The U.S. landscape has undergone substantial changes since Europeans first arrived. Many land use changes are attributable to human activity. Historical data concerning these changes are frequently limited and often difficult to develop. Modeling historical land use changes may be necessary. We develop annual population series from first European settlement to 1999 for all 50 states and Washington D.C. for use in modeling land use trends. Extensive research went into developing the historical data. Linear interpolation was used to complete the series after critically evaluating the appropriateness of linear interpolation versus exponential interpolation.Our objective was to develop an annual population data series from the first nonindigenous settlements to 1999 for each present day state that could be used to model landscape change presumed to be a direct result of activities associated with the settlement of nonindigenous people.

This dataset was created as suplementary material for research article: Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package). The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office. The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files: Data This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table: Data Point Coordinates X Y X Y ... A1 0.85 0.1 B1 1.85 0.1 ... A2 0.85 1.1 B2 1.85 1.1 ... A3 0.85 2.1 B3 1.85 2.1 ... ... ... ... ... ... ... ... A11 0.85 10.1 B11 1.85 10.1 ... Data_Eval This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected. Processed_Data Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values. The CSV files are in a format: X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 Data_Scenarios This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table: Scenario Descriptions Data Name Scenario Description GPR00 Only measured data, 50 samples per reference position GPR01 Measured data with empty spots filled using Linear interpolation, 50 samples per reference position GPR02 Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position GPR03 Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position GPR04 Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position GPR05 Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position GPR06 Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position GPR07 Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position GPR08 Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position GPR09 Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position GPR10 Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position GPR11 Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position GPR12 Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position GPR13 Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table: File Suffix Descriptions Suffix Suffix Description _trncrd Training Labels _trnrss Training RSSI Values _tstcrd Evaluation Labels _tstrss Evaluation RSSI Values These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc. The RSSI data are in format: RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0) X, Y, 0, 0, 0

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset was created to establish a unified streamflow dataset from the source dataset provided by the BoM. The data will be used for the summarising the streamflow characteristics in the South Sydney Basin context report and surface water and ground water modelling (if required). ## Dataset History Data were extracted from the raw data .csv format to corresponding unified .csv files for surface water sites within the South Sydney Basin. The process steps are as follows 1. To move one day backward to match precipitation data since the original 9:00am data is for the period of the current 10:00 am to next 9:00 am 2. To identify gauge stuck issue 3. To identify data linear interpolation issue 4. To regard the issue data as missing data 5. To generate streamflow data with the unified quality codes: (1: Good; 2: Fair; 3: Poor; 4: Unverified; 5: Non-conforming; 6: Missing) 6. To separate daily streamflow into baseflow and quick flow using the standard filtering method (Lyne and Hollick (1979)). The data was created in MATLAB using scripts and functions. ## Dataset Citation Bioregional Assessment Programme (2015) SYD ALL Unified Stream Gauge Data v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fbcf2377-fc55-489e-a432-c7fa430efbd6. ## Dataset Ancestors * Derived From SYD ALL Raw Stream Gauge Data BoM v01

Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).

Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.

Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).

As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.

Comparison by week of the first detected influenza-negative ILI time series outlier and the first reported COVID-19 case and peak by country.

The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agriculture Handbook Number 703 (Renard et al.,1997). Lines connecting points of equal rainfall ersoivity are called isoerodents. The iserodents plotted on a map of the Island of Kauai were digitized, then values between these lines were obtained by linear interpolation. The final R-Factor data are in raster GeoTiff format at 30 meter resolution in UTM, Zone 4, GRS80, NAD83.

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset Paper (Open Access)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the ESA CCI Soil Moisture science data records community

The data are sourced from the China Urban Statistical Yearbook, China Environmental Statistical Yearbook and statistical bulletins. Individual missing data was processed using linear interpolation, ARIMA filling, and other methods.

