Projects LHWM

Current research projects

  1. DRY-2-DRY: Do droughts self-propagate and self-intensify? (read more)
  2. HYDRAS+: Improving drought monitoring through assimilating multi-source remote sensing observations in hydrologic models (read more)
  3. SAT-EX: Global impacts of hydrological and climatic extremes on vegetation (read more)
  4. STR3S: Stress on TRanspiration Sensed from Satellite Systems (read more)
  5. SMOS+ET2: On the value of SMOS data to derive global evaporation (read more)
  6. LandFlux: The GEWEX LandFlux Project (read more)
  7. Optimized discharge assessment based on hydrodynamic simulation results (read more)
  8. Use of in situ soil moisture observations for data assimilation in distributed hydrological modelling (read more)

Finished research projects

  1. SMOS+ET1: On the value of SMOS data to derive evaporation over Australia (read more)
  2. SMOS+Hydrology (read more)
  3. Integrating Radar Remote Sensing, Hydrologic and Hydraulic Modelling for Surface Water Management (read more)
  4. Epidemoist: Improving epidemiological modelling using satellite derived soil moisture proxies (read more)
  5. Copula-based models for generating design rainfall (read more)
  6. Assessment of the use of rainfall generators for the development of flood risk maps (read more)
  7. Assessment of land surface characterization and implications for distributed hydrological modelling (read more)
  8. Hydrologic modeling for surface water management – estimation of the catchment averaged evapotranspiration (read more)
  9. Stochastic rainfall generation accounting for climate changes (read more)
  10. Floodmoist: Flood mapping and soil moisture retrieval for improved water management (read more)
  11. Spatial scaling uncertainty in hydrological variables based on remote sensing (read more)
  12. Water and sediment budgets of Lake Tana for optimisation of land management and water allocation (Ethiopia) (read more)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DRY-2-DRY: Do droughts self-propagate and self-intensify?dry2dry

Droughts cause agricultural loss, forest mortality and drinking water scarcity. Still today, our knowledge of how meteorological droughts start and evolve remains limited. Yet land feedbacks have been suggested as a key reason for their occurrence. Climate and forecast models seem immature when it comes to representing these feedbacks. Do climate models underestimate the feedback from land on climate? If so, future drought aggravation may be greater than expected. DRY–2–DRY uses novel satellite observations in combination with new mechanistic models. We aim to uncover the triggers of meteorological droughts and the potential of land management to dampen them. This will enable progress towards accurate short-term and long-term meteorological drought forecasts.

website: http://www.dry2dry.org/

 

HYDRAS+: Improving drought monitoring through assimilating multi-source remote sensing observations in hydrologic modelsSmos + Hydrology

Motivated by climate change and its impact on the availability or excess of water in several parts of the world, several agencies and research institutions have taken initiatives in the monitoring of the hydrologic cycle at global scale. Monitoring systems are often based on a combination of land surface modelling and remote sensing, where satellite data are assimilated into the model. The main objective of HYDRAS+ is to develop and improve techniques for the processing and assimilation of multi-source remote sensing products into large-scale hydrologic models with the aim to improve current worldwide early-warning systems for droughts. This will be based on an intelligent use of complementary remote sensing data and innovative modelling techniques. Existing algorithms will be further evaluated and adapted to the current needs and supplemented with new techniques to optimally use the available remote sensing data sets.

website: http://www.hydrasplus.ugent.be/

 

SAT-EX: Global impacts of hydrological and climatic extremes on vegetation

Recent advances in satellite Earth observation – with the development ofSAT-EX consistent global historical records of crucial environmental and climatic variables – provide new means to start unraveling the processes driving long-term changes in climate extremes, and understanding the impact of these changes on terrestrial ecosystems. In addition, these datasets offer an observational benchmark to evaluate the skill of climate models at representing climatic extremes and vegetation dynamics. SAT–EX raises with the goals of revealing how droughts, heatwaves and extreme rain events have changed in frequency and intensity over the past three decades, to uncover the causes behind these changes and the consequences for terrestrial vegetation. The ability of our current IPCC climate models to estimate these processes will be evaluated by comparison to novel satellite-based data records.

website: http://www.sat-ex.ugent.be

 

STR3S: Stress on TRanspiration Sensed from Satellite Systems

Accurate, large-scale observations of plant stress are needed to STR3Simprove modelling of global ecosystem transpiration and the implications of vegetation stress for the global carbon and water cycles. These observations may already be available. The GOME-2 and OCO-2 instruments can sense chlorophyll fluorescence, emitted by the chemical reactions that occur during photosynthesis, and is thus (a priori) sensitive to plant stress. Recent studies have concentrated on using GOME-2 data to investigate forest primary production. Here, we propose a different use: to uncover how vegetation stress impacts ecosystem transpiration. Finally, STR3S goals are in line with the European Space Agency (ESA) top priorities, in anticipation of the launch of the fluorescence-dedicated FLEX mission.

websitehttp://www.str3s.org/

 

SMOS+ET2: On the value of SMOS data to derive global evaporation

SMOS+ET2 is a continuation of SMOS+ET1 in which the emphasis isSMOS+ET2 placed on the global scales. Results from SMOS+ET2 will enable us to (a) assess the quality of SMOS-derived geophysical variables at large spatial scales using an alternative to more traditional approaches (i.e. validation of model performance after assimilation of SMOS data, as opposed to standard comparisons to in situ soil moisture data), (b) show the value of SMOS data for the estimation of global-scale terrestrial evaporation, and consequently contribute to a better understanding of the global water and energy budgets, and (c) increase the awareness for the value of SMOS data for global-scale hydrological applications through the dissemination of the results of this study. As a result, SMOS+ET2 will highlight the value of SMOS for an application that was not explicitly defined in the main mission objectives, thus it will help expand the SMOS scientific community. 

