These datasets are associated with the manuscript "Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas." The datasets include (1) ZIP code-level daily average temperature for 2000-2017, (2) ZIP code-level daily counts of Medicare hospitalizations for cardiovascular disease for 2000-2017, and (3) ZIP code-level population-weighted urban heat island intensity (UHII). There are 9,917 ZIP codes included in the datasets, which are located in the urban cores of 120 metropolitan statistical areas across the contiguous United States. (1) The ZIP code-level daily temperature data is publicly available at: https://doi.org/10.15139/S3/ZL4UF9. A data dictionary is also available at this link. (2) The ZIP code-level daily counts of Medicare hospitalizations cannot be uploaded to ScienceHub because of privacy requirements in the data use agreement with Medicare. (3) The ZIP code-level UHII data is attached, along with a data dictionary describing the dataset. Portions of this dataset are inaccessible because: The ZIP code-level daily counts of Medicare cardiovascular disease hospitalizations cannot be uploaded to ScienceHub due to privacy requirements in data use agreements with Medicare. They can be accessed through the following means: The Medicare data can only be accessed internally at EPA with the correct permissions. Format: The Medicare data includes counts of the number of cardiovascular disease hospitalizations in each ZIP code on each day between 2000-2017. This dataset is associated with the following publication: Cleland, S., W. Steinhardt, L. Neas, J. West, and A. Rappold. Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 178(108005): 1, (2023).

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

US Average, Maximum, and Minimum Temperatures

Analyzing Daily Temperatures Across the USA

By Matthew Winter [source]

About this dataset

This dataset features the daily temperature summaries from various weather stations across the United States. It includes information such as location, average temperature, maximum temperature, minimum temperature, state name, state code, and zip code. All the data contained in this dataset has been filtered so that any values equaling -999 were removed. With this powerful set of data you to explore how climate conditions changed throughout the year and how they varied across different regions of the country. Dive into your own research today to uncover fascinating climate trends or use it to further narrow your studies specific to a region or city

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How to use the dataset

This dataset offers a detailed look at daily average, minimum, and maximum temperatures across the United States. It contains information from 1120 weather stations throughout the year to provide a comprehensive look at temperature trends for the year.

The data contains a variety of columns including station, station name, location (latitude and longitude), state name zip code and date. The primary focus of this dataset is on the AvgTemp, MaxTemp and MinTemp columns which provide daily average, maximum and minimum temperature records respectively in degrees Fahrenheit.

To use this dataset effectively it is useful to consider multiple views before undertaking any analysis or making conclusions:
- Plot each individual record versus time by creating a line graph with stations as labels on different lines indicating changes over time. Doing so can help identify outliers that may need further examination; much like viewing data on a scatterplot looking for confidence bands or examining variance between points that are otherwise hard to see when all points are plotted on one graph only.
- A comparison of states can be made through creating grouped bar charts where states are grouped together with Avg/Max/Min temperatures included within each chart - thereby showing any variance that may exist between states during a specific period about which it's possible to make observations about themselves (rather than comparing them). For example - you could observe if there was an abnormally high temperature increase in California during July compared with other US states since all measurements would be represented visually providing opportunity for insights quickly compared with having to manually calculate figures from raw data sets only.

With these two initial approaches there will also be further visualizations possible regarding correlations between particular geographical areas versus different climatic conditions or through population analysis such as correlating areas warmer/colder than median observances verses relative population densities etc.. providing additional opportunities for investigation particularly when combined with key metrics collected over multiple years versus one single year's results exclusively allowing wider inferences to be made depending upon what is being requested in terms of outcomes desired from those who may explore this data set further down the line beyond its original compilation starter point here today!

Research Ideas

• Using the Latitude and Longitude values, this dataset can be used to create a map of average temperatures across the USA. This would be useful for seeing which areas were consistently hotter or colder than others throughout the year.
• Using the AvgTemp and StateName columns, predictors could use regression modeling to predict what temperature an area will have in a given month based on it's average temperature.
• By using the Date column and plotting it alongside MaxTemp or MinTemp values, visualization methods such as timelines could be utilized to show how temperatures changed during different times of year across various states in the US

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2015 USA Weather Data FINAL.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Matthew Winter.

Files: ‘zip.temp.data_[year].rds’, where [year] is between 2000-2017 Data frame with arithmetic (.Mean) and population-weighted (.Wght) averages of mean/max/min temperature, dew point, relative humidity, and apparent temperature for 9,917 ZIP codes located in the urban cores of 120 metropolitan areas in the contiguous United States for 01/01/2000 to 12/31/2017. A data dictionary describing all variables included in the dataset can be found in: 'Data Dictionary.docx'

Feel free to FORK THIS NOTEBOOK in order to correctly load the data for your project!

