ABSTRACT:

The CSEM data acquired during 8 days as part of the IkeWai marine Geophysics survey conducted in Sep-Oct 2019 offshore of the Kona coastline, west of Hawaii.

The three zipped files attached below contains the following CSEM data/information:
1) CSEM raw data recordings (binary files).
2) Survey towlines time windows of the CSEM data recording.
3) Power spectrograms images of the recorded raw CSEM data.

ABSTRACT:

Abstract: Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.

ABSTRACT:

Tutuila water budget model recharge coverage for present day scenario 50m cell size

ABSTRACT:

All input output and model code for the Tutuila SWB2 water budget model. This is version 0.0, compiled for release of a preprint. This version can also be accessed at Zenodo: https://doi.org/10.5281/zenodo.3466114, and the working (dynamic) repository can be accessed at https://github.com/cshuler/Tutuila-SWB-Scenarios. This work was funded by the Pacific RISA and Ike Wai projects located at the East West Center and UH Manoa.

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A review of existing water quality studies and data as they relate to agricultural impacts on coastal and inland watersChristopher K. Shuler* and Michael Mezzacapo*Corresponding author: cshuler@hawaii.edu

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A review of existing water quality studies and data that show how nutrients from sources including fertilizers may impact coastal and inland watersChristopher K. Shuler* and Michael Mezzacapo*Corresponding author: cshuler@hawaii.edu

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These data are results of prioritization for watershed protection and restoration in Hawaiʻi County Department of Water Supply priority areas as completed in a UHERO study and published in the Journal of Environmental Management. Continuous rasters give actual values of estimated groundwater recharge saved. Data are provided in imperial (million gallons per acre over 50 years OR gallons per acre per day) as well as metric (thousands of cubic meters per hectare over 50 years OR cubic meters per hectare per day). For priority rasters, values are as follows: 0: outside priority area; 1: priority 1 (highest priority); 2: priority 2; 3: priority 3; 4: priority 4; 5: priority 5 (lowest priority); 6: no change in recharge; 7: negative change in recharge (only relevant for reforestation rasters). The Protection_Benefits folder contains priority rasters estimating recharge benefits through protecting native forest from invasion and conversion to non-native forest and non-native grassland. Within this folder are the following rasters:1. invasion_priority_cumulative_nonnative_10 --- prioritization of benefits over 50 years from native forest protection assuming equal fog interception in native and invaded forests (priority rankings 1-5; 0=outside priority area; 6=no change). Priority 1=>5.2 million gallons per acre (>48.6 thousand m3 per hectare); Priority 2 = 4.0-5.2 million gallons per acre (37.4-48.6 thousand m3 per hectare); Priority 3=2.8-3.9 million gallons per acre (26.2-37.3 thousand m3 per hectare); Priority 4= 1.6-2.7 million gallons per acre (15.0-26.1 thousand m3 per hectare); and Priority 5=<1.6 million gallons per acre (<15.0 thousand m3 per acre).2. invasion_priority_cumulative_nonnative_09 ---prioritization of benefits over 50 years from native forest protection assuming 10% lower fog interception in invaded forests vs. native forests (priority rankings 1-5; 0=outside priority area; 6=no change). Priority 1=>5.3 million gallons per acre (>49.6 thousand m3 per hectare); Priority 2 = 4.1-5.3 million gallons per acre (38.4-49.6 thousand m3 per hectare); Priority 3=2.9-4.0 million gallons per acre (27.1-38.3 thousand m3 per hectare); Priority 4= 1.7-2.8 million gallons per acre (16.0-27.0 thousand m3 per hectare); and Priority 5=<1.7 million gallons per acre (<16.0 thousand m3 per acre).3. invasion_snapshot_priority_nonnative_10 --- snapshot prioritization of annual benefits of forest protection assuming full invasion and equal fog interception in native and invaded forest (priority rankings 1-5; 0=outside priority area; 6= no change). Priority 1=>670 gallons per day per acre (>6.3 m3 per hectare per day); Priority 2 = 580-670 gallons per acre per day (5.4-6.3 m3 per hectare per day); Priority 3= 490-579 gallons per acre per day (4.6-5.3 m3 per hectare per day); Priority 4= 400-489 gallons per acre per day (3.7-4.5 m3 per hectare per day); and Priority 5=<400 gallons per acre per day (<3.7m3 per acre per day).4. invasion_snapshot_priority_nonnative_09 --- snapshot prioritization of annual benefits of forest protection assuming full invasion and 10% greater fog interception in native vs. invaded forest (priority rankings 1-5; 0=outside priority area; 6=no change). Priority 1=>750 gallons per day per acre (> 7.0 m3 per hectare per day); Priority 2 = 650-750 gallons per acre per day (6.1-7.0 m3 per hectare per day); Priority 3= 550-649 gallons per acre per day (5.2-6.0 m3 per hectare per day); Priority 4= 450-549 gallons per acre per day (4.2-5.1 m3 per hectare per day); and Priority 5=<450 gallons per acre per day (<4.2 m3 per acre per day).The Reforestation_Benefits folder contains the prioritization raster estimating recharge benefits through reforestation of non-native grasslands. Raster list:5. reforestation_priority -- prioritization of benefits over 50 years from reforestation (priority rankings 1-7; 0=outside of priority area; 6=no change; 7=negative change). Priority 1=>9.5 million gallons per acre (>88.9 thousand m3 per hectare); Priority 2 = 7.5-9.5 million gallons per acre (70.2-88.9 thousand m3 per hectare); Priority 3=5.5-7.4 million gallons per acre (51.4-70.1 thousand m3 per hectare); Priority 4= 3.5-4.4 million gallons per acre (32.7-51.3 thousand m3 per hectare); and Priority 5=<3.5 million gallons per acre (<51.3 thousand m3 per acre).

