29 April 2014
description
Transcript of 29 April 2014
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
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Difference was less than 15% with some exceptions
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Reported cotton irrigated areas
Reported rice production
Independent evaluation: areas and production
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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
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Irrigated
Dryland
Floodplain
Global irrigation mapping: covariates not depending on time of year
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Irrigated
Dryland
Floodplain
Global irrigation mapping: covariates not depending on time of year
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Irrigated
Dryland
Floodplain