critical datasets & potential new tools for detection of climate impact on the water cycle
DESCRIPTION
Critical datasets & potential new tools for detection of climate impact on the water cycle. Dr Stuart Minchin CSIRO. Outline. Water as an integrator and amplifier of climate change signals The attribution challenge C andidates for measureable signal and new technologies for measurement - PowerPoint PPT PresentationTRANSCRIPT
Critical datasets & potential new tools for detection of climate impact on the water cycle
Dr Stuart MinchinCSIRO
Outline
• Water as an integrator and amplifier of climate change signals
• The attribution challenge • Candidates for measureable signal and new
technologies for measurement• CO2 Enrichment
• Seasonality shifts
• Groundwater and storage
• Soil moisture and vegetation trends
• Rainfall intensity and atmospheric fluxes
• New approaches to communication of climate/water links
• Common threads
Water as an integrator and amplifier of climate change signals
source: Australian Water Resources 2005
Minimal variations and errors in rainfall and ET estimation accumulate in ground and surface water resource assessment• Streamflow is ~9% of rainfall nation-wide (30 mm/y)• Groundwater recharge <2% of rainfall nation-wide (6 mm/y)• A 10 mm/y change in streamflow can be considered pretty significant in most systems..• Any change in rainfall is amplified 2 or 3 times in streamflow• Water resources generation and use is very localised• The actual resource is only very partially gauged thus needs to be estimated from sparse obs and indirect information
Keywords: Accuracy, Interpolation, Estimation
Percent difference in rainfall and runoff
Over-allocation to Irrigation
Bushfire Recovery Impacts
Expanding Plantations
Drying & Warming Climate
Uncapped Groundwater Extraction
Expanding Farm Dams
Growing Urban Demand
The Environmental Flows Imperative
The big
8water scarcity factors
The Attribution challenge
±0 500250
Kilometres
Legend
Net water balance
High : 1000 mm/y
Low : -1000 mm/y
River water resources (MDBSY)
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±0 520260
Kilometres
Legend
# Streamflow gauging stations
E Rainfall stations
Reporting region
±0 500250
Kilometres
Legend
Perennial watercourse
Major ephemeral watercourse
Perennial lakes and storages
Irrigation area
Ephemeral wetlands
Reporting region
Murray
Lachlan
Barwon-Darling
Warrego
Condamine-Balonne
Murrumbidgee
Namoi
Paroo
Gwydir
Wimmera
Macquarie-Castlereagh
Border Rivers
Moonie
Loddon-Avoca
Ovens
Goulburn-Broken
Campaspe
Eastern Mt Lofty Ranges
13
28
8
18
3
23
14
7
2
4
24a
11
6
1
25
22
29
16
26
9b
19
10
24b
21
5
12
17
3a
9a & 9b
9a
20
9
15a & 15b
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12
1
7
3
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6
7
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3
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3
3
1
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2
3
4
11
3
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3
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4
13
12
2
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1
1
7
5
1
1
1
3
12
51
1
9
1
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1 Reach with number
Legend
Reporting region
Accounting reach
Contributing catchment
Not included
Accounting gauge
Research by Mac Kirby, Albert van Dijk and many others
Bringing together different data types• Gauging data• Classification• Water use estimates• River network information
Murray Darling Basin:
Modelling climate impacts the challenge of mega-data
The Attribution challenge is a two way problem
4 IPCC Scenarios
Stochastic Rainfall generation 150 y
5 km Gridded Rainfall/Runoff
Security of supply for every demand node in river basin under
all 16 scenarios over 150 years
Murray Darling Basin Sustainable Yields Project
4 Land Use scenarios
Model 1 Model 2 Model 3
Model 70
Model 15 Model 16
Surface and groundwater models used in the 18 MDB reporting regions
Paroo IQQM
Warrego IQQMNebine IQQM
Condamine MODFLOW
Middle Condamine IQQM
St George SGCS13NT
Lower Balonne IQQM
Upper Condamine IQQM
Border Rivers IQQMBorder Rivers MODFLOW
Moonie IQQM
Gwydir IQQMLower Gwydir MODFLOW
Eastern Mt Lofty Ranges 6*WATERCRESS
DailyWeeklyMonthly
Barwon-Darling IQQM
Menindee IQQM
Peel IQQMUpper Namoi MODFLOW
Namoi IQQMLower Namoi MODFLOWMacq-Castlereagh 6*IQQMMacquarie MODFLOW
Wimmera REALMLachlan IQQMMid-Lachlan MODFLOWLower Lachlan MODFLOW
Ovens REALMGSM REALM
Avoca REALMSnowy SIM_V9
Murray BigModMurray MSM
Southern Riverine Plains MODFLOW
Upper Bidgee IQQMACTEW REALM
Mid Bidgee MODFLOW
Bidgee IQQMLower Bidgee MODFLOW
Lessons from this modelling effort
•Transparency
•Auditability
•Provenance and archiving
•Court challenges
In the challenging world of terabyte data
Candidates for Measurable Climate Change Signal in
the Water Cycle?