Overview: This dataset offers a comprehensive collection of Daily weather readings from major cities around the world. In the first release, it included only capitals, but now it also adds main cities worldwide and hourly data as well, making up to ~1250 cities. Some locations provide historical data tracing back to January 2, 1833, giving users a deep dive into long-term weather patterns and their evolution.

Data License and Updates: This dataset is updated every Sunday using data from Meteostat API, ensuring access to the latest week's data without overburdening the data source.

Cities DataFrame (cities.csv)

This dataframe offers details about individual cities and weather stations. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city. - country: The country where the city is located. - state: The state or province within the country. - iso2: The two-letter country code. - iso3: The three-letter country code. - latitude: Latitude coordinate of the city. - longitude: Longitude coordinate of the city.

Countries DataFrame (countires.csv)

This dataframe contains information about different countries, providing insights into their geographic and demographic characteristics. - Columns: - iso3: The three-letter code representing the country. - country: The English name of the country. - native_name: The native name of the country. - iso2: The two-letter code representing the country. - population: The population of the country. - area: The total land area of the country in square kilometers. - capital: The name of the capital city. - capital_lat: The latitude coordinate of the capital city. - capital_lng: The longitude coordinate of the capital city. - region: The specific region within the continent where the country is located. - continent: The continent to which the country belongs. - hemisphere: The hemisphere in which the country is located (e.g., Northern, Southern).

Daily Weather DataFrame (daily_weather.parquet)

This dataframe provides weather data on a daily basis. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city where the station is located. - date: Date of the weather record. - season: Season corresponding to the date (e.g., summer, winter). - avg_temp_c: Average temperature in Celsius. - min_temp_c: Minimum temperature in Celsius. - max_temp_c: Maximum temperature in Celsius. - precipitation_mm: Precipitation in millimeters. - snow_depth_mm: Snow depth in millimeters. - avg_wind_dir_deg: Average wind direction in degrees. - avg_wind_speed_kmh: Average wind speed in kilometers per hour. - peak_wind_gust_kmh: Peak wind gust in kilometers per hour. - avg_sea_level_pres_hpa: Average sea-level pressure in hectopascals. - sunshine_total_min: Total sunshine duration in minutes.

These dataframes can be utilized for various analyses such as weather trend prediction, climate studies, geographic analysis, demographic insights, and more.

Dataset Image Source: Photo credits to 越过山丘. View the original image here.

PRISM data converted into FIPS, ZIP Code, and census tract summaries in the USA Introduction: Parameter-elevation Regressions on Independent Slopes Model (PRISM) by PRISM Climate group Oregon State temperature, precipitation 4km daily weather variable grids that I have converted to daily county FIPS, ZIP Code, and census tract summaries for use in several papers. Available for download (see Data below) in RDS (compact) format. CSV available on request. In Python it is easy to load RDS files and much more compact files than CSVs too. Note that ZIP Code throughout is actually ZIP Code Tabulation Area (ZCTA), which was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code. Defining the extent of an area is necessary in order to tabulate census data for that area.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

Daily and hourly historical weather data for latitude/longitude points, Zip/Postal Codes, Designated Market Areas, and OnPoint™ points. The Weather Source History API is built upon the OnPoint™ Platform which ensures data that is gap-free, homogeneous, and ready for immediate analysis. We offer the highest resolution grid on the market, covering every landmass in the world and up to 200 miles offshore.

The Heat and Health Index (HHI) helps identify communities where people are most likely to feel the effects of heat on their health, in order to build towards a healthier and more heat-resilient future for all. The Heat and Health Index (HHI) is a national tool that incorporates historical temperature, heat-related illness, and community characteristics data at the ZIP code level to identify areas most likely to experience negative health outcomes from heat and help communities prepare for heat in a changing climate. Each ZIP code has a single ranking for the overall HHI and rankings for individual components so that users can make informed decisions to prepare for and prevent the negative health impacts from heat in their communities.

