The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached 38.9 million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to 18.3 million in 2020. The flight volume increased again in the following years and was forecasted to reach 38.7 million in 2024. The global airline industry The number of flights performed increased year-on-year continuously to transport both passengers and freight. The industry’s recent growth can be attributed to a combination of increasing living standards and decreasing costs of air travel. While North American and European airlines currently dominate in terms of both revenue and passengers flown, it is predicted that future growth will be highest in markets of Asia.

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately 4.5 billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below five billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for one third of the global total.

The average for 2021 based on 152 countries was 15 million passengers. The highest value was in the USA: 666.15 million passengers and the lowest value was in Guatemala: 0 million passengers. The indicator is available from 1970 to 2021. Below is a chart for all countries where data are available.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,574.000 Unit in Jan 2025. This records an increase from the previous number of 18,324.000 Unit for Dec 2024. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,783.000 Unit from Apr 2001 (Median) to Jan 2025, with 281 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.

Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API); this field may not be very reliable;

icao24: the transponder unique identification number;

registration: the aircraft tail number (when available);

typecode: the aircraft model type (when available);

origin: a four letter code for the origin airport of the flight (when available);

destination: a four letter code for the destination airport of the flight (when available);

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

day: the UTC day of the last message received by the OpenSky Network;

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page: https://traffic-viz.github.io/gallery/covid19.html

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 https://doi.org/10.5194/essd-13-357-2021

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

The United States had the largest commercial air travel market in 2021, with just over 666 million passengers boarding planes registered to American and international air carriers. The next largest market was China, with more than 440 million passengers, while the Eurozone ranked in third place, with almost 252 million passengers.

Passenger measurement Measuring the number of passengers boarded by carriers registered in a country provides an indication of the size of that country’s airline industry, but it does not measure the amount of air travel in that country. For example, as Ryanair is registered in Ireland, all Ryanair flights count as Irish, even if the flight was between, say, Berlin and London. One way to measure the number of air passengers within a country is to look at the number of passengers passing through airports in that country. Alternatively, the level of travel within an airline market can be considered at the regional level, in which case North America slips back to third behind the Asia Pacific region and Europe. Erasing two decades of growth in global air travel Regardless of how passenger numbers are measured, global air travel increased rapidly over the last decade. However, this was not the case in 2020, when the COVID-19 pandemic erased two decades of global passenger traffic growth, cutting the number of air passengers to only 1.8 billion and the number of flights globally to 16.9 million. Looking at this period, the Middle East region was affected the most, with seat capacity down 63 percent compared to 2019.

(UNCLASSIFIED) - In general, transportation infrastructure in Liberia is sub-par by most standards. Likewise, air transportation and modern infrastructure lags behind due to both conflict and a lack of capital investment. That being said, several major airlines operate out of the two international airports in Liberia including Astraeus, Bellview and SN Brussels Airlines as well as Slok Air International and Weasua Air Transport. Roberts International Airport is actually located outside of the capital of Monrovia, but remains the nation’s busiest aviation facility. Spriggs Payne Airport is centrally located in Monrovia but is a smaller facility with only a few arrivals per day. The remaining aviation facilities in the nation consist of unpaved runways in various cities. Some are finished, maintained runways of packed dirt while others are simply grass.Further complicating the travel situation has been the recent outbreak of the Ebola virus. Several airlines have suspended all flights to the country and currently it is unknown when or whether regular service will resume. Many other international airlines have begun considering suspending flights to and from Liberia as well.Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name ADM3_NAME - Administration level three identification / name NAME - Name of airfield TYPE - Classification in the geodatabase (Civil, Military, Dual) ICAO - International Civil Aviation Organization four letter airport location indicator IATA - International Air Transport Association three letter airport location indicator RUNWAY - Paved or unpaved runway N_RUNWAYS - Number of runways R1_SURFACE - Runway surface type (Asphalt, Dirt, Grass, Concrete) R2_SURFACE - Second runway surface type (Asphalt, Dirt, Grass, Concrete) R_LENGTH - Length of runway (meters) R_WIDTH - Runway width (meters) USE - Use description (Regional, Local, International) CUSTOMS - Presence of customs (Yes or No) SPA_ACC Spatial accuracy of site location (1- high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding the airfield SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from various air transportation websites as well as open source databases. DigitalGlobe imagery was used to assess and when necessary, improve the location of features. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aircraft Charter World, "Airports in Liberia." Last modified January 2009. Accessed September 29, 2014. http://www.aircraft-charter-world.com.DigitalGlobe, "DigitalGlobe Imagery Archive." Last updated September 2014. Accessed September 29, 2014. Falling Rain Global Gazetteer, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Great Circle Mapper, "Liberia." Last modified January 2013. Accessed September 29, 2014. http://gc.kls2.com.GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. http://www.geonames.org.Google, "Liberia." Last modified September 2014. Accessed September 29, 2014. http://www.google.com.World Airport Codes, "Directory of Airports in Liberia." Last modified 2010. Accessed September 29, 2014. http://www.fallingrain.com.Sources (Metadata)"Transport in Liberia." The Lonely Planet. September 29, 2014. Accessed October 2, 2014. http://www.lonelyplanet.com.Zennie, Michael. "U.S. Airlines in Contact with Government about Ebola Concerns." The Daily Mail, October 2, 2014. Accessed October 2, 2014. http://www.dailymail.co.uk.

