The Australian water resources assessment system - Group on Earth ...

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The Australian water resources assessment system - Group on Earth ...

ong>Theong> ong>Australianong> ong>waterong> ong>resourcesong> ong>assessmentong> ong>systemong>

integrating hydrological models with on-ground and satellite observations

Albert van Dijk, Luigi Renzullo, Edward King, Juan Pablo Guerschman, Steve Marvanek, Garth

Warren, Randall Donohue, Jamie Vleeshouwer and many others

GEOSS, Melbourne, 15 September 2009


Key points

• More comprehensive and up to date ong>waterong>

information will benefit ong>waterong> management.

• A prototype ong>waterong> observation ong>systemong> for

Australia exists.

ong>Theong> ong>systemong> follows a model-data fusion

approach; combining on-ground and

satellite observations with models.

• Simulations have been evaluated against

observations.

• Results can be used for resource

accounting, understanding change,

reporting present state and forecasting.

• All conditions are met to start developing a

global ong>waterong> ong>resourcesong> ong>assessmentong>

technology.


Mud map

Background

ong>Theong> ong>Theong> need need

Research Research

context context

System System

requirements requirements

System System

design design

Approach Approach

Model-data Model-data

fusion fusion

Components Components

Evaluation

Flux Flux towers towers

Streamflow Streamflow

GRACE GRACE

Example

uses uses

Interpreting Interpreting

the the past past

Understanding

Understanding

change change

Current Current

awareness awareness

A global global

System?

Precipitation Precipitation

Streamflow Streamflow

Other Other

observations observations


Background


Murray Basin: beyond reasonable drought?

7,000

6,000

5,000

4,000

3,000

2,000

1,000

-

150

Eildon

Hume

1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009

rice

cotton

100

50

0

1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009

Combined storage (GL)

Production index (1990-2000 mean)

sources: Goulburn-Murray Water, MDBC, Indexmundi, ‘Beyond Reasonable Drought’ exhibition

5 of 43


ong>Theong> need for ong>waterong> information

Lack of timely, accurate and insightful

ong>waterong> information has been identified as

an obstacle to addressing the ong>waterong>

crisis in the National Water Initiative.

6 of 43


A new national ong>waterong> information agency

A ong>waterong> information agency was established within the Bureau of Meteorology to

‘enhance the quality and utility of ong>waterong> information’ :

• issue national ong>waterong> information standards

• collect and publish ong>waterong> information

• conduct regular national ong>waterong> ong>resourcesong> ong>assessmentong>s

• publish an annual National Water Account

• provide regular ong>waterong> availability forecasts

• give advice on matters relating to ong>waterong> information

• enhance understanding of Australia's ong>waterong> ong>resourcesong>.

//www.bom.gov.au/ong>waterong>/


Water information R&D alliance (WIRADA)

Through the 5-year WIRADA alliance, CSIRO and the Bureau of Meteorology

are collaborating to develop ong>waterong> information infrastructure and services.

Water Informatics

• Flexible automation and discovery

• Water data standards

Foundation data

• SRTM Digital Elevation Model

• Precipitation and Actual Evapotranspiration Products*

Water Resources Assessment and Accounting

• Continental ong>waterong> balance observation ong>systemong>*

• Water accounting methods

Water Forecasting

• Flood observation and forecasting

• Seamless ong>waterong> availability forecasting (days to decades)

//www.csiro.au/partnerships/WIRADA.html


A national ong>waterong> observation ong>systemong>

Sample questions

• How much resource has been generated?

• How much ong>waterong> is used by whom, for what?

• How much ong>waterong> have we got left?

• How does this compare with the past?

• Is a trend or shift emerging?

• How can we expect ong>waterong> availability to develop?

• What is the observed impact of extraction/land use/farm

dams/bushfires on ong>waterong> security and environment?

