Download 10649 - ESA Conference Bureau

Close water cycle at the river basin scale using remote sensing data (ID. 10649)
Prof. Xin Li (李新)
Cold and Arid Regions
Environmental and Engineering
Research Institute, CAS
Prof. Harry Vereecken
Institute of Bio-and Geosciences
(IBG), Jülich Research Centre
Germany
Chinese Investigators European Investigators •
Xin Li (CAREERI)
•
Harry Vereecken (IBG, G)
•
Jian Wang (CAREERI)
•
Kurt Roth (IEP, G)
•
Jiemin Wang (CAREERI)
•
Massimo Menenti (TU Delft, NL)
•
Shaomin Liu (BNU)
•
Mingguo Ma (CAREERI)
•
Bob Su (ITC, NL)
•
Ling Lu (CAREERI)
•
Henk de Bruin (TU Delft, NL)
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Weizhen Wang (CAREERI)
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Wolfgang Kinzelbach (ETH, CH)
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Chunlin Huang (CAREERI)
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Haijing Wang (ETH, CH)
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Zhuotong Nan (CAREERI)
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Carsten Montzka (IBG, G)
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Tao Che (CAREERI)
•
Patrick Klenk (IEP, G)
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Rui Jin (CAREERI)
•
Xiaoduo Pan (CAREERI)
•
Shuguo Wang (CAREERI)
•
Xujun Han (CAREERI)
Outline
• Objectives and background • Research contents
• EO data acquisition plan
• Project schedule
• Field campaigns and expected data sets
• Conclusions and outlook
1.1 Objective
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The overall objective of this project is to close the water cycle
at river basin scale.
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Water budget components including precipitation,
evapotranspiration (ET), soil moisture (SM), snow water
equivalent (SWE), runoff, and groundwater storage will be
estimated using multi-source remote sensing observations and
corresponding time series products of these variables will be
generated.
•
These products, in combination with hydrology and land surface
modeling will be integrated by data assimilation methods to close
the land water budget at basin scale. 4/33
1.2 Background
1.2 Study area
China: Heihe River Basin
1.2 Study area
Germany: Rur Catchment
Grassland test site “Kall”
• Eddy Correlation station
• Soil moisture sensor network
• Soil temperature measurements
• Soil CO2 flux measurements
X-band Doppler
Weather Radar
Agricultural test site
“Selhausen”
Forest test site “Wüstebach”
• Eddy Correlation station
• Soil moisture sensor network
• Soil temperature measurements
• Groundwater monitoring
• Runoff and solute monitoring
• Soil CO2 Flux measurements
• Eddy Correlation station
• Soil moisture measurements
• Soil temperature measurements
• Soil CO2 flux measurements
• Ground-based remote sensing
• LIDAR
2. Research contents
• Estimation approaches on
precipitation, ET, SM, SWE and
groundwater storage;
• Corresponding time series
products for these variables；
• Closing water budget through
integration of remote sensing
products, hydrological modeling,
and a land data assimilation
system.
8/33
2.1 Precipitation
Input for precipitation products
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•
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WRF-25km: TRMM/GPM
WRF-1km: Doppler-radar（200-250m）
WRF-200m: Rain gauges
2.2 Evapotranspiration
General roadmap
2.3 Soil moisture
Time series SAR
imagery
Backscattering
coefficients
Retrieval scheme
Assuming surface roughness is invariant,
vegetation cover grows periodically
Temporal dynamics of σ0 with in situ soil moisture
(e.g. linear relationship)
Sensor
configurations,
media dielectric
properties,
vegetation, surface
roughness
To detect soil freeze/thaw status dependent on
temporal dynamics of σ0 , in situ soil moisture and
temperature
Change
detection
method
Spatial variability of σ0 due to land
heterogeneities
To apply change detection method to derive soil
moisture sequence
Forward model, iterative loop, ANN, data assimilation etc.
