Jorge Peña Arancibia, Francis Chiew, Tim McVicar, Yongqiang Zhang, Albert Van Dijk, Mohammed Mainuddin and others 29 April 2014 CSIRO 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 km 2 ): 41% national agricultural production  Irrigation: Only 2% of the total agricultural land in the MDB  66% of Australia's agricultural water consumption (7.7bn m 3 )  31% of the basins’ gross value of agricultural production ($ 4bn).  Precipitation: High spatiotemporal variability  P=457 mm y -1 (ET a is 96%)  Periods of drought and flooding  Large regulation.

Mapping of irrigation using remote sensing Recurrent NDVI at 250 m resolution: the 353 th 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 Models Monthly values for each water year of: Total of 120 covariates fPAR rec,i d/dt(fPAR rec,i ) fPAR per,i d/dt(fPAR per,i ) ET a,i d/dt(ET a,i ) P i d/dt(P i ) ET a,i -P i d/dt(ET a,I -P i ) 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 km 2 )  Model with 50% train/predict sample  ‘Pruning’ the tree  Covariance importance and optimisation  Observed agreement of 99%, kappa of 96% GreennessWater use ‘Pruning’ ‘Covariate importance’ ‘Optimisation’ only 20 covariates

Independent evaluation: maps and statistics Yearly basin-wide statistics Composite 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 irrigationWinter rainfall, summer irrigation

Irrigation | Jorge Pena| Page 10 Different outcomes when using all covariates

Global irrigation mapping: ET a development and evaluation Rolled-out globally at 5 km resolution, potentially at 500 m resolution. Evaluated at 500 m resolution against flux tower ET a located in 13 cropland and 22 grassland sites. Crops Grass o Flux tower evapotranspiration Remote sensing evapotranspiration  Potential evapotranspiration Water limited: Southern Italy Energy limited: The Netherlands Seasonally water limited: Nebraska, USA Irrigation | Jorge Pena| Page 11

Conclusion Irrigation | Jorge Pena| Page 12  Accurate random forest mapping in a basin with extreme climatic variability and dissimilar irrigation practices  Inclusion of remotely-sensed ET a, P, and ET a -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