China CN; Air: Number of Flight: Domestic data was reported at 2,467,400.000 Unit in 2022. This records a decrease from the previous number of 3,855,300.000 Unit for 2021. China CN; Air: Number of Flight: Domestic data is updated yearly, averaging 2,016,565.000 Unit from Dec 1999 (Median) to 2022, with 21 observations. The data reached an all-time high of 4,477,800.000 Unit in 2019 and a record low of 562,203.000 Unit in 1999. China CN; Air: Number of Flight: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Number of Flight.

This dataset contains predictions of whether temperature inversions will occur at locations in Allegheny County.

This dataset is still under active development and should be considered to be in "beta".

Motivation

Temperature inversions occur when there is a warmer layer of air above the air at or near ground level. This represents a reversal of the normal flow of heat near the earth and results in the cooler air being trapped near the ground. Temperature inversions can lead to the formation of fog or dew. Pollution or smoke from fires, which would rise and dissipate in the atmosphere under normal conditions, become trapped near the ground in a temperature inversion, potentially leading to hazardous concentrations of pollutants in the air.

This dataset was extracted from NASA's Goddard Earth Observing System Forward-Processing (GEOS-FP) system as a collaboration between NASA's Goddard Space Flight Center and the Western Pennsylvania Regional Data Center, to provide access to 1-day, 3-day, and 5-day predictions of temperature inversions in Allegheny County.

Preprocessing/Formatting/Methodology

This dataset is generated using data-processing scripts written by partners at NASA Goddard Space Flight Center. The scripts extract from the GEOS-FP model the predicted air temperature as a function of latitude/longitude/date/height, and then, starting near surface level, search upward for the height of the local maximum in air temperature. This determines whether a temperature inversion is expected.

Each record is a prediction of whether there will be a temperature inversion, for a particular day at 12pm UTC (7am EST) within five days after the prediction, and for a particular cell in a coarse grid overlaying Allegheny County. If an inversion is predicted, the height of top of the inversion above the ground and the temperature difference between the ground and the top of the inversion are given, as well as an estimate of the inversion strength on a scale of 0 to 4 (where the strength of the inversion is calculated based on the value of the temperature difference). For some locations, we've also added the name of a place (e.g., "Pittsburgh" or "Monroeville") within that cell, to make look-ups easier.

Additionally, we've created forecast maps for the region and 5-day timeline forecasts (for particular locations) of both inversion strength and PM2.5 concentration.

Known Uses

If you are using this dataset, please write to the data steward (listed below) and let us know! Your stories support the development of future datasets like this.

Recommended Uses

This data could provide an early-warning system for certain kinds of unhealthy air-quality events, such as dangerously high PM_2.5 levels from wildfire-induced smog or pollution, trapped near the ground.

Known Limitations/Biases

The spatial resolution of the forecast is pretty coarse.