This interactive mapping application easily searches and displays global tropical cyclone data. Users are able to query storms by the storm name, geographic region, or latitude/longitude coordinates. Custom queries can track storms of interest and allow for data extraction and download.Searches and displays tropical cyclone track data by ZIP Code, latitude and longitude coordinates, city, state, or geographic region and then displays the selected tracks on a mapDisplays coastal population data and hurricane strike data for coastal counties from Maine to TexasProvides access to storm reports written by hurricane specialists at the National Hurricane Center. Reports are available for the Atlantic and East-Central Pacific BasinsBuilds custom Uniform Resource Locator (URL) strings that users can follow from personal websites to the on-line mapping application with specific storm tracksThese data were derived from National Hurricane Center HURDAT data (http://www.nhc.noaa.gov/pastall.shtml) and International Best Track Archive for Climate Stewardship (IBTrACS) data (http://www.ncdc.noaa.gov/oa/ibtracs/). Metadata for each dataset can be found on their respective websites.

Comprehensive database of first and last frost dates for US ZIP codes based on weather station data

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

This model application data release provides the data processing and model code used to generate predictions of daily stream water temperature across the contiguous United States from 1979-2021. We used a recurrent graph convolutional network (RGCN) algorithm to make daily stream temperature predictions. Stream water temperature observations, along with forcing data consisting of daily meteorological information, a stream distance matrix, and static stream characteristics were used to predict daily stream temperature summaries (minimum, mean, and maximum) for 57,810 stream segments across the contiguous United States. This model application data release is organized as follows: • data_processing_code.zip contains the instructions and code needed to assemble inputs to the model. This directory contains a README.txt file that describes all major processing steps and outputs of this code. • model_code.zip contains code to process the outputs from data_processing_code.zip into model-r ...

This dataset includes climate change hazard projections and combined climate hazard index values for 42,786 drinking water utilities accross the continental United States (US). The projections are compiled from multiple sources, including the Climate Mapping for Resilience and Adaptation tool (CMRA) and Climate Risk and Resilience Portal (ClimRR), and use mid-century (2050) Representative Concentration Pathway 4.5 CMIP5 Localized Constructed Analogs (LOCA) CMIP5 Projections for North America. The included climate hazards are extreme heat, energy demand, freeze-thaw cycles, extreme precipitation, wildfires, water supply stress, and sea level rise. Each row of the dataset corresponds to a different community water system within the contiguous US, each identified using their assigned Public Water System Identification number More details about the data sources and modeled combined climate hazard index can be found in the publication: Lyle et al 2025, Environ. Res.: Climate, https://doi.org/10.1088/2752-5295/adab10. Code can be found here: https://github.com/zialyle/DW-climate-change-hazard-index

The columns in the database are as follows:

pwsid: Public Water System Identification Number

primacy_agency_code: Two character postal code for the state or territory having regulatory oversight for the water system.

pws_name: Name of the water system

State: State in which water system is located

city_served: City in which water system is located

County: County in which water system is located

population_served_count: Number of customers served by water system

service_connections_count: Number of service connections maintained by water system

service_area_type_code: Service area type code

owner_type_code: Code that dentifies the ownership category of the water system consisting of: F (Federal Government), L (Local Government), M (Public/Private), N (Native American), P (Private), or S (State Government)

is_wholesaler_ind: Indicates whether the system is a wholesaler of water

primacy_type: Code that indicates if the water system is regulated by a state, tribal, or territorial primacy program. Note that EPA direct implementation programs, except for Wyoming, are tribal primacy programs

primary_source_code: The code showing the differentiation between the sources of water: ground water (GW),groundwater purchased (GWP), surface water (SW), surface water purchased (SWP), groundwater under influence of surface water (GU), or purchased ground water under influence of surface water source (GUP)

centroid_lat: Latitude ocation of water system

centroid_lon: Longitude ocation of water system

NOAA.Region: NOAA Climate Region in which water system is located

heat_index: Extreme heat index value

historic_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], historical mean

RCP4.5_mid_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], RCP 4.5 mid-century

RC_maxtemp_5d: Relative change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

Diff_maxtemp_5d: Absolute change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

extremeprecip_index: Extreme precipitation index value

historic_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , historical mean

RCP4.5_mid_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , RCP 4.5 mid-century

RC_highest_precip_5d: Relative change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

Diff_highest_precip_5d: Absolute change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

SLR_index: Sea level rise index value

SLR_indicator: Sea level rise indicator, where 0 indicates utility is not in a county expecting some amount of sea level rise by 2100 and 1 indicates utility is in a county expecting some amount of sea level rise by 2100.

wildfirerisk_index: Wildfire index value

RC_avg_wildfire: Relative change in Fire Weather Index from historical to RCP 4.5 mid-century

