ENSEMBLES WORK PACKAGE 6.2 MEETING

1. ENSEMBLES WORK PACKAGE 6.2 MEETING DISAT contribution:Development of a methodology for probabilistic assessments of climate change impacts on typical Mediterranean agric. crops (e.g. durum wheat)R. Ferrise, M. Moriondo and M. Bindi HELSINKI, 26-27 APRIL 2007

2. Structure of the presentation • What are the main objectives of our study? • What have we achieved so far? • What are we planning to do in the next 6 months ? • What are our main questions requiring discussion in this meeting?

3. 1. What are the main objectives of our study? • Select/Test impact models to simulate different Mediterranean ecosystem tasks: • Forestry - damage due to forest fire • Agriculture - losses due to water and heat stresses • Apply the selected models to estimate a range of impacts using the probabilistic representation provided by RT1

4. 2. What have we achieved so far? • Previous months • Impact model selection and calibration for typical Mediterranean crops (e.g. olive, grapevine and durum wheat) and forest fire risk assessments • Collection of data required for the calibration and testing of impact models and as reference data for model input • Last 6 months 2.1 Develop a simple statistical model that emulate process-based crop yield models (e.g. SIRIUS-Quality durum wheat model) and can be used in probabilistic climate change assessments 2.2 Create yield response surfaces altering the baseline climate 2.3 Define critical thresholds of impactsusing yield cumulative distribution from the last 30-years (e.g. yield thresholds) 2.4 Obtain preliminary estimates of risk probabilities overlapping response surfaces and joint distribution of T and P changes

5. 2.1 Statistical model to emulate process-based crop yield models • 9 representative sites over the Mediterranean Basin were selected to perform a scenario sensitivity analysis • Crop yield model SIRIUS simulations were carried out for each scenario with different soils and N-rates • The outputs of the model were used to train a neural network back-propagation model • The Artificial Neural Network (ANN) was tested using the One-Leave-Out Cross Validation

6. 2.1 Statistical mode to emulate process-based crop yield models Scenario Sensitivity Analysis: • Sites: 9 grid points (50 Km side), representatives of the Mediterranean Basin climatic variability • Baseline Climate: 30 years (1975-2005) of daily Temperature (min and max), Rainfall and Global Radiation (from MARS JRC archive) • Temperature changes: from 0°C to 8°C with 2°C step • Precipitation changes: from -40% to +20% with 20% step • CO2 Scenarios: from 350 ppm to 650 ppm with 100 ppm step

7. 2.1 Statistical model to emulate process-based crop yield models SIRIUS simulations: • For each of the 9 grid cells SIRIUS was run for the combination of the different climatic scenarios with 3 different soils and 3 levels of Nitrogen fertilization • Sowing Date was set using a climatic criterion: at least 5 consecutive days with mean Temperature < 14°C and Rainfall < 2mm, starting from October 1st and not later than February 14th Soil types used for SIRIUS simulations • Nitrogen Fertilization was split in three times: 1/4 at sowing, 1/4 at tillering and 2/4 at jointing Nitrogen levels used for SIRIUS simulations

8. 2.1 Statistical model to emulate process-based crop yield models • Training the Artificial Neural Network: • A neural network back-propagation model was trained for emulating the SIRIUS outputs: • Network layers: 3 • Input nodes: 5 (variables: CO2, SWC, N level, T(AMJ), Prec.(AMJ)) • Hidden layer nodes: 20 • Output: 1 ANN model structure Testing results

9. 2.1 Statistical model to emulate process-based crop yield models • Leave-One-Out Cross Validation Test: Pearson’s correlation coefficients between ANN and SIRIUS estimates of crop yields for each of the 9 grid cells with all climate scenarios, 3 soils and 3 N-rates.

