Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-5187-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-5187-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The UWO dataset – long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales
Frank Blumensaat
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093, Zurich, Switzerland
Landesdirektion Sachsen, Stauffenbergallee 2, 01099 Dresden, Germany
Simon Bloem
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Christian Ebi
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Andy Disch
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Christian Förster
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Max Maurer
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093, Zurich, Switzerland
Mayra Rodriguez
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Jörg Rieckermann
CORRESPONDING AUTHOR
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
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Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
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Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Kire Micev, Jan Steiner, Asude Aydin, Jörg Rieckermann, and Tobi Delbruck
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This paper reports a novel rain droplet measurement method that uses a neuromorphic event camera to measure droplet sizes and speeds as they fall through a shallow plane of focus. Experimental results report accuracy similar to a commercial laser sheet disdrometer. Because these measurements are driven by event camera activity, this approach could enable the economical deployment of ubiquitous networks of solar-powered disdrometers.
Anna Špačková, Vojtěch Bareš, Martin Fencl, Marc Schleiss, Joël Jaffrain, Alexis Berne, and Jörg Rieckermann
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An original dataset of microwave signal attenuation and rainfall variables was collected during 1-year-long field campaign. The monitored 38 GHz dual-polarized commercial microwave link with a short sampling resolution (4 s) was accompanied by five disdrometers and three rain gauges along its path. Antenna radomes were temporarily shielded for approximately half of the campaign period to investigate antenna wetting impacts.
Cited articles
Abdel-Aal, M., Villa, R., Jawiarczyk, N., Alibardi, L., Jensen, H., Schellart, A., Jefferson, B., Shepley, P., and Tait, S.: Potential influence of sewer heat recovery on in-sewer processes, Water Sci. Technol., 80, 2344–2351, https://doi.org/10.2166/wst.2020.061, 2019.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Allemang, D. and Sequeda, J.: Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!, arXiv [preprint], https://doi.org/10.48550/arXiv.2405.11706, 20 May 2024.
Auer, A., Gauch, M., Kratzert, F., Nearing, G., Hochreiter, S., and Klotz, D.: A data-centric perspective on the information needed for hydrological uncertainty predictions, Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024, 2024.
AWEL: Mikroverunreinigungen – Messkampagne zu Belastungen aus Industrie und Gewerbe, AWEL, Zürich, https://www.zh.ch/content/dam/zhweb/bilder-dokumente/themen/umwelt-tiere/wasser-gewaesser/gewaesserschutz/abwasserreinigungsanlagen-ara/themen-und-projekte/2021_awel_bericht_mikroverunreinigung_industrie_und_Gewerbe.pdf (last access: 8 June 2026), 2021.
Benyon, R.: Letter from Richard Benyon MP to CEOs of water companies regarding monitoring of combined sewer overflows, Department for Environment, Food & Rural Affairs, https://www.gov.uk/government/publications/letter-from-richard-benyon-mp-to-water-and-sewerage-companies (last access: 8 June 2026), 2013.
Bertrand-Krajewski, J.-L., Laplace, D., Joannis, C., and Chebbo, G.: Mesures en hydrologie urbaine et assainissement, Éditions Lavoisier, Tec & Doc, France, 793 pp., ISBN 978-2-7430-1097-3, 2008.
Bertrand-Krajewski, J.-L., Lepot, M., and Invernon, N.: UDMT – Urban drainage metrology toolbox: a software tool facilitating the adoption of better metrology practices, Water Pract. Technol., 20, 1784–1796, https://doi.org/10.2166/wpt.2025.102, 2025.
Bianchi, B., Rieckermann, J., and Berne, A.: Quality control of rain gauge measurements using telecommunication microwave links, J. Hydrol., 492, 15–23, https://doi.org/10.1016/j.jhydrol.2013.03.042, 2013.
Blumensaat, F., Ebi, C., Dicht, S., Rieckermann, J., and Maurer, M.: Langzeitüberwachung der Raum-Zeit-Dynamik in Entwässerungssystemen mittels Niedrigenergiefunk – Ein Feldexperiment im Großmaßstab, Korresp. Abwasser, 64, 594–603, 2017.
Blumensaat, F., Leitão, J. P., Ort, C., Rieckermann, J., Scheidegger, A., Vanrolleghem, P. A., and Villez, K.: How urban storm- and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond – a horizon scan, Environ. Sci. Technol., 53, 8488–8498, https://doi.org/10.1021/acs.est.8b06481, 2019.
