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Impact of projected climates on drought occurrence in the Australian wheatbelt James Watson1, Bangyou Zheng2, Scott C. Chapman2 and Karine Chenu1 The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), 203 Tor Street, Toowoomba, QLD 4350, [email protected] 2 CSIRO Agriculture Flagship, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, QLD 4067 1 Abstract Wheat is a staple crop, and in Australia it is primarily produced in rainfed environments. Climate change projections indicate an increase in future rainfall variability and in temperature across the Australian wheatbelt. Coupled with the continued increase in demand for this crop due to rising populations and living standards, climate change may significantly impact the Australian wheat industry. The lead times involved in adapting cultivars and management practices mean that planning for adaptation must often begin many years before implementation. Thus timely, realistic assessments of the crop-level implications of climate change are critical to the long-term planning of breeders, farmers and policy makers. Such assessments require extensive analysis of the complex interplay between local environment, genotype, and adaptive management practices. In this study we capture these interactions for 60 representative sites using the APSIM-Wheat crop model, and simulated the impact that 33 climate model projections had on the distribution of drought environment types across the Australian wheatbelt. Simulation results indicate that changes in future drought patterns are highly region-specific. Significant variations in projected changes were found across climate models, giving local ranges of uncertainty to consider in planning efforts. However, simulations for the majority of climate models projected increased frequencies of severe drought conditions in the Western area of the wheatbelt, and fewer severe droughts in other regions. Overall, simulations indicate that all areas of the Australian wheatbelt will continue to experience drought conditions this century, and that adaptation planning is necessary to match future wheat demand. Key words wheat, climate change, water deficit, environment characterisation, crop modelling, APSIM Introduction Due to the combination of increasing population and rising living standards, demand for staple foods such as wheat continues to increase. Most Australian wheat is produced in water-limited environments, and is exported. Recent climate projections indicate that Australia will experience increased temperature and rainfall variability this century (Zheng et al. 2012; Reisinger et al. 2014), which may have substantial implications for the local wheat industry. As it takes 5-15 years to produce new cultivars, timely assessments of the plausible impacts of climate change are of vital importance (Chapman et al. 2012). Both the degree and timing of environmental stressors influence crop development (e.g. Fischer 2011). Thus, assessing the impact of abiotic stressors on wheat requires careful consideration of the complex interactions between the local environment, crop genotype, and management practices (Chenu 2015). In this study, we used the APSIM-Wheat model (Holzworth et al. 2014) to capture these interactions at 60 representative sites across the Australian wheatbelt. Seasonal patterns in the water-stress index output by the model were classified to identify key seasonal patterns of water-stress, termed ‘drought environment types’ (Chenu et al. 2013). For each site, a set of future climate scenarios were generated using the projections of 33 climate models from the Coupled Model Intercomparison Phase 5 Project (CMIP5; Taylor et al. 2012). The wheat model was run for each site’s current and future climate scenarios, and every simulated season was classified into one of four drought environment types. The main aims were to (1) assess likely changes in occurrence of drought across the wheatbelt, and (2) identify the range of potential changes projected by this large ensemble of climate models. Materials and Methods Characterizing current drought environment types Crop simulations were performed using the Agricultural Production Systems Simulator (APSIM Version 7.6) for the wheat variety ‘Hartog’ (Triticum aestivium L.). For each of the 60 representative sites, two sets of © 2015 “Building Productive, Diverse and Sustainable Landscapes “ Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au simulations were performed using historical weather observations from 1955 to 2013, obtained from SILO simulations performed historical weather observations from 1955 to 2013,dates obtained SILO (Jeffrey et al. were 2001). The first using set was performed to identify five representative sowing and from five initial (Jeffrey et al. 2001). The first set was performed to identify five representative sowing dates and five initial soil water levels for each site, according to local conditions and management practices (see Chenu et al. 2013 soil water levels each dates site, according local conditions and management practices (see20% Chenu et al. for details). These for sowing and initialtosoil water values were selected to each represent of the 2013 for details). These sowing dates and initial soil water values were selected to each represent 20% the sowing opportunities and soil water conditions, respectively. The resulting 5x5 initial conditions for eachofsite sowing opportunities and soil water conditions, respectively. The resulting 5x5 initial conditions for each site were used in the second set of simulations, to characterize the drought patterns experienced by current