Post on 28-Dec-2015
Observing catchments, rivers and wetlands from space: assimilating hydrologic information into distributed models
Peter A. Troch
Outline of presentation
• Data needs for (surface) water resources management
• Satellite based observations of rivers and wetlands
• Satellite based observations of soil moisture and latent heat fluxes
• Satellite based observations of rainfall• Satellite based observations of catchment
storage changes• Data assimilation into distributed models• Recommendations/conclusions
Data needs for Water Resources ManagementSound water resources management is hampered by uncertainties in quantifyingthe water balance components at the catchment scale.
Water balance components at the catchment scale are traditionally estimatedby means of in-situ measurements and distributed hydrological models.
A wide variety of distributed hydrological models has been developed over the pastdecade. A major problem plaguing distributed modelling is parameter identifiability,owing to a mismatch between model complexity and the level of data which istraditionally available to parametrize, initialize, and calibrate models, and to uncer-tainty and error in both models and observational data.
New data sources for observation of hydrological processes (ENVISAT, MSG, SMOS)can alleviate some of the problems facing the validation and operational useof hydrological models.
Data assimilation provides a means of integrating these data in a consistent mannerwith model predictions.
Lack of Q?
Lack of Q and S Measurements: An example from Inundated Amazon Floodplain
100% Inundated!
Singular gauges are incapable of measuring the flow conditions and related storage changes in these photos whereas complete gauge networks are cost prohibitive. The ideal solution is a spatial measurement of water heights from a remote platform.
How does water flow through these environments?
(L. Mertes, L. Hess photos)
Example: Braided Rivers
It is impossible to measure discharge along these Arctic braided rivers with a single gauging station. Like the Amazon floodplain, a network of gauges located throughout a braided river reach is impractical. Instead, a spatial measurement of flow from a remote platform is preferred.
Resulting Science Questions
– How does this lack of measurements limit our ability to predict the land surface branch of the global hydrologic cycle?
• Stream flow is the spatial and temporal integrator of hydrological processes thus is used to verify predicted surface water balances.
• Unfortunately, model runoff predictions often do not agree with observed stream flow during validation runs.
Solutions from Radar Altimetry
Water surface heights, relative to a common datum, derived from
Topex/POSEIDON radar altimetry. Accuracy of each height is about the
size of the symbol.
Topex/POSEIDON tracks crossing the Amazon Basin. Circles indicate locations of water level changes measured by T/P radar altimetry over rivers and wetlands.
Presently, altimeters are configured for oceanographic applications, thus lacking the spatial resolution that may be possible for rivers and wetlands.
0 km 20
Solutions from Interferometric SAR for Water Level Changes
JERS-1 Interferogram spanning February 14 – March 30, 1997. “A” marks locations of T/P altimetry profile. Water level changes across an entire lake have been measured (i.e., the yellow marks the lake surface, blue indicates land). BUT, method requires inundated vegetation for “double-bounce” travel path of radar pulse.
These water level changes, 12 +/- 2 cm, agree with T/P, 21 +/- 10++ cm.
River Velocity & Width & Slope Measurements
Example of measurement of the radial component of surface velocity using along-track interferometry
Measure +Doppler Velocity
Measure -Doppler Velocity
Measure Topography
Concept by Ernesto Rodriguez of JPL
Basic configuration of the satellite
Global Wetlands
• Wetlands are distributed globally, ~4% of Earth’s land surface
• Current knowledge of wetlands extent is inadequate
Saturated extent from RADARSAT - Putuligayuk River, Alaska
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Variable source areas detected from ERS-1/2
Verhoest et al. (1998)
Hillslope-storage dynamics
European contribution to GPM (SRON)
3
GPM
January 28 , 2002Eric A. Smith
Potential GPM Validation Sites
Supersite Regional Raingage Site Supersite & Regional Raingage Site
Australia
NASA Ocean
Japan
South Korea
India
France (Niger & Benin)
Italy
Germany
Brazil
England
Spain
NASA KSC
NASA Land
Canada
Taiwan
ARM/ UOK
8
GPM
January 28 , 2002Eric A. Smith
Focused Field Campaigns
Meteorology-MicrophysicsAircraft
GPM Core SatelliteRadar/Radiometer
Prototype Instruments
Piloted
UAVs
150 km
Retrieval ErrorSynthesis
AlgorithmImprovement
Guidance
Validation Analysis
Triple Gage Site(3 economy scientific gages)
Single Disdrometer/Triple Gage Site(1 high quality-Large Aperture/2 economy scientific gages)
150 km
100-Gage Site Lo-Res DomainCentered on Multi-parm Radar
5 km
50-Gage Site Hi-Res DomainCenter-Displaced with
Uplooking Matched Radiom/Radar[10.7,19,22,37,85,150 GHz/14,35 GHz]
