AWAP Soil moisture

web.science.unsw.edu.au

AWAP Soil moisture

Australian Water Availability Project

Soil Moisture: ENSO and IOD Signatures in the MDB

Peter Briggs

Michael Raupach, Vanessa Haverd, Edward King,

Matt Paget, Cathy Trudinger

CSIRO Marine and Atmospheric Research

Acknowledgements

Colleagues in CMAR, CLW, BoM, BRS

Helen Cleugh, Damian Barrett, Luigi Renzullo, Francis Chiew, Tim McVicar,

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

to name a few…


Australian Water Availability Project

An Introduction,

Some Early AWAP Science Highlights,

Peter Briggs

Michael Raupach, Vanessa Haverd, Edward King,

Matt Paget, Cathy Trudinger

CSIRO Marine and Atmospheric Research

Acknowledgements

Colleagues in CMAR, CLW, BoM, BRS

Helen Cleugh, Damian Barrett, Luigi Renzullo, Francis Chiew, Tim McVicar,

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

to name a few…


Australian Water Availability Project

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

Michael Raupach, Vanessa Haverd, Edward King,

Matt Paget, Cathy Trudinger

CSIRO Marine and Atmospheric Research

Acknowledgements

Colleagues in CMAR, CLW, BoM, BRS

Helen Cleugh, Damian Barrett, Luigi Renzullo, Francis Chiew, Tim McVicar,

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

to name a few…


Australian Water Availability Project

A Mesmerising Short Film To Make

You Forget Everything Else

Peter Briggs

Michael Raupach, Vanessa Haverd, Edward King,

Matt Paget, Cathy Trudinger

CSIRO Marine and Atmospheric Research

Acknowledgements

Colleagues in CMAR, CLW, BoM, BRS

Helen Cleugh, Damian Barrett, Luigi Renzullo, Francis Chiew, Tim McVicar,

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

to name a few…


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 nearreal

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


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)

change in

soil water

layer

1



change in

soil water

layer

2



rainfall transpiration from layer 1 soil evaporation


surface runoff drainage from layer 1 to layer 2

drainage from layer 1 to layer

2 deep drainage out of layer 2



transpiration from layer 2


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

Murray-Darling Basins

South-East Coast Basins

Murrumbidgee Basin

Unimpaired Catchments

Major Rivers

425

426

413

414

408

239

415

407

424

409

405

423

412

410

404

402

403

422

421

401

420

417

418

419

219

210

416

212

214

213

215

216

411

217

218

211

201

202

203

204

205

206

207

208

209

220

238

237

236

234

235

229

233

232

406 231 230

228

227

225

226

223

224

222

221

*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


Outflow (m/mth)

Outflow (m/mth)

WaterDyn Performance (Sample Time Series)

Modelled

0.2

0.15

Goobarragandra

Monthly runoff

410057 410057

Measured

0.1

0.05

0

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

0.01

0.008

0.006

Goobarragandra

daily runoff

410057 410057

0.004

0.002

0

17 18 19 20

• 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


AWAP Operational System

King et al. 2009, IEEE JSTARS, submitted

A. External

• Data made available via internet in

various formats

A

Gridded

Met. Data

(Govt.

agency)

Daily Updates

AVHRR

Reception

Station

(Aust.)

Hourly Updates

MODIS

Data

(US)

Daily Updates

AATSR

Data

(Europe)

Daily Updates

B. Independent Local Servers

• Fetch, preprocess, reformat to

common standard, assign metadata,

B

Reformat

Catalog

Pre-proc.

Remap

Reformat

Catalog

Reformat

Catalog

LST Gen.

Remap

Reformat

Catalog

place in ftp-accessible archive

C. Model-Run Apparatus

• Marshall data, initial model state, run

model

D. Visualisation & Dissemination

Data and

Obs.

Assembly

Model

Run

Prep.

Model

Framework

• Create maps for www, data to ftp

server, experimental OPeNDAP

Parameter

Archive

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

1

C

D

Distinct Output Series

Output Visualisation and Dissemination

WWW FTP OPeNDAP


www.csiro.au/awap


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


Science Highlights

• Near-real-time soil moisture results (nearly)

• 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


Soil Moisture in Near-Real-Time: March 9-15, 2009

A Tale of Two Time Scales

Upper Layer

% rank

Lower Layer

% rank

Rainfall

mm d -1

Max Daily

Temperature

% rank


MDB Soil Moisture: March 9-15, 2009


Science Highlights

• Near-real-time soil moisture results (nearly)

• 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


The Complicated Story of Australian Rainfall Variability

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

BoM

CSIRO

UNSW

UTas

et al.

