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PhD

Open PhD positions
All topics of Ph.D. research are open in the framework of recent grant projects.
The closest deadline for application is April 30, 2021 - however, the candidates should contact me in advance to discuss the details of the research program.
Further details and application are available in the Study Information System:  tinyurl.com/y64rdjob


Impact of climate change and landscape alteration on the dynamics of hydrological extremes


Changing climate, forest disturbance and stream modifications are the most significant drivers of runoff regime alterations in mid-latitude mountain catchments. Rising variability of runoff response, prolonged periods of drought, and changes in seasonal runoff distribution emerge as the key symptoms. However, the observed changes are complex and have different scope and magnitude in different environments.


The goal of the Ph.D. research project is the attribution of the effects of climate change and the landscape alteration on runoff response in mid-latitude montane catchments with a focus on extreme events - peak flows and droughts. The research will be carried out at different spatial scales, ranging from the small experimental catchments, equipped with the own sensor network monitoring and requiring field monitoring, to the complex basins with long-term observations. The attribution of the drivers to the given aspects of hydrological change will be done mostly using geostatistical analysis and modeling.


The proposed Ph.D. research will require skills in hydrological analysis and/or modeling, interest in learning new approaches, the ability of teamwork as well as independent research. Motivated students from geographic, hydrologic, or geoscientific disciplines with scientific curiosity and relevant skills are encouraged to apply.


This topis is launched under the STARS support scheme




Machine learning approaches in the modeling of hydrological extremes


The PhD project is focused on the applications of machine learning (ML) and deep learning (DL) models for the analysis of hydrological extremes in montane basins.

The selected ML and DL techniques are used for the analysis of conditionality and links between causal factors, changes in frequency, seasonality, magnitude or for the search for regularities and possibilities of prediction of hydrological extreme phenomena, including both floods and droughts.

The principal modeling techniques, used for this thesis include neural networks, support vector machines, and Deep Learning models. 

The study area presents the selected stations of the headwaters of montane streams in different physiographic conditions with long observation time series, supplemented by experimental high-frequency monitoring at the stations, operated by the Department of Physical Geography and Geoecology.


Changing dynamics of hydrological extremes in montane areas

The PhD project is focused on the analysis of changing occurrence of hydrological extremes in montane basins, identification of their driving forces and predictions of the effects in conditions of climate change.

Geostatstic and modeling techniques are used to analyze the changes in frequency, variability, seasonality, and magnitude of extreme events including both floods and droughts and to predict the potential changes in conditions of climate change. The research is based on long time series of observations, supplemented by experimental high-frequency monitoring at the stations, operated by the Department of physical geography and geoecology.   

Various approaches will be used to study the conditionality and links between causal factors, changes in frequency, seasonality, magnitude or for the search for regularities and possibilities of prediction of hydrological extreme phenomena including both floods and droughts. ranging from geostatistic analysis to non-linear modeling. 

The research is focused on headwater catchments of the Czech boundary mountains. The study sites will comprise selected catchments in headwaters of montane streams in different physiographic conditions with long observation time series, supplemented by experimental high-frequency monitoring at the stations, operated by the Department of physical geography and geoecology.


UAV monitoring of the dynamics of fluvial processes 


The PhD project is focused on the exploration of recent fluvial dynamics of streams using  advanced non-invasive monitoring techniques.

The research focuses on the analysis of the changing dynamics of fluvial processes in relation to the triggering factors - climate change, forest and landscape disturbances, changes in fluvial connectivity and anthropogenic modifications of the riverscape.

The research employs a combination of advanced techniques of instrument monitoring and numerical analysis and simulation. In particular, the monitoring involves the application of unmanned imaging techniques (UAV), ground LiDAR imaging, RFID monitoring of daytime running and optical granulometry. The tools for geoinformatic analysis and modeling are used for analysis.

The study area is primarily the headwaters of the montane streams in the Šumava (Bohemian Forest) region, possibly completed by the reference streams in areas with different dynamics of fluvial processes.


UAV monitoring and modeling of streamflow dynamics

The PhD project explores new techniques for monitoring streamflow dynamics using unmanned aerial systems (UAV, UAS). 

The aim is to develop a robust method for streamflow monitoring by UAV, applicable for flow velocity determination in ungauged streams or for assessment of extreme flows. 

The research should develop and test new approaches for indirect quantitative assessment of flow velocity and its distribution in montane channels, using UAV imaging. UAV image velocimetry analysis will be based on the identification and tracking of seeded particles in the stream from hi-speed UAV imaging. The image analysis will extract the particle trajectories and velocities from the imagery stack and determine the flow velocity patterns. The results will be validated by continuous monitoring and in-situ velocity measurement and used for accuracy improvements of streamflow modeling of montane streams. 

The study area is located in the headwater part of the Sumava mountains (Czech Republic), where the research will benefit from the long-term research efforts and the experimental monitoring network.



What you could expect?

  • An international team of Czech and foreign students
  • Active team-wide field campaigns for imaging, monitoring, measurement
  • Working with new device monitoring and mapping technologies
  • Working with new geoinformatics tools
  • Regular discussion meetings and workshops giving you feedback on your work


What will be expected from you?

  • Interest in science
  • Passion for technologies
  • Independence and initiative in research
  • Interest in learning new approaches and techniques
  • Geoinformatic literacy
  • Team spirit
  • Engagement in fieldwork campaigns



How to apply?
For more details and for application please contact me at jakub.langhammer@natur.cuni.cz.

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