improving near real-time flood forecasting using multi-sensor soil
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1
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
4
The project
Cooperation between:
● Utrecht University
● JRC-ISPRA
● CESBIO
● ESA-ESTEC
● TU Wien
Funded by: ● NWO-SRON/GO
5
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
6
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
7
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
8
Microwave remote sensing
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)
10
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
11
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
13
SMOS
Properties
● Dielectric constant
● Accuracy of 4% volumetric soil moisture
● Spatial resolution 35-50 km
● Revisit time 0.5-3 day
● Penetration depth ~5cm
● Visit time 6:00 and 18:00
Data
● Reprocessed data 2010
● Operational data 2011
14
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
15
AMSR-E
Properties
● Dielectric constant
● Accuracy of 6% volumetric soil moisture
● Spatial resolution 38-56 km
● Revisit time 0.5-3 day
● Penetration depth ~2cm
● Visit time 13:30 and 1:30
Data
● From 2002 until 4 October 2011
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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)
19
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
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
23
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%
24
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
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
27
All satellites for one location
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)
28
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
29
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
30
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
31
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
32
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)
35
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
36
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
37
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
39
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
SMOS ( 227 Obs.) compared with SWAP (R2= 0.00013 ,RMSE= 0.152 )
Days
So
il M
ois
ture
(m
3/m
3)
R2 = 0.05 R2 = 0.68
40
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
41
0 100 200 300
0.0
0.2
0.4
0.6
0.8
SMOS ( 133 Obs.) compared with SWAP (R2= 0.315 ,RMSE= 0.22 )
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
SMOS ( 184 Obs.) compared with SWAP (R2= 0.0346 ,RMSE= 0.119 )
Days
So
il M
ois
ture
(m
3/m
3)
R2 = 0.15 R2 = 0.68
43
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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