australian water availability project - university of...

Australian Water Availability Project

Peter Briggs

Michael Raupach, Vanessa Haverd, Edward King,

Matt Paget, Cathy Trudinger

CSIRO Marine and Atmospheric Research

Acknowledgements

Colleagues in CMAR, CLW, BoM, BRSHelen Cleugh, Damian Barrett, Luigi Renzullo, Francis Chiew, Tim McVicar,

David Jones, William Wang, John Sims, Dave Barratt, James Risbey

to name a few…

Soil Moisture: ENSO and IOD Signatures in the MDB

Peter Briggs




Acknowledgements



to name a few…

An Introduction,

Some Early AWAP Science Highlights,


Peter Briggs




Acknowledgements



to name a few…

An Introduction,

Some Early AWAP Science Highlights,


(including a few very preliminary slides about the

ENSO and IOD Signature in AWAP Soil Moisture),

and...

Peter Briggs




Acknowledgements



to name a few…


A Mesmerising Short Film To Make

You Forget Everything Else

Outline

A Brief Overview of AWAP

Early AWAP Science Highlights

Near-real-time soil moisture results

The signature of SOI and IOD in AWAP soil moisture

The MDB water loss cascade

Improving the CABLE Soil Model (Vanessa Haverd)

How does uncertainty in forcing met propagate to uncertainty

in the water balance

AWAP in the future

Out of our hands: AWAP take-up in the community

AWAP The Movie: Deep soil moisture and the SOI 1900-2007

A Joint Project CSIRO, BoM, BRS, and ANU

Project Aims

Monitor the state & trend of Australia‟s terrestrial water balance at

5 km resolution

Create a prototype operational system to automate the data

gathering, modelling, visualisation and delivery of results in near-

real time

Use model-data fusion methods to combine measurements

(satellite and hydrological) and model predictions

Mean relative water content in lower soil layer, Jan-Dec 2002

Red: 25th percentile and lower

Blue: 75th percentile and higher

Project Outputs

Soil moisture, all fluxes contributing to changes in soil moisture:

rainfall, transpiration, soil evaporation,

surface runoff, deep drainage

Automated web based delivery of data and visualisations

Weekly near-real-time updates

Historical monthly series updates

Historical monthly climatologies

Mean relative water content in lower soil layer, Jan-Dec 2002

Red: 25th percentile and lower

Blue: 75th percentile and higher

transpiration from layer 1 soil evaporatirainfa on

surface runoff drainage from layer 1 to

change in soil water

layer

layer

1

2

ll

WaterDyn

Dynamic model for two-layer soil water and green-leaf carbon

Daily time steps

No horizontal transport between grid cells

Transpiration each layer = min (energy-limited [P-T], water limited

rate) (then combined in a simple yet elegant way…)

When soil saturated, all precip runs off; no runoff otherwise

Runoff and deep drainage are losses to the system (someone elses

problem)

Leaf carbon allocation response to soil water (when implemented): will

use ecological optimality principles (Raupach 2005)

drainage from layer 1 to la deep drainage change in soil water

layout of layer 2

transpi

er

ration from l

yer

ayer 2

22

change in

leaf carbon

= [ net primary production ] − [ leaf decay ]

WaterDyn Testing:200 Unimpaired* catchments (mostly wetter areas) in SE Australia

Comparison:

Observed river

discharges at gauging

stations

vs.

