, Mg, Na, K, HCO3, Cl, SO4, electrical conductivity and pH) which were measured across most hydrochemical records were selected. Prior to the multivariate statistical analysis, all variables except for pH were log-transformed to ensure that each variable more closely follows a normal distribution. The multivariate statistical technique is described in more details by Raiber et al. (2012) and Raiber et al. (2016).

description: WATER MONITORING STATION ANALYSIS CALENDAR YEAR 2013 to 2016 SITE NUMBER: 393937093090901 SITE NAME: Turkey Creek nr Sumner MO, Fulbright Rd COOPERATION: Swan Lake National Wildlife Refuge WATERSHED: 48.9 square miles EQUIPMENT: Sutron Satlink in metal gage housing in communication with a transducer mounted on a fencepost in the streambed. Instruments are powered by two solar panels run to a battery inside the gage housing. Stage data are collected at 15 minute intervals, and water temperature data are collected at hourly intervals. Prior to 09/29/2015, data logger was a Sutron Monitor 1. SITE CHARACTERISTICS: The site is located approximately one mile north of where Turkey joins Elk Creek just upstream of Swan Lake National Wildlife Refuge and Silver Lake. The transducer is deployed throughout the year collecting water temperature and water level data. The bottom is mucky with a significant portion of clays and fine sediments. Water levels respond rapidly to rainfall, but low flows are typical where the control is often affected by Silver Lake water levels downstream. GAGE RECORD: The record from 2013 to 2016 has numerous gaps due to equipment malfunction at this site. Site visits were made by Region 3 Hydrologists or Refuge staff to download the data, take logger readings and record surface water levels. One stage reading per site visit was used for datum corrections(Table 1)....; abstract: WATER MONITORING STATION ANALYSIS CALENDAR YEAR 2013 to 2016 SITE NUMBER: 393937093090901 SITE NAME: Turkey Creek nr Sumner MO, Fulbright Rd COOPERATION: Swan Lake National Wildlife Refuge WATERSHED: 48.9 square miles EQUIPMENT: Sutron Satlink in metal gage housing in communication with a transducer mounted on a fencepost in the streambed. Instruments are powered by two solar panels run to a battery inside the gage housing. Stage data are collected at 15 minute intervals, and water temperature data are collected at hourly intervals. Prior to 09/29/2015, data logger was a Sutron Monitor 1. SITE CHARACTERISTICS: The site is located approximately one mile north of where Turkey joins Elk Creek just upstream of Swan Lake National Wildlife Refuge and Silver Lake. The transducer is deployed throughout the year collecting water temperature and water level data. The bottom is mucky with a significant portion of clays and fine sediments. Water levels respond rapidly to rainfall, but low flows are typical where the control is often affected by Silver Lake water levels downstream. GAGE RECORD: The record from 2013 to 2016 has numerous gaps due to equipment malfunction at this site. Site visits were made by Region 3 Hydrologists or Refuge staff to download the data, take logger readings and record surface water levels. One stage reading per site visit was used for datum corrections(Table 1)....

This data is associated with the paper O’Grady, J.G., McInnes, K.L., Hemer, M. A., Hoeke, R. K., Stephenson, A., and Colberg, F. (in press), "Extreme Water Levels for Australian Beaches using Empirical Equations for Shoreline Wave Setup", Journal of Geophysical Research: Oceans.

Understanding how high ocean water levels can reach up the coast is important for designing coastal protection from coastal inundation and erosion. This is particularly important as climate change affects wind and weather conditions and sea-level rise with the subsequent modification to the occurrence of the largest storm-driven water levels. While the height of storm-driven water levels are well understood for protected harbours and estuaries, new research is providing estimates of how high water levels can reach for coastlines exposed to dangerous wave/surf conditions. This study uses mathematical model simulations spanning ~30 years of historical water levels and ocean waves. Statistical analysis is performed to determine how high the largest storm events will likely reach on natural sandy beaches directly exposed to large wave/surf conditions.