CO2 Enrichment effects on water cycle
• More CO2, better photosynthetic efficiency• Some early projections of higher stream flow due to
stomata response in energy limited environments• Much debate about whether observed increases in
runoff in some areas are evidence of this• Some emerging evidence of increased Vegetation
vigour in water limited environments (much of Australia), consistent with CO2 enrichment prediction
• There is a clear role here for continental scale observation of possible CO2 enrichment effects using a combination of RS and in-situ measurement
Potential tools:
Requires long time-series of consistent remote sensing and stream gauging data
Also need ability to correct for local anthropogenic effects (land use, water use, etc)
DA-09-03a GlobalLandCover
Shifts in seasonality of rainfall
• Seasonality shifts predicted due to Climate Change• Impacts of seasonal shifts on water cycle can be
enormous (SE Australia an example)• Can be quite spatially variable
BUT• Perhaps amenable to low tech
measurement?• Date and spatial info perhaps more
valuable than precise volume measurement?• A role for crowdsourcing, citizen science or community
monitoring efforts in developed and developing world?
Groundwater and Soil moisture measurement
• Traditionally harder to measure than surface water• Hugely important water sources with often long
residence times (meaning trends in recharge can take a long time to see in the water supply)
• With surface water drought, reliance on Groundwater grows
• There are a number of new techniques available to us for measurement of these factors, but all come with constraints.
• Our best chance is combining these observation sources with models to account for partitioning in the water cycle
New tools for Groundwater and Soil Moisture
• GRACE: Great potential but very low resolution. Integrates deep groundwater, surface water, soil moisture and reservoir storage. Very useful for constraining modelling outcomes
• SMOS/SMAP- Both~ 35km resolution 3 day revisit. Direct measure of soil moisture but only surface layer.
• Cosmic Ray Soil moisture probes- Integrates soil moisture across medium footprint. Low power requirement, still relatively expensive
• Time-series vegetation trends from space: integrates across root zone, CO2 enrichment issue. Large archive already potentially available.
Independent calibration of continental ET
-10
-8
-6
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-2
0
2
4
1 2 3 4 5 6 7 8 91
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11
2 1 2 3 4 5 6 7 8 91
01
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2 1 2 3 4 5 6 7 8 91
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2 1 2 3 4 5 6 7 8 91
01
11
2 1 2 3
2003 2004 2005 2006 2007
Gra
vity
an
om
aly
(cm
)
-150
-100
-50
0
50
100
Wa
ter
sto
rag
e a
no
ma
ly (
mm
)
GRACE gravity anomaly (cm)
Estimated water storage anomaly(mm)
y = 0.0402x - 2.3497
R2 = 0.7231
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0
2
4
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Storage anomaly (mm)
GR
AC
E e
nsem
ble
anom
aly
(mm
)
Additional constraints on the water balance are needed to improve accounting
GRACE: measures regional, monthly changes in the mass of the earth (i.e. mainly water storage).
Agrees well with model water balance estimates (for the whole MDB).
But how does it verify ET?
Research by Albert van Dijk, Luigi Renzullo and others
If ET is out we see a bias in storage
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9 0.95 1 1.05 1.1 1.15 1.2 1.25
AET scaling factor
R2
(wat
erba
lanc
e-G
RA
CE
)
It turns out to be a valuable new test of credibility and bias in ET estimates over large scales!
Therefore can also be used as a global constraint in calibration over large scales
-10
-8
-6
-4
-2
0
2
4
1 2 3 4 5 6 7 8 91
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2 1 2 3
2003 2004 2005 2006 2007
Gra
vity
an
om
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(cm
)
-150
-100
-50
0
50
100
Wa
ter
sto
rag
e a
no
ma
ly (
mm
)
GRACE gravity anomaly (cm)
Estimated water storage anomaly (mm)
same but 10% lower ET
SMOS/SMAP- Soil Moisture from space
• Early results from SMOS promising
• Numerous CAL-VAL efforts underway
• Continuity may be an issue
• Images: ESA and NASA
Cosmic Ray Probes
Time series vegetation trends for soil moisture
Mobile phones for rainfall intensity and flux measurements
Mobile rainfall intensity•Uses degradation in mobile phone signal•Potential for huge international network of rainfall intensity data
Flux tower packages•Cheap sensor packages for deployment on Mobile phone towers?•Potentially huge network for flux measurement
Both need cooperation with mobile phone companies in the private sector
http://www.eawag.ch/medien/bulletin/20100126/index_EN
The need for communication and
visualisation innovation?
Google Earth: 4d water visualisation potential
Visualisation of climate risk
Winning the communication war
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Catchment detox
Common Threads?
Common Threads?
• Water is a challenging domain for climate science• Need to combine models, space and in-situ
observations to achieve anything in the water domain• Advantages of amplification but many confounding
influences cause attribution issues• Standard of evidence more critical (cred.\court action).• Lots of potential for new tools but continuity and
historical availability a general issue for most. • Nevertheless a multiple lines of evidence approach
and the use of combined observation/modelling systems has promise
• Critical that high level impacts are translated and communicated effectively to the local/regional scale
Contact UsPhone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au
Thank you
CSIRO Land and WaterDr. Stuart MinchinResearch Director, Environmental Sensing, Prediction and Reporting
Phone: 02 62465790Email: [email protected]: www.csiro.au/clw