Waterdyn total runoff

(surface runoff +

leaching) for catchment

area above gauging

stations

Unimpaired catchment

data provided by

Francis Chiew

422

425

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Murray-Darling Basins

South-East Coast Basins

Murrumbidgee Basin

Unimpaired Catchments

Major Rivers

*Unimpaired catchment: discharge not significantly affected by dams or water extraction

WaterDyn Performance (Mean Annual Discharge)

Predicted vs observed mean annual discharge for 200 unimpaired

catchments, 1981-2006

Forward mode, no data-assimilation (will improve)

Substantial better than original single-layer model

WaterDyn Performance (Sample Time Series)

0

0.002

0.004

0.006

0.008

0.01

17 18 19 20

Outflo

w (

m/m

th)

410057 410057

0

0.05

0.1

0.15

0.2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Outflo

w (

m/m

th)

410057 410057

Goobarragandra

Monthly runoff

Goobarragandra

daily runoff

Modelled

Measured

Broad success predicting total runoff with an extremely simple model

Source of failures:

Process descriptions oversimplified (but beware parameterisation problems with

increased complexity)

Some “unimpaired” catchments have water extraction from the stream

Inadequate soil and hydrogeological information

A. External

• Data made available via internet in

various formats

B. Independent Local Servers

• Fetch, preprocess, reformat to

common standard, assign metadata,

place in ftp-accessible archive

C. Model-Run Apparatus

• Marshall data, initial model state, run

model

D. Visualisation & Dissemination

• Create maps for www, data to ftp

server, experimental OPeNDAP

AWAP Operational SystemKing et al. 2009, IEEE JSTARS, submitted

1

Output Visualisation and Dissemination

WWW FTP OPeNDAP

Daily Updates Daily Updates Hourly Updates Daily Updates

Distinct Output Series

AATSR

Data (Europe)

LST Gen. Remap

Reformat Catalog

Reformat

Catalog

Pre-proc. Remap

Reformat Catalog

Reformat

Catalog

Model

Framework

Data and Obs.

Assembly

Model Run

Prep.

AVHRR Reception

Station (Aust.)

Gridded Met. Data

(Govt. agency)

Parameter

Archive

C

D

MODIS

Data (US)

A

B

Key elements Processes tied together with Perl scripts

Simple, general data format enables

modularity, scability, redundancy

Rigourous testing & logging at each step to

prevent „disguised errors‟

Indistinguishable from magic

www.csiro.au/awap

Relative Soil Moisture (Lower Layer) Fraction (0-1)

Science Highlights

Near-real-time soil moisture results (nearly)




How does uncertainty in forcing met propagate to

uncertainty in the water balance

Soil Moisture in Near-Real-Time: March 9-15, 2009A Tale of Two Time Scales

Upper Layer Lower Layer

Rainfall Max Daily

Temperaturemm d-1 % rank

% rank% rankUpper Layer Lower Layer

Rainfall Max Daily

Temperaturemm d-1 % rank

% rank% rank

MDB Soil Moisture: March 9-15, 2009

Science Highlights







The Complicated Story of Australian Rainfall Variability

ENSO, IOD, MJO, SAM, Blocking Highs, Subtropical Jet, etc.

Thanks James Risbey et al. 2009

On the remote drivers of rainfall

variability in Australia

BoM

CSIRO

UNSW

UTas

et al.

Climate driver with highest correlation to

monthly rainfall for each season

Influences vary

seasonally

regionally

Decadal variability

Interaction between

drivers

Thanks James Risbey et al. 2009

On the remote drivers of rainfall

variability in Australia

Blocking SAM IOD ENSO

Summer Autumn

Winter Spring

Major Australian Drainage Divisions(With Subdivided NE Coast and MDB)

Drainage Division

11

12

20

30

41

42

43

50

60

70

80

90

100

110

120

NE Coast Sea

NE Coast Brd-Ftz

SE Coast

Tasmania

MDB Wet

MDB Agric

MDB Semi-Arid

SA Gulf

SW Coast

Indian Ocean

Timor Sea

Gulf of Carpentaria

Lake Eyre

Bulloo-Bancannia

Western Plateau

MDB: Subdivided by Mean Annual Rainfall

Mean Annual Rainfall(mm yr-1)

< 460

460 to 1000

1000 to 1200

Murray-DarlingAWRC Basin

Numbers

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409408

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401403

404

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411

Semi-arid

Agricultural

Wet

Corr(SOIm,W2)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

% Rank Correlations

with −SOI 1960-2007(Eastern drainage divisions)

