Sub-seasonal to Seasonal Prediction for Dynamic Cropping Calendars in Indonesia Andrew W. Robertson, Dong Eun Lee, Columbia University

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1 ub-seasonal to easonal Prediction for Dynamic Cropping Calendars in Indonesia Andrew W. Robertson, Dong Eun Lee, Columbia University Rizaldi Boer, Ahkmad Faqih, Agus Buono Bogor Agricultural University

2 Outline 1. The application context: rice-cropping in West Java and cropping calendars 2. Empirical 2 forecasts of monsoon onset date, merging ENO and MJO information 3. ub-seasonal drought and flood indices: relationship with MJO 4. tochastic rainfall downscaling

3 Rice-cropping in West Java

4 Typical climate risks for farming in Java rice planting area Rainfall (mm) Technical Irrigated Conventional irrigated/ Rainfed Drought risk (false rain or delay onset of rainy season) Flood risk Drought risk O N D J F M A M J J A rice rice fallow rice Vegetable Vegetable/fallow rice Upland crops Vegetable/fallow Dry land Maize Maize Vegetable/fallow Climate impact on farming system Types of climate risks on lowland rice 1 st Rice: flood risk in the period of between Jan and Feb 2 nd Rice: drought risk due to early onset of dry season (rainy season ends earlier than normal) Maize: risk to be exposed to long dry spell at the start of rainy season (false rain) Type of climate risks on dry-land farming 1. Maize/nuts: risk to be expose to drought risk (long dry spell or season break or rainy season ends earlier than normal) 2. Maize expose to high wind speed (Jan-Feb) ource: Boer, 2005

5 Mean planting area in West Java: Relationship with ENO Planting Area (1000 ha) La Niña Peak of planting in El Nino years delayed one month compared to normal El Niño tart of planting delay 1 month Normal El Nino La Nina Many farmers still plant rice crops in June ( ) Month

6 Dynamic Cropping Calendar Current system uses estimates May August rainfall vs drought area Meteorological and agricultural drought are closely related Drought Area vs MJJA rainfall in Indramayu Bogor Agricultural University works with local government to define the threshold of the probability when specific actions need to taken to manage the drought risk. The actions could be switching crop, change variety, preparing pumping etc.

7 Empirical 2 forecasts of monsoon onset date, merging ENO and MJO information

8 Canonical Correlation Analysis (CCA) of onset dates vs OLR at 8 previous pentads before start date tart dates at each pentad, beginning Jul CCA uses PCs of 5 extended EOFs of latitude-averaged NOAA pentad OLR [10-10N] vs PCs of onset date (4 modes, 20cm accumulation from July 30), for the Aug Dec season, with seasonal cycle subtracted y(t) =A.x(t)+C t : 1979, 1980,...,2009. Onset date PCs PCs of lagged OLR

9 Extended EOFs of Zonally-averaged OLR [10-10N, Aug Dec]

10 Power pectra of PC Timeseries 500 days PC 1 PC days PC 3 PC 4 PC 5 The curves are power*frequency normalized, so that the area under the curve adds up to the percentage variance explained by each mode.

11 Mean onset dates from CMAP and station rainfall

12 Onset date forecast skill from different start dates Cross-validated anomaly correlation Jul-Aug Aug-ep ep-oct Oct-Nov Nov-Dec

13 Model skill excluding interannual variability (EEOF 1) Jul-Aug Aug-ep ep-oct Oct-Nov Nov-Dec

14 ub-seasonal drought and flood indices

15 Daily Agricultural Drought & Flood Index After rainfall, soil moisture diminishes on a daily basis as a function of runoff and evapotranspiration. hallow soils can dry out quickly and sub-seasonal forecasts have potential Weight Daily Weights L = 15 days Effective precipitation index (EP) Byun and Wilhite (1999): sum of past daily rainfall multiplied by a decreasing daily weight Day before present

