Research Topic Hydrosphere
Transcript of Research Topic Hydrosphere
Research Topic: Hydrosphere Quantifying water cycle dynamics for hydrological prediction
C. Montzka, J. Bendix, H. Bogena, P. Dietrich, A. Güntner, I. Hajnsek, H.-J.
Hendricks Franssen, S. Itzerott, T. Jagdhuber, H. Kunstmann, A. Lehmann,
L. Samaniego, M. Schwank, S. Suchandt, H. Vereecken, J. Wickert
Goals of the Research Topic Hydrosphere
• Retrieval and sensor data fusion: How
can we optimally retrieve and
synergistically merge different remote
sensing technologies in order to improve
hydrological variables/parameters?
• Validation: How precise are hydrological
variables observed using remote sensing
technologies at different spatial and
temporal scales?
• Data assimilation: How can remotely
sensed hydrological products be further
used in order to better understand and
predict the hydrological cycle? SEITE 5
⇒ Quantitative understanding of dynamic processes in the Hydrosphere
⇒ Strong focus on soil moisture
Why is soil moisture important for hydrology?
•Plays an essential role in the prediction of …
• regional weather (lower atmospheric boundary)
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Soil moisture remote sensing data not yet routinely included in operational forecast models (e.g., ECMWF)
Tem
pera
ture
Why is soil moisture important for hydrology?
•Plays an essential role in the prediction of …
• regional weather (lower atmospheric boundary)
• floods (antecedent soil moisture)
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Cedar Rapids, Iowa, USA, 13-06-2008
Why is soil moisture important for hydrology?
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•Plays an essential role in the prediction of …
• regional weather (lower atmospheric boundary)
• floods (antecedent soil moisture)
• impact of droughts (reduced plant productivity)
Why is soil moisture important for hydrology?
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•Plays an essential role in the prediction of …
• regional weather (lower atmospheric boundary)
• floods (antecedent soil moisture)
• impact of droughts (reduced plant productivity)
• groundwater recharge (sustainable WRM)
Why is soil moisture important for hydrology?
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•Plays an essential role in the prediction of …
• regional weather (lower atmospheric boundary)
• floods (antecedent soil moisture)
• impact of droughts (reduced plant productivity)
• groundwater recharge (sustainable WRM)
⇒Exhaustive characterization of soil moisture in very high spatial and temporal resolution
⇒Better hydrological, meteorological and climatic predictions
Where are we now and what are the problems?
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In-situ soil moisture measurements:
150 Sensor units 18 Router units 900 Soil moisture sensors 300 Temperature sensors
Where are we now and what are the problems?
In-situ soil moisture measurements:
• Only few measurements (∼dm3), high spatio-temporal variability
• Hardly long time series
• No standard procedure for intermediate scale (∼ hm2)
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Where are we now and what are the problems?
Available remote sensing offers large scale data, but with limitations:
Model (~200m)
SMOS (~35km) ASCAT (~25km)
High temporal resolution, but low spatial resolution
Not adequate for integration into regional scale models
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Where are we now and what are the problems?
Available remote sensing offers large scale data, but with limitations:
⇒ Large differences between prediction and measurements
⇒ No relevance for utilization in hydrological models by data assimilation
46 d
ays
te
mpo
ral r
esol
utio
n (A
LOS)
Top soil
20 cm depth
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Where are we now and what are the problems?
Available remote sensing offers large scale data, but with limitations:
⇒ Small differences between prediction and measurements
⇒ Improved estimation of soil moisture profile in the root zone by integration into hydrological models
1 da
y
tem
pora
l res
olut
ion
Top soil
20 cm depth
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Where are we now and what are the problems?