Thanks James Risbey et al. 2009

On the remote drivers of rainfall

variability in Australia


Climate driver with highest correlation to

monthly rainfall for each season

Thanks James Risbey et al. 2009

On the remote drivers of rainfall

variability in Australia

Blocking SAM IOD ENSO

Summer

Autumn

• Influences vary

• seasonally

• regionally

• Decadal variability

• Interaction between

drivers

Winter

Spring


Major Australian Drainage Divisions

(With Subdivided NE Coast and MDB)

Drainage Division

11NE Coast Sea

12 NE Coast Brd-Ftz

20 SE Coast

30 Tasmania

41 MDB Wet

42 MDB Agric

43 MDB Semi-Arid

50 SA Gulf

60 SW Coast

70 Indian Ocean

80 Timor Sea

90 Gulf of Carpentaria

100 Lake Eyre

110 Bulloo-Bancannia

120 Western Plateau


MDB: Subdivided by Mean Annual Rainfall

Murray-Darling

AWRC Basin

Numbers

Mean Annual Rainfall

(mm yr -1 )

423

Semi-arid

Agricultural

< 460

460 to 1000

424

422

417

416

Wet

1000 to 1200

418

420

419

425

421

426

412

413

414

415

408

409

407 404

405

410

402401

403

411

406


0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Lower Layer

Soil Moisture

-0.2

-0.3

-0.4

-0.5

-0.6

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

0.4

-30 -20 -10 -0.1 0 10 20 30

Total Runoff

(Outflow)

-0.2

-0.3

-0.4

-0.5

-0.6

Corr(SOIm,Precip)

Lag (months)

0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Rainfall

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

Lag (months)

Corr(SOIm,Precip)

0.4

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

−SOI

smoothed

−SOI

smoothed

% Rank Correlations

with −SOI 1960-2007

(Eastern drainage divisions)

-30 -20 -10 -0.1 0 10 20 30

−SOI

unsmoothed

Lag (months)

Key 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)

0: Australia

1: NE Coast

1.1: NE Coast (sea)

1.2: NE Coast (Burd-Fitz)

2: SE Coast

3: Tasmania

4: MDB

4.1: MDB (w et)

4.2: MDB (agric)

4.3: MDB (semiarid)


-30 -20 -10 -0.1 0 10 20 30

-30 -20 -10 -0.1 0 10 20 30

Lower Layer

Soil Moisture

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

0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Total Runoff

(Outflow)

0.4

0.3

0.2

0.1

-0.2

-0.3

-0.4

-0.5

-0.6

-0.2

-0.3

-0.4

-0.5

-0.6

Corr(SOIm,Precip)

Lag (months)

0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Rainfall

-0.2

-0.3

-0.4

-0.5

-0.6

0

Dry

Lag (months)

Agric

-0.2

Wet

-0.3

-0.4

-0.5

-0.6

−SOI

smoothed

Lag (months)

−SOI

smoothed

−SOI

unsmoothed

% Rank 0: Australia Correlations

1: NE Coast

with −SOI 1.1: NE Coast 1960-2007

(sea)

1.2: NE Coast (Burd-Fitz)

MDB ONLY 2: SE Coast

3: Tasmania

4: MDB

4.1: MDB (w et)

4.2: MDB (agric)

4.3: MDB (semiarid)

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)


-30 -20 -10 -0.1 0 10 20 30

Lower Layer

Soil Moisture

Corr(IODcn,Outflow)

Lag (months)

0.4

-30 -20 -10 -0.1 0 10 20 30

Total Runoff

(Outflow)

0.4

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

Corr(IODcn,Precip)

Lag (months)

0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Rainfall

-0.2

-0.3

-0.4

-0.5

-0.6

Lag (months)

Corr(SOIm,Precip)

0.4

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

DMI

smoothed

DMI

smoothed

% Rank Correlations

with DMI 1960-2007

-30 -20 -10 -0.1 0 10 20 30

DMI

unsmoothed

Lag (months)

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)?

0: Australia

1: NE Coast

1.1: NE Coast (sea)

1.2: NE Coast (Burd-Fitz)

2: SE Coast

3: Tasmania

4: MDB

4.1: MDB (w et)

4.2: MDB (agric)

4.3: MDB (semiarid)


Science Highlights

• Near-real-time soil moisture results (nearly)

• 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


Murray-Darling Basins

South-East Coast Basins

Murrumbidgee Basin

423

Unimpaired Catchments

Flow in the River Murray

Major Rivers

422

417

416

201

203

202

424

418

204

• Gauge at Wentworth

• Flows since 2002 have been

less than 25% of long-term

average

• Where did the water go?