D_avg_wildfire: Absolute change in Fire Weather Index from historical to RCP 4.5 mid-century

FT_index: Freeze-Thaw cycle index value

RCP_mid_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), RCP 4.5

historical_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), historical mean

RC_FT: Relative change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

Diff_FT: Absolute change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

waterrisk_index: Water stress index value, using (Dickson & Dzombak, 2019)

water_stress: Change in water supply stress from historical to RCP 4.5 mid-century, using Water Supply Stress Index from (Dickson & Dzombak, 2019)

energydemand_index: Energy demand index value, using regression model developed by (Sowby & Burian, 2022)

energy_demand: Change in energy demand by mid-century under RCP 4.5 scenarios, using utility energy use model from (Sowby & Hales, 2022).

historic_mean_avg_temp: Daily average temperature [degF] , historical mean

RCP4.5_mid_mean_avg_temp: Daily average temperature [degF] , RCP 4.5 mid-century

RC_avg_temp: Relative change in daily average temperature [degF] from historical to RCP 4.5 mid-century

Diff_avg_temp: Absolute change in daily average temperature [degF] from historical to RCP 4.5 mid-century

historic_mean_avg_precip: Daily average precipitation [inches] , historical mean

RCP4.5_mid_mean_avg_precip: Daily average precipitation [inches] , RCP 4.5 mid-century

RC_avg_precip: Relative change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

Diff_avg_precip: Absolute change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

hazard_index: Combined climate change hazard index value, normalized from 0 to 1

hazard_index_group: Classification group for combined climate change hazard index value (minimal, low, moderate, high)

heat_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme heat (0 indicating no, 1 indicating yes)

precip_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme precipitation (0 indicating no, 1 indicating yes)

SLR_threshold: Binary value indicating whether PWS exceeded risk threshold level for sea level rise (0 indicating no, 1 indicating yes)

wildfire_threshold: Binary value indicating whether PWS exceeded risk threshold level for wildfires (0 indicating no, 1 indicating yes)

FT_threshold: Binary value indicating whether PWS exceeded risk threshold level for freeze-thaw cycles (0 indicating no, 1 indicating yes)

waterstress_threshold: Binary value indicating whether PWS exceeded risk threshold level for water stress (0 indicating no, 1 indicating yes)

energydemand_threshold: Binary value indicating whether PWS exceeded risk threshold level for enegery demand (0 indicating no, 1 indicating yes)

sum: Total number of climate hazard risk threshold values exceeded

exposure: Product of combined climate change hazard index value and population served

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:

1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B', 'C', 'D', and 'AuxIn'. These experiment names are shown in the file name.
12. sample_scripts.zip: A zip file containing scripts for creating sample output to demonstrate how the modeling workflow was executed.
13. sample_output.zip: A zip file containing sample output data. Similar files are created by running the sample scripts provided.

A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D

N. Addor, A. Newman, M. Mizukami, and M. P. Clark, 2017. Catchment attributes for large-sample studies. Boulder, CO: UCAR/NCAR. https://doi.org/10.5065/D6G73C3Q

Sadler, J. M., Appling, A. P., Read, J. S., Oliver, S. K., Jia, X., Zwart, J. A., & Kumar, V. (2022). Multi-Task Deep Learning of Daily Streamflow and Water Temperature. Water Resources Research, 58(4), e2021WR030138. https://doi.org/10.1029/2021WR030138

U.S. Geological Survey, 2016, National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), accessed Dec. 2020.

code.zip: Stata code. See the readme in there.data.zip: Files called in the Stata code, some of which are also generated by the Stata codeoutputdir.zip: Complete set of results files from running Stata codePaper abstract: The climate is already changing. The present study shows that these changes have already affected the U.S. economy. It develops a formal framework that accounts for how climate change has affected each county's economy by altering current and past weather, both locally and elsewhere around the country. The results show that climate change is already reducing annual U.S. income by 0.32% [95% confidence interval: -0.17--0.82%] by altering counties' current, local temperatures, with losses concentrated in the Great Plains and Midwest. Accounting for effects on past temperatures and on temperatures in other counties increases income losses to 12% [2.0--22%] and makes them more widely distributed, with suggestive evidence that trade networks propagate effects around the U.S. Central estimates can change with different indices of nonlocal weather or models of cross-county heterogeneity. Calculations like those developed here could be updated annually as a way of measuring and communicating the progress of climate change.