Blumensaat, F., Bloem, S., Ebi, C., Disch, A., Förster, C., Rodriguez, M., Maurer, M., and Rieckermann, J.: The Eawag Urban Water Observatory 710 – UWO, ERIC [data set], https://doi.org/10.25678/000C5K, 2024a.
Blumensaat, F., Bloem, S., Ebi, C., Disch, A., Förster, C., Rodriguez, M., Maurer, M., and Rieckermann, J.: UWO – Accompanying data (2019 to 2021), ERIC [data set], https://doi.org/10.25678/000991, 2024b.
Blumensaat, F., Bloem, S., Ebi, C., Disch, A., Förster, C., Rodriguez, M., Maurer, M., and Rieckermann, J.: UWO – Data access (2019 to 2021), ERIC [data set], https://doi.org/10.25678/000980, 2024c.
Blumensaat, F., Bloem, S., Ebi, C., Disch, A., Förster, C., Rodriguez, M., Maurer, M., and Rieckermann, J.: UWO – Data viewer (2019 to 2021), ERIC [data set], https://doi.org/10.25678/00092Z, 2024d.
Blumensaat, F., Bloem, S., Ebi, C., Disch, A., Förster, C., Rodriguez, M., Maurer, M., and Rieckermann, J.: UWO – Field observations (2019 to 2021), ERIC [data set], https://doi.org/10.25678/00091Y, 2024e.
Boebel, M., Frei, F., Blumensaat, F., Ebi, C., Meli, M. L., and Rüst, A.: Batteryless Sensor Devices for Underground Infrastructure – A Long-Term Experiment on Urban Water Pipes, J. Low Power Electron. Appl., 13, 31, https://doi.org/10.3390/jlpea13020031, 2023.
Bustamante, G. R., Nelson, E. J., Ames, D. P., Williams, G. P., Jones, N. L., Boldrini, E., Chernov, I., and Sanchez Lozano, J. L.: Water Data Explorer: An Open-Source Web Application and Python Library for Water Resources Data Discovery, Water, 13, 1850, https://doi.org/10.3390/w13131850, 2021.
Caradot, N., Sonnenberg, H., Riechel, M., Matzinger, A., and Rouault, P.: The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring, Water Pract. Technol., 8, 417–424, https://doi.org/10.2166/wpt.2013.042, 2013.
Clemens, F., Lepot, M., Blumensaat, F., Leutnant, D., and Gruber, G.: Data validation and data quality assessment, in: Metrology in Urban Drainage and Stormwater Management: Plug and Pray, IWA Publishing, 327–390, https://doi.org/10.2166/9781789060119_0327, 2021.
Crowley, G., Tait, S., Panoutsos, G., Speight, V., and Esnaola, I.: Information-theoretic sensor placement for large sewer networks, Water Res., 268, 122718, https://doi.org/10.1016/j.watres.2024.122718, 2025.
Deheer, K.: A Look Behind Using Machine Learning for Anomaly Detection, 2022, Trinnex, https://www.trinnex.io/insights/a-look-behind-using-machine-learning-for-anomaly-detection (last access: 8 June 2026), 2022.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.: Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Del Giudice, D., Löwe, R., Madsen, H., Mikkelsen, P. S., and Rieckermann, J.: Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors, Water Resour. Res., 51, 5004–5022, https://doi.org/10.1002/2014WR016678, 2015.
Del Giudice, D., Albert, C., Rieckermann, J., and Reichert, P.: Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation, Water Resour. Res., 52, 3162–3186, https://doi.org/10.1002/2015WR017871, 2016.
Deletic, A., Dotto, C. B. S., McCarthy, D. T., Kleidorfer, M., Freni, G., Mannina, G., Uhl, M., Henrichs, M., Fletcher, T. D., Rauch, W., Bertrand-Krajewski, J. L., and Tait, S.: Assessing uncertainties in urban drainage models, Phys. Chem. Earth Pt. ABC, 42–44, 3–10, https://doi.org/10.1016/j.pce.2011.04.007, 2011.
DHI: MIKE URBAN (Release 2020), DHI Group [software], https://www.dhigroup.com/technologies/mikepoweredbydhi/mikeplus, (last access: 5 July 2026), 2020.
Disch, A. and Blumensaat, F.: Messfehler oder Prozessanomalie? – Echtzeit-Datenvalidierung für eine zuverlässige Prozessüberwachung in Kanalnetzen, in: Regenwasser weiterdenken - Bemessen trifft Gestalten, edited by: Burkhardt, M. and Graf. C., Tagungsband – Aqua Urbanica 2019, 73–78, HSR Hochschule für Technik Rapperswil, https://www.dora.lib4ri.ch/eawag/item/eawag:36337 (last access: 8 June 2026), 2019.
