This chart shows the 2-Year Impact of Frontiers of Environmental Science and Engineering over time and its percentile among journals.

This dataset contains change factors for the 2- to 100-year daily (24-hour) extreme rainfall storms for the Continental United States from publicly available downscaled climate projections, namely BCCAv.2, LOCA, MACA and NA-CORDEX data sets. Change factors were estimated as the ratio between the historical (period between1950-2005) climate simulations of extreme rainfall and the future (period between 2044-2099) climate simulations of rainfall depths corresponding to the average recurrence interval (e.g. 2-, 5-year). These change factors were computed using the Generalized Extreme Value Distribution, which is widely used to describe rainfall extremes.
This data archive was prepared as part of the outputs of the published article Lopez‐Cantu, T., Prein, A. F., & Samaras, C. (2020). Uncertainties in Future U.S. Extreme Precipitation from Downscaled Climate Projections. Geophysical Research Letters. https://doi.org/10.1029/2019GL086797. When using the data in this archive, citation must be given to the original article.

This chart shows annual citations to peer-reviewed articles affiliated with Faculty of Earth and Environmental Science and Engineering, grouped by publication year.

SWAT model efficiency statistics for the Upper Colorado River Basin. (XLSX)

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

The operational (ACTIVE, PASSIVE, COMBINED) ESA CCI SM products are available at https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/

Documentation

1) Algorithm Theoretical Baseline Document (ATBD)

An up-to-date summary of the latest algorithmic implementation at v9.2 is provided in the ATBD:

Preimesberger, W., Lems, J., D.Kovács, D., Kidd, R., & Dorigo, W. (2025). Appendix to Algorithm Theoretical Basis Document (ATBD) - Freeze Thaw (FT) D2.6 Version 1.0. Zenodo. https://doi.org/10.5281/zenodo.22285150

2) Evaluation Report (ER)

Validation results for this version of the dataset are provided in the ER:

Lems, J., Rodriguez-Fernandez, N. J., mialon, . arnaud ., Duchemin, D., & Prajapati, R. (2025). Evaluation Report - Freeze Thaw product (ER FT) D4.7 Version 1.0. Zenodo. https://doi.org/10.5281/zenodo.22285208

Abstract

Understanding whether the soil surface is frozen or thawed is crucial for interpreting satellite-based soil moisture measurements and for many Earth system applications. The physical state of water in the soil strongly affects its dielectric properties, which in turn determine how satellites sense moisture content. Current ESA CCI Soil Moisture products exclude data when the surface is likely frozen, as reliable retrievals are not possible under such conditions. Yet, the freeze/thaw state itself carries valuable environmental information: it reflects the changing energy and water exchange between land and atmosphere, shapes seasonal hydrological cycles, and influences agriculture, ecosystems, and climate feedbacks across much of the Northern Hemisphere.

This dataset provides global estimates of the soil moisture freeze/thaw state for the period from 11-1978 to 12-2024 derived from PASSIVE (radiometer) and ACTIVE (scatterometer) satellite observations within the ESA CCI Soil Moisture framework. These sensors, operating in the K- and C-band frequency range, are sensitive to surface temperature, enabling the detection of frozen versus thawed conditions at daily temporal and ~25 km spatial sampling. Data from L-band missions (e.g., SMAP, SMOS) are not included, resulting in a total number of 12 satellites.

The classification algorithm, described in Van der Vliet et al. (2020), was originally developed to flag frozen conditions in passive soil moisture retrievals and has since evolved into a dedicated data product. It applies a decision-tree approach using multi-frequency satellite measurements to classify the surface state for each sensor. Similarly, Naeimi et al. (2012) have developed an algorithm based on ASCAT backscatter for freeze/thaw classification in C-band scatterometer retrievals. Individual classifications are then merged into a single spatiotemporal record using a conservative unanimity rule—if any contributing satellite detects a frozen surface, the merged product is classified as “frozen.”

While this approach ensures reliability, it may lead to some over-flagging, which could be refined in future versions. The current product achieves an estimated accuracy of 75% against in situ surface temperature observations and 92% compared to ERA5 reanalysis data.

