The European Commission s science and knowledge service. Joint Research Centre

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1 The European Commission s science and knowledge service Joint Research Centre

2 Crop yield anomaly predictions by the Combined Stress Index (CSI) at the global and regional scales Matteo Zampieri 1 Andrea Toreti 1, Thomas Chatzopoulos 2 and Ignacio Perez-Dominguez 2 1 JRC D5, 2 JRC D4 Workshop: Indicators for climate extremes and socio-economic impacts under different emission targets Ispra, 5th October 2017

3 Global assessment of heat wave magnitudes since 1901 Zampieri, Russo et al STOTEN 2016

4 <U.S. Dust Bowl > during summer 2010 Jeddah reached 52.0 C (WMO 2011) 2010 Russian Heat Wave in Jeddah: dust outside, mildew inside Unprecedented demand for electricity for air conditioning led to blackouts in the whole Saudi kingdom (Gulf News, June 22, 2010, Heatwave in Middle East no cause for panic ). Image from MODIS Terra 24 July 2010

5 Exploratory project: Andrea Toreti, Matteo Zampieri, Ignacio Perez Dominguez, Thomas Chatzopoulos, Paolo Paruolo, Alessandro Dosio, Frank Dentener

6 FOCUS: present inter-annual variability, seasonal forecasts and near-future projections Global WHEAT Production How much of these fluctuations is a result of climate anomalies? Climate variation explains a third of global crop yield variability Ray et al. Nature 2015 The remaining variability is presumably due to other factors connected to agronomical practices, unknown events, policies and farmer choices, but also uncertainties in the data and in the assessment of climate anomalies

7 Data and Methods DATA: FAOSTAT Wheat, Maize, Soybeans and Rice, MIRCA2000 calendar METHOD: Statistical model combining heat and water anomalies and extremes (bilinear ridge regression) Yield detrend,std ~ CSI = a HMD detrend,std + b SPEI detrend,std HMD: Heat Magnitude Day (AgMERRA) > HEAT STRESS SPEI: Standardized Precipitation Evaporation Index (CRU) -> SOIL MOISTURE PROXY SRDI : Standardized River Discharge Index (GloFAS) -> FRESHWATER AVAILABILITY

8 MIRCA2000 spatial distribution and calendar Portmann et al. GBC 2010 Climate indicators defined on a global grid at 0.5 (HMD is computed on AgMERRA ) Spatially-dependent timewindow corresponding to the sensitive period of the crop (e.g. 3 month from harvesting for WHEAT) Multiple cropping (e.g. irrigated/rainfed winter/spring WHEAT) Spatial averages (harvested area weighting) over same grid and at country level

9 Heat Stress: Heat Magnitude Day (HMD, Zampieri et al. ERL 2017) Visual example of M d summation for WHEAT The HMD accounts for heat anomalies during the grain filling period, from flowering to harvesting. A heat anomaly is defined as a period where daily T max exceeds the 90 th percentile (T 90 ) The HMD is computed similarly to the Growing Degree Days index, but instead of summing up a temperature difference above a base value, it integrates the Magnitude Function (M d, Russo et al. ERL 2015*) over the sensitive period of the crop: HMD Σ [flow-harv] Ո T max>t90 (T max-t 25 )/(T 75 -T 25 ) *while the HWMId is defined as the maximum M d sum over consecutive days where T max >T 90 at annual or seasonal time-scales, the HMD sums up the M d over all events during the sensitive period of the crop

10 Global distribution of 90th maximum temperature percentiles Observed T 90 global distribution for WHEAT crops T 90 at harvesting is consistent with physiological critical values reported in the literature T 90 over wheat cropping regions displays significant variability, reflecting different practices (as irrigation) and crop varieties We account for the threshold increase during the period from flowering to harvesting, reflecting the increase heat tolerance of the crop during the grain filling period

11 Green Water Stress: Standardized Precipitation Evaporation Index (SPEI) Vicente-Serrano et al J.Clim The SPEI is a multi-temporal-scale index that quantifies persistent anomalies in the local soil moisture balance (i.e. precipitation minus potential evaporation) over different time-periods (1-24 months). Anomalies are standardized to allow the comparison of the values in different regions (necessary for the spatial aggregations). Blue Water Availability: Standardized Precipitation Evapotraspiration Index (SRDI) This study Computed as the SPEI, but on river discharge data simulated by GloFAS global gridded hydrological model (Alfieri et al. 2013, Kauffeldt et al. 2015)