ABSTRACT:

This dataset contains gridded monthly Standardized Precipitation Index (SPI) at 10 timescales: 1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month intervals from 1920 to 2012 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, organized by SPI interval, as indicated by the GeoTIFF file name. Thus, for example, the file “spi3_1999_11.tif” would contain the gridded 3-month SPI values calculated for the month of November in the year 1999. Currently, the data are available from 1920 to 2012, but the datasets will be updated as new gridded monthly rainfall data become available.SPI is a normalized drought index that converts monthly rainfall totals into the number of standard deviations (z-score) by which the observed, cumulative rainfall diverges from the long-term mean. The conversion of raw rainfall to a z-score is done by fitting a designated probability distribution function to the observed precipitation data for a site. In doing so, anomalous rainfall quantities take the form of positive and negative SPI z-scores. Additionally, because distribution fitting is based on long-term (>30 years) precipitation data at that location, SPI score is relative, making comparisons across different climates possible.The creation of a statewide Hawai‘i SPI dataset relied on a 93-year (1920-2012) high resolution (250 m) spatially interpolated monthly gridded rainfall dataset [1]. This dataset is recognized as the highest quality precipitation data available [2] for the main Hawaiian Islands. After performing extensive quality control on the monthly rainfall station data (including homogeneity testing of over 1,100 stations [1,3]) and a geostatistical method comparison, ordinary kriging was using to generate a time series of gridded monthly rainfall from January 1920 to December 2012 at 250 m resolution [3]. This dataset was then used to calculate monthly SPI for 10 timescales (1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month) at each grid cell. A 3-month SPI in May 2001, for example, represents the March-April-May (MAM) total rainfall in 2001 compared to the MAM rainfall in the entire time series. The resolution of the gridded rainfall dataset provides a more precise representation of drought (and pluvial) events compared to the other available drought products.Frazier, A.G.; Giambelluca, T.W.; Diaz, H.F.; Needham, H.L. Comparison of geostatistical approaches to spatially interpolate month-year rainfall for the Hawaiian Islands. Int. J. Climatol. 2016, 36, 1459–1470, doi:10.1002/joc.4437.Giambelluca, T.W.; Chen, Q.; Frazier, A.G.; Price, J.P.; Chen, Y.-L.; Chu, P.-S.; Eischeid, J.K.; Delparte, D.M. Online Rainfall Atlas of Hawai‘i. B. Am. Meteorol. Soc. 2013, 94, 313–316, doi:10.1175/BAMS-D-11-00228.1.Frazier, A.G.; Giambelluca, T.W. Spatial trend analysis of Hawaiian rainfall from 1920 to 2012. Int. J. Climatol. 2017, 37, 2522–2531, doi:10.1002/joc.4862.

ABSTRACT:

Precipitation chemistry data for all sampling trips in Kona, West Hawaiʻi. Data were collected between August 2017 and November 2019. There are twenty sites between central and west Hawaiʻi Island. The data include pH, precipitation (mm), fluoride (uM), chloride (uM), bromide (uM), sulfate (uM), sodium (uM), ammonium (uM), potassium (uM), magnesium (uM), calcium (uM), d18O (pmil) and dD (pmil). Each sampling trip is arranged by site elevation. Latitude and Longitude are reported in decimal degrees to the second decimal place (~1 km resolution). Ions and isotopes are reported to the first decimal place. Blank cells for ions represent non-detect, double dashed lines (--) represent samples that were not analyzed.

