Jorge Peña Arancibia, Francis Chiew, Tim McVicar, Yongqiang Zhang, Albert Van Dijk, Mohammed Mainuddin and others

29 April 2014CSIRO LAND AND WATER

Dynamic identification of summer cropping irrigated areas in a large basin under extreme climatic variability

Irrigation EGU 2014 | Jorge Pena| Page 2

Irrigation in the Murray-Darling Basin The MDB (1,059,000 km2): 41%

national agricultural production Irrigation: Only 2% of the total

agricultural land in the MDB 66% of Australia's agricultural water

consumption (7.7bn m3) 31% of the basins’ gross value of

agricultural production ($ 4bn).

Precipitation: High spatiotemporal variability

P=457 mm y-1 (ETa is 96%) Periods of drought and

flooding Large regulation.

Mapping of irrigation using remote sensingRecurrent NDVI at 250 m resolution: the 353th day of the year (white = summer crops):

Dry period

Wet period

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Objectives

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Identify the location and extent of irrigated areas on a year

by year basis from 2004/05 to 2010/11

Use these outputs to constrain existing hydrological and

river models

Identification of areas that require better monitoring

Supervised classification: Random Forest

‘Bagging approach’

Random perturbation to generate an ensemble of

classification trees

Reduces the variance without overfitting

Training: phenology and water use Phenology: TS remotely sensed

inputs of vegetation greenness from MODIS

Water use: TS remotely sensed evapotranspiration estimates

Two Random Forest ModelsMonthly values for each water year of:

Total of 120 covariates

fPARrec,i d/dt(fPARrec,i)

fPARper,i d/dt(fPARper,i )

ETa,i d/dt(ETa,i)

Pi d/dt(Pi)

ETa,i-Pi d/dt(ETa,I -Pi)Irrigation EGU 2014 | Jorge Pena | Page 5

Irrigation | Jorge Pena| Page 6

Random Forest Model Training sample for each: average of

332 pixels (roughly 21 km2) Model with 50% train/predict sample ‘Pruning’ the tree Covariance importance and

optimisation Observed agreement of 99%, kappa

of 96%

Greenness Water use

‘Pruning’

‘Covariate importance’‘Optimisation’ only 20

covariates

Independent evaluation: maps and statistics

Yearly basin-wide statisticsComposite map of irrigated areas for 2004–2010 versus static map

Irrigation | Jorge Pena | Page 7

Difference was less than 15% with some exceptions

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Reported cotton irrigated areas

Reported rice production

Independent evaluation: areas and production

Water resource assessment | Jorge Pena| Page 9

Independent evaluation: metered water withdrawals

Summer rainfall, summer irrigation Winter rainfall, summer irrigation

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Different outcomes when using all covariates

Global irrigation mapping: ETa development and evaluation

• Rolled-out globally at 5 km resolution, potentially at 500 m resolution.

• Evaluated at 500 m resolution against flux tower ETa located in 13 cropland and 22 grassland sites.

Crops

Grass

o Flux tower evapotranspiration• Remote sensing evapotranspirationₓ Potential evapotranspiration

Water limited: Southern Italy

Energy limited: The Netherlands

Seasonally water limited: Nebraska, USA

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Conclusion

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Accurate random forest mapping in a basin with extreme

climatic variability and dissimilar irrigation practices

Inclusion of remotely-sensed ETa, P, and ETa-P enhanced

the accuracy of the mapping

Summer irrigation in winter rainfall areas can be identified

using greenness only during years with average rainfall.

Thank you

Global irrigation mapping: potential covariates

Irrigation | Jorge Pena| Page 14

Irrigated

Dryland

Floodplain

Global irrigation mapping: covariates not depending on time of year

Irrigation | Jorge Pena| Page 15

Irrigated

Dryland

Floodplain

Global irrigation mapping: covariates not depending on time of year

Irrigation | Jorge Pena| Page 16

Irrigated

Dryland

Floodplain