Remote Sensing and Water management - FEFlow

Remote Sensing and 

Groundwater Modeling 

Institut für 

UmweltingenieurwissenschaftenIfU 

Wolfgang Kinzelbach 

Institute of Environmental Engineering 

ETH Zürich, Switzerland 

with 

Philip Brunner, Harrie-Jan Hendricks Franssen, Lesego Kgotlhang, 

Peter Bauer and Carmen Alberich

Contents 

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• Data problems in groundwater modeling 

• Possible uses of remote sensing data in 

groundwater models with examples 

• Conclusions

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Data problems of groundwater 

modelling 

• Data fields required: Recharge, transmissivity, 

leakage factor, evaporation rate, storage coefficient 

∂ h 

S = ∇( T∇ h) + q 

∂t 

• Only point values known, often few 

• Calibration usually not unique – predictive power 

poor 

• Reduction of number of degrees of freedom 

required (e.g. zonation, pivot points)

Typical problem 

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• Fractured rock aquifer 

• Few boreholes 

• Recharge known to plus minus 1 order of magnitude 

• Storage coefficient known to plus minus 1 order of 

magnitude 

• Regional transmissivity known to plus minus 1 order of 

magnitude 

• Calibration non-unique 

• Management strategy unclear, worst case too pessimistic 

• Stop modelling or get new data!

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Reduction of degrees of freedom 

Example recharge 

Recharge is a function of space and time 

Decompose in a spatial and a temporal factor 

 

Rxt ( , ) = gt ( ) f( xR ) average 

Time factor from lysimeters, spatial factor from 

landuse or data from remote sensing shown later 

Remains one degree of freedom: temporal and 

spatial average

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Physical interpolation from point 

data to areal distribution 

Example: Precipitation from METEOSAT 

Station data 

mm/a

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Conditioning of stochastic models 

• Produce many equally likely realizations of the aquifer 

by the self-calibrating method 

• One source of observations maybe remote sensing fields 

• Set a term in the self-calibration procedure which 

penalizes the distance of the realization from data like 

the pattern obtained by remote sensing

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Types of remote sensing data 

• Frequency range in electromagnetic 

spectrum: visible, infrared, microwave 

(passive-active), radar 

• Resolution from cm to 500 km 

• Time sequence 

• Platform: satellite, airplane, balloon, tower 

• Geophysical remote sensing: magnetism, 

electromagnetic methods, gravity

Looking underground 

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• Aeromagnetics (based on differences in magnetisation of 

rocks) 

– Structure: limits of aquifer 

– Intrusion dikes or fracture zones 

• Airborne Electromagnetics (TEM) 

– differences in electric conductivity from water (freshwater-saline 

water contrast), 

– or from lithology differences: e.g. clay versus sand 

• Gravity variations in time 

– Water content (Storage term) 

– GRACE mission data

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UmweltingenieurwissenschaftenIfU

Aeromag data Kanye 

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Lineaments from Landsat and aerial 

photography

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Implementation of dikes in model 

Model without dikes Model with three classes of dikes

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Thickness of Kalahari sand aquifer in the 

Okavango Delta

Magnetic depth (m) 

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Calibration with ground truth 

400 

350 

300 

250 

200 

150 

100 

50 

0 

y = 0.9177x + 15.876 

R 2 = 0.9228 

0 50 100 150 200 250 300 350 400 

Borehole depth (m) 

Structural index 

N = 0.5

10 km 

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Delineation of freshwater lenses in Okavango Delta 

with airborne TEM: Proxy for leakage factor

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Looking at the surface elevation 

• DTM for depth to groundwater (evaporation 

from groundwater) 

• DTM for drainage patterns 

• Temporal differences in DTM indicate 

settling caused by cone of depression 

• DTM from radar satellites, shuttle 

topography mission, LIDAR altimetry

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DTM from radar satellite images

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DTM from radar satellite images

LIDAR Scan 

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Information related to areal recharge 

R= P−ET −ΔS−R • Determination of vegetation cover (NDVI) 

• Characterisation of land use, land use map (Optical range) 

• Soil map (Hyperspectral satellites) 

• Distribution of precipitation (weather radar, Meteosat) 

• Distribution of ET from energy balance

Precipitation 

sum of year 2000 from 

METEOSAT5 [mm/yr] 

Evapotranspiration 

over 24 hours 

ET 24 [mm d -1 ] 

from NOAA images 

via radiation 

energy balance 

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23.5 24.5 

- 17.5 

- 18.5 

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10-year Average of Precipitation-ET (mm/yr) 