10. 2.2 Create yield response surfaces • SIRIUS and ANN model yield response surfaces were compared for a study area in France (43.6 N, 5.0 E): • Yield Response Surfaces were estimated altering the 30-years baseline climate (from MARS-JRC archive): • Temperature changes: from 0°C to +8°C • Precipitation changes: from -40% to +20% • CO2 concentration scenarios: 350 ppm and 550 ppm • Soil Water Content: 115 mm • Nitrogen Fertilization: 170 Kg N ha-1

11. 2.2 Create yield response surfaces • The ANN reproduced quite well the SIRIUS crop yields in the different scenarios Comparison between ANN and SIRIUS estimates of crop yields used to draw the response surfaces SIRIUS and ANN estimated response surfaces for a grid box in Southern France, for two CO2 scenarios

12. 5.35 Mg ha-1 2.3 Define critical thresholds • Critical threshold of impact was obtained: • Calculating the cumulative probability of selected parameter (in this case yield) • Selecting, as threshold, the values that correspond to the 20% of probability Cumulative distribution of yield in a pilot study area

13. 2.4 Estimating risk probability • The trained ANN was applied to estimate response surfaces in a study area (Tuscany 50x 50 km grid cells) • The statistical software “R” was adopted to calculatea polynomial regression model based on response surfaces • The regression model was applied to calculate yield using data from perturbed physics experiment of Hadley Centre for future scenarios • The perturbed yields were compared with yield threshold to define risk probability

14. 2.4 Estimating risk probability • Sites: 9 grid cells (50 Km side) • Baseline climate: from MARS JRC Archive • CO2 concentration scenario: a1b • Soil properties: from the Eusoils database • Nitrogen level: 170 Kg ha-1 • Study Area: Tuscany

15. 2.4 Estimating risk probabilities • Risk probability overlapping response surfaces and joint distribution of T an P changes for a grid box in Tuscany (2000-2020) • Coor.: 43.7N, 11.2E • CO2 Scenario: a1b • SWC: 115 mm • N level: 170 Kg N ha-1

16. 2.4 Estimating risk probabilities • Risk probability overlapping response surfaces and joint distribution of T an P changes for a grid box in Tuscany (2020-2040) • Coor.: 43.7N, 11.2E • CO2 Scenario: a1b • SWC: 115 mm • N level: 170 Kg N ha-1

17. 2.4 Estimating risk probabilities • Risk probability overlapping response surfaces and joint distribution of T an P changes for a grid box in Tuscany (2040-2060) • Coor.: 43.7N, 11.2E • CO2 Scenario: a1b • SWC: 115 mm • N level: 170 Kg N ha-1

18. 2.4 Estimating risk probabilities • Risk probability overlapping response surfaces and joint distribution of T an P changes for a grid box in Tuscany (2060-2080) • Coor.: 43.7N, 11.2E • CO2 Scenario: a1b • SWC: 115 mm • N level: 170 Kg N ha-1

19. 2.4 Estimating risk probabilities • Risk probability overlapping response surfaces and joint distribution of T an P changes for a grid box in Tuscany (2080-2100) • Coor.: 43.7N, 11.2E • CO2 Scenario: a1b • SWC: 115 mm • N level: 170 Kg N ha-1

20. 2.4 Estimating risk probabilities • Change in risk probability in the 9 grid cells in the next decades

21. 3. What are we planning to do in the next 6 months ? • Next 6 months (months 33-38) • Move on other agric. Crops (grapevine and olive) and forest fire risks to: • Development simple statistical models that emulates process-based crop yield models • Create preliminary yield response surfaces altering the baseline climate • Define critical thresholds of impactsusing yield cumulative distribution from the last 30-years • Obtain preliminary estimates of risk probabilities overlapping response surfaces and joint distribution of T an P changes

22. 4. What are our main questions requiring discussion in this meeting? • To get information from: • Chrisabout overall progress in ENSEMBLES (i.e. new developments, ongoing activities and future plans) • Clare(from RT2B) about the latest status of RT2B on the provision of climate model outputs or their derivatives for use in impact assessment (i.e. by WP 6.2) • To discuss with: • Chris, Clare and Glen about: • the various methods of applying probabilistic climate projections in impact studies, • the format and delivery of climate information for use in impact assessments in ENSEMBLES