  

LandFlux: The GEWEX LandFlux Project

The World Climate Research Programmes (WCRP) Global Energy andLandflUX Water Exchanges (GEWEX) Data and Assessments Panel (GDAP) is currently running the LandFlux initiative, aiming at deriving global land surface heat flux products that can be used together with other GEWEX endorsed satellite products to allow a comprehensive observation-based analysis of the water and energy cycles. LandFlux is coordinating two interrelated research efforts that seek to: (i) intercompare long-term gridded surface flux data sets and identify their skill and reliability (i.e. product-benchmarking), and (ii) simulate and intercompare evaporation models to identify algorithms appropriate for developing a global flux product (i.e. model-benchmarking). Intercomparison efforts have resulted in some published product evaluations and the generation of a multi-year global merged benchmark synthesis product based on the analyses of existing land evaporation datasets. This monthly product covers the periods 1989-1995 and 1989-2005; work is ongoing to produce a similar product at daily resolution and covering 1980-2010.

website: http://hydrology.kaust.edu.sa/Pages/GEWEX_Landflux.aspx

 

Optimized discharge assessment based on hydrodynamic simulation results

Discharge assessment through rating curves is a widespread technique in the field of hydrologic monitoring. A rating curve combines simultaneous discharge and height information in a measurement point and describes a relationship between both. Thus, high frequency water level records can be transformed into discharge information based on only a limited number of flow measurements. This is an effective method to avoid the installation of expensive discharge measurement stations needing an intensive maintenance. However, a few limitations occur when applying this technique. First, most of the frequently used rating curve equations presume a steady state situation with uniform flow. Second, a well scattered availability of calibration measurements is essential to establish a convenient curve for the whole discharge reach. This is particularly a problem when focusing on flood applications where extreme, often unmeasured situations are point of interest and extrapolation is hence necessary. In practical applications, all three assumptions are regularly violated, inducing important uncertainties in the calculated discharges.

 

Use of in situ soil moisture observations for data assimilation in distributed hydrological modelling

Modelled soil moisture state estimates can be improved by incorporating soil moisture observations. A so called ‘data assimilation’ algorithm calculates a weighted average of the modelled and observed state while accounting for both the model and observation uncertainty. Most often remote sensing products are used as observations because of their ability to catch the horizontal variability of soil moisture patterns. However, some limitations exist. Long return periods and limited vertical extents hinder a full spatio-temporal characterisation. Another important issue is the potential large biases induced by vegetated landcover. In certain cases it could therefore be worthwhile to replace/combine the remote sensing products with in situ observations which address the abovementioned issues more properly. First of all it is examined whether a ‘smart’ configuration of the in situ network can compensate for the poor horizontal extent of this type of measurements. Furthermore the optimal integration of in situ observations and remote sensing products in the data assimilation framework is a subject of research.

 

SMOS+ET1: On the value of SMOS data to derive evaporation over Australia

Terrestrial evaporation is an essential component of the climate system that smoset1links water, energy and carbon cycles. Evaporation regulates the interaction between land and atmosphere through multiple feedbacks on climate, shaping local precipitation and temperature. Despite this crucial importance, evaporation remains one of the most uncertain components of the global hydrological cycle. The main objective of SMOS+ET1 is to investigate the potential of the Soil Moisture and Ocean Salinity (SMOS) observations to improve satellite-based evaporation estimates. The study considers the entire continental Australia and focuses on the period from 2010 to 2013. The GLEAM (www.GLEAM.eu) evaporation model is used as core method. SMOS soil moisture observations are assimilated in GLEAM (www.GLEAM.eu), whereas retrievals of the vegetation optical depth are used in the forcing dataset. In addition, in-situ eddy-covariance measurements of evaporation from FLUXNET and soil moisture measurements at different soil depths from the International Soil Moisture Network (ISMN) are used for independent validation of the simulations.

 

SMOS+Hydrology

satellite

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.

 

Integrating Radar Remote Sensing, Hydrologic and Hydraulic Modelling for Surface Water Management

Radarsat

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 is performed at 4 different laboratories (Ghent University, Université catholique de Louvain and Centre de Recherche Public-Gabriel Lippmann). (read more)

 

Epidemoist: Improving epidemiological modelling usisatellite derived soil moisture proxies

Bluetongue is an arboviral disease that causes high mortality, mainly in certain breeds of sheep 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)

 

Copula-based models for design rainfall

Copula

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)

 

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)

  

Assessment of land surface characterization and implications for distributed hydrological modelling

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)

 

Hydrologic modeling for surface water management – estimation of the catchment averaged evapotranspiration

Scintillometer

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)

 

Stochastic rainfall generation accounting for climate changes

overstroming1

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)

 

Floodmoist: Flood mapping and soil moisture retrieval for improved water management

The objective of the FLOODMOIST project is to investigate the benefits of jointly assimilating remotely sensed observations of soil moisture and flood extent into coupled hydrologic-hydraulic models. The research will mainly focus on the improvement of soil moisture estimations from remotely sensed microwave observations, the estimation of the uncertainty in flood maps derived from remote sensing data and the joint assimilation of both products into coupled hydrologic-hydraulic models.

 

Spatial scaling uncertainty in hydrological variables based on remote sensing

Downscaling

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)

 

Water and sediment budgets of Lake Tana for optimisation of land management and water allocation (Ethiopia)

The overall developmental objective of the project is to contribute to sustainable management of land and water resources in Lake Tana basin. The project will provide scientific evidence and raise awareness about water and sediment problems associated to the lake and show alternative options to minimize these problems and bring sustained use of the land resources and lake. Academically, this project will strengthen the capacity of BDU, BWR and EPLAUA staff.