wheat wereacross used inthe thewheatbelt. second set of simulations, to characterize the drought patterns experienced by current wheat crops crops across the wheatbelt. For each model run, a daily water-stress index was computed to reflect crop ‘water supply/demand ratio’, i.e. the potential water available toindex the crop the amount that the crop couldsupply/demand use for potential Forratio eachofmodel run,soil a daily water-stress was to computed to reflect crop ‘water ratio’, transpiration. index ranged fromavailable 0 (no water available crop) that to 1 the (no crop watercould stress). i.e. the ratio This of potential soil water to the crop to to thethe amount useWater-stress for potential patterns for each environment (i.e.from each0unique site,available year, sowing andtoinitial water combination) transpiration. This index ranged (no water to thedate crop) 1 (no soil water stress). Water-stress were defined as theenvironment water-stress(i.e. indices every 100°Cd, from aftersoil emergence to 450°Cd patterns for each eachaveraged unique site, year, sowing date100°Cd and initial water combination) after wereflowering. defined as the water-stress indices averaged every 100°Cd, from 100°Cd after emergence to 450°Cd after flowering. The ‘clara’ clustering function (Maechler et al. 2015) was used to group these water-stress patterns into four sets to define four drought ‘environment types’ (ET; see Figure 1). The ‘clara’ clustering function (Maechler et al. 2015) was used to group these water-stress patterns into four sets to define four drought ‘environment types’ (ET; see Figure 1). Figure1.1.Water-stress Water-stresspatterns patternsdefining definingthe thefour fourmain mainenvironment environmenttypes types(ET) (ET)identified identifiedfor forthe theAustralian Australian Figure thth wheatbelt.For Foreach eachenvironment environmenttype, type,the themedian median(coloured (colouredline) line)and andvariations variationsfrom fromthe the1010 the90 90thth totothe wheatbelt. percentile percentileofofthe thedistribution distribution(shading) (shading)are arepresented. presented. Projecting future environment types Projecting future environment typesmodels from CMIP5 (Taylor et al. 2012) were used to generate the set of The monthly output of 33 climate The monthly output of 33 climate models from CMIP5 (Taylor al. 2012) weredata usedfrom to generate the set of future scenarios by downscaling daily observations from SILO.etWe employed the Representative future scenarios Pathway by downscaling daily observations SILO. We employed data the Representative Concentration (RCP) 8.5 scenario, whichfrom assumes ‘business as usual’ COfrom emissions. For each 2 emissions. For each site, Concentration Pathway (RCP) 8.5 scenario, which assumes ‘business as usual’ CO site, future climate scenarios were generated according to each climate model for 2three distinct time future climate scenarios were generated each to climate threescenarios distinct time periods centred on 2030, 2050 and 2070according (baseline:to1955 2013).model All 99for future wereperiods the result of centred on 2030, 2070 (baseline: 1955 to 2013). All 99 future scenarios were the result of local transforming the2050 localand daily historical temperature and precipitation values by their projected future transforming the local dailyclimate historical temperature and precipitation bysite, theiryear, projected future monthly means. For each scenario, the water-stress patternsvalues of each sowing date,local and monthly means. each climate scenario, the water-stress of each year, sowing date, and initial soil waterFor combination were simulated as described patterns above (with onesite, set of simulations to identify initial water combination wereclimate simulated as described (with one settoof simulationsdrought to identify initialsoil conditions specific to each model, and one above set of simulations characterize patterns). initial conditions specific to each climate model, and one set of simulations to characterize drought patterns). In total, 9.2 million crop simulations were performed (1 historical scenario + 99 future climate scenarios) x sites 59 years) + (60 sites x 59were years x 5 planting dates x 5scenario initial soil water values)), required In((60 total, 9.2xmillion crop simulations performed (1 historical + 99 future climatewhich scenarios) x the sites equivalent of over 100sites daysxof59computing time on adates current Intel Xeon ((60 x 59 years) + (60 years x 5 planting x 5 processor initial soil(2.93GHz water values)), whichX5570). requiredIn real time, these were completed in approximately days on(2.93GHz the University of Queensland’s the equivalent of simulations over 100 days of computing time on a current 2.5 processor Intel Xeon X5570). In high performance computing facility. Results were stored as compressed NetCDF files using the Python real time, these simulations were completed in approximately 2.5 days on the University of Queensland’s 1 netCDF4 packagecomputing , and the differences between and theNetCDF baselinefiles for the frequency of high performance facility. Results werefuture storedscenarios as compressed using the Python 2 1 occurrence of different ETs were processed and analysed using R . netCDF4 package , and the differences between future scenarios and the baseline for the frequency of occurrence of different ETs were processed and analysed using R2. Results and Discussion Future projections for drought patterns varied significantly across climate models, even by 2030, primarily 1 https://github.com/Unidata/netcdf4-python due to differences in projected precipitation. This is consistent with recent work