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Raoul Collenteur

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Raoul Collenteur

Hydrologist

Abteilung Wasserressourcen & Trinkwasser

Über mich

Hydrologist focusing on groundwater related problems and developing open-source software to solve them. In my free time I enjoy exploring the mountains by foot, rope, or kayak.

Software projects


I strongly believe in the use and development of open-source software. I currently lead or contribute to the development of the following open-source softwares:

  • Pastas - Open-source Python package for the analysis of groundwater time series. 
  • PyEt - Open-source Python package for the estimation of evapotranspiration. 
  • pHydrus - Open-source Python package for Hydrus-1D unsaturated zone modeling. 

Curriculum Vitae

2024 - Ongoing Part-time (40%)  self-employed at HydroConsult (Switzerland)
2022 - Ongoing Part-time (60%) PostDoc at Eawag (Switzerland)
2018 - 2022 PhD at the University of Graz (Austria)
2016 - 2018 Hydrologist at Artesia Water (The Netherlands)
2012 - 2016 MSc. in Water Management at TU Delft (The Netherlands)
2009 - 2012 BSc. in Earth Sciences and Economics at VU Amsterdam (The Netherlands)

Project: Swiss Groundwater Database (v1.0)

Groundwater level data contains invaluable information about the subsurface conditions and the driving forces and environmental factors causing water level fluctuations. More and more hydrological studies are using large data sets to improve our understanding of this important resource. Large data sets of groundwater are, unfortunately, not easy to obtain and often contain heterogeneous data. In this project, started at the end of 2023, we are collecting groundwater data in Switzerland to develop the first-ever Swiss Groundwater Database.  The goal of this project is to develop a fully open-source (FAIR) database of groundwater data for Switzerland. The first release is planned mid-2025. Contact Raoul.Collenteur@eawag.ch for more info and collaboration!

Project:AI-generated unit tests for Pastas

Pastas is currently tested with continuous integration using a moderately-sized set of unit tests written by the developers. This system has enabled us to improve code quality and catch errors more easily before new releases. The current test suite (~150 tests) contains rudimentary tests covering ~70% of the code, but not all methods are tested. Recently, generative AI-tools such as CodiumAI [13] have become available to generate unit tests. Initial application of these tools gave promising results, although manual oversight remains necessary. We will use AI-tools with human oversight to generate a comprehensive set of unit tests for Pastas. The goal is to cover 95% of the code, with more than two tests per method (on average). A protocol will be developed to aid new commits with AI-generated tests.

Project: Improved understanding and forecasting of the impact of climate extremes on groundwater systems

Groundwater has historically been perceived as a secure source of freshwater that is relatively

resistant to changes in meteorology. Extreme hydrological events in recent years (floods and droughts)
have shown that groundwater may be more vulnerable than previously assumed. To support decision
making on groundwater resource management, a good understanding and forecasting of groundwater
dynamics is required. These dynamics depend on a variety of stresses such as precipitation,
evaporation, river dynamics, and groundwater pumping. In a recent collaboration, we explored the
application of lumped-parameter models using impulse response functions to simulate observed
groundwater levels from the Swiss nationwide groundwater monitoring network (NAQUA). High levels
of accuracy were achieved with this approach. In the proposed project, we aim to further develop and
validate the method and apply this new type of models to (i) systematically analyze the sensitivity of
groundwater systems in Switzerland to extreme events and (ii) forecast groundwater levels. The results
from the project will be used to develop an operational groundwater information system, helping water
decision makers to make short- and long-term decisions on the management of groundwater.

Publikationen

Collenteur, R. A., Vonk, M. A., & Haaf, E. (2025). Quantification and analysis of hydrograph behavior using groundwater signatures. Groundwater. doi:10.1111/gwat.13486, Institutional Repository
Vremec, M., Seelig, M., Seelig, S., Collenteur, R., Haslinger, K., Wagner, T., … Winkler, G. (2025). Trend analysis of Alpine spring discharge: Interplay between climate and discharge characteristics. Science of the Total Environment, 993, 179875 (12 pp.). doi:10.1016/j.scitotenv.2025.179875, Institutional Repository
Berghuijs, W. R., Collenteur, R. A., Jasechko, S., Jaramillo, F., Luijendijk, E., Moeck, C., … Allen, S. T. (2024). Groundwater recharge is sensitive to changing long-term aridity. Nature Climate Change, 14, 357-363. doi:10.1038/s41558-024-01953-z, Institutional Repository
Collenteur, R. A., Haaf, E., Bakker, M., Liesch, T., Wunsch, A., Soonthornrangsan, J., … Meysami, R. (2024). Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge. Hydrology and Earth System Sciences, 28(23), 5193-5208. doi:10.5194/hess-28-5193-2024, Institutional Repository
Kokimova, A., Collenteur, R. A., & Birk, S. (2024). Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria. Hydrogeology Journal, 32, 1729-1749. doi:10.1007/s10040-024-02830-x, Institutional Repository
Moeck, C., Collenteur, R. A., Berghuijs, W. R., Luijendijk, E., & Gurdak, J. J. (2024). A global assessment of groundwater recharge response to infiltration variability at monthly to decadal timescales. Water Resources Research, 60(6), e2023WR035828 (19 pp.). doi:10.1029/2023WR035828, Institutional Repository
Vonk, M. A., Collenteur, R. A., Panday, S., Schaars, F., & Bakker, M. (2024). Time series analysis of nonlinear head dynamics using synthetic data generated with a variably saturated model. Groundwater, 62(5), 748-760. doi:10.1111/gwat.13403, Institutional Repository
Vremec, M., Collenteur, R. A., & Birk, S. (2024). PyEt v1.3.1: A Python package for the estimation of potential evapotranspiration. Geoscientific Model Development, 17(18), 7083-7103. doi:10.5194/gmd-17-7083-2024, Institutional Repository
Collenteur, R. A., Moeck, C., Schirmer, M., & Birk, S. (2023). Analysis of nationwide groundwater monitoring networks using lumped-parameter models. Journal of Hydrology, 626, 130120 (15 pp.). doi:10.1016/j.jhydrol.2023.130120, Institutional Repository
Jemeļjanova, M., Collenteur, R. A., Kmoch, A., Bikše, J., Popovs, K., & Kalvāns, A. (2023). Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries. Journal of Hydrology: Regional Studies, 47, 101416 (18 pp.). doi:10.1016/j.ejrh.2023.101416, Institutional Repository
Rudolph, M. G., Collenteur, R. A., Kavousi, A., Giese, M., Wöhling, T., Birk, S., … Reimann, T. (2023). A data-driven approach for modelling karst spring discharge using transfer function noise models. Environmental Earth Sciences, 82(13), 339 (19 pp.). doi:10.1007/s12665-023-11012-z, Institutional Repository

Diese Person arbeitet nicht mehr an der Eawag. Bitte wenden Sie sich an info@eawag.ch für weitere Auskünfte.

  • https://bsky.app/profile/rcollenteur.bsky.social
  • https://github.com/raoulcollenteur
  • https://hydroconsult.ch

Forschungsgruppen

Hydrogeology