16 Impact of MJO on Effective precipitation anomalies EP, Phase 1 EP, Phase 2 6 o 30 7 o 30 8 o 30 9 o 30 6 o 30 7 o 30 8 o 30 9 o 30 6 o 30 7 o 30 8 o 30 9 o o E 108 o E 110 o E 112 o E 114 o E EP, Phase o E 108 o E 110 o E 112 o E 114 o E EP, Phase o E 108 o E 110 o E 112 o E 114 o E EP, Phase o 30 7 o 30 8 o 30 9 o 30 6 o 30 7 o 30 8 o 30 9 o 30 6 o 30 7 o 30 8 o 30 9 o o E 108 o E 110 o E 112 o E 114 o E EP, Phase o E 108 o E 110 o E 112 o E 114 o E EP, Phase o E 108 o E 110 o E 112 o E 114 o E EP, Phase Composites vs MJO Phase (Oct Apr) Data: Aphrodite,! Units: mm/15-days 95% t-test masking 6 o 30 7 o 30 8 o 30 9 o o E 108 o E 110 o E 112 o E 114 o E o 30 7 o 30 8 o 30 9 o o E 108 o E 110 o E 112 o E 114 o E

17 What could farmers do with this information? Effective Precipitation could give drought warning for rainfed agriculture system, particularly for cash crops such as horticulture. Help farmers to irrigate more efficiently during dry periods. For flood, could help fish/ prawn pond farmers avoid washing away by floods. Harvest before the flood occurs less yield, but better than nothing!

18 Downscaling Given degree grid, downscaling is still needed to link with crop and hydrology at local scales. Bayesian NHMM-GLM tochastic Hidden Markov Models (HMMs) provide a flexible framework for simulating daily localscale rainfall

19 Rainfall downscaling over Indramayu from ENO & MJO indices (Oct Jun, ) Year Number of Years tate sequence 1 Oct 1 Dec 1 Feb 1 Apr 1 Jun averaged over years 1 Oct 1 Dec 1 Feb 1 Apr 1 Jun Rainfall probability (a) tate 1 5mm 10mm 20mm (b) tate 2 Mean Rainfall intensity (e) tate mm 10mm 20mm (f) tate (c) tate 3 5mm 10mm 20mm (g) tate (d) tate 4 5mm 10mm 20mm (h) tate 4 Fig. 3. Indramayu district with seasonal input and K =4states. Top: probabilityof Bottom: mean daily intensity. The relative frequencies of the 4 states are 20%, 18%, 2 and 34% for states 1 (wet) through 4 (dry) respectively. tate 1 tate 2 tate 3 tate 4 4. (a) Estimated state sequence and (b) its seasonality. Panel (b) shows the average er of days per calendar date that fall into each state, summed over the 23-year period.

20 tation Predictor Coefficients from Observed Indices Coef. for W Coef. for W tation tation (a) easonality Coefficients (b) Nino34 Coefficients Coef. for W Coef. for W tation tation (c) RMM1 Coefficients (d) RMM2 Coefficients 95% CLs ig. 5. Coefficient values for seasonality, Nino34 and RMM inputs, with the median coeffi ent values (circles) and 95% probability intervals.

21 Conclusions Agricultural drought and flood are serious climate stresses on rice growers in Java, Indonesia 2 forecasts need to be tailored to agricultural needs monsoon onset date is a key variable, although it appears much more impacted by ENO than MJO the daily Effective Precipitation index is a potentially useful simple filter for agricultural drought/flood 2 forecasts will often still need to be downscaled/bias corrected for use in agricultural applications the HMM provides a flexible methodology

22 thank you!

23 easonal Hindcasts x(t) Canonical correlation analysis of CMAP onset dates vs. July monthly T field y(t) =A.x(t)+C y(t) t : 1979, 1980,...,2009. Cross-validated anomaly correlation skill r(ŷ(t),y(t)) after Moron, Robertson & Boer (2009)

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