ABSTRACT:

This self-potential dataset has been acquired at Makapuu/Kaiwi Coast (Oahu, Hawaii, USA)in the frame of a Summer Class entitled "Hydrogeophysics in Volcanic Environments" given at the University of Hawaii at Manoa. The data helps understanding and mapping underground water circulations in this study area.. Self Potential survey for ‘Ike Wai aim at understanding underground water circulations in the coastal area and across the High-Low divide (Big Island) as well as in valley/ridge systems and across the natural hydrogeological “dams” (O’ahu). The objective is to enhance our understanding of ground water flows and aquifer depths in the areas studied. Combined with other datasets (seismic noise and Electric Resistivity Tomography), the Self Potential method gives valuable structural and geological information (faults, lithological transitions/interfaces, etc).the self-potential is a difference of electrical potential naturally occurring in the ground, measured between two electrodes placed at the surface of the Earth or in boreholes. SP can be generated by redox potentials associated with ore bodies or contaminant plumes that are rich in organic matter. A second source of self-potential anomalies is the thermoelectric effect associated directly with a gradient of the temperature affecting the chemical potential gradient of charge carriers. A third source is related to gradients of the chemical potential of the ionic charge carriers at constant temperature. A fourth source of self-potential signals is the streaming potential contribution related to the flow of the pore water relative to the mineral grain framework in saturated and unsaturated conditions.Basic corrections have been applied to all the datasets. Detailed analysis and interpretations are ongoing for Queen Lili`uokalani Trust (Big Island) and Dole (O’ahu) datsets.The data for each study site is stored in one Excel file composed of several datasheets. Each sheet represents one profile or part of a profile or a final table (tab found under the name TOTAL in each Excel file) containing data ready to be plotted or interpolated for maps. The sheets of a file are linked together and at this stage they should not be separated because they are connected together for the processing, and together they are used to create maps- Raw data for each profile within a study site is located in each sheet corresponding to individual profiles or sections of profiles- Names of participants to field surveys are detailed at the top of each excel sheet, in each excel file

ABSTRACT:

Contains all grab sample data collected, including location, date, lat long, salinity, water depth, radon concentration in water, carbamazepine concentrations, caffeine concentrations, fluoroquinolones concentrations, and dissolved nutrient concentrations (including phosphate, nitrate + nitrite, ammonium, total dissolved nitrogen, and total dissolved phosphorus).

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Time series results by study site and sampling date (including king tide vs. spring tide sampling for coastal sites). Data collected include time, water temperature, water salinity, water depth, and radon concentrations in water.

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Raw data converted into a SeisComP Data Structure (SDS) filled with daily miniseed files for each sensor and each component, as well as instrument response files.Required

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Groundwater geochemistry nutrient data, collected between November 2017 and March 2019. The dataset includes: Sample Name, Well ID, Longitude (dd), Latitude (dd), Time Stamp, pH, Temperature (C), Specific Conductance (uS/cm), Salinity, Dissolved oxygen (% and mg/L), Si (ug/L), PO4 (ug/L), NO3 & NO2 (ug/L), NH3 & NH4 (ug/L), TP (ug/L), TN (ug/L), and alkalinity (mg/L).

ABSTRACT:

