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
Irrigation EGU 2014 | Jorge Pena| Page 3
Objectives
Irrigation | Jorge Pena| Page 4
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
Irrigation | Jorge Pena| Page 8
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
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
Irrigation | Jorge Pena| Page 11
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.
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
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