Available remote sensing offers large scale data, but with limitations:
• Model needed for translating measurement in soil moisture (uncertainty, bias)
• Disturbances: vegetation, surface roughness
• Lack of ground truth verification
• Infrequent visits (e.g., ALOS) OR
• Low spatial resolution (e.g., SMOS, ASCAT)
• Limited to upper 5 cm of soil
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Remote sensing for improving hydrological predictons:
⇒ Spatio-temporal scales of Tandem-L observations move us forward to enhanced hydrological predictions
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Synthetic study, (potential of data)
Small well controlled studies with real data
Large scale studies incl. verification of improved predictions
Operational use for improving large scale predictions
WORK PACKAGES
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WP H1: Derivation of surface soil moisture under the vegetation cover using multi-parametric SAR Jagdhuber & Hajnsek (DLR-HR)
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H-pol V-pol
Soil
Vegetation
Azimuth
Nadir
Range
Surface Volume Dihedral Scattering
Soil
Removal of Vegetation Component and Inversion for Soil Moisture
Surface Soil moisture
Ground: Surface & Dihedral
- = + Vegetation Total Scattering
Polarimetric Decompositions
19. April Land use
= Mask for forested and urban areas
Validation with in situ data for all crops
RMSE=5.22vol.% 0
9
18
27
36
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[vol.%]
WP H2: Multi-scale ground measurements data for the validation of remotely sensed soil moisture products - Bogena & Montzka (FZJ)
Spat
ial s
cale
WP H3: GNSS based soil moisture measurements – Wickert & Güntner (GFZ)
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Global GNSS network
GNSS antenna
Correlation of the GNSS multipath signal (L-band) (reflected from ground) with soil moisture
Sutherland, South Africa, 2013
WP H4: Ocean Surface Current Measurement with SAR along-track Interferometry - Suchandt & Lehmann (DLR-IMF/GEOMAR)
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Tidal currents at Orkney Islands measured with TerraSAR-X (2009, 2010)
3 m/s 3 m/s
23.11.09 26.12.09 12.11.09 26.04.10
Surface current data processing
system
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Understanding & modeling the ocean response to climate variability and
extremes is essential for climate projection studies. Models must be validated by
observations.
Space borne SAR Along-track interferometry provides large-area information
on ocean surface motion with high spatial and velocity resolution.
Main objective: Combination of remote sensing data, models and
in-situ measurements to obtain climatologic relevant information
Milestones:
Adaption & validation of TerraSAR-X data processing system
Cross-data analysis for a pilot study area: in-situ data (ADCP),
numerical circulation model (BSIOM), surface current velocities (TanDEM-X)
TanDEM-L mission requirements for ocean current measurements
Tidal currents measured
by TerraSAR-X
3 m/s 3 m/s
WP H4: Ocean Surface Current Measurement with SAR along-track Interferometry - Suchandt & Lehmann (DLR-IMF/GEOMAR)
WP H5: Airborne active and passive microwave data fusion for soil moisture retrieval - Montzka & Bogena (FZJ)
Das, Entekhabi and Njoku (2011) F-SAR backscatter overlayed with PLMR brightness temperature
Backscatter (3 km)
Soil moisture (36 km)
Soil moisture (9 km)
The SMAP active/passive fusion approach
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WP H6: Demand-driven observation of soil moisture - Dietrich & Wollschläger (UFZ)
Aim: provide demand-driven, detailed information on near surface to deeper vadose zone soil water content and subsurface architecture Approach: improve process understanding and predictive capability by demand generated through:
- interaction with modeling and data assimilation - interaction with ground based measurement - consideration of underlying characteristics of observed processes
for calibration and gathering of additional data suitable for processing of remote sensing data
predictions and decisions mobile platforms
permanent installations
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Milestones: • Parameters influencing signal and definition of test sites and methods • Parameter estimation and analysis for 3D model of plants • Transfer of influence parameter knowledge to airborne data processing • Validated method for soil moisture estimation from HSS and radar data
Optical Hyperspectral
SAR
Platform airborne/spaceborne airborne/spaceborne Radiation reflected sunlight own radiation Spectrum visible/infrared microwave Frequency hyper-frequency Several frequencies (C,
L) Polarimetry N.A. polarimetric phase Interferometry N.A. interferometric phase Acquisition time day time day/night Weather blocked by clouds see through clouds Influenced by vegetation vegetation
polarimetric decomposition and inversion
Detection of
)()(
21191800
21191800
RRRRNSMI
+−
=
NSMI (Haubrock et. al 2007)
Detection of promising band ratios
Bron
ster
t et.
al 2
012
WP H7: The use of hyperspectral optical and L-band radar data for retrieving surface soil moisture – Itzerott (GFZ)
WP H8: Data assimilation of multi-scale soil moisture - Hendricks Franssen (FZJ)
Assimilating SMOS, SMAP, Tandem-L, CRP, TDR: scale dependence.
Multi-scale data assimilation with two different algorithms will be tested in synthetic and real world application:
Prior downscaling
Utilization of retrieval operator for
downscaling during assimilation
Main Result: Value of Tandem-L data for improving predictions with land surface models.