showing that these climate 2 http://www.R-project.org/ "Building https://github.com/Unidata/netcdf4-python © 2015 Productive, Diverse and Sustainable Landscapes " 2 Proceedings ofhttp://www.R-project.org/ the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au 1 © 2015 “Building Productive, Diverse and Sustainable Landscapes “ Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au 2 Results and Discussion Future projections for drought patterns varied significantly across climate models, even by 2030, primarily due to differences in projected precipitation. This is consistent with recent work showing that these climate models modelsbroadly broadlyagree agreeon onfuture futuretemperatures, temperatures,but butdo donot notagree agreeon onfuture futureprecipitation precipitationpatterns patternsin inAustralia Australia (Reisinger et al. 2014). (Reisinger et al. 2014). Two key aspects of these results are (1) the range of projected environment type responses, and (2) Two key aspects results arethe (1)climate the range of projected environment responses, consensus that canofbethese found among models, which indicate likelytype future impacts.and (2) consensus that can be found among the climate models, which indicate likely future impacts. To To assess assess the the range range of of impacts, impacts, climate climate models models were were ranked ranked according according to to their their projected projected changes changes in in occurrence of severe drought environments (ET 3 and 4; Figure 1). Figure 2 presents the range of occurrence of severe drought environments (ET 3 and 4; Figure 1). Figure 2 presents the range of projected projected changes changesin inET EToccurrence occurrence for for the the most most optimistic optimistic (CESM1-BGC), (CESM1-BGC), the the central central (MRI-CGCM3), (MRI-CGCM3), and and the the most most pessimistic pessimistic(GFDL-ESM2M) (GFDL-ESM2M)climate climatemodels. models. Figure 2. Regional averages for projected changes in the frequency of occurrence of environment types across Figure 2. Regional averages for projected changes in the frequency of occurrence of environment types across the wheatbelt for three future periods (rows) and three contrasting climate models (columns). The coloured the wheatbelt for three future periods (rows) and three contrasting climate models (columns). The coloured proportion of each box indicates the change in occurrence of each environment type compared to the baseline proportion of each box indicates the change in occurrence of each environment type compared to the baseline (1955 to 2013). Projections are presented for 2030, 2050 and 2070 and optimistic, central, and pessimistic climate (1955 to 2013). Projections are presented for 2030, 2050 and 2070 and optimistic, central, and pessimistic climate models(see (seetext). text). models Clearvariations variationsamong amongclimate climatemodels modelswere werefound, found,even evenby by2030. 2030.While While increased increased frequencies frequencies of of severe severe Clear droughtconditions conditionsin inthe theWest West of of the the wheatbelt wheatbelt were were projected projected by by both both the the central central and and pessimistic pessimistic climate climate drought models,the theoptimistic optimisticclimate climatemodel modelprojected projectedaadecrease decreasein inthese theseconditions. conditions.In Incontrast, contrast,in inthe theEast Eastand and models, South-Eastof ofthe thewheatbelt, wheatbelt,the theoptimistic optimisticand andcentral centralclimate climatemodels modelsindicated indicatedaadecrease decreasein insevere severedrought drought South-East conditionslater laterthis thiscentury. century. This This reduction reduction was was primarily primarily driven driven by by shorter shorter crop crop cycles cycles arising arising from from higher higher conditions temperatures,and andincreased increasedtranspiration transpirationefficiency efficiency due due to to raised raised CO CO22 concentrations. concentrations. Note Note that that the the primary primary temperatures, differences in in the the future future drought drought predictions predictions presented presented in in Figure Figure 22 derive derivemore morefrom fromthe theclimate climatemodel model differences consideredthan thanfrom fromthe theprojection projectiontime. time. considered When assessing the projections of all 33 climate models, occurrences of severe drought environments (ET 3 When assessing the projections of all 33 climate models, occurrences of severe drought environments (ET and 4) were found to decrease this century in the East, South and South-East of the wheatbelt (Figure 3). In 3 and 4) were found to decrease this century in the East, South and South-East of the wheatbelt (Figure 3). contrast, the majority of climate models projected that the Western area of the wheatbelt will experience In contrast, the majority of climate models projected that the Western area of the wheatbelt will experience significant significant increases increases in in drought drought conditions. conditions. © 2015 "Building Productive, Diverse and Sustainable Landscapes " Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au © 2015 “Building Productive, Diverse and Sustainable Landscapes “ Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au 3 Figure 3. Projected occurrence of drought environments (ET 3 and 4) per major wheat-producing area, Figure 3. Projected occurrence of drought (ETsummarize 3 and 4) per wheat-producing area,for each of compared to historical occurrence (dottedenvironments lines). Boxplots themajor distribution of percentages compared to historical occurrence (dotted