To validate the forecast, a comparison was made of its predictions with actual temperature-inversion measurements made by weather balloon (or sodar/RASS acoustic upper air profiler) by the Allegheny County Health Department Air Quality Office. The results are shown in this table, which is accompanied by some additional analysis. When the 1-day forecast predicted a strong or moderate inversion, there was about a 90% chance that it was historically correct, and when the 3-day or 5-day forecast confirmed this forecast for the same date, the accuracy increased, with more than 96% historical accuracy when confirmed by the 5-day forecast.

Also, sometimes the model results can not be computed on the expected schedule. (These delays are reported on the "geos5-fp-users" mailing list.) In these instances, the WPRDC's automated processes fall back to the previous day's forecasts; the forecast_version field provides the date and hour that the forecast simulation was started.

Related Datasets

The Allegheny County Health Department's measurement of pollutant concentrations (and other parameters) at several measurements stations are published in the Allegheny County Air Quality dataset.

We are also publishing a dataset that forecasts concentrations of three air quality parameters: carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5).

Credits

This work is the result of a collaboration between the WPRDC and NASA's Goddard Space Flight Center. This dataset would not have been possible without the efforts of NASA Goddard Space Flight Center personnel to apply NASA's atmospheric models and domain expertise to the problem of forecasting temperature inversions, yielding this prototype forecast, tailored to Allegheny County. Thanks also to Jason Maranche and Angela Wilson of the Allegheny County Health Department's Air Quality Program for providing us with, and helping us understand, their historical temperature-inversion measurement data (used to validate the predictions).

Sample data files from realtime micro-scale weather simulations that were performed to support UAV (Unmanned Aerial Vehicles) flights during ISARRA Lower Atmospheric Process Studies at Elevation – Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field experiment. Two types of data are available: (1) ascii files that contain profiles and surface variables at select grid points that correspond with 3 key observing locations, (2) grib2 files contain 2D and 3D grids of variables described below and stored on levels AGL as described more below. The sample data includes all leadtimes from both domains. Domain 01 (1 km grid spacing) is initialized at 0400 UTC, lead times = 00h00m to 18h00m by 10m increments (108 files) and DO2 is initialized at 1000 UTC with leadtimes of 06h00m-18h00m by 10 min increments (72 files). Each grib2 file from the WRF-LES domain (D02) are around 100 MB per lead time. The full dataset is available here: https://doi.org/10.5065/83r2-0579

These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Subgrid-scale turbulence is diagnosed and stored within the TKE variable using Lilly (1966, 1967).

The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. A next-day simulation was run using forcing data from NCEP's Global Forecast System (GFS) while a day-of simulation was run using data from the High Resolution Rapid Refresh (HRRR). Both simulations were valid between 04:00 and 16:00 local time providing an opportunity to explore the impact of lateral boundary conditions on forecast skill. The dataset consists of a series of two sets of files: 3D grids and point profiles. The 3D grids consist of all relevant basic state parameters (p,T,U,RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields (and compute wind in earth-relative coordinates), interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity.

Profile and surface data are stored in ascii format for select grid points coincident with up to 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield) with a time resolution of 0.666 sec. See README files for details. The 3D output files are stored in grib2 format which are available every 10 min. The grib2 data can be converted to netCDF using a variety of tools such as ncl_convert2nc command available on many linuxOS.

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Details of ascii file names:

XXX.dnn.VV.yyyymmddhh.gz

where

XXX is the location name (SAG - Saguache, MOF - Moffat, LEA - Leach)

nn is the domain number (01,02,03)

VV is the variable (see readme file for details)

yyyymmddhh is the model initialization time (UTC)

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Details of grib2 data files:

Data are stored as follows: one file per forecast lead time using the following file naming convention:

WRFPRS_YYYYMMDDhhmm_dnn.lh_lm

where

hhmm is the hour and minute of the day the model run was initialize

nn = domain number

lh = forecast outlook hour

lm = forecast outlook min

valid_time = hhmm + lhlm

zlevels = (30,80,150,300,500,750,1000,1250,1500,1750,2000,2500,3000,3500,4000,4500,5000 m AGL)

DatasetTemporalCoverage: 14 - 19 July 2018
Forecasts were issued twice per-day and valid between 04:00 and 16:00 LT (10:00 and 22:00 UTC)

Gridded data are stored at 10 min intervals

Interpolated 3D Variables: temperature,pressure,u,v,w,RH,sub-gridscale turbulence kinetic energy, energy dissipation, etc at selected heights AGL

2D Diagnosed variables:
Ceiling height, visibility, precip rate, accumulated precip, precipitable water, vertically- integrated condensed water, downwelling shortwave and longwave radiation at surface, sensible and latent heat flux, 10 m U and V, 2 m T and specific humidity

Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, Geophysical Research Letters, https://doi.org/10.1002/2018GL077914

The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: https://doi.org/10.5281/zenodo.1219784

Abstract

Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.