System specifications

• Continental coverage

• Local accuracy and relevance

• Confidence through using all relevant observations

• Up to date and insightful information

• Technology and theory that is robust, benchmarked and

evolving

9 of 43


Model-data fusion – the best of three worlds

On-ground observations

• relatively direct

• sparse and/or infrequent

• not predictive

Satellite observations

• full and frequent coverage

• relatively indirect

• not predictive

Biophysical models

• predictive

• directly interpretable

• full and continuous coverage

• unhindered by reality

11 of 43


On-ground observations: few and far between

Rainfall gauging network

Surface ong>waterong> gauging network

(each colour is another agency)

12 of 43


Satellites: drowning in data

station rainfall

rainfall radar

satellite rainfall

rainfall

station meteo

atmospheric vars

atmospheric RS

station radiation

radiation

cloud cover

greenness

albedo

roughness

vegetation

ong>waterong> content

evapotranspiration

LS temperature

contour map

SRTM DEM

runoff

airborne geophysics

geology map

bore data

groundong>waterong>

soil properties

soil ong>waterong>

soil map soil moisture

flood recharge

gravity anomaly

land use map

land cover

station streamflow

metering

rivers, ong>waterong> bodies

ong>waterong> bodies

irrigation


Ensuring evolving model theory

data-driven model

development

Occam’s Razor

Adopt, Adapt, Invent

Modify

performance

benchmarks

Evaluate

Accept

new theory

new observations

14 of 43


Conceptual ong>systemong> architecture

gridded

landscape

hydrological

model

river

model

stream

network

Aquifer

model

3-D

15 of 43


Current landscape hydrological model

radiation

radiation

temperature

temperature

albedo

albedo

precipitation

precipitation

fraction

fraction

deep-rooted

deep-rooted

vegetation

vegetation

available

energy

maximum

transpiration

ET

ET

surface soil

shallow root

zone

saturated area

maximum

root uptake

deep root

zone

surface

ong>waterong>

vegetation

adjustment

ground

ong>waterong>

16 of 43


River flows and use

Key difference with land surface models: satellite ET

estimates that do not require a ong>waterong> balance assumption

are needed to identify areas where lateral flows from rives

and groundong>waterong> are evaporated.

4000

3000

Diversions (GL/y)

2000

1000

Satellite based estimate

MDBC reported

0

1990 1993 1996 1999 2002 2005

Water year starting

17 of 43


Prototype ong>systemong> technical details

Dimensions

• daily time step

• continental coverage

• variable resolution

Input data

• daily grids of precipitation

• daily grids of potential ET or climate variables

• fraction deep-rooted vegetation (from vegetation remote sensing)

• single set of 8 parameter values (calibrated to flux tower ET &

catchment streamflow)

Code

• Modular configurable scripts

• Easy to parallellise

• Execution sufficiently fast for large scales at fine resolution

Not yet implemented

• Explicit river and groundong>waterong> routing & extraction

• Spatial model-data assimilation


Evaluation Evaluation


Test sites (OzFlux)

HoSp - Howard Springs

Open forest savanna

(Lindsay Hutley, CDU)

ViPa -

Virginia Park

Open

woodland

savanna

(Ray

Leuning,

CSIRO)

550

mm.y -1

Tumb - Tumbarumba

Net rainfall (P-AET)

Wet open sclerophyll

Guerschman et al., 2009

forest (Ray Leuning,

CSIRO)

Kyem - Kyemba

grassland

(Jeff Walker, UoM)


Observations used

Vegetation greenness

• Enhanced Vegetation Index (EVI), similar to NDVI

• measured by MODIS satellite instrument

• strongly correlated to transpiration and carbon

assimilation

• footprint 250 m

Flux tower evapotranspiration

• measured by eddy covariance instruments

• require correction and gap-filling

• measurements of rainfall interception losses

questionable

• footprint usually assumed 25 km

21 of 43


Evaluation: vegetation greenness

observed

modelled

0.6

Howard Springs, NT

0.8

Kyemba, NSW

0.5

0.7

0.6

0.4

0.5

Enhanced vegetation index

0.3

0.2

0.1

0

2000 2001 2002 2003 2004 2005 2006

0.6

0.5

Tumbarumba, NSW

0.4

0.3

0.2

0.1

0

2000 2001 2002 2003 2004 2005 2006

0.6

0.5

Virginia Park, Qld

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

2000 2001 2002 2003 2004 2005 2006

0

2000 2001 2002 2003 2004 2005 2006

22 of 43


Evaluation: evapotranspiration

6

Howard Springs, NT

6

Kyemba, NSW

observed

modelled

5

5

4

4

Weekly average ET (mm/d)