lines). Boxplots summarize the distribution of percentages for each of the 33 climate models. the 33 climate models. Conclusions Climate model projections indicate that the West of the Australian wheatbelt is likely to experience increased Conclusions droughtmodel conditions as early as 2030. the lead times involved for crop is adaptation, and the western Climate projections indicate thatGiven the West of the Australian wheatbelt likely to experience increased wheatbelt’s significant contribution towards total national production, these results provide a strong case drought conditions as early as 2030. Given the lead times involved for crop adaptation, and the western for immediate initiation of adaptation planning this area. Other areas ofresults the wheatbelt projected wheatbelt’s significant contribution towards totalfor national production, these provideare a strong caseto for experience decreased frequencies of drought conditions, with rising temperatures shortening the crop immediate initiation of adaptation planning for this area. Other areas of the wheatbelt are projected to cycle and CO2 concentrations allowingofmore efficient water use. even whereshortening the frequencies of severe experience decreased frequencies drought conditions, withHowever, rising temperatures the crop cycle drought conditions are projected to decrease, their occurrences are projected to remain substantial. Overall, and CO2 concentrations allowing more efficient water use. However, even where the frequencies of severe increased demandare forprojected wheat means that Australia must continue improvetocultivars management drought conditions to decrease, their occurrences aretoprojected remain and substantial. Overall, practises to mitigate the drought conditions experienced across its wheatbelt. increased demand for wheat means that Australia must continue to improve cultivars and management practises to mitigate the drought conditions experienced across its wheatbelt. References Chapman, S.C., et al. 2012. Plant adaptation to climate change – opportunities and priorities in breeding. References Crop and Pasture Science. 63:251-268. Chapman, S.C., et 2013. al. 2012. Plant adaptation to climate opportunities and priorities in breeding. Chenu, K., et al. Large-scale characterization ofchange drought– pattern: a continent-wide modelling Crop and Pasture Science. 63:251-268. approachapplied to the Australian wheatbelt – spatial and temporal trends. New Phytologist 198:801-820. Chenu, al. 2013. Large-scalethe characterization of drought pattern: a continent-wide modelling approach Chenu,K., K.et2015. Characterising crop environment – nature, significance and applications. In: SadrasV.O., applied to the Australian – spatialApplications and temporalfor trends. New Phytologist and 198:801-820. Calderini, D.F., editors. wheatbelt Crop Physiology. Genetic Improvement Agronomy. Chenu, K. 2015. Characterising the crop environment – nature, significance and applications. In: Sadras Academic Press. pp. 321-348. V.O., Calderini, editors. Crop Physiology. for Genetic Improvement and Fischer, R.A. 2011.D.F., Wheat physiology: a review ofApplications recent developments. Crop & Pasture Science 62:95–114. Agronomy.D.P., Academic Press. pp. 321-348. Holzworth, et al. 2014. APSIM - Evolution towards a new generation of agricultural systems Fischer, R.A. 2011. Wheat physiology: a review of recent developments. Crop & Pasture Science 62:95–114. simulation. Environmental Modelling & Software. 62:327-350. Holzworth, D.P., et 2001. al. 2014. APSIM - Evolution towards a new generation of agricultural Jeffrey, S.J., et al. Using spatial interpolation to construct a comprehensive archive systems of simulation. Environmental Modelling & Software. 62:327-350. Australianclimate data. Environmental Modelling and Software. 16:309-330. Jeffrey, S.J.,A. et et al.al. 2001. Using spatial interpolation to construct comprehensive archive Australian Reisinger, 2014: Australasia. In: Climate Change 2014:a Impacts, Adaptation, andofVulnerability. climate Environmental Modelling and Software.Group 16:309-330. PartB:data. Regional Aspects. Contribution of Working II to the Fifth Assessment Report of the Reisinger, A. et al. 2014: Australasia. In: Climate 2014: Impacts, Press, Adaptation, and Vulnerability. Intergovernmental Panel on Climate Change. Change Cambridge University Cambridge, UK and NewPart B:York, Regional Aspects. NY, USA, pp. Contribution 1371-1438. of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Press,RCambridge, UK and New Maechler, M., et al. 2015. Cluster: Cluster Analysis BasicsUniversity and Extensions. package version 2.0.1. York, K.E., NY, USA, pp. 1371-1438. Taylor, et al. 2012: An Overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society. 93:485-498. Maechler, M., et al. 2015. Cluster: Cluster Analysis Basics and Extensions. R package version 2.0.1. Taylor, K.E., et al. 2012: An Overview CMIP5what and are the the experiment offrost the American Zheng, B., et al. 2012. Breeding for theoffuture: potentialdesign. impactsBulletin of future and heat eventson sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) Meteorological Society. 93:485-498. varieties? Biology. Zheng, B., et al.Global 2012. Change Breeding for the 18:2899-2914. future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Global Change Biology. 18:2899-2914. © 2015 "Building Productive, Diverse and Sustainable Landscapes " Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au © 2015 “Building Productive, Diverse and Sustainable Landscapes “ Proceedings of the 17th ASA Conference, 20 – 24 September 2015, Hobart, Australia. Web site www.agronomy2015.com.au 4