improving near real-time flood forecasting using multi-sensor soil

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Improving near real-time flood forecasting using multi-sensor soil moisture

assessment

Niko Wanders

Derek Karssenberg

Marc Bierkens

Steven de Jong

2

Content

Introduction

● Project

● Current research

Objectives

Microwave remote sensing

● Theory

● Satellites

Satellite validation

● Scaling

● Modelling

● Comparison

Conclusions

3

Introduction

Niko Wanders

● 7 Months at UU

MSc Hydrology and quantitative water management WUR

● Catchment hydrology

● Hydrological modelling

EU-WATCH

● Drought indicators

● Drought propagation

● Global hydrological modelling

EU-XEROCHORE

● Drought policy

● Drought early-warning

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The project

Cooperation between:

● Utrecht University

● JRC-ISPRA

● CESBIO

● ESA-ESTEC

● TU Wien

Funded by: ● NWO-SRON/GO

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JRC European Data

Member States data

0

500

1000

1500

2000

2500

8/23/02 0:00 8/24/02 0:00 8/25/02 0:00 8/26/02 0:00 8/27/02 0:00 8/28/02 0:00 8/29/02 0:00 8/30/02 0:00 8/31/02 0:00

Dessau/Rosslau Wittenberg Torgau Riesa Dresden Labe Decin Labe/Usti N.L. Vltava/Prague

River basin management

Meteodata

Flood simulation & forecasting

Courtesy: Ad de Roo - JRC

European Flood Alert System

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Soil moisture

Soil moisture key variable for:

● Infiltration

● Evapotranspiration

● Groundwater replenishment

● Overland flow

● Propagation of droughts

- From precipitation to discharge

Flood and drought forecasting

Data-assimilation

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Overall objectives

Determine uncertainty of modeled and satellite derived soil moisture

Improve overall discharge simulation

Forecast of (flash) floods:

● Better overland flow estimation

Drought forecasting skills

● Prolonged soil moisture drought

● Propagation to discharge droughts

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Advantages:

● Cloud penetration

● Not light dependent

● (Almost) no solar interference

● Very sensitive to soil moisture (<10 GHz)

Likely problems:

● Dense vegetation

● Highly topographic areas

● Land sea contamination

● Radio Frequency Interference (RFI)

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ASCAT (ESA)

Properties:

● Metop-A satellite

● Predecessor ERS-1 and ERS-2

● Active microwave

● 5.3 Ghz (C-Band)

● Triple beam

● Two swaths of 550 Km

● L1 and L2 Near Real time product

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ASCAT

Properties

● Change detection

● Saturation index of soil moisture (change detection)

● Spatial resolution 25 km

● Revisit time 0.5-2 day

● Penetration depth ~2cm

● Visit time 9:30 and 21:30

Data

● From 2007 onwards

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SMOS (ESA)

Properties:

● SMOS satellite

● Passive microwave

● 1.41 Ghz (L-Band)

● Lifetime 5 year (until 2014)

● Many incident angles

● ≈ 1000 km swath

● L1 product Near Real Time

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SMOS

Properties

● Dielectric constant

● Accuracy of 4% volumetric soil moisture

● Spatial resolution 35-50 km




Data

● Reprocessed data 2010

● Operational data 2011

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AMSR-E (NASA)

Properties:

● Aqua satellite

● Passive microwave

● 6.9 GHz - 10.65 GHz (C-Band to X-Band)

● Operational use

● 55°incident angle

● ≈ 1450 km Swath

● L1 and L2 product Near Real Time

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AMSR-E

Properties

● Dielectric constant

● Accuracy of 6% volumetric soil moisture

● Spatial resolution 38-56 km




Data

● From 2002 until 4 October 2011

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Future satellites

SMAP (November 2014) Sentinel-1 (2013)

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Scaling problems

Scales

● Satellite scale

● Model scale

● Observation scale

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Other problems

Penetration depth

● ASCAT (0-2 cm)

● AMSR-E (0-2 cm)

● SMOS (0-5 cm)

● In-situ (5 cm)

Timing

● ASCAT (9:30 & 21:30)

● AMSR-E (1:30 & 13:30)

● SMOS (6:00 & 18:00)

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Remote sensed soil moisture validation

Calibration

● REMEDHUS network Spain

● 20 x 30km

● 22 locations

● 5cm depth

● 2006-2010

Validation

● 79 Meteorological stations Spain

● Daily precipitation

● Calculation of daily Penman evapotranspiration

● CORINE soil texture map

● 50 x 50 km

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REMEDHUS

50km

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REMEDHUS

Soil moisture year 2010

Month

So

il m

ois

ture

(m

3/m

3)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

J F M A M J J A S O N D

Mean

Loc 1

Loc 2

Loc 3

Mean

Loc 1

Loc 2

Loc 3

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SWAP

Soil-Water-Atmosphere-Plant

Richard equation

Topsoil 10 layers of 1 cm

Input:

● CORINE soil texture map

● LAI (MODIS)