(from 97 images)

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Recharge rates from chloride method („ground truth“) 

Recharge by Chloride 

Method (mm/yr) 

60 chloride samples from boreholes 

Average chloride concentration: 21 mg/l 

⇒ Average recharge: 6.8 mm/yr 

25.00 

20.00 

15.00 

10.00 

5.00 

0.00 

-5.00 

Chloride (mg/l) 

10000.0 

1000.0 

100.0 

10.0 

1.0 

y = 0.057x - 0.8448 

R 2 = 0.734 

0.1 

0 10 20 30 40 50 60 

Sample number 

-100 0 100 200 300 400 

Average Net Exchange (mm/yr)

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Recharge distribution from combination 

Area with nonnegligible 

surface runoff 

Only for area where 

surface runoff is neglible 

(mm/a)

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Conditioning of Stochastic Modelling 

Application to Kavimba Aquifer 

Data: 

• Transmissivities from pumping tests and variogram 

• Head observations 

• Digital terrain model 

• Recharge distribution from remote sensing plus its error 

from correlation analysis 

Two variants with 300 realizations each: 

• A: without using recharge distribution 

• B: with recharge distribution as conditioning data

Results: 

Application to Kavimba Aquifer 

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Ensemble standard deviations of transmissivity, recharge and head 

Ensemble of realizations μ logT σ logT μ R σ R σ h 

A (without remote sensing 

information) 

B (with remote sensing 

information) 

-2.36 0.71 6.5 8.0 16.0 

-2.38 0.61 6.4 3.3 10.3 

Uncertainty reduction in input and output variables

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Information related to surface waters 

• Determination of water covered area 

• Seasonal and permanent swamps 

• Change in ponding as measure for recharge 

• Water surface elevation, elevation changes 

• Using radar satellite information or 

optical/infrared satellite information

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Observed water covered area 

NOAA AVHRR (optical, 5 bands) 

- unsupervised classification 

9 June 2005 

Envisat ASAR (radar) 

- temporal variance and edge detection 

11 June 2005

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Information related to soil 

• Mapping of salinity with multispectral data 

• Mapping of soil type (hyperspectral data) 

• Passive microwave for soil moisture 

• Infrared at night for soil temperatures

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Mapping soil salinity with remote sensing 

Based on the spectrum of extremely saline pixels, a spectral match to 

that signature is defined :

GCP- conductivity (micrS/cm) j 

160000 

140000 

120000 

100000 

80000 

60000 

40000 

20000 

0 

Calibration with ground truth 

0 0.2 0.4 0.6 0.8 1 

Spectral match between GCP and Reference 

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Non-irrigated 

areas 

Irrigated areas

The Yanqi Basin and its problems 

(due to doubling of population over the last 50 years) 

First Control 

Point Kaidu River 

Huang Shui River 

Qing Shui River 

Groundwater table rise due to irrigation 

Soil salinization 

Second 

Control Point 

Die-off of fish 

Kongque 

Drying up of „greenRiver corridor“ 

Bostan 

Lake 

Decline of water level in lake 

Increase of salinity in lake

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Method used to gain system understanding 

Coupled modeling of groundwater and surface water resources 

First layer of a two-layer model

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ET in Yanqi basin year 2000 (mm/a)

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Calibration of model using phreatic evaporation 

Model 

Using depthdependence 

from stable 

isotopes 

Remote 

Sensing 

Using ET, 

NDVI and 

stable isotopes

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Usefulness of remote sensing data 

• New data source for regional hydrology and water 

resources management especially in dry regions 

• Allowing step from point process to area 

• Mostly indirect data which only unfold their 

usefulness in connection with modelling 

(conditioning data, data assimilation) 

• Indispensable in regions with weak infrastructure 

and low accessibility 

• Great potential for improving quality of our work 

in the arid and semiarid environments

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Problems with remote sensing data 

• Correlation to required information may be 

weak (noisy information) 

• Resolution problem 

• Availability of longer time series poor 

• Ground truth required and expensive 

• Clouds in optical and infrared window often 

crippling

Conclusions 

• The potential of remote sensing data in 

groundwater modelling is considerable 

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• Data are indirectly related and therefore best used 

as conditioning data in a stochastic approach 

• Pattern information is more reliable than actual 

numbers 

• Ground truth is absolutely necessary 

• On the whole the new data sources have great 

potential to improve our work

Remote Sensing and Water management - FEFlow

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