Lower Layer

Soil Moisture

Corr(SOIm,Outflow [total runoff])

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Total Runoff

(Outflow)

Rainfall

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

0: Australia1: NE Coast1.1: NE Coast (sea)1.2: NE Coast (Burd-Fitz)2: SE Coast3: Tasmania4: MDB4.1: MDB (w et)4.2: MDB (agric)4.3: MDB (semiarid)

−SOI

unsmoothed

−SOI

smoothed

−SOI

smoothedKey points

Deep soil moisture & runoff, higher

correls than rainfall, but lagged (~4

months)

Lag producing highest correl varies

regionally

Highest correls in the wettest, and

coastal areas, except Tasmania (lowest

correl)

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(SOIm,Outflow [total runoff])

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(SOIm,W2)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

% Rank Correlations

with −SOI 1960-2007MDB ONLY

Lower Layer

Soil Moisture

Total Runoff

(Outflow)

Rainfall

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)


−SOI

unsmoothed

−SOI

smoothed

−SOI

smoothed

Wet

Agric

Dry

Key points

Wet MDB: shorter lag

Agric: highest correl

Dry: lowest correl

Rainfall plots are similar, why do

soil moisture & runoff differ?

Differing regional rainfall regimes lead

to different hydrologic responses?

Regional differences in soil parameters,

vegetation cover?

Seasonality? (Undoubtedly)

Corr(IODcn,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(IODcn,Outflow)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(IODcn,W2)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

% Rank Correlations

with DMI 1960-2007

Lower Layer

Soil Moisture

Total Runoff

(Outflow)

Rainfall

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)


DMI

unsmoothed

DMI

smoothed

DMI

smoothed

Key points

For deep soil moisture and runoff,

Tasmania, SE Coast, and the wet MDB

stand out.

These 3 divisions are major sufferers in

the current „Big Dry‟ (all three still very red

2 weeks ago)

Is this support for Ummenhofer et al.

2009? (IOD responsible for Aust‟s worst

droughts)?

Science Highlights







Flow in the River Murray

Gauge at Wentworth

Flows since 2002 have been less than 25% of long-term average

Where did the water go?

Irrigators?

Murray flow at Wentworth (GL/mth)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1950 1960 1970 1980 1990 2000 2010

Flow2

Data: MDBC, via

Geoff Podger,

April 2008

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Murray-Darling Basins

South-East Coast Basins

Murrumbidgee Basin

Unimpaired Catchments

Major Rivers

Drivers of Murray flow: The water loss cascade

using AWAP results and Wentworth gauging

Soil water balance:

River water balance:

Cascade leading to river flow (F) is:

F = P x (R/P) x (F/R)

Precipitation Total Runoff Evapotranspiration Soil water

storage change

P R E dW dt

Total Runoff River Flow Irrigation Flux to Storage

and offtakes groundwater changes

R F I G dS dt

F (TL/y) P (TL/y) R/P F/R

Average 1951:2001 9.01 518 0.109 0.16

Average 2002:2006 2.21 395 0.045 0.13

Ratio 0.24 0.76 0.41 0.79

= x x

= x x

= x x

Science Highlights







Ti-1 ψi-1

Ti ψi

qlqHqv

qv

( / )lq K d dz K

Ti-1 ψi-1

Ti ψi

qlqH

Ti-1 ψi-1

Ti ψi

qlqHqv

Soil-Snow Soil-Litter Soil-Litter: with litter

( / )lq K d dz K

H H

dTq k

dz

,

,

v satr

v v v sat v r

dc Tdhq D c D h

dz dz

,

,

H H

v satr

E v v sat v r

dTq k

dz

dc TdhD c D h

dz dz

New CABLE Soil Scheme(Haverd)

Old CABLESoil Scheme

Adelong Soil Moisture: 0-8 cm

OLD

CABLE 1.4 with Soil-Snow

NEW

CABLE 1.4 with Soil-Litter

Data from Oznet (Jeff Walker U. Melb.)