12 Example of National Aggregation (France) Yield Cor(CSI,Yield anom )=-0.43 p=0.02 Summary of method CSI Non-linear trend is removed from yield data Consistently, non-linear trend is removed from HMD and SPEI Spatial aggregation of HMD and SPEI performed using harvested area as weighting factor (including double cropping system and irrigation fraction) HMD The CSI is computed by bilinear (ridge) regression of the yield anomalies i.e. Y detrend,std ~ CSI a HMD detrend,std + b SPEI detrend,std France is more sensitive to water excess than to drought (i.e. b < 0) Robustness of the procedure checked at subnational level (NUTS3) SPEI

13 Combined heat and water stresses index (CSI) 42% variability explained Wheat WHEAT

14 Regional skill and sensitivities to heat and water scarcity/excess BAD GOOD linear correlations between FAOSTAT yield data and CSI (CSI a HMD + b SPEI) relative importance of heat w.r.t. water stress in determining the yield anomalies (i.e. the a parameter) sensitivity to drought or to water excess (i.e. the sign of the b parameter) Zampieri M., Ceglar A., Dentener F. and A. Toreti ERL 2017 HEAT WATER DRY WET #jrcsciencehub: Extreme weather conditions and climate anomalies account for 40% of global wheat production variability (4th July)

15 Combined heat and water stresses index (CSI) MAIZE correlation of the CSI with national yields data BAD GOOD fraction of variability explained by heat vs. water stress HEAT WATER sensitivity to drought or to water excess 50% variability explained DRY WET

16 Application to Seasonal Forecasts Barcelona Supercomputing Center Application to EU countries Evidence of seasonal forecasts ability in predicting crop yields Improved skill initializing soil moisture in the seasonal forecasts Improved skill in simulations started later (i.e. June vs May) Ceglar et al., SREP, submitted

17 Application to Economical Modeling: Aglink-Cosimo (JRC D4*) - dynamic economic model of global agriculture - annual supply, demand, price and trade simulations: 93 commodities, 44 countries/regions, 10-year horizon ONGOING WORK: More countries and commodities Deterministic scenarios of single-crop, single-country, and singleyear events Stochastic scenarios of multi-crop, concurrent, and multi-year events (with the stochastic version of Aglink-Cosimo) Presentation in the Medium-Term Outlook Workshop (18-20 October, Brussels, Scenario: Extreme weather in the EU and effects on cereals markets, 2018) *thomas.chatzopoulos@ec.europa.eu ignacio.perez-dominguez@ec.europa.eu

18 SOYBEANS Main producers well captured: corr = -0.5 for Brazil, for Argentina, -0.5 for China, -0.7 for India Significant but poor skill over US (the dominant producer, correlation = -0.37) Decoupling of yield variability from climate in irrigated regions or SPEI not adequate? (Observed fro other crops as well)

19 CSI computed with SRDI RICE explained variance per country Main rice producers (i.e. China and India) are significantly better captured considering nonlocal freshwater availability than local surface water balance CSI computed with SPEI Correlation between SRDI and SPEI is low in many regions, indicating the non-local information delivered by the SRDI 35% global variability explained

20 RICE increase in explained variance Heterogeneous phenomenology comprise freshwater limited regions, which include the top producers (China, India, Cambidia, ) There are regions, as Italy, where rice yield anomalies are negatively correlated with river discharge, which is still a better regressor w.r.t the SPEI no dependency on freshwater Some regions (Vietnam, Japan, ) are better captured by the local water balance sensitivity to SRDI

21 CONCLUSIONS - the Combined Stress Index (CSI) helps explaining crop yield interannual variability - the CSI has been coupled to Aglink-Cosimo and implemented into Seasonal Forecasts (BSC) - blue water limitation should also be accounted for

22 Any questions? You can find me at

23 MIRCA2000

24 C 2 ESAM - Wheat

25 C 2 ESAM French wheat Adapted from previous work by Ceglar et al. AFM 2016

26 C 2 ESAM Global maize* (*) maize is less sensitive sensitive to climate anomalies over the last two months of the growing season untill harvesting

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