ENWAT Project
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Publication date: Sep 4, 2024
| Position-ID: | 1584 |
| Faculty/ Facility: | Civil- and Environmental Engineering |
| Institute/ Facility: | Civil- and Environmental Engineering : IWS – Institute for Modelling Hydraulic and Environmental Systems |
| Research Association: | N/A |
| Teaching Obligation: | No |
| Application deadline: | 10/31/2024 |
| Anticipated Start Date: | 10/01/2025 |
About Us
The international Doctoral Program “Environment Water” (ENWAT) of the Faculty of Civil and Environmental Engineering Sciences, University of Stuttgart, Germany, in collaboration with the German Academic Exchange Service (DAAD) opens a call for max. 2 PhD positions for research in Environment Water. Each project involves high-quality research and state-of-the-art techniques and is supervised by excellent researchers. We are looking for highly motivated and talented students with a passion for science. Candidates must demonstrate an excellent performance in their previous academic education.
Title: Learning Deep Insights into Hydrological Processes using Bayesian Neural Hydrology
Advisor: Prof. Dr.-Ing. Wolfgang Nowak, Dr. rer. nat. Jochen Seidel, apl. Prof. Dr.-Ing. Sergey Oladyshkin
Research group / department:
Chair of Stochastic Simulation and Safety Research for Hydrosystems (LS3)
Institute for Modelling Hydraulic and Environmental Systems (IWS)
and Stuttgart Centre for Simulation Technology (SC SimTech)
Keywords: Hydrological modelling, Model development, Deep learning, Neural Ordinary differential equations, Bayesian inference, Knowledge infusion
Introduction / Background
Errors in hydrological modelling can stem from model uncertainties and errors (epistemic uncertainties) as well as input data. This holds particularly true for precipitation, which is highly variable in space and time, especially when dealing with intense local events. Therefore, it is very important to accurately estimate precipitation, both for understanding and modelling of hydrological processes and when designing and planning for extreme rainfall events. Weather radars provide high resolution spatial and temporal rainfall estimates. Yet, their measurements can suffer from several types of errors, such as the measurement height above ground or attenuation due to intense rainfall. Interpolated rainfall fields using common rain gauge data often miss extreme events due to an insufficient density of rain gauges. Both approaches often lead to a systematic underestimation of flood peaks in hydrological modelling. A fairly new approach that can improve rainfall quantification uses so-called opportunistic sensors (OS). OS are sensors that were not originally designed to provide high-quality rainfall data or any rainfall data at all. However, they typically have a much larger density than official rain gauges. Examples include commercial microwave links (CML) or personal weather stations (PWS). The potential of OS for improving rainfall estimates has be shown by Bárdossy et al. (2021) and Graf et al. (2021), but a systematic investigation of this data source using hydrological models is still to be carried out.
Your Tasks
Research goals:
One research goal will be to investigate how rainfall interpolations using information from opportunistic sensors can improve hydrological modelling. The underlying research question was posed by Baŕdossy and Anwar (2023), namely “[w]hy do our rainfall–runoff models keep underestimating the peak flows?”. They conclude that the rain gauge density used for interpolating rainfall fields has a significant impact on this question. Given their much larger density, using data from opportunistic rainfall sensors should have two benefits: They should yield better estimates of catchment precipitation and also capture more extreme precipitation events. Hence, it is a promising research goal to improve and evaluate the performance of OS data with sub-hourly temporal resolution. For this, techniques to merge OS data with rain gauge data or weather radars have to be developed or improved, implemented, tested and compared.
References:
Bárdossy, A., Seidel, J., & El Hachem, A. (2021). The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrology and Earth System Sciences, 25(2), 583–601.
Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., & Bárdossy, A. (2021). Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37, 100883.
Bárdossy, A. Anwar, F. (2023). Why do our rainfall–runoff models keep underestimating the peak flows?, Hydrol. Earth Syst. Sci., 27, 1987–2000.
Your Profile
Prerequisites:
- MSc in hydrology, environmental sciences, hydrogeology, water management (or similar) or in data sciences, statistics, applied mathematics.
- Skills in programming (e.g. python, matlab, julia)
- Skills at scientific writing and presentation
- Ability to work independently and in a team
- Willingness to learn new concepts and methods
- Experience (e.g., coursework, thesis work) in hydrological modelling
- Willingness to contribute to the goals and culture of the research group
Further Prerequisites:
- Resume/CV showing the applicant’s background, professional skills, a list of publications and oral and poster presentations as well as additional achievements (scholarships, awards etc.)
- M.Sc., Dipl.-Ing. or equivalent degree in Civil Engineering, Water Resources Management, Environmental Engineering or related sciences
- B.Sc. in Civil Engineering, Water Resources Management, Environmental Engineering or related sciences
Copies of Certificates and Transcripts, including all undergraduate level certificates and university degrees. All documents, which are not in English or in German, must be accompanied by copies of a legally certified English translation (for the application we will accept copies; but please be aware, that originals or legally certified copies will be needed for the final phase. In case any differences between the copies and the originals show up, the application will be dismissed.)
Please make sure, that the copies of the transcripts show not only the grades but also explain the home grades’ system (please add copy of the description of grade scale).
- At the time of application, generally no more than 6 years should have passed since the last degree was gained.
- Only international (non-German) applicants can be accepted. At the time of application the candidate must not have been resident in Germany for more than the last 15 months.
- Unless native speaker: proficiency in English (e.g. TOEFL, IELTS, etc.), or proof that M.Sc. and B.Sc. programs were held in English.
- 2 Reference letters from university professors from the applicants home university, issued during the last 2 years.
- Motivation letter describing the applicant’s work experience and research goals (1 page).
Your Benefits
Research Environment:
This research will be embedded into the Chair of Stochastic Simulation and Safety Research for Hydrosystems (LS3) at the IWS, Faculty of Civil and Environmental Engineering. Depending on qualification of the candidate, a formal association of the project to the SC SimTech and the Cluster of Excellence in Data-Integrated Simulation Science is possible and advisable.
Employment and compensation information
Maximal Funding Period or Duration of Employment: 48 months
Type of Funding: Scholarship
Compensation: 1300 € per month
Percentage of weekly working hours (usually 39.5h = 100%):100%
Employment at the cooperation partner:
Location: Stuttgart, Campus Vaihingen
If Location other than Stuttgart or additional location(s): N/A
Contact Details
Contact person: Dr. Gabriele Hartmann
Mail: gabriele.hartmann@f02.uni-stuttgart.de
Phone: +49 711 685 66585
Website: https://www.iws.uni-stuttgart.de/ls3/
At the University of Stuttgart, we actively promote diversity among our employees. We have set ourselves the goal of recruiting more female scientists and employing more people with an international background, as well as people with disabilities. We are therefore particularly pleased to receive applications from such people. Regardless, we welcome any good application.
Women who apply will be given preferential consideration in areas in which they are underrepresented, provided they have the same aptitude, qualifications and professional performance. Severely disabled applicants with equal qualifications will be given priority.
As a certified family-friendly university, we support the compatibility of work and family, and of professional and private life in general, through various flexible modules. We have an employee health management system that has won several awards and offer our employees a wide range of continuing education programs. We are consistantly improving our accessibility. Our Welcome Center helps international scientists get started in Stuttgart. We support partners of new professors and managers with a dual-career program.
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