Context

Data description

The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Content

Statistical concepts and definitions

Statistical standards: Data in the Temperature Change domain are not an explicit SEEA variable. Nonetheless, country and regional calculations employ a definition of “Land area” consistent with SEEA Land Use definitions, specifically SEEA CF Table 5.11 “Land Use Classification” and SEEA AFF Table 4.8, “Physical asset account for land use.” The Temperature Change domain of the FAOSTAT Agri-Environmental Indicators section is compliant with the Framework for the Development of Environmental Statistics (FDES 2013), contributing to FDES Component 1: Environmental Conditions and Quality, Sub-component 1.1: Physical Conditions, Topic 1.1.1: Atmosphere, climate and weather, Core set/ Tier 1 statistics a.1.

Statistical unit: Countries and Territories.

Statistical population: Countries and Territories.

Reference area: Area of all the Countries and Territories of the world. In 2019: 190 countries and 37 other territorial entities.

Code - reference area: FAOSTAT, M49, ISO2 and ISO3 (http://www.fao.org/faostat/en/#definitions). FAO Global Administrative Unit Layer (GAUL National level – reference year 2014. FAO Geospatial data repository GeoNetwork. Permanent address: http://www.fao.org:80/geonetwork?uuid=f7e7adb0-88fd-11da-a88f-000d939bc5d8.

Code - Number of countries/areas covered: In 2019: 190 countries and 37 other territorial entities.

Time coverage: 1961-2023

Periodicity: Monthly, Seasonal, Yearly

Base period: 1951-1980

Unit of Measure: Celsius degrees °C

Reference period: Months, Seasons, Meteorological year

Acknowledgements

Documentation on methodology: Details on the methodology can be accessed at the Related Documents section of the Temperature Change (ET) domain in the Agri-Environmental Indicators section of FAOSTAT.

Quality documentation: For more information on the methods, coverage, accuracy and limitations of the Temperature Change dataset please refer to the NASA GISTEMP website: https://data.giss.nasa.gov/gistemp/

                                                                          Source: http://www.fao.org/faostat/en/#data/ET/metadata

Inspiration

Climate change is one of the important issues that face the world in this technological era. The best proof of this situation is the historical temperature change. You can investigate if any hope there is for stopping global warming :)

• Can you find any correlation between temperature change and any other variable? (Using ISO3 codes for merging any other countries' data sets possible.)

• Prediction of temperature change: there is also an overall world temperature change in the country list as 'World'.

We aggregate 800m estimates of daily temperature (minimum, mean, and maximum) and total precipitation from Parameter-elevation Regressions on Independent Slopes Model (PRISM) to the zip code tabulation area (ZCTA). We first aggregate the raw raster files from PRISM to the census block based on areal weighting, and then apply a 2010 census block to ZCTA crosswalk to population weight from the census block to the larger ZCTA area. National ZCTA-level temperature and precipitation are provided for 2010 zip code tabulation area geographies. From the PRISM website (https://prism.oregonstate.edu/): The PRISM Group gathers weather observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial datasets to reveal short- and long-term weather patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present. Whenever possible, we offer these datasets to the public, either free of charge or for a fee (depending on dataset size/complexity and funding available for the activity). We have aggregated from the native raster format to the ZCTA for use in health analyses which often rely on zip code level data. Further crosswalk from the ZCTA to zip code will be required for this use. For more information, see: https://github.com/Climate-CAFE/zip_codes_and_zctas.

Data, metadata and code for paper published in Earth System Science Data:

A high density observational dataset of daily precipitation and air temperature for the Extended Alpine Region

Code (working copy all written in R statistical software): scripts.zip

• to read and process data in from different sources
• to perform intra and inter-stations quality control
• to perform break detection and homogenization
• to read results of quality control and homogenization

Data:

• Daily time series of air temperature (mean, minimum and maximum) and precipitation as .zip files, grouped by data provider.
• Information on column content is provided in separate files "data_readme.txt"
• about 10000 stations from Italy, France, Switzerland, Austria, Germany, Slovenia, Croatia, Bosnia-Herzegovina, Czech Republic, Slovakia and Hungary
• Meta data (code, name, longitude, latitude, elevation, measurements availability for each variable, starting date, ending date) in "metadata.zip", including a file for each data provider
• If you use the data you agree to adhere to the respective data provider's terms as listed in "License.pdf"
• The license terms especially (and additionally to any other terms of the single data providers) include: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from CC BY 4.0]

Version history:

v1.0: initial upload

v2.0: update of data policies; addition of France and Croatia time series