Detailed project information

In many parts of the world, groundwater is an important source for drinking water. Also, the magnitude and

persistence of low flows of major rivers heavily depend on groundwater discharge. Moreover, levels of inland

water such as lakes, smaller rivers and brooks are dependent of groundwater sapping, especially in times of

drought. It is expected that climate change and population growth will adversely affect groundwater resources in

many parts of the world. Monitoring and predicting such changes is therefore imperative. Although remote sensing

is increasingly used for mapping hydrological states, up to now, groundwater depth and groundwater storage

fluctuations have been largely exempt from remote sensing applications. The reason is that most remote sensors

are either unable to penetrate deep enough to encounter groundwater, or (in case of gravity remote sensing) only

detect very large-scale storage changes. The goal of this project is to extend the domain of remote sensing

application to groundwater monitoring and assessment. We aim to use multi-sensor remote sensing together with

a large-scale groundwater model to map groundwater dynamics in the combined Rhine-Meuse basin.At this stage of the research, we have successfully developed a coupled groundwater-land surface model. The

documentation about it can be found in our first paper (Sutanudjaja et al., 2011). Using this groundwater model

and a remote sensing soil moisture product called as European Remote Sensing Soil Water Index (ERS SWI,

Wagner et al., 1999) , we try to answer the following research questions:

a. To what extent can a remote sensing product improve groundwater models when used for groundwater

model calibration?

b. What are the improvements in groundwater level predictions when remote sensing information is

assimilated into groundwater models?

Following those research questions, we will perform two exercises: model calibration and data assimilation. This requires extensive computing facilities.