The changing patterns of extreme hydrological and meteorological phenomena, such as floods and droughts, present significant challenges to both society and science. Accurately predicting and modeling these phenomena is crucial for effective disaster management, designing climate change adaptation strategies, and sustainable water resources management. The coupled effects of climate change with concurrent rapid landscape changes, such as forest disturbance, lead to increased uncertainty in hydrological predictions. In this field, the role of Machine Learning (ML) and Artificial Intelligence (AI) is emerging as promising approaches enabling us to more reliably simulate dynamic natural systems under changing boundary conditions, reducing uncertainties in predictions, and disentangling cross effects of principal driving forces of hydrological change.
The proposed research project aims to explore the potential and limitations of the use of Machine Learning (ML) models coupled with data from automated hydrometeorological sensor networks. Flood and drought modeling will be done on different spatial and temporal scales, using the network of experimental catchments in montane catchments, from event-scale to long-term perspective. Simulations will focus on hydrological extreme phenomena in mid-latitude montane basins, experiencing effects of climate warming, forest disturbance, or land use change, being a sensible indicator of hydrological change. Different ML algorithms will be employed, such as SVM, ANN, CNN, LSTM, or DL. Data from the sensor network in experimental catchments, comprising hydrological and meteorological monitoring at the high temporal resolution, combined with long-term observations at complex basins and supporting spatial data will be used as a basis.
We are seeking a highly motivated, independent, early-career researcher with a clear research vision and team spirit. The candidate should have a strong background in hydrology and/or geosciences, with a focus on hydrological modeling, hydroinformatics, and skills in machine learning and Python coding. Results of the candidate's independent research should be evidenced by relevant publications.
This project will be carried out under the supervision and mentoring of prof. Jakub Langhammer, and the candidate will be fully involved in the research activities of the Hydrology Research Group (http://hydro.natur.cuni.cz/) at Faculty of Science, Charles University. It is expected that the candidate will be involved in the ongoing research projects focusing on the hydrological impacts of climate change on peat and snow hydrology, as well as in the broader team activities, and will have the opportunity to collaborate with the partner team from the University of Zurich. We expect the candidate to publish results in high-quality hydrological journals such as Journal of Hydrology, Hydrology and Earth System Sciences, or Water Research.
Salary: co-founding 1000 EUR/month is ensured
Co-founding resources: Department of Physical Geography and Geoecology
Department: Department of Physical Geography and Geoecology
Supervisor: prof. RNDr. Jakub Langhammer, Ph.D.
Phone: +420 - 739 488 268
Position available from: January 1, 2024
Deadline date for applications: July 21, 2023