The Luxembourg Institute of Science and Technology offers an outstanding opportunity for joining its newly created doctoral training unit (DTU) in hydrological sciences : HYDRO-
CSI : Towards a holistic understanding of river systems : Innovative methodologies for unraveling hydrological, chemical and biological interactions across multiple scales .
The total number of positions in this DTU is 14 embracing four complementary thematic clusters on (1) high frequency monitoring of hydrological processes, (2) new hydrological tracers, (3) remote sensing applied to hydrology, and (4) hydrological forecasts and predictions under change.
Academic partners are the TU Vienna (Austria), University of Luxembourg, Wageningen University (The Netherlands) and the Karlsruhe Institute of Technology (Germany).
The DTU HYDRO-CSI is funded in the framework of the PRIDE scheme of the Luxembourg National Research Fund (FNR), and coordinated by Prof. Laurent Pfister.
The main objective of the DTU HYDRO-CSI is to train a new generation of highly skilled experts with a view to contribute to solving some of the most pressing challenges related to water resources research and management : hydrological system complexity, non-
stationarity of boundary conditions, high-frequency monitoring of environmental processes, global change impact assessment.
The offered fully paid PhD position is embedded in cluster (3) of the DTU and focuses on the estimation of the different terms of the soil water balance equation for different layer depths by interpreting and combining the remote sensing-
based microwave measurements. The position is envisaged to start between September 1st 2018 and December 1st 2018 and will extend over a maximum duration of 4 years.
The PhD candidate will be part of the Remote sensing and natural resources modelling research group (Remote) at the Department of Environmental Research and Innovation (ERIN) at LIST.
Furthermore, the PhD candidate will obtain his / her PhD from Wageningen University (Prof. R. Teuling) and will also work in collaboration with Prof.
N. Teferle (University of Luxembourg).
As climate change impacts the shortage or surplus of water in many parts of the world there is an increased need for monitoring and predicting different hydrology-
related fluxes and states at both local and global scale. A common way to do this is to combine hydro-meteorological modelling and in situ measurements.
Over the last decade developments and applications of different microwave remote sensing-based techniques have strengthened our capacity to monitor the hydrological cycle at high temporal and spatial resolution at large scale, most significantly in data-
poor areas of the world, where ground observations are sparse. Such measurements are used not only to improve our understanding of hydrological processes, to build and drive adequate hydrological models, but also to calibrate, evaluate and periodically control these numerical models.
Among the most critical hydrological variables to monitor, one can mention precipitation, soil moisture at different soil depths, evaporation and transpiration, as well as groundwater recharge and surface and subsurface runoff.
Previous studies have shown that many of the terms of the soil water balance equation can be estimated using advanced microwave remote sensing-
based measurement techniques, such as GNSS, scintillometers and scatterometers.
The main objective of the PhD project will be to estimate directly or indirectly the different terms of the soil water balance equation for different layer depths by interpreting and combining the signals recorded by different measurement instruments.
The PhD applicant will use state-of-the-art retrieval techniques developed by the three partner organisations to derive soil moisture from scatterometer measurements, precipitation, soil moisture and atmospheric vapour content from GNSS and heat flux from scintillometer measurements.
Within a data assimilation framework, the estimates of both measured and non-measured hydro-meteorological variables shall be obtained and their uncertainties characterized.
The combination of the different estimates shall ultimately provide new insights on water storage dynamics across different temporal and spatial scales, as well as land surface-atmosphere interaction.
The second objective is to make use of these improved estimates and possible synergies to reduce the predictive uncertainty of hydrological models.
The PhD candidate will be jointly supervised by Drs. P. Matgen and Marco Chini (LIST) and Prof. R. Teuling (Wageningen Uni.
The selected candidate has a strong background in hydrology and microwave remote sensing measurements and she / he is confident in managing big and heterogeneous datasets.
Good skills in programming are therefore essential. Experience in signal processing and / or numerical modelling and / or data assimilation are considered as assets.
Place of employment and main place of work