The digital data was generated from the Geographic Information System of the Railroad Commission of Texas. Base map information was obtained directly from U.S. Geological Survey 7.5 minute quadrangle maps. Patent Survey lines from Texas General Land Office maps were interpreted as accurately as possible over the US Geological Survey base. Oil and gas well data or pipeline data (if included) was obtained from public records at the Railroad Commission. The information provided by this system is being continually updated and refined. The data is intended solely for the internal use of the Railroad Commission, which makes no claim as to its accuracy or completeness.Field Definitions can be found at: https://rrc.texas.gov/media/kmld3uzj/digital-map-information-user-guide.pdf

The GIS Viewer allows you to view information about wells, pipelines, surveys, LPG/CNG/LNG, Operator Cleanup Program, Voluntary Cleanup Program, Brownfield Response Program, Commercial Waste Disposal sites, Discharge Permits and related features in a map view. Contact Email: records@rrc.texas.gov

The digital data was generated from the Geographic Information System of the Railroad Commission of Texas. Base map information was obtained directly from U.S. Geological Survey 7.5 minute quadrangle maps. Patent Survey lines from Texas General Land Office maps were interpreted as accurately as possible over the US Geological Survey base. Oil and gas well data or pipeline data (if included) was obtained from public records at the Railroad Commission. The information provided by this system is being continually updated and refined. The data is intended solely for the internal use of the Railroad Commission, which makes no claim as to its accuracy or completeness.Field Definitions can be found : https://rrc.texas.gov/media/kmld3uzj/digital-map-information-user-guide.pdf

This layer is sourced from wwwgisp.rrc.state.tx.us.

The Digital Geologic Map of Big Thicket National Preserve and Vicinity, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Texas Water Development Board and Railroad Commission of Texas. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (bith_metadata.txt; available at http://nrdata.nps.gov/bith/nrdata/geology/gis/bith_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (bith_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 15N. That data is within the area of interest of Big Thicket National Preserve.

This layer is sourced from wwwgisp.rrc.state.tx.us.

This layer is sourced from wwwgisp.rrc.state.tx.us.

This layer is sourced from wwwgisp.rrc.state.tx.us.

Oil and gas well locations in Denton County, provided by the Railroad Commission of Texas.

Oil and gas pipeline data for Denton County, provided by the Railroad Commission of Texas.

This layer is sourced from wwwgisp.rrc.state.tx.us.

DFD Multifamily location Dallas County with master gas meter. Railroad Commission of Texas Pipeline Evaluation System list

This data layer can be used for a variety of purposes, including: the plotting of BSA sites on maps; utilization by field personnel; and performing spatial analysis on how the sites affect their surroundings. There are several aspects of the Brownfields Program, all of which begin with the EPA providing funding for Petroleum and/or Hazardous Waste assessments and/or cleanups. This is outlined in the Memorandum of Agreement between the EPA and TCEQ. The EPA provides funding directly for voluntary cleanup programs to approved state agencies. In Texas, this money goes to the Railroad Commission (RRC) and TCEQ.Brownfields are properties that may have hazardous substances, pollutants or contaminants present. EPA’s Brownfields Program provides grants and technical assistance to communities, states, cities, local governments, nonprofit organizations, regional councils of government, tribes and others to assess, safely clean up and sustainably reuse these contaminated properties. Cleaning up and reinvesting in brownfields protects human health and the environment, reduces blight, and takes development pressures off greenspaces and working lands. The TCEQ offers technical advice and education, through its relationship with the EPA, for some brownfields redevelopment projects where local government is supporting or playing a key role. The TCEQ will assess eligible sites and accept applications as long as resources are available. The Brownfields Site Assessments Program web URL is: (https://www.tceq.texas.gov/remediation/bsa).

Profitability and investment risk of Texan power system winterization This data repository contains interim and final results of the paper “Profitability and investment risk of Texan power system winterization” published in Nature Energy. Code used to generate these results can be found at github Abstract A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure. Date and time format Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in input/ is in UTC, all other input datasets are in local Texas time (GMT-6). In interim, temperatures/temppop/, temperatures/temp_gas_powerplant.csv, temperatures/temp_gas_outages.csv, temperatures/temp_coal_powerplant.csv, temperatures/temp_coal_outages.csv and the wind power simulation output (windpower/) is in UTC. All other datasets are in local Texas time. Data cache/ Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R) figures/ Figures shown in the manuscript raw_data: includes raw data for reproducing the figures in the main part of the manuscript outage_model: figures representing the outage function as derived with our model input/ Input data from external sources (with exception of orcd not included due to licensing issues) ERA5_windspeeds_USA: available from the CDS. Download with scripts/download_era5_USA.py gas_production: available from the Texas Railroad Commission in PDF format here. We extracted the data manually. Load: available from ERCOT here orcd: Scarcity prices as regulated by ERCOT. Manually extracted from J. Zarnikau et al. outages: Outage Events from ERCOT with geo locations provided by Edgar Virguez here resulting from unit outage data provided by Ercot population: population density data provided by arcgis here powerplants: locations of power plants in Texas provided by the Energy Information Administration here shp: shapefile of Texas state boundaries provided by arcgis here temperatures: available from the CDS here. Can be downloaded with script scripts/download_era5_TX_temp.py USWTDB: US wind turbine data base provided by the US Geological Service here. We used version: uswtdb_v3_3_20210114 GWA2: Global Wind Atlas Version 2.1 accessible here interim/ Intermediary files from the analysis bootstrap_year.csv: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb bootstrap_year2020.csv: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb turbine_data.csv: turbine data for Texan wind turbines Generated by scripts/prepare_TX_turbines.py Columns: capacity: turbine capacity (kW) height: turbine height (m) lon: longitude coordinate (°) lat: latitude coordinate (°) sp: specific power (W/m²) ind: running index interim/load/ Temperature dependent estimates of electricity load for Texas. load_est70_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh) temp: population weighted temperature (°C) load_est10_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: load: observed load in period 2012-2021 as published by ERCOT (MWh) load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh) load_est9_LR24temptrend2021_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh) load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 fo...

In compliance with OPP 8.11 and 8.12, the alias for the AGENCY, LATITUDE, and LONGITUDE, and fields have been set to the agency’s spatial data standards of HORZ_ORG, LAT_DD, and LONG_DD, respectively. Similarly, the HORZ_METH, HORZ_ACC, HORZ_REF, HORZ_DESC, HORZ_DATE, and HORZ_DATUM fields have also been added; however, their values of UNKNOWN, -9999, OTHER, UNKNOWN, 01/01/1900, and UNKNOWN, respectively, for every record are not precise because these records were provided by the Railroad Commission of Texas (RRC) and multiple programs within TCEQ. The data owner of this feature class cannot determine the actual values for these six added fields. You can obtain more spatial information about a specific record by going to https://www.tceq.texas.gov/groundwater/groundwater-planning-assessment/sfr-056-joint-groundwater-monitoring-contamination-report and following the instructions under the Requesting Information heading.

Geospatial data about Texas Pipelines. Export to CAD, GIS, PDF, CSV and access via API.

This layer is sourced from wwwgisp.rrc.state.tx.us.

This layer is sourced from wwwgisp.rrc.state.tx.us.

This layer is sourced from wwwgisp.rrc.state.tx.us.

This layer is sourced from wwwgisp.rrc.state.tx.us.