Geobrief 8 - kngmg

In liquid form, groundwater is the largest
accessible source of freshwater. It serves
as an essential source for drinking water
and it supplies water for agricultural and
industrial activities. During times of drought,
groundwater has a crucial role to sustain
water flow in rivers, lakes and wetlands
(and thus supports ecosystem habitat and
biodiversity), and its large natural storage
provides a buffer against water shortage.
Nevertheless, groundwater is known as a
vulnerable resource. In many areas, ground-
water is being consumed faster than it is
naturally replenished. Due to climate
change and increased human population,
the pressure upon groundwater resources
is expected to intensify. Hence, monitoring
and predicting groundwater variability and
change are imperative.
Remote sensing
However, assessing groundwater condition
– especially over large areas – is difficult
because it requires large amounts of groundbased
measurement data. The most common
way to measure groundwater states is by
measuring groundwater head or level in a
drilled observation well. Yet, in-situ head
measurements are often sparsely distributed
and the coverage of such head monitoring
networks is mostly limited. Consequently,
large-extent groundwater assessments (at
the sub-continental scale and comprising
multiple basins, aquifers and countries)
are still challenging.
During the last decades, spaceborne remote
sensing applications for hydrological studies
have received increased attention due to
their ability to provide spatially and temporally
exhaustive maps of hydrological properties.
Examples include mapping of global
land surface elevation (SRTM: Shuttle
Radar Topography Mission), precipitation
(TRMM: Tropical Rainfall Measuring Mission)
and soil moisture (AMSR-E: Advanced
Microwave Scanning Radiometer-EOS,
SMOS: Soil Moisture and Ocean Salinity
satellite), as well as global land surface
temperature, vegetation and land cover
(MODIS: Moderate-resolution Imaging
Spectroradiometer). Yet, the use of satellitebased
data in groundwater hydrology is still
limited. Up to now, only the mission of
GRACE (Gravity Recovery and Climate
Experiment satellite) is recognized as a
groundwater assessment tool, specifically
to detect groundwater storage dynamics
at a coarse resolution (400 km).
Soil Water Index (SWI)
In my Ph.D. research, we intend to explore
the possibilities of using a satellite-based
soil moisture product – having a better
spatial resolution (50 km) than the GRACE
product – for large-scale groundwater studies.
In this research, we used the spatiotemporal
maps of Soil Water Index (SWI) –
derived by Vienna University of Technology
(Austria), based on the scatterometer signals
from European Remote Sensing (ERS)
satellites. We used the monthly time series
of SWI – which represents the moisture
content of the upper profile soil (≤ 1 m
thickness). Figure 1d is a snapshot of SWI
indicating areas with low soil moisture (dry,
indicated in red) and high soil moisture
(wet, blue) soil conditions.
As a test-bed of this research, we used
the Rhine-Meuse basin (Fig. 1), covering
about 200,000 km 2 and supported by
more than four thousands of in-situ
groundwater head time series (Fig. 1c).
In this research, we explore the potential
of using SWI for large-scale groundwater
modeling, specifically in an empirical
Transfer Function-Noise (TFN) model and
a physically-based groundwater flow model
PCR-GLOBWB-MOD.
Groundwater head prediction
In the first part of the thesis, it is shown
that a correlation exists between observed
groundwater head and SWI dynamics. This
is apparent mostly for shallow groundwater
areas (see the example as shown in Fig. 2a).
For deep groundwater areas, the correlation
may become apparent if a time-lag is
considered (indicating the response time
between the moisture in upper soil layers
and groundwater compartments). Given
this correlation, a groundwater head prediction
based on SWI should be feasible.
Hence, we performed two exercises in
which SWI time series were used as TFN
model input.
In the first exercise – focusing on forecasting
in time – the TFN model parameters
were estimated based on available groundwater
head measurements in the period
1995-2000. Subsequently, groundwater
head forecasts were made and validated
in the period 2004-2007. In the second
exercise – focusing on spatio-temporal
prediction – the TFN model parameters
were interpolated by using the global digital
elevation map. Using these estimated
parameters, spatio-temporal groundwater
head maps were then made.
Both exercises show good results. Results
of temporal predictions from the first
exercise well reproduce observed head
time series, especially at shallow groundwater
locations (see example in Figs. 2b
and 2c). In such areas, groundwater head
fluctuations are dominated by meteorological
forcing that is also reflected in SWI
soil moisture dynamics (as shown in the
correlation analysis in Fig. 2a).The spatiotemporal
prediction method is able to
predict the head variation in terms of timing
and amplitude reasonably well (see
Fig. 2d). However, the absolute value of
the groundwater head is hardly estimated
(as indicated by the bias between observation
and prediction, Fig. 2d). This is mainly
due the limited accuracy of the currentlyavailable
global digital elevation map
(≤ 16 m).
Fig. 1. The Rhine-Meuse basin used as the study area: The average groundwater head (a) and depth (b) calculated from the PCR-GLOBWB-MOD model;
the head measurement stations used in the study (c); and a snapshot of SWI in August 1995 (d). Figure 1a includes the point x indicating the measurement
station for the graphs in Fig. 2 and the points a, b, c, d, e and f indicating the measurement stations for the graphs in Fig. 3.
december 2012 Geo.brief 13