● Precipitation

● Evapotranspiration

● Van Genuchten parameters

10 cm

50 cm

200 cm

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Swap (uncertainty)

Van Genuchten soil parameters:

● Depended on soil texture

● Selected from correlate distributions

Soil map:

● 20% error

Precipitation:

● Variance of 20%

Evapotranspiration:

● Variance of 10%

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REMEDHUS compared with SWAP

Year 2010 R2 R RMSE

ASCAT 0.446 0.668 0.0423

SMOS 0.106 0.326 0.0807

AMSR-E 0.623 0.789 0.1608

SWAP (median) 0.764 0.874 0.0331

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All satellites for one location

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260 280 300 320 340 360

0.0

0.2

0.4

Days

So

il M

ois

ture

(m

3/m

3)

260 280 300 320 340 360

0.0

0.2

0.4

So

il M

ois

ture

(m

3/m

3)

ASCAT AMSR-E

SMOS

SWAP model

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R2 R RMSE

ASCAT 0.604 0.777 0.0611

SMOS 0.007 0.083 0.0198

AMSR-E 0.147 0.383 0.0961

260 280 300 320 340 360

0.0

0.2

0.4

Days

So

il M

ois

ture

(m

3/m

3)

260 280 300 320 340 360

0.0

0.2

0.4

So

il M

ois

ture

(m

3/m

3)

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Some summary statistics

-0.2 0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.1

0.2

0.3

0.4

Comparison of satellites and SWAP model

Correlation

RM

SE

SMOS

ASCAT

AMSR-E

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Some summary statistics

-0.2 0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.1

0.2

0.3

0.4

Comparison of satellites and SWAP model

Correlation

RM

SE

SMOS

ASCAT

AMSR-E

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Conclusions

SMOS:

● Underestimation

● Very noisy (RFI?)

● Only trend is represented

ASCAT:

● Good correlation

● Low RMSE

● Noisy in low values

AMSR-E:

● Good correlation

● Overestimation

● High RMSE

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Discussion and future research

Quality of satellite data

● Large differences between satellites

From top-soil moisture to total soil moisture

Improvement of flood forecast or drought monitoring

● Added value of soil moisture observations

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Thanks for your attention

Special thanks to

Richard de Jeu (VU)

Jennifer Grant (ESTEC)

Matthias Drusch (ESTEC)

Wouter Dorigo (TU Wien)

Jos van Dam (WUR)

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Statistics for one location

SWAP ASCAT SMOS AMSR-E

SWAP 0.620 0.153 0.347

ASCAT 0.0492 0.118 0.232

SMOS 0.1299 0.1375 0.159

AMSR-E 0.2002 0.1995 0.2717

Green: R2

Yellow: RMSE

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ASCAT rescaling

Input

● CORINE soil map

● 1 x 1 km resolution

● Derive van Genuchten parameters

Determine

● Average saturation point = 1

● Average field capacity = 0

Linear transformation

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SWAP (assumptions)

Rooting depth

● Winter 20 cm

● Summer 70 cm

● Uniform

Free drainage

No lateral flow

Ponding up to 2mm

Uniform soil texture in vertical

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Locations in Spain

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SWAP compared with SMOS

0 100 200 3000

.00

.20

.40

.60

.8

SMOS ( 172 Obs.) compared with SWAP (R2= 0.527 ,RMSE= 0.17 )

Days

So

il M

ois

ture

(m

3/m

3)

0 100 200 300

0.0

0.2

0.4

0.6

0.8


Days

So

il M

ois

ture

(m

3/m

3)

R2 = 0.05 R2 = 0.68

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SWAP compared with ASCAT

0 100 200 3000

.00

.10

.20

.30

.4

ASCAT ( 350 Obs.) compared with SWAP (R2= 0.244 ,RMSE= 0.0867 )

Days

So

il M

ois

ture

(m

3/m

3)

0 100 200 300

0.0

0.1

0.2

0.3

0.4

ASCAT ( 346 Obs.) compared with SWAP (R2= 0.764 ,RMSE= 0.0589 )

Days

So

il M

ois

ture

(m

3/m

3)

R2 = 0.78 R2 = 0.22

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0 100 200 300

0.0

0.2

0.4

0.6

0.8


Days

So

il M

ois

ture

(m

3/m

3)

SWAP compared with AMSR-E

0 100 200 300

0.0

0.2

0.4

0.6

0.8


Days

So

il M

ois

ture

(m

3/m

3)

R2 = 0.15 R2 = 0.68

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REMEDHUS compared with SWAP

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Conclusion

SWAP is highly sensitive to the soil parameters

Meteorological forcing very important

Impact of vegetation on SWAP is limited

ASCAT can be rescaled (with good results) using soil characteristics

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Active Passive

Measures Backscatter Brightness temperature

Determines Change detection Dielectric constant

# Satellites 1 (2) 2 (1)

Operational Yes No

improving near real-time flood forecasting using multi-sensor soil

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