3

2

1

0

2001 2002 2003 2004 2005 2006

6

5

Tumbarumba, NSW

3

2

1

0

2001 2002 2003 2004 2005 2006

6

5

Virginia Park, Qld

4

4

3

3

2

2

1

1

0

2001 2002 2003 2004 2005 2006

0

2001 2002 2003 2004 2005 2006

23 of 43


Evaluation: top soil moisture

1.2

Howard Springs, NT

1.2

Kyemba, NSW

observed

modelled

Top soil moisture (normalised by mean and st. dev.)

1.0

0.8

0.6

0.4

0.2

0.0

1999 2000 2001 2002 2003 2004 2005 2006

1.2

1.0

0.8

0.6

0.4

0.2

Tumbarumba, NSW

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

1999 2000 2001 2002 2003 2004 2005 2006

1.2

1.0

0.8

0.6

0.4

0.2

Virginia Park, Qld

0.0

0.0

-0.2

1999 2000 2001 2002 2003 2004 2005 2006

-0.2

1999 2000 2001 2002 2003 2004 2005 2006


Streamflow from test catchments

243 catchments with

‘unimpaired’ streamflow and

good quality data

Size 51–1979 km 2 (median

~300 km 2 )

Mean streamflow (1980-2008) and location of test

catchments

25 of 43


Evaluation: streamflow

10000

2500

Estimated streamflow (mm/y)

1000

100

10

Estimated streamflow (mm/y)

2000

1500

1000

500

1

1 10 100 1000 10000

Observed streamflow (mm/y)

0

0 500 1000 1500 2000 2500

Observed streamflow (mm/y)

1000

1000%

Abs Q difference (mm/y)

100

10

Rel Q difference

100%

10%

1

0% 25% 50% 75% 100%

1%

0% 25% 50% 75% 100%

Fraction catchments exceeding

Fraction catchments exceeding

26 of 43


Gravity Recovery and Climate Experiment

Satellite mission

that can measure

total terrestrial

ong>waterong> storage

over large areas

Anomaly for April 2009

Source://geoid.colorado.edu/grace/grace.php


Evaluation: total ong>waterong> storage

Total ong>waterong> storage (mm)

300

250

200

150

100

50

0

2002 2003 2004 2005 2006 2007 2008

model estimates

Total ong>waterong> storage (mm)

GRACE observations

150

125

100

75

50

25

0

2002 2003 2004 2005 2006 2007 2008

Total ong>waterong> storage (mm)

150

125

2003-2008 trends

100

(model estimated

total

75

storage)

50

25

0

2002 2003 2004 2005 2006 2007 2008

28 of 43


Example uses


Interpreting the past: word of caution

Trends in annual rainfall 1980-2008

%/dec

20

-20

p-value

< 90%

> 90%

> 95%

> 99%

Linear trend

Significance

2050 prediction

(IPCC A1FI GCM ensemble)

decrease very likely

decrease more likely

even probabilities

increase more likely

increase very likely

Climate change prediction

Gauge network

Source: www.climatechangeinaustralia.gov.au, SILO, BoM

30 of 43


Interpreting change

Estimated trends in ong>waterong> ong>resourcesong> (1980-2008)

Total ong>waterong> (sw+gw) storage

(mm per decade)

mm per 10y

+30

Annual streamflow

(mm per decade)

mm per y

+1+10

-30

-1-10

31 of 43


Natural resource accounting

Murray-Darling Basin ong>waterong> accounts (1990-2006)

Soil ong>waterong> account

mm/year

GL/year

River network ong>waterong> account

mm/year

GL/year

River inflows

41

43,041

Rainfall

428

452,730

Riparian and floodplain losses

27

28,521

Evapotranspiration

421

445,765

Net extraction

10

10,697

Runoff

26

28,035

Net open ong>waterong> evaporation

3

3,102

Deep drainage

14

15,005

End of ong>systemong> outflow

6

6,156

Net storage change

-34

-36,075

Net storage change

5

5,435

Dryland ET

90.2%

System outflow

1.2%

Irrigation

2.2%

Wetland ET

6.4%

32 of 43


Understanding hydrology – is it changing?

