Projects LHWM
Current research projects
Spatial scaling uncertainty in hydrological variables based on remote sensing
The objective of this project is to investigate the spatial scaling of remotely sensed soil moisture and rainfall based on different remote sensing products. The project aims at developing a methodology which allows for up- and downscaling of remotely sensed observations, and to assess the uncertainty in the obtained products. Deriving scaling laws and modelling the uncertainty on scaled products are both of uttermost importance when such data are applied in hydrological model studies. (read more)
Hydrologic modeling for surface water management – estimation of the catchment averaged evapotranspiration
An accurate understanding of the behavior of the different water and energy balance terms at the catchment scale is of interest for hydrologic modeling and operational flood forecasting. Flood forecast models are usually based on two different kinds of meteorological inputs, more specifically the catchment averaged precipitation and evapotranspiration rates. (read more)
Copula-based models for design rainfall
For the dimensioning of hydraulic structures one often uses design storms, with a certain return period, or long timeseries of simulated rainfall to make sure the design satisfies all risk-related norms. Until recently, this exercise was usually carried out using extreme value distribution functions (e.g. Gumbel) for the estimation of intensity-duration-frequency (IDF) relations and stochastic rainfall models. (read more)
Stochastic rainfall generation accounting for climate changes
Rainfall is an essential variable of the hydrological cycle, which in turn is an indispensable part of many of the environmental sciences. Therefore, it is to everyone's best interest to have decent rainfall records at one's disposal. In some cases, the mere use of historical rainfall records can be justified. For certain other applications (e.g. hydrological design studies), a more extensive dataset will be required to obtain satisfactory results. In such cases, stochastic rainfall models can be regarded as valuable alternatives to using observed rainfall time series. (read more)
Assessment of the use of rainfall generators for the development of flood risk maps
In order to make reliable flood risk maps, coupled hydrologic-hydraulic models are used to predict the chance of flooding at different locations on and along a river system. However, such modelling requires long time series of rainfall and corresponding evapotranspiration rates (>500 year) which are not available from measurements. In this project, rainfall generators are tested and improved for their use in developing flood risk maps. (read more)
Uncertainty assessment for hydrologic modeling
The research aims at developing new insights with respect to uncertainty assessment and uncertainty analysis related to distributed hydrological model predictions. The general objective of this study is to integrate uncertainty into a distributed hydrological model with a focus on soil and vegetation related inputs and parameters. The designed uncertainty model should allow for the quantification of the impact of the input errors on hydrologic model predictions. In particular, the study wants to gain insight into the uncertainty associated with some 'standard' information, as often used in hydrologic modelling if simulations in ungauged basins are to be made. (read more)
SMOS+Hydrology
The Soil Moisture and Ocean Salinity (SMOS) satellite mission, launched in November 2009, is routinely providing novel accurate data at the global scale. However, the integration of low resolution SMOS observations into finer resolution land surface models poses significant challenges, through which its potential for operational hydrology is at present poorly understood. Therefore, the SMOS+Hydrology project, funded by the European Space Agency (ESA) support to science program, aims at developing a robust end-to-end method for the integration of SMOS data into land surface models, in order to assess the usefulness of SMOS with respect to flood forecast in two large river basins, the Upper Mississippi Basin in central USA, and the Murray Darling Basin in Eastern Australia. This project is in collaboration with Centre d'Etudes Spatiales de la Biosphère, France, Princeton University, USA, and Monash University, Australia.
Project website: http://www.hydro-smos.be
Assessing drought impacts of climate change on future water resources
Climate change is expected to result in more extreme events with respect to precipitation. Many research efforts are being made to improve the prediction of extreme rainfall events, assess the impact of the consequential floods. However, less research has been performed with respect to the impacts of the increased extreme drought periods on the future water resources. This research project aims at estimating droughts under future climate change and its impact on the future water resources in Flanders, Belgium. (read more)
Finished research projects
Integrating Radar Remote Sensing, Hydrologic and Hydraulic Modelling for Surface Water Management
The main aim of this project is to investigate how radar remote sensing of soil moisture and flood extents can be used to improve predictions of hydrologic and hydraulic models through data assimilation. This project consists is performed at 4 different laboratories (Ghent University, Université catholique de Louvain and Centre de Recherche Public-Gabriel Lippmann). (read more)
Improving epidemiological modelling usisatellite derived soil moisture proxies
Bluetongue is an arboviral disease that causes high mortality, mainly in certain breeds ofsheep but also in other domestic and wild ruminants. It is caused by the bluetongue virus that is transmitted between ruminant hosts almost entirely by the bites of Culicoides biting midges (Diptera: Ceratopogonidae). (read more)
Determination of hydraulic soil parameters through a combination of remote sensing and hydrologic modeling
The main objective of this project is to develop a methodology to retrieve spatially distributed soil physical parameter values using a combination of remote sensing and hydrologic modeling. This project is performed in association with five partners, where every group focuses on a specific topic. (read more)