Description of repository datasets:

1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:

• "ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758
• "UP_CELLS" : number of upstream cells (pixels)
• "AREA" : upstream drainage area (km²)
• "DISCHARGE" : discharge (m³/s)
• "WIDTH" : mean bankfull river width (m)
• "WIDTH5" : 5th percentile confidence interval bankfull river width (m)
• "WIDTH95" : 95th percentile confidence interval bankfull river width (m)
• "DEPTH" : mean bankfull river depth (m)
• "DEPTH5" : 5th percentile bankfull river depth (m)
• "DEPTH95" : 95th percentile confidence bankfull river depth (m)
• "LENGTH_KM" : segment length (km)
• "ORIG_FID" : original ID of segment
• "ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg
• "POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)
• "POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)
• "SLOPE" : average slope of segment (m/m)
• "CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/
• "CITY_POP_M" : population of joined city (max N inhabitants)
• "DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html
• "DAM_AREA_S" : surface area of joined dam (m²)
• "DAM_CAP_MC" : volumetric capacity of joined dam (m³)
• "CELER_MPS" : modeled river flow wave celerity (m/s)
• "PROPTIME_D" : travel time of flow wave along segment (days)
• "hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins
• "GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0
• "FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution
• "SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.
• "UPSTR_DIST" : upstream distance to the basin outlet (km)
• "UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)
• "CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)
• "DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)
• "MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)
• "MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)
• "MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)
• "MC_LENGTH" : mean of Monte Carlo simulated river length (m)
• "MC_SLOPE" : mean of Monte Carlo simulated river slope (-)
• "MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)
• "MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))
• "CONTINENT" : integer indicating the HydroSHEDS region of shapefile

2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:

• Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles
• Col2: Downstream UID
• Col3: Number of upstream UIDs
• Col4 – Col12: Upstream UIDs

3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:

• FID : unique identifier of each polygon
• CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)
• CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)
• COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)

4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table.

5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:

• GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv
• GAUGE_SITE: USGS gauge site number of joined gauge
• GAUGE_HUC8: which hydrological unit code the gauge is located in
• OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R.
• OBS_CEL_MPS: gauge-based celerity estimate (m/s).

6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.

7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.

8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:

• tabdTT_b.csv : basin outlet travel times for all rivers
• tabdTT_b_swot.csv : basin outlet travel times for SWOT
• tabdTT_c.csv : next downstream city travel times for all rivers
• tabdTT_c_swot.csv : next downstream city travel times for SWOT
• tabdTT_d.csv : next downstream dam travel times for all rivers
• tabdTT_d_swot.csv : next downstream dam travel times for SWOT

In fiscal year 2023/2024, Qatar Airways carried roughly 40 million passengers. This was an increase of approximately eight million over the previous year. Established in 1994, the eponymous state flagship carrier of Qatar, Qatar Airways, is the world’s youngest yet fastest-growing airline. Its headquarters are in Doha at the five-star rated Hamad International Airport.   Regional context    Prior to joining the oneworld alliance in 2013, Qatar Airways was the second largest airline in the Middle East in 2010, second only to Emirates. Since having become a member of oneworld, the airline has partnerships with global airlines from different world regions allowing passengers to travel to more than 150 countries worldwide.   Qatar Airline     In 2019, the total revenue generated by Qatar Airways grossed nearly 51 billion Qatari riyal. The state-owned airline’s assets also totaled well over 127.5 billion Qatari riyal. Back in 2015, the airline recorded an approximate of 37 million passenger movements at Hamad International Airport alongside a capacity of almost 127 billion available seat kilometers that grew by 61 percent by 2018.  

In 2023, U.S. airlines recorded 862.8 million passengers on domestic and international flights. The number of passengers at U.S. airports has begun recovering following the COVID-19 pandemic but was still below the approximately 926.4 million passengers recorded in 2019.

