Multi-source satellite data assimilation to detect water cycle changes at global scale
Albert van DijkWater and Landscape DynamicsThe Australian National University, Canberra
Princeton, 2 June 2016
A survey of past successes and failures, opportunities and ideas to make more progress in the future
Questions to ask
• How do temperature and rainfall changes propagate through the water cycle?
• Are risk and impact of drought or flooding increasing?• Does terrestrial water distribution change in connection to
ice and sea mass changes?• Does our water engineering affect the global water cycle?
Can we answer these questions empirically, in hindsight? Not at global scale, except perhaps for the last ~15 years.
Can we actually predict them in advance?No way!
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Area in drought (PDSI <−3.0) using Thornthwaite (blue line) or Penman-Monteith (red line) ET equations
Are our models good enough?
Sheffield, Wood & Roderick (2012) Nature; Trenberth et al. (2014) NCC
5-year running average global PDSI calculated with Penman-Monteith ET and different rainfall data sets
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Total profile water storage anomalies (mm) over the land area
Schellekens (2015) OzEWEX 2015 conference
• Ensemble of ‘state-of-the-art’ land surface/hydrological models (eartH2Observe Tier 1)
• all use the same meteorological forcing data…!
Are our models good enough?
Oceans
Lakes
New dams
Rivers
Snow & Ice
Sub-surface
altimetry (Aviso)
altimetry (Crop Explorer)
Inventory (GranD, ICOLD)
GLDAS-NOAH
GLDAS-Vic
GLDAS-CLM
GLDAS-Mosaic
W3RA
Bias correction & routing
Groundwater extraction (Wada et al.)
River extraction (Wada et al.)Convolve to
GRACE signal
Level-mass conversion
Hydrological M
odels
Gaussian smoother
Evaluation: forward ‘GRACE’ modelling
GRACE: Gravity Recovery and Climate Experiment mission
Van Dijk, Renzullo, Wada et al (2014) HESS
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GRACE retrieval (4 products)
Prior estimate (average 5 models)
The result is TERRIBLE !
Reanalysis (i.e., retrospective data assimilation) helps reduce some of these errors and improve the estimates (for the past).
Also helps to diagnose, perhaps address, model and forcing issues.
Water storage trends (2003-2012)
Van Dijk, Renzullo, Wada et al (2014) HESS
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Greatest differences (= model issues)• ice sheets• mountain glaciers• seasonal tropics• large rivers
We will need to do a lot better if we are ever going to credibly predict the future.
Root mean square difference between prior and posterior time series of total water storage
Global Water Cycle Reanalysis v1.0
Van Dijk, Renzullo, Wada et al (2014) HESS
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polaricecaps
mountainglaciers
seasonalsnowpack
newimpoundments
othersurfacewaterbodies
groundwaterdepletion
subsurface(temperate,monsoon)
Trend2003-2012 (Gt/y,km3/y)
Net result: 495 km3 flows from land to ocean each year(ca. 1.4 mm sea level rise)
Terrestrial water storage trends
Van Dijk, Renzullo, Wada et al (2014) HESS
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-90-60-3003060
Subs
urfa
ce w
ater
sto
rage
tren
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m3
y-1)
Latitude
prior
posterior
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Linear 2003–2012 trends in sub-surface water storage by 10° latitude band
• Current generation of models have about zero skill in reproducing multi-annual trends.
• Several reasons: precipitation errors, lacking processes, water engineering, parameter estimation.
So much for modelling
Van Dijk, Renzullo, Wada et al (2014) HESS
Past successes and FailingsOur meteorological data are not good enough for globalscale water cycle reconstruction pre ~2000. We cannot fix that, and should probably stop pretending.
Perhaps worse, our models are usually not even good enough when and where we do have good meteorological data. That we should be able to fix – but we’ll need to use more observations, better.
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1) Model formulation
2) Model configuration
3) Model calibration
4) Model selection
5) Model-data
assimilation
6) Model evaluation
Karmic wheel of model-data fusion
Van Dijk (2011) MODSIM
Ideas and Opportunities• There are a lot of ancillary (satellite) observations we
can use to improve our lousy models, or their estimates.• Data assimilation can help a great deal to improve water
cycle estimates for the intensive satellite period.• Calibration has served us well (…) at local scale – can it
do the same at global scale?
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Can we calibrate some problems away?
data via http://www.wenfo.org/wald/data-software/
GCWR v1.0 monthly anomalies (2003-2012, 1°):• sub-surface (soil and ground-) water• snow and ice• river channels• lakes• oceans
Can we calibrate ourmodels to these?
We will be needing (better) global scalesub-models for several water cycle components.
Ideas and Opportunities• There are a lot of ancillary (satellite) observations we
can use to correct our lousy models, or their estimates.• Data assimilation can help a great deal to improve water
cycle estimates for the intensive satellite period.• Calibration has served us well (…) at local scale – can it
do the same at global scale?• No need to always wait for the next fancy satellite
mission! There are still lots of underused satellite observations out there.
• One example follows.
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Zambezi River @ Senanga, Zambia
Satellite gauging reaches
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Rank correlation
Van Dijk, Brakenridge, et al (in review) WRR
MODIS & passive microwave inundation detection
Van Dijk, Brakenridge, et al (in review) WRR
Validation correlation of MODIS- vs. station-based monthly discharge time series.
Next challenge: where we don’t have any station data..
Does it work?
Key points• Our hydrological models are too lousy to reliably
reproduce or detect water cycle trends, much less predict them.
• Satellite observations can help improve water cycle estimation through data assimilation
• Resulting water cycle re-analyses can be used to develop the next generation of models.
• We can still make much better use of already existing observation records.
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