Summary

• Daily binary (true/false) freeze/thaw surface soil moisture state classification dataset (~25 km spatial sampling) for the period November 1978 to December 2024.
• Based on a satellite brightness temperature (K-band) classification algorithm (Van der Vliet et al., 2020) from 12 satellite radiometers and a satellite backscatter (C-band) classification algorithm (Naeimi et al., 2012).
• A pixel is classified as "frozen" if it was classified accordingly for at least one satellite. This can lead to potential over-flagging in the current version.
• Approximately 75% agreement with in situ surface temperature measurements (Dorigo et al., 2021) and 92% with ERA5-Land reanalysis temperature fields (Muñoz-Sabater et al., 2021)

Programmatic (bulk) download

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

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"

# Loop through years 1978 to 2024 and download & extract data
for year in {1978..2024}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

Filename template

The dataset provides global daily estimates for the 1978-2024 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:

ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc

Data Variables

Each netCDF file contains 3 coordinate variables

• lon: longitude (WGS84), [-180,180] degree W/E
• lat: latitude (WGS84), [-90,90] degree N/S
• time: datetime, encoded as "number of days since 1970-01-01 00:00:00 UTC"

and the following data variables

• ft: (int) Soil moisture freeze-thaw state binary indicator (0=not frozen, 1=frozen, -1=missing data)
• ft_agreement (float): Classification agreement between available sensors. 1 means that the frozen/unfrozen classification was the same for all merged sensors. The number decreases as the classification results between available satellites contradict.
• sensor_count (int): Total number of merged sensors/overpasses
• sensor_count_frozen (int): Total number of measuring sensors/overpasses that detected frozen soils
• mode: (int) Indicator for satellite orbit(s) used in the retrieval (1=ascending, 2=descending, 3=both, 0=missing data)
• sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.

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

Version Changelog

Changes in v9.2 (first released version):

• This version applies the classification algorithms described by Van der Vliet et al. (2020) and Naeimi et al. (2012) to 17 sensors and a unanimous merging approach. Covers the period from 11-1978 to 12-2024.

Software to open netCDF files

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

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

Related Records

This record and all related records are part of the ESA CCI Soil Moisture science data records community.

This repository contains three Python scripts supporting the modelling and validation of outdoor thermal satisfaction. The study investigates whether multidimensional subjective perceptions provide incremental predictive information beyond objective microclimatic and spatial predictors and evaluates predictive performance on held-out survey days. The research data were collected through concurrent microclimatic measurements and questionnaire surveys at two study sites and six outdoor microenvironments over four summer and four winter survey days. The predictors comprise five objective microclimatic variables, two spatial descriptors, and 12 subjective perception variables. Thermal satisfaction is classified into three categories: dissatisfied, neutral, and satisfied. The three scripts cover seven-family machine-learning model screening, hyperparameter optimization and selection among five retained model families, and final XGBoost evaluation under leave-one-day-out validation. Adding subjective perceptions improved predictive performance across all seven model families in both seasons, although performance varied across seasons, survey days, and satisfaction classes. This repository contains analysis code and supporting documentation but does not include the complete analytical dataset. Data access requests are subject to applicable ethical and privacy restrictions.

TRACC Performance Evaluation Dataset

This dataset supports the paper “TRACC: A Trusted IoT–Blockchain Architecture for Real-Time Carbon Compliance and Device-Level Emission Accountability.” TRACC links authenticated methane sensor reports to on-chain, device-indexed liability through ERC-6551 device-bound accounts and non-transferable Carbon Burden Tokens (CBTs).

The revised dataset separates physical prototype validation, synthetic workload generation, controlled Polygon Amoy benchmarking, reliability monitoring, security testing, latency characterization, and fee-model inputs.

The fixed-seed 30-day synthetic workload models 1,500 sensors across 100 companies. Report arrivals follow a Poisson process with mean 2.79 reportable readings per sensor per day. Methane concentrations follow a Pareto Type-I distribution with shape α=1.75 and minimum (x_m=200) ppm. Using NumPy 2.3.5 default_rng (PCG64), seed 42, and a 500 ppm threshold, the realization contains 125,837 reportable readings, 25,282 exceedances (20.09%), and 34,104 policy-assigned CBT units. These are synthetic workload outcomes, not field-observed methane frequencies or 125,837 Amoy transactions.

The controlled offered-load benchmark uses 200 registered devices, one authenticated 1200 ppm report per device per trial, three trials per load, and real-time pacing. Valid offered loads are 0.2222, 0.6667, and 1.6667 transactions/s. All 1,800 submissions across the nine valid trials confirmed on the first attempt. Higher-rate 3.3333 and 6.6667 transactions/s attempts are retained for transparency but excluded from contract-capacity inference because free-tier JSON-RPC throttling prevented reliable contract-level measurement.

Additional files preserve the 30-day reliability monitoring series, six-stage latency characterization, 146 scripted adversarial scenarios, 24-hour gas-price/confirmation-time observations, and validation summaries. Reliability distinguishes attempt-level pipeline completion from service availability; 99.78% is the descriptive mean of component availabilities and 98.92% is an independence-based all-service estimate. The 6.25 s latency is a composed total from separately characterized stages, not a distribution of complete end-to-end trials.

Reference implementation: https://github.com/CryptoGuy1/TRACC-On-Chain-Emission-Tracking-via-DePIN

This chart shows annual citations to peer-reviewed articles affiliated with Faculty of Environmental Science and Engineering, grouped by publication year.

