1. Dr. Eric W. Harmsen Associate Professor, Dept. of Agricultural and Biosystems Engineering email: eharmsen@uprm.com. The Potential Impact of Climate Change on Agricultural in Puerto Rico USDA TSTAR

2. Acknowledgements Norman L. Miller, Atmosphere and Ocean Sciences Group, Earth Sciences Division, Berkeley National Laboratory. Nicole J. Schlegel, Department of Earth and Planetary Science, University of California, Berkeley Jorge E. Gonzalez, Santa Clara University I would like to thank the NASA-EPSCoR and USDA- TSTAR projects for their financial support.

3. What might Puerto Rico’s agriculture look like in the future?

4. One Possible Scenario Fewer, but more intense tropical storms will cause increased soil erosion, reduce surface water quality and fill our reservoirs with sediment. Flooding of fields will increase during the wet season, resulting in the loss of crops. During the dry season, evapotranspiration increases lead to drier soils, which produces crop stress and reduced yields. Crop water requirements will increase during certain months of the year, therefore the agricultural sector’s demand for water will increase, which may result in water conflicts between different sectors of society. http://academic.uprm.edu/abe/PRAGWATER

5. Agenda Downscaling GCM data Limitations of Climate Modeling Estimation of Potential ET and rainfall Penman-Monteith method Rainfall Deficit (or Excess) Yield Reduction Results Summary Conclusions and Recommendations

6. Objective The purpose of this study was to estimate evapotranspiration and rainfall deficit (or excess) under climate change conditions for three locations in western Puerto Rico: Adjuntas, Mayagüez and Lajas. Estimates of future crop yields are also provided.

7. Statistical and Dynamic Downscaling

8. Aerosol effect on clouds and precipitation Radiative Forcing Direct/diffuse solar irradiance change due to aerosols Diffuse radiation feedback with the terrestrial biosphere The cloud versus aerosol feedback on diffuse radiation changes Role of aerosols on radiative energy redistribution Biological effect of increased CO2 (e.g., stomatal resistance) Land use changes Economic factors Some sources of uncertainty in climate modeling

9. Disclaimer “Global and regional climate models have not demonstrated skill at predicting climate change and variability on multi- decadal time scales.” “Global and regional climate models have not demonstrated skill at predicting climate change and variability on multi- decadal time scales.” “Beyond some time period, our ability to provide reliable quantitative and detailed projections of climate must deteriorate to a level that no longer provides useful information to policymakers.” “Beyond some time period, our ability to provide reliable quantitative and detailed projections of climate must deteriorate to a level that no longer provides useful information to policymakers.” (Nov. 17, 2006, Roger Pielke Sr. Weblog, http://climatesci.atmos.colostate.edu)

10. WHAT IF? What if questions are routinely addressed in engineering. What if questions are routinely addressed in engineering. What if the dam fails?What if the dam fails? What if the wind velocity reaches 150 mph?What if the wind velocity reaches 150 mph? What if the climate changes in certain ways, how might agriculture be affected? What if the climate changes in certain ways, how might agriculture be affected?

11. The GCM data were obtained from the Department of Energy (DOE)/National Center for Atmospheric Research (NCAR) Parallel Climate Model (PCM). The scenarios considered were the Intergovernmental Panel on Climate Change (IPCC) a2 (mid-high CO2 emission) and b1 (low CO2 emission). METHODS

12. IPCC SCENARIOS B1. B1. A convergent worldA convergent world Population peaks in mid-century and declines thereafterPopulation peaks in mid-century and declines thereafter Rapid change in economic structures towards a service and information economy, with reductions in material intensity and the introduction of clean and resource-efficient technologies.Rapid change in economic structures towards a service and information economy, with reductions in material intensity and the introduction of clean and resource-efficient technologies. The emphasis is on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives.The emphasis is on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives. A2. A2. A very heterogeneous world.A very heterogeneous world. Continuously increasing population.Continuously increasing population. Economic development is primarily regionally orientedEconomic development is primarily regionally oriented Per capita economic growth and technological change more fragmented and slower than in other storylines.Per capita economic growth and technological change more fragmented and slower than in other storylines. A1FI A1FI The A1 storyline and scenario family describe a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafterThe A1 storyline and scenario family describe a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter Rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income.Rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. Fossil Intensive (FI)Fossil Intensive (FI)

13. Study Area Table 1. Latitude, elevation, average rainfall, average temperature, NOAA Climate Division and distance to the coast for the three study locations. Location Latitude (decimal degree) Elevation (m) Annual Rainfall (mm) T mean ( o C) Tmin ( o C) T max ( o C) NOAA Climate Division Distance to Coast (km) Adjuntas18.18549187121.615.227.9622 Mayaguez18.3320174425.719.830.543 Lajas18.0027114325.318.831.7210

14. KsKs KcKc Evapotranspiration (ET)

15. where ET o is the Penman-Monteith reference or potential evapotranspiration,  is slope of the vapor pressure curve, R n is net radiation, G is soil heat flux density,  is psychrometric constant, T is mean daily air temperature at 2-m height, u 2 is wind speed at 2-m height, e s is the saturated vapor pressure and e a is the actual vapor pressure. Potential Evapotranspiration (ET o )