The data comprises Gumbel distribution parameters from regression fitting to the hindcast model data. The file ST_rGUM_25m_sta.1981-2013.nc is for the storm-tide SWL heights from the ROMS storm surge hindcast. The file SU_GT81_rGUM_25m_sta.1981-2013.nc is for wave setup calculated with the Guza, R. T., & Thornton 1981 method.
The file SU_GT81_ST_rGUM_25m_sta.1981-2013.nc is for the time-series combined storm-tide and wave setup.
Notes: 1) The data datum is relative to the model bathymetry mean sea level (Geoscience Australia’s 2009 250m dataset). Haigh corrected their dataset of storm tide to AHD by comparing modelled 1-year ARI to the tide gauge measurements. “The predicted levels have been artificially adjusted so that the 1-year return period levels exactly match those of the measured estimates at each site. This was done because the predicted water levels are relative to MSL, whereas the measured levels are relative to AHD. Around mainland Australia, AHD was defined using MSL records between 1966 and 1968 at 30 sites and hence differs from present day MSL. Around Tasmania, AHD was defined using two records from 1972.” 2) To convert to AHD, the netcdf file ‘ST_rGUM_25m_sta.1981-2013.nc’ has a variable ‘toAHD’, you will need to add this onto the location parameter ‘mu’. Alternatively add it to the predicted return levels. 3) Wave setup is really only valid for open coastlines exposed to waves, so be careful applying it in estuaries. Lineage: Created with R's ismev Gumbel function on selected datasets (ROMS storm surge hindcast, CAWCR wave hindcast, and combined data).

Long-term freshwater quality data from federal and federal-provincial sampling sites throughout Canada's aquatic ecosystems are included in this dataset. Measurements regularly include physical-chemical parameters such as temperature, pH, alkalinity, major ions, nutrients and metals. Collection includes data from active sites, as well as historical sites that have a period of record suitable for trend analysis. Sampling frequencies vary according to monitoring objectives. The number of sites in the network varies slightly from year-to-year, as sites are adjusted according to a risk-based adaptive management framework. The Great Lakes are sampled on a rotation basis and not all sites are sampled every year. Data are collected to meet federal commitments related to transboundary watersheds (rivers and lakes crossing international, inter-provincial and territorial borders) or under authorities such as the Department of the Environment Act, the Canada Water Act, the Canadian Environmental Protection Act, 1999, the Federal Sustainable Development Strategy, or to meet Canada's commitments under the 1969 Master Agreement on Apportionment.

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.

An objective review (Asquith and others, 2018 and 2020) of the distribution of the first two significant figures of a water-level measurement (depth below land surface) was done on the 10,295 measurements (one per well) that met the threshold criteria. The purpose of this review was to ascertain the degree to which substantial rounding of values might exist in the dataset. It was evident that the dataset has a large number of values rounded to the nearest integer foot with a tendency for more rounding towards even integers. For values between 10 and 99 feet, there is a large number of values rounded to the even 10 feet and for values less than about 35 feet, there are an excessive number of values rounded the nearest. There are also numerous values of 12 and 14. This suggests that considerable estimation or rounding of water levels have been made probably by use of apparatus other than graduate tapes. Systematic review of original data sources is not possible and insufficient metadata exist for a manual of each value in the data set. However, the database is large and offers an opportunity for data mining and machine learning to foster further review. A large, objective, technically-demanding, and rigorous spatial-temporal review of the water-level data, expressed in altitude, was made for the 10,295 water level records comprising this data release.

Contains direct file downloads available through the Water section of the EPA data site, including data from: - Community Financing (FACT, Water Finance Clearinghouse) - FRS - NHDPlus - NLFA - NPS - WATERS - WQP - WSIO - BEACON 2.0

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri AWQMN AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri AWQMN AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri AWQMN

Abstract

Dataset combines multiple parameters measured in groundwater and surface water samples and interpreted using Hierarchical Cluster Analysis. This dataset combines groundwater and surface water hydrochemical (major ions, EC, TDS) sourced from GA datasets that were originally sourced from the NT government database and other sources listed under column 'Source'.