The literature search was conducted in Scopus and Web of Science and covered peer-reviewed articles and review papers published in English between 2010 and 2024. Records were processed in RStudio using the Bibliometrix package for duplicate removal and data organization. Study identification, screening, eligibility assessment, and selection followed the PRISMA framework. After preprocessing, 129 records were screened, resulting in a final portfolio of 51 publications, comprising 48 empirical LCA studies and three review papers.

The Excel workbook contains a README and 13 tables documenting the search and screening process, exclusion decisions, adapted InOrdinatio ranking, final bibliographic portfolio, and harmonized methodological information. The variables include functional units, technological pathways, analytical classifications, LCA modeling approaches, Life Cycle Impact Assessment methods and impact categories, software, databases, inventory data sources, sensitivity and uncertainty analyses, transport modeling, multifunctionality treatment, and system boundaries. The updated dataset also includes a comparative synthesis of the 12 studies classified as LCA-TP, documenting the ELT management pathways assessed, the reported environmentally preferable alternatives, and key methodological and environmental findings. These preferences are specific to the functional units, system boundaries, geographical contexts, impact assessment methods, and modeling assumptions adopted in the original studies.

The dataset introduces two complementary classification frameworks: technological classes based on recycling (REC), applications of ELT-derived materials or coproducts (APP), and energy recovery (ENE); and analytical classes based on Functional Comparison (LCA-FC) and Technological Pathways (LCA-TP). It also documents terminology-harmonization criteria, explicit decision rules for derived variables, and the System Boundary Consistency Index (SBCI), applied to the 36 empirical studies classified as LCA-FC to assess consistency between reported and effectively modeled system boundaries. Missing information was retained as not reported or not applicable according to predefined rules and was not inferred from customary LCA practice.

The dataset can support replication and updating of the review, comparative assessments of ELT management pathways, methodological research in LCA, and evidence-based decision-making related to waste management and circular economy strategies.

This model estimates at high spatial and temporal resolution carbon and sulfur emissions of Indian power generation in 2017-2018. It estimates current emissions and emission under future policies such as carbon taxes, sulfur emissions control, and market integration.

A better understanding of greenhouse gas (GHG) emissions resulting from oil sands (bitumen) extraction can help to meet global oil demands, identify potential mitigation measures, and design effective carbon policies. While several studies have attempted to model GHG emissions from oil sands extractions, these studies have encountered data availability challenges, particularly with respect to actual fuel use data, and have thus struggled to accurately quantify GHG emissions. This dataset contains actual operational data from 20 in-situ oil sands operations, including information for fuel gas, flare gas, vented gas, production, steam injection, gas injection, condensate injection, and C3 injection.

Town Brook watershed, Catskills, New York. SWATWB was also tested on the Town Brook watershed (Figure 2) in the USA, a 37 km2 sub-catchment of the Cannonsville reservoir basin. The region is typified by steep-to-moderate hillslopes of glacial origins with shallow permeable soils, underlain by a restrictive layer. The climate is humid with an average annual temperature of 8 ¢XC and average annual precipitation of 1123 mm. Elevation in the watershed ranges from 493 to 989 m above mean sea level. The slopes are quite steep with a maximum of 91%, and a mean of 21%. Soils are mainly silt loam or silty clay loam with soil hydrological group C ratings (USDA–NRCS, 2000). Soil depth ranges from less than 50 cm to greater than 1 m and is underlain by a fragipan restricting layer (e.g. coarse-loamy, mixed, active, mesic, to frigid Typic Fragiudepts, Lytic or Typic Dystrudepts common to glacial tills) (Schneiderman et al., 2002). The lowland portion of the watershed is predominantly agricultural, consisting of pasture and row crops (20%) or shrub land (18%), whereas the upper slopes are forested (60%). Water and wetlands comprise 2%. Impervious surfaces occupy

Financial overview and grant giving statistics of Environmental Science And Engineering Student Organization

Description of aridity indices ranges [60].

The world is urbanizing while it undergoes dramatic environmental change, especially climate change. These two drivers are on a collision course that will exaccerbate challenges for cities. At the global and local scales, new solutions will have to be found to build resilience in the cities of tomorrow. This assertion is explored through the lens of urban water systems: supply and delivery at the global scale, and stormwater management at the local scale.

Radar range: standard. Max rain level: strong rain. Parent item: IDRA weather radar measurements - month 2012-06 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

Radar range: far. Max rain level: moderate rain. Parent item: IDRA weather radar measurements - month 2017-01 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

Radar range: standard. Max rain level: weak rain. Parent item: IDRA weather radar measurements - month 2014-07 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

This chart shows annual citations to peer-reviewed articles affiliated with Center for Environmental Science and Engineering, grouped by publication year.

Radar range: far. Max rain level: moderate rain. Parent item: IDRA weather radar measurements - month 2016-04 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.