• Irrigators?

426

239

414

415

238

237

421

425

412

413

410

408

409

407

404

401

405

402

403

223

236

229

225

234

233

224

232 231 230

228

226

235 406

227

419

420

219

222

221

210

212

211

213

214

215

216

411

217

218

220

205

206

207

208

209

5000

4500

4000

3500

3000

2500

2000

1500

1000

500

0

Murray flow at Wentworth (GL/mth)

Flow2

Data: MDBC, via

Geoff Podger,

April 2008

1950 1960 1970 1980 1990 2000 2010


Drivers of Murray flow: The water loss cascade

using AWAP results and Wentworth gauging


Soil water balance:


P


R


E dW dt


Precipitation Total Runoff Evapotranspiration Soil water

storage change


River water balance:


R


F


I


G dS


dt

Total Runoff River Flow Irrigation

and offtakes

Flux to

groundwater

Storage

changes


Cascade leading to river flow (F) is:

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

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

Average 1951:2001 9.01 = 518 x 0.109 x 0.16

Average 2002:2006 2.21 = 395 x 0.045 x 0.13

Ratio 0.24 = 0.76 x 0.41 x 0.79


Science Highlights

• Near-real-time soil moisture results (nearly)

• 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


Old CABLE

Soil Scheme

New CABLE Soil Scheme

(Haverd)

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

T i-1 ψ i-1

q l

q

T i-1 ψ i-1

H q l q v

q H

T i ψ i T i ψ i

q K( d

/ dz)

K

l

q K( d

/ dz)

K

q

l

H

k

H

dT

dz

q v

T i-1 ψ i-1

q l q v

q H

T i ψ i


dc T


dh

q D c D h

dz

q

r

v v v,

sat v r

H

dT

kH

dz



D c


dh

v,

sat

dz





dc T


D h

r

E v v,

sat v r

dz

v,

sat

dz


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

NEW

w. Litter

New,

No Litter

Old

Old

New,

No Litter

NEW

w. Litter


Daytime fluxes of energy and CO 2 : Tumbarumba

OLD

CABLE 1.4 with Soil-Snow

NEW

CABLE 1.4 with Soil-Litter


Science Highlights

• Near-real-time soil moisture results (nearly)

• 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


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”


Lower Layer

Soil Moisture

Monthly 1900-2007

Wetter

% Rank

Drier

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

climatology for each pixel

El Nino

−SOI

La Nina


-30 -20 -10 -0.1 0 10 20 30

Lower Layer

Soil Moisture

0.4

0.3

0.2

0.1

-0.2

-0.3

-0.4

-0.5

-0.6

Corr(SOIm,Precip)

Lag (months)

0.4

-0.1

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

-0.2

-0.3

-0.4

-0.5

-0.6

0

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

0.4

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Total Runoff

(Outflow)

Rainfall

-0.2

-0.3

-0.4

-0.5

-0.6

Lag (months)

−SOI

smoothed

Corr(SOIm,Precip)

0.4

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

−SOI

smoothed

−SOI

unsmoothed

Correlations with

ENSO 1960-2007

(Western divisions)

-20 -10 0 10 20 30

Lag (months)

0: Australia

5: SA Gulfs

6: SW Coast

7: Indian Ocean

8: Timor Sea

9: Carpentaria

10: Lake Eyre

11: Bulloo-Bancannia

12: Western Plateau


-30 -20 -10 -0.1 0 10 20 30

Lower Layer

Soil Moisture

Corr(IODcn,Outflow)

Lag (months)

0.4

-30 -20 -10 -0.1 0 10 20 30

Total Runoff

(Outflow)

-0.2

-0.3

-0.4

-0.5

-0.6

0.3

0.2

0.1

-0.2

-0.3

-0.4

-0.5

-0.6

Corr(IODcn,Precip)

Lag (months)

0.4

-0.1

0.3

0.2

0.1

0

-30 -20 -10 -0.1 0 10 20 30

Rainfall

0.4

0.3

0.2

0.1

-0.2

-0.3

-0.4

-0.5

-0.6

0

0

Lag (months)

DMI

smoothed

Corr(SOIm,Precip)

0.4

0.3

0.2

0.1

0

-0.2

-0.3

-0.4

-0.5

-0.6

DMI

smoothed

DMI

unsmoothed

Correlations with

IOD 1960-2007

(Western divisions)

-20 -10 0 10 20 30

Lag (months)

0: Australia

5: SA Gulfs

6: SW Coast

7: Indian Ocean

8: Timor Sea

9: Carpentaria

10: Lake Eyre

11: Bulloo-Bancannia

12: Western Plateau

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