One possible multi-scale DA approach (Montzka et al., 2012)
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WP H9: Assimilation of soil moisture and snow water into the mesoscale hydrologic model - Samaniego (UFZ)
Samaniego et al. 2011
Soil moisture index at 2011-04-30
Wet Dry
Research Questions
• Assimilation SM & SWC → minimize parameter uncertainty of mHM?
• Subgrid-scale variability of state variable → better effective parameters at larger scales (e.g. 1-5 km)?
• Statistical data exploration (dimensionality, stochastic dependency)
• Data assimilation, parameter inference, and model improvement
• Evaluation at TERENO sites
Phases
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WP H10: High resolution soil moisture parameterization of land surface models – Bendix & Thies (Uni Marburg)
Investigation of growth and competition processes of pasture grass (setaria sphacelata) and southern bracken in southern Ecuador
Adaptation of the Community Land Model
Soil moisture information with high spatio-temporal resolution by Tandem-L:
Improved model accuracy
Enhanced understanding of growth and competition processes
Development of DA scheme for level 3 Tandem-L soil moisture product
1. Development of a downscaling method for SMOS and SMAP soil moisture product to 50m Tandem-L resolution
2. Model validation (EC data)
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Location of the study site and vegetation units from satellite classification
Göttlicher et al. 2011
Curatola et al. 2012
WP H10: High resolution soil moisture parameterization of land surface models – Bendix & Thies (Uni Marburg)
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WP H11: Copula Based Merging of Modeled and Satellite-Derived Soil Moisture Fields and Ecosystem Fluxes – Kunstmann & Lorenz (KIT)
Problem: Inconsistencies in the spatial and temporal resolution as well as spatial and temporal availability of current (large-scale) soil moisture products.
Main objective: Data merging of satellite- and airbourne-based, in-situ, and modeled soil moisture in order to provide reliable and consistent estimates of gridded soil moisture.
Methods: Application of Copula-techniques in order to describe the dependency between different sources of soil moisture and other climatic variables (e.g. temperature, precipitation, …).
Data assimilation based on the statistical dependency between the different input-datasets.
Empirical (left) and theoretical (right) Copula density between observed and modeled soil moisture
First experiments over the Mississippi basin using SMOS, NLDAS-data and observations from the ISM-Network
Observed (black) and modeled (gray) soil moisure over five observation stations of the ISM-Network
WP H11: Copula Based Merging of Modeled and Satellite-Derived Soil Moisture Fields and Ecosystem Fluxes – Kunstmann & Lorenz (KIT)
L-band test data sets
DLR F-SAR on Do228
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TERENO – Terrestrial Environmental Observatories
Expected impact
• A strong improvement in the characterization of soil moisture contents and associated hydrological and energy fluxes:
• A better understanding of what is exactly measured and therefore a better conversion of measured signals of individual products (e.g., radar, microwave) to soil moisture.
• The spatial and temporal interpolation of soil moisture products to achieve a higher resolution (fusion, downscaling).
• Additional interpolation and extrapolation in space and time (e.g., root zone soil moisture), making use of land surface models and data assimilation techniques.
• Insights in the relative value of different data products.
• Main impact: improved hydrological, meteorological and climatic predictions and sufficient understanding for operational use.
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Direction towards multi-sensor approaches Link with passive sensors / multi-sensor platform efforts to link active and
passive sensors (SMAP-mission)
Link with hyperspektral sensors -> hybrid soil moisture inversion
Link with GNSS reflectrometry-based soil moisture patterns
Link with ground-based measurements (pattern comparison with geophysical techniques)
Direction towards data assimilation into models Assimilation
Direct input
Link through common test sites
Links and joint efforts with project partners: Within Hydrosphere and beyond
Scientific questions
• Does the assimilation of soil moisture help to minimize parameter uncertainty of
hydrological models?
• To what extent does the assimilation of subgrid-scale (e.g. 50-500 m) variability of
state variables contribute to the derivation of effective parameters at larger scales
(e.g. 1-5 km) in order to characterize hydrological processes?
• Is Tandem-L able to improve the regionalization of land surface models?
• What are further techniques to be used for a global soil moisture monitoring and
validation system in conjunction with remote sensing products?
• Can an improved land surface model enhance our understanding about soil-
vegetation – atmosphere water fluxes and coupled plant water/nutrient dynamics?
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Scientific questions…
You may find first answers to these questions in the Hydrosphere session on
Wednesday 9th October, 9:00 – 11:45
and the Poster session on
Tuesday 8th October, 15:00
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