The Hydrological and Climate Variability Research Group of the Czech University of Life Sciences Prague (Faculty of Environmental Sciences) is opening positions for PhD students.
High-profile international candidates from engineering, geosciences or computer science, wishing to achieve a further step in their career in the dynamic research environment of the Czech University of Life Sciences Prague are welcome to apply. Candidates with previous experience in data analysis of big data sets and solid background in basic science will be considered with particular attention. Potential research specializations involve the study of hydroclimatic extremes, the application of remote sensing data in hydrology and climatology, the integration of paleoclimatic reconstructions in hydrological modeling, the quantification/modelling of global water cycle, modelling of hydrological balance and extremes at ungauged sites, snow hydrology, hydrodynamics of porous media etc.
Within the 4 years study period, the PhD students will be rewarded with 20,000-25,000 CZK/month net (785-980 EUR) + bonuses. We also currently seek a candidate to work at the 3-year research project “XEROS: eXtreme EuRopean drOughtS: multimodel synthesis of past, present and future events”, see here. This project position should be combined with PhD study. The PhD study starts in September 2019, postdocs and project researchers could start as soon as possible.
Annotated topics for PhD research are given bellow. In addition, the applicants are welcome to propose their own research fitting into the scope of Hydrological and Climate Variability Research Group.
How to apply
The applicants selected in the first round for an interview have to apply via the admission procedure at the Faculty of Environmental Sciences and will be guided by Ms. Petra Kadlecova. Do not hesitate to contact her per email or visit our webpage for detail information.
Application deadline: March 15 2019
- Motivation letter
- Academic letter of recommendation (by your supervisor)
- Diploma + Diploma supplement (Transcript of records)
Enhancement of satellite data efficiency for hydrological modeling of ungauged mountainous catchments
Although satellite missions have enhanced our hydrological data collection efficiency, it has become apparent that hydrological processes demonstrate a non-linear relationship with both temporal and spatial scale, which hinders our ability to incorporate them into existing hydrological modeling applications. This PhD aims to remedy this challenging issue by exploring the structural and spatial consistency of satellite data and examining the effect of spatial aggregation of precipitation as observed in different scales to study its areal representation. The region of study will be Nepal/Tibetan plateau, since there is a relatively dense station network with long hydroclimatic records, that can help to cross-validate the efficiency of the satellite products. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Multi-source quantification of global water cycle components
Our understanding of Earth’s water cycle has improved dramatically due to the vast amount of different data sources. Remote sensing data and model simulations, complemented the traditional surface measurements and offered an unprecedented coverage in a global scale. However, this unique opportunity to obtain a robust quantification of global water cycle fluxes has been hindered by the uncertainty revealed in the first attempts of the unification of different data-products. Thus, it remains a challenge to constrain the variability stemming from observations by various sources, which will help us understand the effect of global warming to water resources in general. The ideal candidate for this position would have strong background in atmospheric sciences and some experience in handling big datasets. Knowledge of R/Python/Julia, solid statistical skills and/or proficiency with parallel computing are also considered valuable assets.
Application of data-driven methods for catchment classification over Earth, Mars and Titan
What is common between Earth, Mars and Titan? All of these three planetary systems have or had active hydrological cycles and their geological fingerprints can be found in their catchments. The aim of this topic is to identify regions that share geomorphological characteristics between these three planets. To achieve this the successful applicant will explore different dimensionality reduction techniques (i.e., support vector machines, self-organizing maps) to effectively classify the catchments and then compare them to decipher the underlying processes. The ideal candidate would have some background in hydromorphology, geology and/or remote sensing. Knowledge of R/Python/Julia, experience with ESA EO data platforms and/or proficiency with classification techniques are considered assets.
Reconstruction of European hydroclimate during the last 2000 years
In the last decades there has been a growing number of reconstructions of past climates from proxy data. Over the same period, our efficiency in hydrologic and climatic modeling increased geometrically. Therefore there is a need of high quality gridded reconstructions of temperature and precipitation that can be used as input for the hydrological models or for validation purposes of Earth System Models. This PhD aims to develop a complete high-resolution hydrological reconstruction for Europe during the last two millennia. Special focus will be given in the major periods of long-term (decadal or longer) drought, also cited as mega-droughts, and their links with global atmospheric circulation and the hydrologic cycle in general. The individual selected for this position should have a degree related to (paleo-) climatic studies or hydrology and will collaborate with researchers from Helmholtz Centre for Environmental Research (UFZ; Germany) and/or Global Institute of Water Security (GIWS; Canada). Knowledge of R/Python, experience with big datasets and/or proficiency with stochastic modelling are considered assets.