Duque, H., Diao, K., Villa, R., Leitao, J. P., Djordjević, S., and Abdel-Aal, M.: Context-aware data driven sensor data analysis: With application to H2S concentration prediction in urban drainage networks, Water Res. X, 28, 100346, https://doi.org/10.1016/j.wroa.2025.100346, 2025.
Dürrenmatt, D. J., Del Giudice, D., and Rieckermann, J.: Dynamic time warping improves sewer flow monitoring, Water Res., 47, 3803–3816, https://doi.org/10.1016/j.watres.2013.03.051, 2013.
Eawag-SWW: UWO Open Data, https://uwo-opendata.eawag.ch/ (last access: 8 June 2026), 2025.
Ebi, C., Schaltegger, F., Rust, A., and Blumensaat, F.: Synchronous LoRa mesh network to monitor processes in underground infrastructure, IEEE Access, 7, 57663–57677, https://doi.org/10.1109/ACCESS.2019.2913985, 2019.
EC: Directive (EU) 2024/3019 of the European Parliament and of the Council of 27 November 2024 concerning urban wastewater treatment (recast) (Text with EEA relevance), Official Journal of the European Union, 2024, https://eur-lex.europa.eu/eli/dir/2024/3019/oj/eng (last access: 8 June 2026), 2022.
Eggimann, S., Mutzner, L., Wani, O., Schneider, M. Y., Spuhler, D., Moy de Vitry, M., Beutler, P., and Maurer, M.: The Potential of Knowing More: A Review of Data-Driven Urban Water Management, Environ. Sci. Technol., 51, 2538–2553, https://doi.org/10.1021/acs.est.6b04267, 2017.
Elías-Maxil, J. A., Hofman, J., Wols, B., Clemens, F., van der Hoek, J. P., and Rietveld, L.: Development and performance of a parsimonious model to estimate temperature in sewer networks, Urban Water J., 14, 829–838, https://doi.org/10.1080/1573062X.2016.1276811, 2017.
Environment Act: Environment Act 2021, https://www.legislation.gov.uk/ukpga/2021/30/contents (last access: 8 June 2026), 2021.
Ferriman, A.: BMJ readers choose the “sanitary revolution” as greatest medical advance since 1840, BMJ, 334, 111, https://doi.org/10.1136/bmj.39097.611806.DB, 2007.
Figueroa, A., Hadengue, B., Leitão, J., Rieckermann, J., and Blumensaat, F.: A distributed heat transfer model for thermal-hydraulic analyses in sewer networks, Water Res., 204, 117649, https://doi.org/10.1016/j.watres.2021.117649, 2021.
Giakoumis, T. and Voulvoulis, N.: Combined sewer overflows: relating event duration monitoring data to wastewater systems' capacity in England, Environ. Sci. Water Res. Technol., 9, 707–722, https://doi.org/10.1039/D2EW00637E, 2023.
Hadengue, B., Joshi, P., Figueroa, A., Larsen, T. A., and Blumensaat, F.: In-building heat recovery mitigates adverse temperature effects on biological wastewater treatment: A network-scale analysis of thermal-hydraulics in sewers, Water Res., 204, 117552, https://doi.org/10.1016/j.watres.2021.117552, 2021.
HBT: GEP Fehraltorf – Gewässer, Hunizker Betatech Engineering, Winterthur, 2016.
Hoppe, H., Fricke, K., Kutsch, S., Massing, C., and Gruber, G.: Von Daten zu Werten – Messungen in Entwässerungssystemen, Aqua Gas, 96, 26–31, 2016.
Huisman, J. L.: Transport and transformation processes in combined sewers, ETH Zürich, https://doi.org/10.3929/ethz-a-004176286, 2001.
International Electrotechnical Commission: Industrial systems, installations and equipment and industrial products – Structuring principles and reference designations — Part 2: Classification of objects and codes for classes, IEC 81346-2:2019, https://webstore.iec.ch/en/publication/29181 (last access: 8 June 2026), 2019.
