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Correlating satellite remote sensing signals with

groundwater head time series

Edwin H. Sutanudjaja, Steven M. de Jong, Frans C. van Geer and Marc F.P. Bierkens Dept. of Physical Geography, Faculty of Geosciences, Utrecht University, The Netherlands

: e.sutanudjaja@geo.uu.nl

Can we monitor groundwater from space ?

Purpose:

To check whether remote sensing signals carry information on groundwater levels. Currently, we focus on the ERS Soil Water Index (SWI) fields (Wagner et al, 1999), time series of which are compared to more than 5000 groundwater head time series in the Rhine-Meuse drainage basin.

Derived from European Remote Sensing (ERS) active scatterometer signals.

Represent soil moisture contents (%) in the first 1 m of soil.

25-50 km spatial resolution.

ERS Soil Water Index

SWI (%)

15 25 35 45 55 65

Rhine-Meuse border

lakes

NETHERLANDS

BELGIUM

GERMANY

GERMANY

AUSTRIA SWITZERLAND

FRANCE FRANCE

Lobith

Borgharen

Liege Namur

Koblenz

Mannheim

Frankfurt

Basel Rekingen Rhine Meuse

Moselle hi R ne

Lahn Sieg

Ruhr Lippe

Nec kar

Aare LUX

0 100km

Soil Water Index August 2005

Groundwater head

Ground measurements from various institutions in the Rhine-Meuse basin.

Only time series from the first upper aquifer were used (> 5000 points).

Point scale.

0 100km

Number of stations in our groundwater head database

1 2-5 6-10 11-25 26-50 51-100 101-250 251-500

>500

# points

Study area:

Rhine-Meuse Basin

The combined basin has ample groundwater head time series.

Figure A and Figure B illustrate mean groundwater head and depth for the period 1974-2008 calculated by using the model of Sutanudjaja et al (2010).

head (+m)

≤ 0 200 400 600 800 1000

≥ 1200

NETHERLANDS

BELGIUM

GERMANY

GERMANY

AUSTRIA

SWITZERLAND FRANCE

FRANCE

Lobith

Borgharen

Liege Namur

Mannheim

Frankfurt

Basel Rekingen LUX

0 100km

Average groundwater head 1974-2008

Koblenz

a

b c

A

0 100km

Average groundwater depth 1974-2008

≤ 0.5 1 5 10 25 50

≥ 100

Results:

R

0 100km

R : Cross correlation at zero lag (k = 0)

C

≤ 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0 100km

R best : Best cross correlation at lag k ≥ 0

D

≤ 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0 100km

0 25 50 75 100 125 150 175 200 225 250

k best

(days)

Lag k corresponding to the best cross

correlation Histogram of R

(cross correlation at zero lag)

-1 -0.5 0 0.5 1

G 0 100 200 300 400 500

No. of stations

depth(m)

≤ 0 0 - 0.5 0.5 - 1 1 - 5 5 - 10 10 - 25

> 25

k best : Lag k corresponding to the best cross

correlation

Lag k corresponding to the best cross

correlation

-1 -0.5 0 0.5 1

H 0 100 200 300 400 500

No. of stations

depth(m)

≤ 0 0 - 0.5 0.5 - 1 1 - 5 5 - 10 10 - 25

> 25

1990 1995 2000 2005

1.0

0.0 -1.0

0.0 -7.5 7.5

1.0

0.0 -1.0

42

0

-42

0 -43 43

E R S S o il W a te r In d e x a n o m a ly (% )

42

0

-42

Examples of comparison between groundwater head time series (red) and ERS Soil Water Index (SWI) time series (black)

R(k=0)= 0.80

R

best

(0

days

)= 0.80 mean groundwater

depth = 1.19 m mean head = 7.09 m

mean SWI = 47.49 %

mean head = 232.19 m mean SWI = 47.17 %

mean head = 32.58 m mean SWI = 55.47 %

0 5 10 15 20

Rpearson

= 0.40

Rspearman

= 0.41

0 5 10 15 20

Rpearson

= 0.26

Rspearman

= 0.34

groundwater depth (m)

0 5 10 15 20

groundwater depth (m)

0

-0.4 -0.6

0.6 0.4

120 100 80 60 40 20

k

best

: la g k ( da ys ) fo r b e st c ro ss c o rr e la ti o n

0 0.2 -0.2

E2 E1

5.0 0.0 -5.0

g ro u n d w a te r h e a d a n o m a ly ( m )

5.0 0.0 -5.0

5.0 0.0

-5.0

1970 1975 1980 1985 1990 1995 2000 2005 2010

a) Maaseik, Belgium

R = 0.87 average values: data (red) = 23.88 m model (black) = 25.10 m

b) Trier-Euren, Germany

R = 0.81 average values: data (red) = 131.00 m model (black) = 130.81 m

c) Kemmern, Germany

R = 0.84 average values: data (red) = 237.79 m model (black) = 237.68 m

Figure I shows examples of comparisons between groundwater head and ERS Soil Water Index (SWI) time series. For each comparison, we calculated the cross correlation (R) between both time series, without considering lag-time (k=0). We also calculated the cross correlations at positive lags (head time series of which are shifted forwardly by k days). Then, we identified the best cross correlation coefficient (R best ) and its corresponding lag-time (k best ).

R, R best and k best were calculated for all stations and summarized in histograms and maps.

We see most of groundwater head time series have strong correlation to SWI time series (Figure G), especially in shallow groundwater areas (Figures E1 & C). By considering lag-time and identifying k best , we see even considerably stronger correlation (Figures H & D).

Moreover, values of k best , which may be physically defined as average water residence time in unsaturated zone, generally increases with average groundwater depths (Figures E2 & F).

g ro u n d w a te r h e a d a n o m a ly (m )

I

Groundwater head time series 1974-2008 in some locations

B

R best

F

R : cr o ss c o rr e la ti o n a t ze ro l a g

Histogram of R best

(best cross correlation at lag k ≥ 0)

R(k=0)= 0.45

R

best

(75

days

)= 0.75 mean groundwater depth = 8.94 m

R(k=0)= 0.18

R

best

(75

days

)= 0.71 mean groundwater depth = 2.22 m

depth(m)

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