Empirical, semi-empirical, theoretical models
Soil moisture
2.3 Soil moisture
Downscaling scheme
Downscaling
algorithms
1km soil moisture
25km Soil moisture
validation
1km MODIS NDVI
Statistical
calculation
1km TVDI
1km MODIS LST
SM_fine=f（SM_coarse, TVDI, TVDI_coarse）Observations or model
simulations
2.4 Snow water equivalent
Snow depth and snow water equivalent retrieval by fusing optical and passive microwave remote sensing data
Optical multispectral (Rf)
Snow grain size at
surface
Snow density
Snow temperature
Passive
microwave Tb
Microwave
transfer model
Look-up table
(Tb vs Snow properties)
Snow depth
Snow water equivalent
2.5 Groundwater storage
Estimating groundwater storage changes in the Heihe River Basin
Inter-annual changes of groundwater estimated
by GRACE in the entire HRB
Spatial variability of groundwater in the HRB
in 2003-2008
2.6 Integration of products and
modeling through data assimilation
High‐resolution Atmospheric forcing Data Multi‐resolution Data Products
Continuous Hydrological Data
ET
ET
HDAS
SM
SWE
DA Algorithm
GW
Spatial Resolution： 1‐25km
Temporal Resolution：1‐30day
LSM
Hydrological model
SM
SWE
GW
Spatial Resolution： 1km
Temporal Resolution：hourly
2.6 Integration of products and modeling
through data assimilation
• Generation of atmospheric forcing data
– Using WRF model and measurement data
• Assimilation of the retrieved products
– Using Multi-scale Ensemble Kalman Filter to fuse the remote
sensing products and model estimation
• Calibration and validation
– Using ground-based, airborne, satellite data
• Analysis of water budget
– Understanding of complex hydrological processes, e.g. surface
and groundwater interaction
3. EO data acquisition plan
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•
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ERS
ENVISAT
GOCE
SMOS
CRYOSAT-2
Sentinel
EarthCARE
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CBERS
HJ
BJ
FY
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PROBA
ALOS
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TerraSAR-X
Cosmo-Skymed
CoReH2O
SMAP
4.
Project schedule
WP1, present-2013, further improvement of the retrieval algorithms for key
hydrological variables based on the outcomes of Dragon 2 period.
WP2, 2014-2015, generation of time series
of remote sensing data products regarding
key water cycle components based on the
results obtained from WP1.
WP3, 2015-2016, integration of remote
sensing products, hydrological modeling
through a data assimilation framework to
close the basin scale water budget.
WP4, data management.
5. Field campaigns and
expected data sets
19/33
5.1
In the middle stream
China: HiWATER
5.1
One superstation, 16 towers equipped with EC and LAS, and four AMSs established on gobi, desert and wetland
China: HiWATER
5.1
China: HiWATER
In the upstream
5.2
Germany: TERENO
Multi‐scale soil moisture monitoring
•Process based modeling of regional
water fluxes taking into account
multi-sensor and multi-scale
observation patterns
•Assimilation of multi-scale soil
moisture data:
• Microwave remote sensing
(regional scale)
• Cosmic ray probes (footprintscale)
• Soil moisture sensor networks
(point-scale)
5.2
Germany: TERENO
Cosmic Ray monitoring network in the Rur Catchment
⇒ Also important for Heihe catchment to get link between in situ
and spaceborne soil moisture observations
5.2
Germany: TERENO
Cosmic Ray monitoring network in the Rur Catchment
⇒ Also important for Heihe catchment to get link between in situ
and spaceborne soil moisture observations
5.2
Germany: TERENO
Multi‐scale ground measurements data for the validation of remotely sensed brightness temperature and soil moisture products Combination of PLMR2 and DLR F-SAR onboard a Dornier DO228 aircraft
+ IR-camera
+ Hyperspectral camera
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F‐SAR is able to operate in 4 frequency bands (X, C, L and P)
Dual (F‐SAR) channel operation
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Polarisation: Dual linear (V and H)
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Incidence angles: +/‐ 8°, +/‐22°, +/‐ 38°
@ pushbroom
5.2
Germany: TERENO
Airborne active and passive microwave data fusion for soil moisture retrieval
The SMAP active/passive fusion approach
Soil moisture (36 km)
Soil moisture
(9 km)
Backscatter (3 km)
Das, Entekhabi and Njoku (2011)
F-SAR backscatter overlayed with
PLMR brightness temperature
6. Training of young scientists
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Young scientists will be actively involved in our study for various
aspects.
•
The annual symposium acts as an ideal platform for young scientists
to exchange their knowledge.
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Experts from both sides will provide joint training of young scientists,
short-term visits or to accomplish their PhD dissertation.
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Exchange of team members, instrument from both sides in field
campaigns.
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Coauthorship in international peer-reviewed journals and conference
proceedings.
7.
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Conclusions and outlook
This project inherits the outcomes of our previous Dragon 2 project,
and will further improve the studies and applications of remote
sensing in hydrology research.
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A comparative analysis in the two well-instrumented catchments is
an important task for a future global application of the established
model-data synthesis product.
7.
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Conclusions and outlook
To close the river basin scale water cycle by integrating remote
sensing, in situ observations and modeling with a land data
assimilation framework.
•
To create time series of remote sensing and assimilated data
products for the key water cycle components in both study.
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To propel the utilization of mature remote sensing observation
methods and examine the innovative technologies in hydrological
studies.
Xin Li
CAREERI/CAS, Lanzhou 730000, China
E-mail: [email protected]
Web: http://www.westgis.ac.cn
water.westgis.ac.cn
Harry Vereecken
IBG/Jülich Research Centre
Forschungszentrum Jülich GmbH, IBG-3, Leo-Brandt-Strasse,
52425 Jülich, Germany
Email: [email protected]
Thank you!