Kerkez, B., Gruden, C., Lewis, M., Montestruque, L., Quigley, M., Wong, B., Bedig, A., Kertesz, R., Braun, T., Cadwalader, O., Poresky, A., and Pak, C.: Smarter Stormwater Systems, Environ. Sci. Technol., 50, 7267–7273, https://doi.org/10.1021/acs.est.5b05870, 2016.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Krejci, V., Fankhauser, R., Gammeter, S., Grottker, M., Harmuth, B., Merz, P., and Schilling, W.: Integrierte Siedlungsentwässerung Fallstudie Fehraltorf, Dübendorf Eawag 1994, 303 P Schriftenreihe Eawag, Vol. 8, ISBN 3-906484-09-2, 1994.
Lepot, M., Torres, A., Hofer, T., Caradot, N., Gruber, G., Aubin, J.-B., and Bertrand-Krajewski, J.-L.: Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers, Water Res., 101, 519–534, https://doi.org/10.1016/j.watres.2016.05.070, 2016.
Manny, L., Duygan, M., Fischer, M., and Rieckermann, J.: Barriers to the digital transformation of infrastructure sectors, Policy Sci., https://doi.org/10.1007/s11077-021-09438-y, 2021.
Mathis, S., Gruber, J.-M., Ebi, C., Bloem, S., Rieckermann, J., and Blumensaat, F.: Energy self-sufficient systems for monitoring sewer networks, in: Sensors and Measuring Systems; 21th ITG/GMA-Symposium, 8 pp., https://doi.org/10.48550/arXiv.2204.03748, 2022.
Mourad, M. and Bertrand-Krajewski, J. L.: A method for automatic validation of long time series of data in urban hydrology, Water Sci. Technol., 45, 263–270, 2002.
Nedergaard Pedersen, A., Wied Pedersen, J., Vigueras-Rodriguez, A., Brink-Kjær, A., Borup, M., and Steen Mikkelsen, P.: The Bellinge data set: open data and models for community-wide urban drainage systems research, Earth Syst. Sci. Data, 13, 4779–4798, https://doi.org/10.5194/essd-13-4779-2021, 2021.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
NYS Combined Sewer Overflows (CSOs): https://kaggle.com/datasets/new-york-state/nys-combined-sewer-overflows-csos (last access: 8 June 2026), 2022.
Ochoa-Rodriguez, S., Wang, L.-P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I., Onof, C., Willems, P., and ten Veldhuis, M.-C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015.
Panasiuk, O., Hedström, A., Langeveld, J., and Viklander, M.: Identifying sources of infiltration and inflow in sanitary sewers in a northern community: comparative assessment of selected methods, Water Sci. Technol., 86, 1–16, https://doi.org/10.2166/wst.2022.151, 2022.
Ramgraber, M.: Data for: Non-Gaussian parameter inference for hydrogeological models using Stein Variational Gradient Descent – ERIC, https://doi.org/10.25678/00035V, 2025.
Regueiro-Picallo, M., Anta, J., Naves, A., Figueroa, A., and Rieckermann, J.: Towards urban drainage sediment accumulation monitoring using temperature sensors, Environ. Sci. Water Res. Technol., https://doi.org/10.1039/D2EW00820C, 2023.
Rieckermann, J. and Chavarría Vargas, A.: Tutorial and SQL query to build a table from the UWO data in sqlite format, Zenodo [video], https://doi.org/10.5281/zenodo.19071302, 2026.
Rieckermann, J. and Disch, A.: Challenges and Prospects in Anomaly Detection of Sewer Monitoring Data: Annotating Synthetic Sewer Data with Known Sensor Failures, engrXiv [preprint], https://doi.org/10.31224/3520, 5 February 2024.
Rieckermann, J., Bertrand-Krajewski, J.-L., Blumensaat, F., Ort, C., Pistocchi, A., and Schellart, A.: Assessing Combined Sewer Overflows (CSOs) – A growing need for evidence base, compliance assessment, and future regulation, 15th ICUD – International Conference on Urban Drainage, https://hal.science/hal-03432045v1 (last access: 8 June 2026), 2021.
Riveraction: Sewage Map, Sewage Map, River Action, https://www.sewagemap.co.uk/ (last access: 8 June 2026), 2025.
Rodriguez, M., Fu, G., Butler, D., Yuan, Z., and Cook, L.: The effect of green infrastructure on resilience performance in combined sewer systems under climate change, J. Environ. Manage., 353, 120229, https://doi.org/10.1016/j.jenvman.2024.120229, 2024.
Rodriguez Bennadji, M.: The influence of Green Infrastructure on the resilience of urban drainage systems, Thesis, University of Exeter, https://hdl.handle.net/10871/131516, 2022.