Title of dataset Water quality data, Wailupe, HIAbstract Coastal groundwater dependent ecosystems take advantage of low salinity, nutrient rich submarine groundwater discharge (SGD). Across the Pacific islands marine macroalgae have been challenged by and adapted to the stress of lowered salinity with a trade-off of nutrient subsidies delivered by SGD. Human alterations of groundwater resources and climate change-driven shifts brought modifications to the magnitude and composition of SGD. This paper discusses how native macroalgae have adapted to SGD nutrient and salinity gradients, but that invasive algae are outcompeting the native ones near SGD with nutrient pollution, due to their higher salinity tolerance. It is important to re-evaluate land and water use practices by modifying groundwater sustainable yields and improving wastewater infrastructure to keep SGD reductions minimal and nitrogen inputs in optimal ranges. This task may be particularly challenging amidst global sea level rise and reductions in groundwater recharge, which threaten coastal groundwater systems and ecosystems dependent on them.Keywords submarine groundwater discharge, salinity water level, nitrate, ammoniumDataset lead author Henrietta DulaiPosition of data author Professor, principal investigatorAddress of data author 1680 East-West Rd POST 707 Honolulu, HI 96822Email address of data author hdulaiov@hawaii.eduPrimary contact person for dataset Henrietta DulaiPosition of primary contact person Professor, principal investigatorAddress of primary contact person 1680 East-West Rd POST 707 Honolulu, HI 96822Email address of primary contact person hdulaiov@hawaii.eduOrganization associated with the data University of Hawaiʻi at MānoaUsage Rights publicly available and free to useGeographic region Wailupe, O’ahu, Hawai’I, USAGeographic coverage 21.2759N, 21.2751N, 157.7624W, 157.7606WTemporal coverage - Begin date Sep 10, 2015Temporal coverage - End date Oct 7, 2015General study design A known coastal spring area at Wailupe, HI was monitored for 28 days.Methods description water salinity measurements were collected at 1-hour intervals with YSI multiparameter sonde (6920 V2-2) deployed 0.3 m below the surface and a 5 m lateral distance from a major spring. The instrument was attached to a float. Water depth measurements at 1-hour intervals were performed using a CTD Diver (Schlumberger Inc. CTD Diver) fixed at the ocean bottom.Discrete sampling was done at low, mid, and high tide for dissolved nutrients. Samples were filtered onsite through a 0.45 μM filter and kept on ice until returning to the lab.Laboratory, field, or other analytical methods Dissolved nutrients (total dissolved nitrogen, total dissolved phosphorus, nirate + nitrite, ammonium, and phosphate) were analyzed with a SEAL AutoAnalyzer 3 HR in the S-Lab at the University of Hawaiʻi at Mānoa. Quality control YSI and CTD salinity were calibrated before each field excursion against a known standard in the lab.For nutrient samples, all bottles were pre-cleaned to appropriate standards. Over 10% of nutrient samples were analyzed in duplicate to assess laboratory analysis accuracy.Additional information

ABSTRACT:

Submarine groundwater discharge (SGD) dataset from Kīholo Bay, Hawai’i Island from 2014-2016. Radon data (Bq/m^3) were collected using the SGD Sniffer (Dulai et al., 2016), an autonomous gamma spectrometer. Salinity was derived from specific conductivity collected by an onboard CTD Diver. SGD (cm/day) was calculated using a radon mass balance following methods described in Dulaiova et al., 2010.

ABSTRACT:

Submarine groundwater discharge (SGD) dataset from Kīholo Bay, Hawai’i Island from 2014-2016. Radon data (Bq/m^3) were collected using the SGD Sniffer (Dulai et al., 2016), an autonomous gamma spectrometer. Salinity was derived from specific conductivity collected by an onboard CTD Diver. SGD (cm/day) was calculated using a radon mass balance following methods described in Dulaiova et al., 2010.

Please contact: Trista McKenzie <tristam@hawaii.edu>, or Henrietta Dulai <hdulaiov@hawaii.edu> for information related to this

References: 1. Dulai, H. et al. Autonomous long-term gamma-spectrometric monitoring of submarine groundwater discharge trends in Hawaii. J. Radioanal. Nucl. Chem. 307, 1865–1870 (2016). 2. Dulaiova, H., Camilli, R., Henderson, P. B. & Charette, M. A. Coupled radon, methane and nitrate sensors for large-scale assessment of groundwater discharge and non-point source pollution to coastal waters. J. Environ. Radioact. 101, 553–563 (2010).

ABSTRACT:

Title of dataset Thermal infrared imagery, Wailupe, HIAbstract Coastal groundwater dependent ecosystems take advantage of low salinity, nutrient rich submarine groundwater discharge (SGD). Across the Pacific islands marine macroalgae have been challenged by and adapted to the stress of lowered salinity with a trade-off of nutrient subsidies delivered by SGD. Human alterations of groundwater resources and climate change-driven shifts brought modifications to the magnitude and composition of SGD. This paper discusses how native macroalgae have adapted to SGD nutrient and salinity gradients, but that invasive algae are outcompeting the native ones near SGD with nutrient pollution, due to their higher salinity tolerance. It is important to re-evaluate land and water use practices by modifying groundwater sustainable yields and improving wastewater infrastructure to keep SGD reductions minimal and nitrogen inputs in optimal ranges. This task may be particularly challenging amidst global sea level rise and reductions in groundwater recharge, which threaten coastal groundwater systems and ecosystems dependent on them.Keywords submarine groundwater discharge, thermal infrared imagery, temperatureDataset lead author Eunhee LeePosition of data author Senior ResearcherAddress of data author Korea Institute of Geoscience and Mineral Resources (KIGAM)Email address of data author eunheelee@kigam.re.krPrimary contact person for dataset Henrietta DulaiPosition of primary contact person Professor, principal investigatorAddress of primary contact person 1680 East-West Rd POST 707 Honolulu, HI 96822Email address of primary contact person hdulaiov@hawaii.eduOrganization associated with the data University of Hawaiʻi at MānoaUsage Rights publicly available and free to useGeographic region Wailupe, O’ahu, Hawai’I, USAGeographic coverage 21.2759N, 21.2751N, 157.7624W, 157.7606WTemporal coverage - Begin date April 1, 2015Temporal coverage - End date April 1, 2015General study design Imagery of known groundwater discharge spots was performed at low tide, during a single flight.Methods description The thermal camera (FLIR T450sc) with a field of view (FOV, 258 3198) was mounted on a S1000 Octocopter drone (DJI Inc) using a three-axis direct drive gimbal (DYS Eagle Eye) mounting system. Laboratory, field, or other analytical methods Infrared images were collected, georeferenced based on AUV flight information, a false color SST map was produced and draped over a visible light image from Google Earth. Quality control The following parameters were checked for quality control: UAV battery, transmitter, and GPS, thermal sensor, flight information dataAdditional information