16. Missing Parameters in the Penman-Monteith Equation e a (T dp ): T dp = T min + K corr (Harmsen et al., 2002)e a (T dp ): T dp = T min + K corr (Harmsen et al., 2002) u 2 : Historical averages for NOAA Climate Divisions (Harmsen et al., 2002)u 2 : Historical averages for NOAA Climate Divisions (Harmsen et al., 2002) R net : Hargreaves radiation equationR net : Hargreaves radiation equation G: Allen et al., 1998G: Allen et al., 1998

17. RAINFALL DEFICIT (RFD) RFD = (RAINFALL – ETo) RFD < 0 MEANS THERE IS A DEFICIT RFD > 0 MEANS THERE IS AN EXCESS

18. Yield Moisture Stress Relationship YR = Yield reduction (%) K y = Yield response factor ET cadj = Adjusted (actual) crop ET ET c = K c ET o ET o = potential or reference ET

19. ET cadj = K c K s ET o Average K c = 1.0 Based on 140 crops (Allen et al., 1998) K c = crop coefficient K s = crop water stress factor RAW = readily available water TAW = Totally available water Actual Evapotranspiration (ET)

20. YIELD RESPONSE FACTOR (K y ) Alfalfa1.1 Banana1.2-1.35 Beans1.15 Cabbage0.95 Citrus1.1-1.3 Cotton0.85 Grape0.85 Groundnet0.70 Maize1.25 Onion1.1 Peas1.15 Pepper1.1 Potato1.1 Safflower0.8 Sorghum0.9 Soybean0.85 Spring Wheat1.15 Sugarbeet1.0 Sugarcane1.2 Sunflower0.95 Tomato 1.05 Watermelon1.1 Winter wheat1.05

21. WATER BALANCE S i+1 = R i – ET cadj,i – RO i – Rech i + S i S i+1 is the depth of soil water in the beginning of month i+1 S i is the depth of soil water in the profile at the beginning of month i R i = rainfall during month i ET i = Actual evapotranspiration during month i RO i = Surface runoff during month i Rech i = percolation or aquifer recharge during month i

22. RO = C R R = monthly rainfall C = monthly runoff coefficient = 0.3 Long term values of Runoff Coefficient Añasco Watershed C = 0.33 Guanajibo Watershed C = 0.2 Surface Runoff (RO)

23. Aquifer Recharge (Rech) S i+1 = R i – ET cadj,i – RO i + S i If S i+1 ≤FC then Rech = 0 If S i+1 > FC then Rech = S i+1 – FC and S i+1 = FC FC = Soil Field Capacity or Soil Water Holding Capacity

24. AIR TEMPERATURE RESULTS

25. Have we seen a warming trend in the Caribbean? Source: Ramirez-Beltran et al., 2007 14 53 1 28 5

26. Lajas, PR SLOPE IS NOT STATISTICALLY SIGNIFICANT

27. Adjuntas, PR SLOPE IS STATISTICALLY SIGNIFICANT AT THE 5% LEVEL

28. Mayagüez, PR SLOPE IS STATISTICALLY SIGNIFICANT AT THE 5% LEVEL

29. Downscaled Minimum, Mean and Maximum Air Temperature ( o C) for Lajas Scenario A2

30. RAINFALL RESULTS

31. Downscaled Rainfall (mm) for Lajas Scenario A2

32. Downscaled Rainfall at Lajas for Scenario A2 February and September

33. IPPC Report, Feb. 2007 “Based on a range of models, it is likely that future tropical cyclones (typhoons and hurricanes) will become more intense, with larger peak wind speeds and more heavy precipitation associated with ongoing increases of tropical SSTs.”

38. Daily Reference Evapotranspiration (ETo) by Month at Lajas, PR

39. RAINFALL DEFICIT RESULTS

43. Relative Change in Rainfall Deficit

44. CROP YIELD RESULTS

48. Historical data for Cuba, Haiti, Dominican Republic and Puerto Rico showed increasing trends in air temperature. Historical data from Adjuntas and Mayagüez indicated significant increasing trend in air temperature Historical data from Lajas did not indicate a significant trend in air temperature. The historical temperature data at Lajas may have been influenced by land cover/land use around the weather station. Future increases were predicted in air temperatures for Adjuntas, Mayagüez and Lajas downscaled from the DOE/NCAR PCM model. SUMMARY

49.  The annual predicted rainfall showed a slight decrease  Rainfall in September increased for all locations and all scenarios.  Rainfall decreased in most months (except September)  The rainfall results from this study were in general agreement with the results reported in the IPCC Feb. 2007 Report SUMMARY-Cont.

50.  Rainfall excess increased during September for all locations and all scenarios (between 2000 and 2090).  The largest increase in rainfall excess occurred for Adjuntas for scenario A1fi (312 mm)  The largest change in rainfall deficit occurred in Mayagüez for scenario A2 (-72 mm) SUMMARY-Cont.

51.  Significant Yield Reduction can be expected during the months that receive less rainfall  Yields improved during September for most scenarios and locations. SUMMARY-Cont.

52. Conclusions and Recommendations With increasing rainfall deficits during the dry months, the agricultural sector’s demand for water will increase, which may lead to conflicts in water use. With increasing rainfall deficits during the dry months, the agricultural sector’s demand for water will increase, which may lead to conflicts in water use. The results indicate that the wettest month (September) will become significantly wetter. The excess water can possibly be captured in reservoirs to offset the higher irrigation requirements during the drier months. The results indicate that the wettest month (September) will become significantly wetter. The excess water can possibly be captured in reservoirs to offset the higher irrigation requirements during the drier months.