Rossi, L., Chevre, N., Fankhauser, R., and Krejci, V.: Probabilistic environmental risk assessment of urban wet-weather discharges: an approach developed for Switzerland, Urban Water J., 6, 355–367, https://doi.org/10.1080/15730620902934801, 2009.
Ruggaber, T. P., Talley, J. W., and Montestruque, L. A.: Using Embedded Sensor Networks to Monitor, Control, and Reduce CSO Events: A Pilot Study, Environ. Eng. Sci., 24, 172–182, https://doi.org/10.1089/ees.2006.0041, 2007.
Russo, S., Disch, A., Blumensaat, F., and Villez, K.: Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data, 10th IWA Symposium on Modelling and Integrated Assessment (Watermatex 2019), ETHZ, Copenhagen, https://doi.org/10.48550/arXiv.2002.03843, 2019.
Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., Hess, A., Lürig, M., Matthews, B., Minaudo, C., Morgenroth, E., Tran-Khac, V., and Villez, K.: The value of human data annotation for machine learning based anomaly detection in environmental systems, Water Res., 206, 117695, https://doi.org/10.1016/j.watres.2021.117695, 2021.
Sant'Anna, M., Souza, R. G., Prudente, G., Brentan, B., and Meirelles, G.: Analysis of the impact of leaks and valve maneuvers on pressure transient data in water distribution networks, RBRH, 29, e45, https://doi.org/10.1590/2318-0331.292420240085, 2024.
Schilperoort, R., Hoppe, H., de Haan, C., and Langeveld, J.: Searching for storm water inflows in foul sewers using fibre-optic distributed temperature sensing, Water Sci. Technol., 68, 1723–1730, https://doi.org/10.2166/wst.2013.419, 2013.
Semtech Corporation: AN1200.22: LoRa™ Modulation Basics, https://it4sec.org/article/semtech-an120022-lora-modulation-basics (last access: 8 June 2026), 2015.
Speck-Fehraltorf Airport: https://skybrary.aero/airports/lszk, last access: 8 June 2026.
Spraakman, S.: IAHR/IWA Joint Specialist Group on Urban Drainage – March 2023, Newsletter 36, 2023.
Staufer, P., Scheidegger, A., and Rieckermann, J.: Assessing the performance of sewer rehabilitation on the reduction of infiltration and inflow, Water Res., 46, 5185–5196, https://doi.org/10.1016/j.watres.2012.07.001, 2012.
Stream – Portal: https://www.streamwaterdata.co.uk/, last access: 8 June 2026.
Sun, S. H. and Yu, R.: Copula Conformal prediction for multi-step time series prediction, International Conference on Learning Representations 2024 (ICLR 2024), https://doi.org/10.48550/arXiv.2212.03281, 2024.
Taylor, P., Cox, S., Walker, G., Valentine, D., and Sheahan, P.: WaterML2.0: development of an open standard for hydrological time-series data exchange, J. Hydroinform., 16, 425–446, https://doi.org/10.2166/hydro.2013.174, 2013.
The Dutch Urban Drainage Ontology (GWSW): https://data.gwsw.nl/, last access: 29 June 2025.
van Kranenburg, R. and Bassi, A.: IoT Challenges, Commun. Mob. Comput., 1, 9, https://doi.org/10.1186/2192-1121-1-9, 2012.
Villez, K., Vanrolleghem, P. A., and Corominas, L.: Optimal flow sensor placement on wastewater treatment plants, Water Res., 101, 75–83, https://doi.org/10.1016/j.watres.2016.05.068, 2016.
Vovk, V., Gammerman, A., and Shafer, G.: Algorithmic Learning in a Random World, SpringerLink, https://doi.org/10.1007/978-3-031-06649-8, 2025.
Wani, O., Maurer, M., Rieckermann, J., and Blumensaat, F.: Does distributed monitoring improve the calibration of urban drainage models?, in: 12th Urban Drainage Modeling Conference, Costa Mesa, California, January 2022, Urban Drainage Modelling Conference (UDM), https://www.dora.lib4ri.ch/eawag/item/eawag:30040, 2022.
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016.
Short summary
Detailed monitoring of urban drainage systems is challenging due to the hazardous environment, the required expertise and resources. The Fehraltorf Urban Water Observatory provides a unique dataset with 124 sensors observing rainfall-runoff, wastewater and in-sewer temperatures as well as wireless sensor network performance for three years. To enhance usability, systematic meta-data, sewer infrastructure, and a hydrodynamic model are included.
Detailed monitoring of urban drainage systems is challenging due to the hazardous environment,...
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