Upward S-/X-band Doppler Radar Profilers & 90GHz Cloud Radar
Data Acquisition-Analysis Facility
DELIVERY
Legend
Multiparameter Radar
Uplk Mtchd Radiom/RadarS-/X-Band Profilers90 GHz Cloud Radar
Meteorological Tower &Sounding System
Supersite Template
Site Scientist (3)
Technician (3)
• 1 core satellite (dual frequency 13.6 / 35 GHz imaging pulsed radar, TMI-like radiometer)
• 8 constellation satellites (passive microwave radiometers)
River basin storage changes through gravity
• GRACE: Gravity Recovery and Climate Experiment– Schatten van bergingsverandering in grote stroomgebieden– Horton Research Grant (AGU) AIO onderzoek
Sensitivity of gravity changes to water storage changes
1 Gal = 9,807 m/s2
Time (days)
Existing Instruments
• Water Surface Area:– Low Spatial/High Temporal: Passive
Microwave (SSM/I, SMMR), MODIS– High Spatial/Low Temporal: JERS-1,
ERS 1/2 & EnviSat, RadarSat, LandSat
• Water Surface Heights:– Low Vertical & Spatial, High
Temporal (> 10 cm accuracy, 200+ km track spacing): Topex/POSEIDON
– High Vertical & Spatial, Low Temporal (180-day repeat): ICESat
• Water Volumes:– Very Low Spatial, Low Temporal:
GRACE– High Spatial, Low Temporal:
Interferometric SAR (JERS-1, ALOS, SIR-C)
• Topography:– SRTM (also provides some
information on water slopes)
Motivation for Data Assimilation
Continued progress in our scientific understanding of hydrological processes at theregional scale relies on making the best possible use of advanced simulation modelsand the large amount of environmental data that are increasingly being made available.
The objective of data assimilation is to provide physically consistent estimates of spa-tially distributed environmental variables.
Geophysical data assimilation is a quantitative, objective method to infer the state ofthe land-atmosphere-ocean system from heterogeneous, irregularly distributed, andtemporally inconsistent observational data with differing accuracies, providing at thesame time more reliable information about prediction uncertainty in model forecasts.
Data assimilation is used operationally in oceanography and meteorology, but inhydrology it is only recently that international research activities have been deployed.
Data assimilation of remote sensing observations
y = 1.0746x
R2 = 0.802
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(26)
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ETact SIMGRO
ETact SEBAL
Data assimilation of remote sensing observations• Rivierenland-project (ICES-KIS3)
– Soil moisture measurements and scintillometer to validate RS
Open Research Issues (1)
Remote sensing technology provides many types of data that are related to landsurface variables of interest to hydrologists. However, very little of this informationis available in a form that can be used directly for hydrological purposes.
Data assimilation research in hydrology should focus on producing data products that are directly useful for water management. Such products need to be carefullydesigned to meet the needs of potential users:
• resolution and spatial configuration of data products;• quantitative measures of data product reliability;• quality control issues;• sensitivity of data products to “hidden” model properties.
There is a need to bridge the gap between continental scale data sets (GLDAS)and catchment scale applications (downscaling and parameterization issues).
Open Research Issues (2)
Classical hydrological models that have been optimized for use with sparse in situobservations are inadequate for extension to work with remote sensing data. Thereis a need for developing more appropriate distributed models at catchment scale.
More research is needed to develop data assimilation algorithms that can handlethe specific problems encountered in hydrological applications:
• subsurface processes are hard to “observe”;• high degree of heterogeneity of physical system;• hydrologic systems function over a wide range of temporal scales.
Geostatistical techniques for describing multi-scale spatial heterogeneity need to beincorporated into algorithms that account for the multi-resolution nature of differentbut complementary hydrologic measurements.
Case studies are needed to introduce and demonstrate the potential of dataassimilation in operational water resources management (e.g. improved floodpredictions).
What is needed?Continued investment and coordination of data assimilation initiatives at theEuropean level is urged:
• wide range of research topics relevant to data assimilation;• strong need for innovation in each of these areas;• clear potential for water resources modelling and management;• transboundary nature of catchments and river basins;• need for common algorithms, models, tools, data standards, etc.• leading role already demonstrated by European researchers.
Expertise from many disciplines will be needed to meet the challenge of dataassimilation for improved river basin water resources management:
• hydrology• meteorology• remote sensing• ecology• mathematics (systems theory, statistics)• information technology• water management• etc.