Dependence of soil evaporation and

transpiration on soil-scheme

Old

Old

NEWw. Litter

NEWw. Litter

New,No Litter

New,No Litter

Daytime fluxes of energy and CO2: Tumbarumba

OLD

CABLE 1.4 with Soil-Snow

NEW

CABLE 1.4 with Soil-Litter

Science Highlights







How does uncertainty in forcing met propagate

to uncertainty in the water balance?

Planning a formal sensitivity analysis of AWAP products--

framework is in place

Case study: AWAP implications of using BoM AWAP met

surfaces versus same-but-different QDNRM Silo met surfaces

A „live issue‟ with several groups, mainly over rainfall

Coordinating with:

Catherine Beesley & Andrew Frost (BoM Water Group)

Luigi Renzullo et al. (CLW WIRADA)

Starting in earnest in the next couple of weeks with arrival of

full new BoM meteorology update

AWAP take-up in the community… Monitoring and reporting water resource conditions and trends at the national, regional and catchment level

Targeting investment in regions with significant current or future water resource management issues

Performance information for agricultural industries and Environmental Management Systems

Development planning and risk assessment at the national regional and catchment level, and

Modelling processes that affect the water resource base and generate problems such as salinity and declines

in water quality and quantity

Radon maps of Australia (soil moisture is important) [ANSTO]

Research on evolutionary biology, and as a GIS teaching tool to

explore historical climatic relationships [UQ]

How to divide up the GST between the states: differences in their

needs to subsidise domestic water & sewerage suppliers

[Enquiry: Commonweath Grants Commission]

Farm outreach: „The Break‟ and „Fastbreak‟ climate risk

newsletters [Vic DPI]

AWAP In the Future (1)

Administrative Issues (Operational Mode)

Now funded by and providing products to SEACI

Handover of operational system to BoM (medium term)

Eventual incorporation in AWRIS

Resolve issues with BoM data

Coordinate AWAP system with BoM update schedule

Discrepancy between monthly reanalysis and sum-of-dailies

Continue supply of products to the Australian community

AWAP In the Future (2)

Science Issues (Development Mode)

Incorporate a new plant carbon dynamics model

Currently using FAPAR climatology

Implementation of full data assimilation of r.s. products

Starting with LST (payoff: potential improved precipitation)

Great progress but not quite ready for prime-time

Substantial additional computational requirement

Answer some questions!

How has climate been driving and interacting with the

Australian landscape for the past 100 years?

How will it do so in the future?

Finally...

…another motion picture about Australia

“But Drover… Australia is so dry. And then it‟s so wet”

% Rank

−SOI

El Nino

La Nina

Lower Layer

Soil Moisture

Monthly 1900-2007

Drier

Wetter

w.r.t. pdf of monthly 1961-90

climatology for each pixel

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(SOIm,W2)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)Corr(SOIm,Outflow [total runoff])

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Correlations with

ENSO 1960-2007(Western divisions)

Lower Layer

Soil Moisture

Total Runoff

(Outflow)

Rainfall −SOI

unsmoothed

−SOI

smoothed

−SOI

smoothed

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

0: Australia5: SA Gulfs6: SW Coast7: Indian Ocean8: Timor Sea9: Carpentaria10: Lake Eyre11: Bulloo-Bancannia12: Western Plateau

Corr(IODcn,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(IODcn,Outflow)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Corr(IODcn,W2)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

Correlations with

IOD 1960-2007(Western divisions)

Lower Layer

Soil Moisture

Total Runoff

(Outflow)

Rainfall DMI

unsmoothed

DMI

smoothed

DMI

smoothed

Corr(SOIm,Precip)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-30 -20 -10 0 10 20 30

Lag (months)

0: Australia5: SA Gulfs6: SW Coast7: Indian Ocean8: Timor Sea9: Carpentaria10: Lake Eyre11: Bulloo-Bancannia12: Western Plateau

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