Trend (1980-2008)

mm/y per decade

Change

% over 29 years

Australia

north

south

Australia

north

south

Rainfall

+18

+20

-3

+10

+23

-1

ET

+13

+11

+1

+9

+17

+2

GW recharge

+2

+4

-2

+19

+49

- 36

Streamflow

+6

+8

-2

+23

+49

- 23

Storage

-1

+1

-2

LAI

-20

+32

-35

300

Streamflow (north)

0.5

Leaf area index (south)

40

Groundong>waterong> recharge (south)

250

30

200

mm/y

150

100

20

10

`

50

0

1980 1985 1990 1995 2000 2005

0.0

1980 1985 1990 1995 2000 2005

0

1980 1985 1990 1995 2000 2005


Coupling between ong>waterong>, carbon and ecoong>systemong>s

Trends in vegetation cover (fPAR, 1980-2006)

Climate can explain some

of the observed trends,

but not all

AVHRR satellite fPAR

(Donohue et al., GCB 2008)

fraction per 10y

+0.5%

Model simulated fPAR

(no data assimilation)

-0.5%

34 of 43


Current awareness: river inflows

Estimated cumulative streamflow for June 8-September 8 (3 months)

Relative to average streamflow (1980-2008) Percentile year (1980-2008)

35 of 43


Current awareness: ong>waterong> storage

Estimated combined soil and ground ong>waterong> storage - 8 September 2009

Anomaly relative to average total storage (1980-2008) Percentile year (1980-2008)

+150 mm

-150 mm

36 of 43


Time for a global ong>waterong>

ong>resourcesong> observation ong>systemong>?


Global precipitation observations

• Precipitation is the main

constraint on ong>waterong> balance

estimation

• Good quality global products

now exist that blend historic

and current data from gauge

and satellites

TMPA 3B42

ong>Theong>re is much potential to

constrain rainfall uncertainty

using additional observations


Global streamflow measurement network

(rapid) availability of hydrometric

measurements is still poor but improving

39 of 43


Other data sources

• inundation (optical, radar)

• soil moisture (passive microwave,

radar)

• vegetation (leaf area, ong>waterong>

content)

• total ong>waterong> storage (GRACE)

• weather interpolation/NWP

reanalysis and forecasts

Mean ERS scatterometer surface soil moisture (1991-2007), TU Wien

Meso-scale LAPS rainfall forecast in Delft Fews

Mean vegetation greenness


Key points

• More comprehensive and up to date ong>waterong>

information will benefit ong>waterong> management.

• A prototype ong>waterong> observation ong>systemong> for

Australia exists.

ong>Theong> ong>systemong> follows a model-data fusion

approach; combining on-ground and

satellite observations with models.

• Simulations have been evaluated against

observations.

• Results can be used for resource

accounting, understanding change,

reporting present state and forecasting.

• All conditions are met to start developing a

global ong>waterong> ong>resourcesong> ong>assessmentong>

technology.


ong>Theong> to-do list (in progress)

Improving (use of) forcing data

• Rainfall and meteorology, blending on-ground and satellite observations

• Simulating interpolation and averaging errors

• More relevant land cover products

Evaluation

• On-ground observations (e.g. groundong>waterong>, extraction metering)

• New satellite observations (e.g. land surface temperature)

Modification

• Connecting with river and groundong>waterong> models

• Inferring use of river and groundong>waterong> flows from satellite ET

• Full coupling of ong>waterong> and carbon with ecoong>systemong> metrics (e.g. crop

production)

Setting performance benchmarks

• Ongoing

Model-data fusion and assimilation techniques

• Sequential data assimilation

• Testing (prior) parameter estimation techniques

43 of 43

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