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Peer-reviewedCC BYEarth system science data

The UWO dataset – long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales

  • Frank Blumensaat1,2,3
  • Simon Bloem1
  • Christian Ebi1
  • Andy Disch1
  • Christian Förster1
  • Max Maurer1,2
  • Mayra Rodríguez1
  • Jörg Rieckermann4,1
4 affiliations
  1. 1Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
  2. 2Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093, Zurich, Switzerland
  3. 3Landesdirektion Sachsen, Stauffenbergallee 2, 01099 Dresden, Germany
  4. 4Swiss Federal Institute of Aquatic Science and Technology (Dübendorf, Switzerland)
Open at sourcePDFDOICite

Abstract

Urban drainage systems are integral infrastructural components. However, their monitoring poses considerable challenges owing to the intricate, hazardous nature of the process, necessitating substantial resources and expertise. These inherent uncertainties act as deterrents, discouraging active involvement of researchers and sewer operators in the rigorous monitoring and utilization of data for a comprehensive understanding and efficient management of drainage-related processes. Consequently, a notable absence of openly available urban drainage datasets hampers exploring their potential for engineering applications, scientific analysis, and societal benefits. In this study, we present a distinctive dataset from the Urban Water Observatory (UWO) in Fehraltorf, Switzerland. This dataset is unique in terms of its completeness, consistency, extensive observation period, high spatio-temporal resolution and its availability in the public domain. The dataset comprises coherent information from 124 sensors that observe rainfall-runoff processes, wastewater and in-sewer atmosphere temperatures. Of these 124 sensors, 89 transmit their signals via a specifically set-up wireless network using long-range, low-power transmission technologies. Sensor data have a temporal resolution of 1–5 minutes and covers a period of three years from 2019–2021. To make the data interpretable and re-useable we provide systematically collected meta-data, data on sewer infrastructure, associated geo-information including a validated hydrodynamic rainfall-runoff model. Basic data quality checks were performed, and we motivate future research on the dataset with five selected research opportunities from detecting anomalies in the data to assessing groundwater infiltration and the capability of the low-power data transmission. We conclude that robust automated data quality checks, standardized data exchange formats, and a systematic meta-data collection are needed to boost interpretability and usability of urban drainage data. In the future, ontologies and knowledge graphs should be developed to expand the application of sewer observation data in solving scientific and practical problems.

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Cite

BibTeXRIS
BibTeX
@article{blumensaat2026dataset,
  title = {{The UWO dataset – long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales}},
  author = {Frank Blumensaat and Simon Bloem and Christian Ebi and Andy Disch and Christian Förster and Max Maurer and Mayra Rodríguez and Jörg Rieckermann},
  journal = {Earth system science data},
  year = {2026},
  month = jul,
  doi = {10.5194/essd-18-5187-2026},
  url = {https://doi.org/10.5194/essd-18-5187-2026},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - The UWO dataset – long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales
AU  - Frank Blumensaat
AU  - Simon Bloem
AU  - Christian Ebi
AU  - Andy Disch
AU  - Christian Förster
AU  - Max Maurer
AU  - Mayra Rodríguez
AU  - Jörg Rieckermann
PY  - 2026
DA  - 2026/07/22
JO  - Earth system science data
DO  - 10.5194/essd-18-5187-2026
UR  - https://doi.org/10.5194/essd-18-5187-2026
AB  - Urban drainage systems are integral infrastructural components. However, their monitoring poses considerable challenges owing to the intricate, hazardous nature of the process, necessitating substantial resources and expertise. These inherent uncertainties act as deterrents, discouraging active involvement of researchers and sewer operators in the rigorous monitoring and utilization of data for a comprehensive understanding and efficient management of drainage-related processes. Consequently, a notable absence of openly available urban drainage datasets hampers exploring their potential for engineering applications, scientific analysis, and societal benefits. In this study, we present a distinctive dataset from the Urban Water Observatory (UWO) in Fehraltorf, Switzerland. This dataset is unique in terms of its completeness, consistency, extensive observation period, high spatio-temporal resolution and its availability in the public domain. The dataset comprises coherent information from 124 sensors that observe rainfall-runoff processes, wastewater and in-sewer atmosphere temperatures. Of these 124 sensors, 89 transmit their signals via a specifically set-up wireless network using long-range, low-power transmission technologies. Sensor data have a temporal resolution of 1–5 minutes and covers a period of three years from 2019–2021. To make the data interpretable and re-useable we provide systematically collected meta-data, data on sewer infrastructure, associated geo-information including a validated hydrodynamic rainfall-runoff model. Basic data quality checks were performed, and we motivate future research on the dataset with five selected research opportunities from detecting anomalies in the data to assessing groundwater infiltration and the capability of the low-power data transmission. We conclude that robust automated data quality checks, standardized data exchange formats, and a systematic meta-data collection are needed to boost interpretability and usability of urban drainage data. In the future, ontologies and knowledge graphs should be developed to expand the application of sewer observation data in solving scientific and practical problems.
SN  - 1866-3508
SN  - 1866-3516
ER  -