ABSTRACT:

This dataset contains gridded monthly rainfall from 1990 to 2019 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, as indicated by the GeoTIFF file name. Contained in the dataset is a statewide complete 30-year partially gap filled monthly rainfall dataset for all stations for the entire date range with station names, ID and location. Also included are month year statewide files for rainfall kriging input files which contain station rainfall, station rainfall transformations, station transformed anomaly, and denotation of inclusion in per county kriging process, statewide gridded rainfall, statewide standard error, statewide gridded rainfall anomaly, statewide gridded rainfall anomaly standard errors, and statewide meta-data that contain per county as well as statewide cross validation statistics, station counts, and readable data quality statement. Monthly rainfall grids were created using an optimized geostatistical kriging approach to interpolate relative rainfall anomalies which are then combined with long-term means to develop the climatologically aided gridded estimates. Optimization of the kriging algorithm consists of: 1) determining an offset (constant) to use when log-transforming data; 2) quality controlling data prior to interpolation; 3) using machine learning to detect erroneous maps; and 4) identifying the most appropriate parametrization scheme for fitting the model used in the interpolation. At present, the data are available from 1990 to 2019, but datasets will be updated as new gridded monthly rainfall data become available. Rainfall products and error metrics are also available by county and can be accessed online for easy download through the Hawaiʻi Data Climate Portal available at http://www.hawaii.edu/climate-data-portal.

ABSTRACT:

Continuous Ambient Noise Seismic Data acquired in the Central part of O‘ahu, Hawai‘i for the ‘Ike Wai project. SeisComP Data Structure (SDS) filled with daily miniseed files for each sensor and each component, as well as instrument response files.

ABSTRACT:

Self Potential survey for ‘Ike Wai aim at understanding underground water circulations in the coastal area and across the High-Low divide (Big Island) as well as in valley/ridge systems and across the natural hydrogeological “dams” (O’ahu). The objective is to enhance our understanding of ground water flows and aquifer depths in the areas studied. Combined with other datasets (seismic noise and Electric Resistivity Tomography), the Self Potential method gives valuable structural and geological information (faults, lithological transitions/interfaces, etc).the self-potential is a difference of electrical potential naturally occurring in the ground, measured between two electrodes placed at the surface of the Earth or in boreholes. SP can be generated by redox potentials associated with ore bodies or contaminant plumes that are rich in organic matter. A second source of self-potential anomalies is the thermoelectric effect associated directly with a gradient of the temperature affecting the chemical potential gradient of charge carriers. A third source is related to gradients of the chemical potential of the ionic charge carriers at constant temperature. A fourth source of self-potential signals is the streaming potential contribution related to the flow of the pore water relative to the mineral grain framework in saturated and unsaturated conditions.Basic corrections have been applied to all the datasets. Detailed analysis and interpretations are ongoing for Queen Lili`uokalani Trust (Big Island) and Dole (O’ahu) datasets.The data for each study site is stored in one Excel file composed of several datasheets. Each sheet represents one profile or part of a profile or a final table (tab found under the name TOTAL in each Excel file) containing data ready to be plotted or interpolated for maps. The sheets of a file are linked together and at this stage they should not be separated because they are connected together for the processing, and together they are used to create maps- Raw data for each profile within a study site is located in each sheet corresponding to individual profiles or sections of profiles- Names of participants to field surveys are detailed at the top of each excel sheet, in each excel file.