Attribution

Geological and Bioregional Assessment Program

History

A hydrochemical dataset composed of 857 groundwater samples and 21 surface water samples was used for Hierarchical Cluster Analysis to investigate inter-aquifer and GW-SW connectivity. Groundwater samples are assigned to the following hydrostratigraphic units: Antrim Plateau Volcanics/Helen Springs/Jindare, Bukalara, Cenozoic, Cretaceous, Jinduckin/AnthonyLagoon/HookerCreek; Proterozoic and Tindall/GumRidge/Montejinni.\r Variables used for the analysis included major ions (Ca, Mg, Na, K, HCO3, Cl, SO4), electrical conductivity (EC) and pH with data normalised for statistical calculation. Datapoints with charge balance error above 10% have been removed prior to interpretation. The interpretation resulted in five clusters with distinct geochemical properties, as described in the respective section of the technical report Hydrogeology of the Beetaloo GBA region.\r Check column source to identify data origin with references in the above mentioned report.

These data are "standard" water quality parameters collected for surface water condition analysis (for example pH, conductivity, DO, TSS). This dataset is associated with the following publication: Kozlowski, D., R. Hall , S. Swanson, and D. Heggem. Linking Management and Riparian Physical Functions to Water Quality and Aquatic Habitat. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 8(8): 797-815, (2016).

Analysis of ‘i08 Stations Discrete Grab Water Quality POR’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2e7e1a6c-c4bd-4511-a369-cee31303bb5e on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Water Data Library Database (WDL) for discrete “grab” water quality sampling stations. The WDL database contains DWR-collected, current and historical, chemical and physical parameters found in drinking water, groundwater, and surface waters throughout the state. This dataset is comprised of a Stations point feature class and a related “Period of Record by Station and Parameter” table. The Stations point feature class contains basic information about each station including station name, station type, latitude, longitude, and the dates of the first and last sample collection events on record. The related Period of Record Table contains the list of parameters (i.e. chemical analyte or physical parameter) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected. The Lab and Field results data associated with this discrete grab water quality stations dataset can be accessed from the California Natural Resources Agencies Open Data Platform at https://data.cnra.ca.gov/dataset/water-quality-data or from DWR’s Water Data Library web application at http://wdl.water.ca.gov/waterdatalibrary/index.cfm.

--- Original source retains full ownership of the source dataset ---

The United Kingdom Water Consumption Market Report is Divided Into Segments Based On Water Procurement (Potable Water, Non-Potable Water, and Other Alternate Sources), Data Center Type (Enterprise, Colocation, and Cloud Service Providers (CSPs)), and Data Center Size (Mega, Massive, Large, Medium, and Small). The Report Provides Market Size and Forecasts for all These Segments, Measured in Volume (Billion Liters).

The Morice Water Management Area (MWMA) was established in 2007 by the Morice Land and Resource Management Plan with the intent to protect the hydrological integrity, water quality, water quantity, and fisheries of the upper Morice River watershed. The major objectives of this report are to summarize water quality monitoring data for the period of record held by the Morice Water Monitoring Trust (MWMT) and the Office of the Wet’suwet’en (OW), interpret the results, and provide recommendations and reference material for a framework of future monitoring, data management, and analyses. Based on water quality data collected at sites monitored from 2015-2017, conditions within the MWMA are generally in the range of values expected for least-impacted, natural surface water bodies in this region, although certain constituents at specific sites were routinely high and regularly exceeded B.C. Water Quality Guidelines for the protection of aquatic life. Where constituents consistently exceed B.C. Water Quality Guidelines, we recommend adopting Water Quality Objectives to protect high quality fisheries and watershed values from future change. Water quality was more different between sites than within sites, and therefore sites were distinct from one another and represent unique water quality conditions at each location. Terms of reference are provided for development of a template approach to long-term monitoring, including consideration of land use and climate change effects on current and future water quality conditions as well as recommendations for future monitoring, data management, data analysis, and reporting.

The dataset and analysis scripts accompanies the scientific article, "Quantifying ecosystem states and state transitions of the Upper Mississippi River using topological data analysis." We coupled this highly dimensional dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years.