In 2023, the number of passengers carried by Air Canada amounted to approximately 44.8 million passengers. Air Canada Air Canada is the national carrier of Canada, being founded by the Canadian government in 1937. The airline was fully privatized in 1989 and in 2001 acquired Canada’s then second largest airline – Canadian Airlines – to become the dominant airline in the Canadian market. The company experienced financial difficulties throughout the 2000’s and into 2010's but has consolidated their financial position in last ten years, recording strong revenue growth in 2018 and 2019, before affected severely by the coronavirus pandemic .Amid the global pandemic, the company's operating revenue decreased dramatically from 19 billion to around six billion Canadian dollars in 2020. As of 2022, the total operating revenue of Air Canada was 13.6 billion Canadian dollars. This was considerably higher than the previous year but still lower than the pre-pandemic year. Air travel in Canada The two main airlines in the Canadian market are Air Canada and low-cost carrier WestJet, who held domestic market shares of 46 percent and 34 percent respectively in 2018. In terms of passenger numbers, WestJet historically carries around half as many as Air Canada. This discrepancy can be explained by the far greater range of international destinations offered by Air Canada. However, the number of customer complaints received about Air Canada is disproportionately, coming in at 1,997 in 2019 – compared to 369 for WestJet.

Visitor Departures: Average Length of Stay: USA & Canada data was reported at 0.000 Day in Jun 2020. This records a decrease from the previous number of 16.800 Day for Mar 2020. Visitor Departures: Average Length of Stay: USA & Canada data is updated quarterly, averaging 15.641 Day from Mar 2004 (Median) to Jun 2020, with 66 observations. The data reached an all-time high of 23.250 Day in Mar 2006 and a record low of 0.000 Day in Jun 2020. Visitor Departures: Average Length of Stay: USA & Canada data remains active status in CEIC and is reported by National Statistics & Census Institute. The data is categorized under Global Database’s Argentina – Table AR.Q008: Visitor Departures: Ezeiza International Airport & Jorge Newbery Airport. Since March 14th 2010, some flights from Argentina to neightboring countries (such as Brazil, Chile and Paraguay) which used to operate in Ezeiza International Airport were transferred to Aeroparque Jorge Newbery. Therefore, this series shows the data for both airports since March 2010.

Argentina Visitor Departures: Average Length of Stay: Rest of the World data was reported at 23.532 Day in Dec 2024. This records a decrease from the previous number of 34.119 Day for Sep 2024. Argentina Visitor Departures: Average Length of Stay: Rest of the World data is updated quarterly, averaging 26.864 Day from Mar 2004 (Median) to Dec 2024, with 80 observations. The data reached an all-time high of 50.261 Day in Dec 2021 and a record low of 0.000 Day in Jun 2020. Argentina Visitor Departures: Average Length of Stay: Rest of the World data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.Q012: Visitor Departures: Ezeiza International Airport & Jorge Newbery Airport: Quarterly. Since March 14th 2010, some flights from Argentina to neightboring countries (such as Brazil, Chile and Paraguay) which used to operate in Ezeiza International Airport were transferred to Aeroparque Jorge Newbery. Therefore, this series shows the data for both airports since March 2010.

In 2021, due to the coronavirus pandemic, only 13.8 billion gallons of fuel were consumed by U.S. airlines, compared to a high of 18.43 billion gallons in 2007. While the consumption of airline fuel in the United States has increased in recent years, it is yet to surpass the levels seen prior to the 2008 recession. Commercial airlines The above figures include all commercial air carriers based in the U.S. who carry cargo and/or passengers on domestic or international flights, and with annual revenue of over 20 million U.S. dollars. Excluded is airline fuel used for military or private flights. Given that the U.S. has the largest business and military aircraft fleets in the world, if included the figures would be appreciably higher. Overall growth in commercial aviation Given the commercial aviation market in the U.S. has experienced strong growth since 2009, with revenue figures and passenger traffic well above pre-recession levels, the fact that fuel consumption is currently lower than in 2007 may appear curious. The likely explanation is that the cost of airline fuel reached record levels around 2012, forcing airlines to find ways to decrease fuel consumption wherever possible.