Investigation of extreme states in Earth’s global hydrological cycle
One of the major hypotheses related with global warming is the intensification of global hydrological cycle. During Earth’s past there have been periods that global climate was dramatically different than the one we experience in Holocene. This extreme states propagated in different time scales with some times abrupt transitions, which still leaves unanswered questions about the links between temperature and water in our planet. The successful applicant will use paleoclimatic reconstructions in order to investigate and simulate the range of hydrological cycle natural variability using process-based and stochastic models. He should demonstrate some solid background in hydrology or atmospheric sciences. Proficiency in R/Python, knowledge of stochastics and prior experience with paleoclimatic data are considered assets.
Assessing the effects of climate change adaptation measures at agricultural and forest sites
Faculty of Environmental Sciences, Czech University of Life Sciences Prague operates several experimental basins dedicated to monitoring, design and assessment of climate change adaptation measures. Currently the most equipped site is small agricultural area (cca 5 km2) with detailed meteorological, hydrological, soil water and ground water monitoring. Several research topics can be related to the dataset obtained at this experimental site ranging from analysis aimed at understanding the processes related to water resources dynamics in the catchment (such as evapotranspiration, connection between soil, stream and ground water, options for water recycling, water quality modelling etc. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Understanding the anthropocentric water use
According to law, water use in the Czech Republic reaching certain level has to be reported. The available data cover the period 1979-present in monthly time step. Number of questions could be addressed, for instance: does the water use relate to climate/hydrological drivers (such as temperature, stream flow)? How large is the uncertainty related to disaggregation of the reported monthly water use data to daily scale? What (if any) information can be transferred to other countries? Are there any similar datasets allowing for similar studies? The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Multi-scale calibration of hydological models
Typically the hydrological models are designed and calibrated to describe the hydrological process at one temporal/spatial scale. However, it is known, that although the model simulation may appear appropriate at target scale, the skill at other scales could be substantially lower. The study is aimed at development of multi-scale hydrological model allowing for robust calibration (possibly at ungauged/partly gauged basins) and disaggregation and aggregation. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
From climate model ensembles to policy relevant climate change scenarios
Recently, large number of climate change simulations are available for Europe and other regions. Since the uncertainty in climate model projections is very large, especially for characteristics of extremes, it is required to flatten the range of available projections to allow for making strategical decisions. The work will utilize the EURO-CORDEX data (or others) and ensemble reduction or weighting strategies or alternatively will develop probabilistic scenarios. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Forecasting of droughts
Medium and long term drought forecasting provides important information to mitigate negative impacts of drought events. This study is aimed on the development of drought forecasting framework based on the use of various drought indicators, spatial hydro-meteorological information, hydrological modeling, data driven modeling tools, capable to make the drought forecast on different temporal and spatial scales. The meteorological, hydrological and agricultural droughts will be considered. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python/other programming language, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Long term average stream flow and the interannual variability of streamflow
The long term average stream flow, precipitation partitioning into streamflow and evapotranspiration on annual and longer time scale, the dependency on catchment characteristics, role of vegetation cover on precipitation partitioning are expected to be analyzed using frameworks based on Budyko curve. The special emphases will be put on its role on improving hydrological modeling over selected temporal and spatial scales. The applicants should have some background in hydrology, remote sensing data or computer science. Knowledge of R/Python/other programming language, solid basis of statistics and/or proficiency with hydrological modeling are regarded assets.
Creative analysis of arbitrary aspects of mathematical modeling of any types of processes in the environment, model development, software creation
It may be a mathematical modeling of nonlinear processes with one independent variable (time) defined as a system of ordinary differential equations or transport processes of different types described by partial differential equations.
For example: Mathematical modeling of the transport of passive admixtures from any source (or sources) in the simulated flow field in the planetary boundary layer. Comparing the results for the Euler type model (part of Fluent Ansys), the Lagrange model and possibly the Gaussian puff type model.