This data was processed from the following 1.2 m surveys presented in Dame, Hartmann, and Thaddeus (2001): https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:10904/10027 https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:10904/10019 https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:10904/10047 https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:10904/10049 https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:10904/10052 all hosted at https://dataverse.harvard.edu/dataverse/rtdc. The processing followed the interpolation scheme of Dame et al. (2001): "interpolated. In each spectrum, <= 2 missing channels are filled by linear interpolation. In each spatial plane, single missing pixels are filled by linear interpolation, first in l direction, then b." and the moment-masking scheme of Dame (2011).

Repeat bathymetric surveys were performed between October 2023 and January 2025 (U.S. Geological Survey field activity number 2023-655-FA) in the shallows of south San Francisco Bay, California using either a 234 kHz SwathPlus interferometric sonar or 200 kHz single beam sonar system. Gridded bathymetric surfaces derived from the processed single beam sonar data were produced with using linear interpolation. The bathymetric datasets are provided in GeoTIFF format.

The FIFE Standard Pressure Level Radiosonde Data Set provides a set of standard level profiles (i.e., 5 mb pressure intervals) from over 450 radiosonde balloon flights, which occurred every one to three hours (daylight hours) during the FIFE IFCs. This derived profile data were computed to 5 mb pressure intervals through simple linear interpolation means. An assumption exists that a linear interpolation scheme may be used with sufficient accuracy to assign meteorological values at 5 mb pressure levels. Some errors are introduced using this method. Several new variables were computed from the original FIFE Radiosonde Data and are included in this derived data set. U (east-west) and V (north-south) winds have been computed from wind speed and direction, and potential temperature has been computed from pressure and temperature. These new parameters are desirable for initial conditions in numerical models as well as forcing functions in models, or as verification and comparison of numerical model's results.

This doctoral thesis is on interpolation and approximation problems in the complex plane which are motivated by questions in numerical linear algebra. In the first part of this thesis, we consider the zeros of rational harmonic functions. In this context we sharpen a bound on the number of zeros of such functions, and show that extremal functions, i.e., rational harmonic functions attaining this bound, are always regular. Moreover, we analyze the change of the number of zeros of rational harmonic functions when adding a pole. This generalizes a construction of Rhie (ArXiv Astrophysics e-prints, 2003), who gave the first examples of extremal functions. Her examples, however, have high rotational symmetry. Our analysis yields in particular a construction principle for general non-symmetric extremal functions. We apply this result in the context of gravitational microlensing in astrophysics, to obtain a construction principle for unsymmetric gravitational point lenses for which maximal lensing occurs. The second part of this thesis is on approximation of analytic functions by series of Faber-Walsh polynomials, which generalize Faber olynomials to compact sets with several components. The Faber-Walsh polynomials are defined through conformal maps of multiply connected domains onto lemniscatic domains, which generalize the Riemann mapping. We first construct two analytic examples of such maps, and give a general construction principle for these maps for certain polynomial pre-images. With these results we derive general properties of the Faber-Walsh polynomials, and relate them to the classical Faber and Chebyshev polynomials. We further present examples of Faber-Walsh polynomials for two real intervals, and also for two nonreal sets consisting of several components.

Low-lying coastal highways are susceptible to flooding as the sea level rises. Flooding events already impact some highways, like Highway 37 which runs across the lowlands at the northern end of San Francisco Bay and is crossed by several creeks/rivers. Short-term operational forecasts are required to enable planning for traffic disruption, evacuation, and protection of property and infrastructure. Traditional physically based numerical models have great predictive capability but require extensive datasets and are computationally expensive which limits their ability to do short-term forecasting. Here we develop a data-driven, site-specific method that can be implemented at multiple vulnerable sites throughout San Francisco Bay and other low-lying coastal areas across the State of California. This method is based on direct observations of the water level at the site and is independent of large computer simulations. For this study, we use a relatively simple statistical model (multiple-linear regression) combined with a forecast error correction inspired by an autoregressive moving average method (ARMA) commonly used in time-series forecasting. The model is then used to produce a 4-day water level forecast at 3 stations near HWY 37, Sonoma/Marin County, California. Methods The input files for the model are grouped into three different datasets: a training dataset, a water level observations dataset, and a weather forecast dataset. All data within those files are sourced from public data servers.
Training Dataset Description: This dataset contains the time series of the four parameters that are used to train the model. It consists of hourly observed meteorological data such as wind, atmospheric pressure, and flow for the period of 2019-01-01 to 2022-09-27. The dataset consists of 4 fields: Ocean Wind, Local Wind, Atmospheric Pressure and River flow. The raw data was collected from publicly available sources. The data was downloaded and resampled to hourly time intervals. Small data gaps were filled by linear interpolation. The wind data was transformed from a polar coordinate system of wind speed and direction to principal component x-y vectors. The principal components were oriented so that the alongshore (y-component) is oriented at 60 degrees North for the wind at Gnoss Field and 100 degrees north for the wind at the NDBC buoy. The listed onshore wind is the shorenormal (x-component) for the 2 locations. Source:

Column Name

Location

Data Type, Unit

Agency Source

Web link to raw data

AtmPres

Buoy 46026

Atmospheric Pressure, mBar

NOAA NDBC

https://www.ndbc.noaa.gov/station_page.php?station=46026

Gnoss_onshorewind

Gnoss Field Airport

Shore-normal component of the wind, m/s

Sonoma County

https://sonoma.onerain.com/site/?site_id=155&site=b4e33d63-e909-4ecd-bb2b-1ee2c587bb00

napa_flow_cfs

Napa River

River flow, cfs

USGS NWIS

https://waterdata.usgs.gov/ca/nwis/uv?site_no=11458000

ocean_onshorewind

Buoy 46026

Shore-normal component of the wind, m/s

NOAA NDBC

https://www.ndbc.noaa.gov/station_page.php?station=46026

Water Level Datasets This dataset consists of three individual files each with 3 fields. The stage_m field is the raw data collected from the water level gauge station, the predicted_m field is the predicted tide as calculated below and the residual_m field is the difference between the two. Description: The raw water level data were collected from 3 stage stations for the period of 2019-01-01 to 2022-09-27 when available. Field stage_m: The data was downloaded, detrended by removing the mean value, and resampled to hourly time intervals. Small data gaps were filled by linear interpolation. Field predicted_m: The predicted tide was calculated using a publicly available Python routine based on a well-documented Matlab routine called Utide (http://www.po.gso.uri.edu/~codiga/utide/utide.htm). Field residual: The residual is the stage-predicted time. It represents the variation of the water level due to non-tidal forcing. Source: The stage data was downloaded from the following sources:

File Name

Location

Data Type, Unit

Agency Source

Web link to raw data

novato_wl_1hr_up.csv

Mouth of Novato Creek

Stage, m

Marin Co

https://marin.onerain.com/site/?site_id=16808&site=a88e57c5-06b1-4855-a65c-92ef0063e6bb

rowland_wl_1hr.csv

Novato Creek at Rowland Bridge

Stage, m

Marin Co

https://marin.onerain.com/site/?site_id=16809&site=82b05ca8-3c86-49cc-9660-63ca3abd3e35

petaluma_wl_1hr.csv

Petaluma River at Horse Ranch

Stage, m

UC Davis, BML

https://coastalocean.ucdavis.edu/ocean-observing/hwy37

Weather Forecast Datasets This dataset is the weather forecast for the 4 parameters used by the model. Description: This dataset contains forecasted meteorological data as obtained from NOAA data servers. The atmospheric pressure forecast was obtained from openweathermap, an open-source weather forecast app. Source:

Column Name

Location

Data Type, Unit

Agency Source

Web link to raw data

AtmPres

Buoy 46026

Atmospheric Pressure, mBar

-

https://openweathermap.org/

Gnoss_onshorewind

Gnoss Field Airport

Shore-normal component of the wind, m/s

NOAA NWS

https://www.weather.gov/documentation/services-web-api

napa_flow_cfs

Napa River

River flow, cfs

NOAA AHPS

https://water.weather.gov/ahps2/hydrograph.php?gage=apcc1&wfo=mtr

ocean_onshorewind

Buoy 46026

Shore-normal component of the wind, m/s

NOAA NWS

https://www.weather.gov/documentation/services-web-api

The FIFE Standard Pressure Level Radiosonde Data Set provides a set of standard level profiles (i.e., 5 mb pressure intervals) from over 450 radiosonde balloon flights, which occurred every one to three hours (daylight hours) during the FIFE IFCs. This derived profile data were computed to 5 mb pressure intervals through simple linear interpolation means. An assumption exists that a linear interpolation scheme may be used with sufficient accuracy to assign meteorological values at 5 mb pressure levels. Some errors are introduced using this method. Several new variables were computed from the original FIFE Radiosonde Data and are included in this derived data set. U (east-west) and V (north-south) winds have been computed from wind speed and direction, and potential temperature has been computed from pressure and temperature. These new parameters are desirable for initial conditions in numerical models as well as forcing functions in models, or as verification and comparison of numerical model's results.

This simulation was performed with the first version of the coupled global model CGCMI with the standard concentration of CO2. Details of the model and an analysis of the simulation are given in Flato et al. (1998). Note: Due to data archival problems, data for April 2041 in the CONTROL run were lost. In order to provide continuous time series, this missing month was filled by linear interpolation between adjacent months. These data represent monthly averaged surface values of selected variables for the IPCC-Data Distribution Centre. (see also http://www.ipcc-data.org/ )

Abstract

The dataset was derived by the Bioregional Assessment Programme from data supplied by the Bureau of Meteorology. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Streamflow timeseries data and gauge locations for selected gauges in the Hunter subregion. This dataset was created to establish a unified streamflow dataset, based on the 24 July 2014 dataset provided by the BoM.

Purpose

The data was used for preparing contextual statement, calibrating and validating surface water modelling, and establishing surface hydrological response variables.

Dataset History

The data was created using the original 24 July 2014 streamflow data. The process steps are as follows

1.\tTo move one day backward to match precipitation data since the original 9:00am data is for the period of the current 10:00 am to next 9:00 am

2.\tTo identify gauge stuck issue

3.\tTo identify data linear interpolation issue

4.\tTo regard the issue data as missing data

5.\tTo generate streamflow data with the unified quality codes: (1: Good; 2: Fair; 3: Poor; 4: Unverified; 5: Non-conforming; 6: Missing)

6.\tTo separate daily streamflow into baseflow and quick flow using the standard filtering method (Lyne and Hollick (1979)).

The data was created in MATLAB using scripts and functions.

Dataset Citation

Bioregional Assessment Programme (XXXX) Streamflow data and locations for selected gauges in the Hunter subregion. Bioregional Assessment Derived Dataset. Viewed 09 May 2017, http://data.bioregionalassessments.gov.au/dataset/0833f695-228a-481a-a18e-2fe32ed67c7b.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Geological Provinces - Full Extent

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From Bioregional Assessment areas v05

• Derived From GEODATA TOPO 250K Series 3

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Bioregional Assessment areas v02

• Derived From Bioregional Assessment areas v04

• Derived From SYD ALL Raw Stream Gauge Data BoM v01

The indicator 'Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]' from the Eurostat statistical product Individuals who used the internet, frequency of use and activities.

- NUTS2013 regional codes are recoded to NUTS2016
- missing data is handled with last observation carry forward, next observation carry back, linear interpolation
-NUTS2 areas are imputed when only NUTS1 level data is available.

The original dataset is available here:
https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en

More about CEEMID: www.ceemid.eu
Get in touch: danielantal.eu/#contact

Digital elevation models (DEMs) were produced from bathymetric and topographic measurements collected offshore of Santa Cruz, CA, from 2014 to 2024. Bathymetric data were collected using personal watercraft (PWCs) equipped with single-beam echosounders and dual frequency global navigation satellite system (GNSS) receivers. Topographic data were collected on foot with GNSS receivers mounted on backpacks. Bathymetric and topographic data were collected primarily along a series of cross-shore transects at 50-m intervals along the coast. Continuous surfaces were produced from all available elevation data using linear interpolation with a resolution of 2 m.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in March 2016. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

Counts of influenza-negative ILI by income group and data missingness.