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Dr. Eric W. HarmsenDr. Eric W. Harmsen
Associate Professor, Dept. of Agricultural and Associate Professor, Dept. of Agricultural and Biosystems EngineeringBiosystems Engineering
email: eharmsen@uprm.comemail: eharmsen@uprm.com
The Potential Impact of Climate Change on Agricultural in Puerto Rico
USDATSTA
R
What might Puerto Rico’s agriculture look like in the future?
One Possible ScenarioOne 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
AgendaAgenda
• Downscaling GCM data • Estimation of Potential ET and rainfall
• Penman-Monteith method• Rainfall Deficit (or Excess)• Yield Reduction• Limitations of Climate Modeling• Results Summary• Conclusions and Recommendations• •Example Calculation of Net Irrigation Requirement
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.
Statistical and Dynamic Downscaling
WHAT IF?WHAT IF?
What if questions are routinely What if questions are routinely addressed in engineering.addressed in engineering.• What if the dam fails?What if the dam fails?• What if the wind velocity reaches 150 What if the wind velocity reaches 150
mph?mph?
What if the climate changes in What if the climate changes in certain ways, how might agriculture certain ways, how might agriculture be affected?be affected?
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).
METHODSMETHODS
Study AreaStudy 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)
Tmean
(oC)Tmin (oC)
Tmax
(oC)
NOAA
Climate Division
Distance to Coast
(km)
Adjuntas 18.18 549 1871 21.6 15.2 27.9 6 22
Mayaguez 18.33 20 1744 25.7 19.8 30.5 4 3
Lajas 18.00 27 1143 25.3 18.8 31.7 2 10
Ks
Kc
Evapotranspiration (ET)Evapotranspiration (ET)
Comparison of ETo from three Comparison of ETo from three different methodsdifferent methods
0
2
4
6
8
3/1/02 4/1/02 5/1/02 6/1/02
Date
ET
o (
mm
)
ETo pan
ETo Penman Monteith
ETo (PRET)
ETo
0.408 Rn G 900
T 273
u2 es ea
1 0.34 u2
.
where ETo is the Penman-Monteith reference or potential evapotranspiration, is slope of the vapor pressure curve, Rn is net radiation, G is soil heat flux density, is psychrometric constant, T is mean daily air temperature at 2-m height, u2 is wind speed at 2-m height, es is the saturated vapor pressure and ea is the actual vapor pressure.
Potential Evapotranspiration (ETPotential Evapotranspiration (EToo))
Missing Parameters in the Missing Parameters in the Penman-Monteith EquationPenman-Monteith Equation
• eeaa(T(Tdpdp): T): Tdpdp = T = Tminmin + K + Kcorrcorr (Harmsen et al., (Harmsen et al., 2002)2002)
• uu22: Historical averages for NOAA Climate : Historical averages for NOAA Climate Divisions (Harmsen et al., 2002)Divisions (Harmsen et al., 2002)
• RRnetnet: Hargreaves radiation equation: Hargreaves radiation equation
• G: Allen et al., 1998G: Allen et al., 1998
RAINFALL DEFICIT (RFD)RAINFALL DEFICIT (RFD)
RFD = (RAINFALL – ETo)RFD = (RAINFALL – ETo)
RFD < 0 MEANS THERE IS A DEFICITRFD < 0 MEANS THERE IS A DEFICIT
RFD > 0 MEANS THERE IS AN EXCESSRFD > 0 MEANS THERE IS AN EXCESS
Yield Moisture Stress RelationshipYield Moisture Stress Relationship
YR = Yield reduction (%)Ky = Yield response factorETcadj = Adjusted (actual) crop ETETc = Kc ETo
ETo = potential or reference ET
YR Ky 1ETcadj
ETc
100.
ETcadj = Kc Ks ETo
Average Kc = 1.0
Based on 140 crops(Allen et al., 1998)
Kc = crop coefficientKs = crop water stress factorRAW = readily available waterTAW = Totally available water
Actual Evapotranspiration (ET)Actual Evapotranspiration (ET)
YIELD RESPONSE FACTOR (KYIELD RESPONSE FACTOR (Kyy))
Alfalfa 1.1
Banana 1.2-1.35
Beans 1.15
Cabbage 0.95
Citrus 1.1-1.3
Cotton 0.85
Grape 0.85
Groundnet 0.70
Maize 1.25
Onion 1.1
Onion 1.1
Peas 1.15
Pepper 1.1
Potato 1.1
Safflower 0.8
Sorghum 0.9
Soybean 0.85
Spring Wheat 1.15
Sugarbeet 1.0
Sugarcane 1.2
Sunflower 0.95
Tomato 1.05
Watermelon 1.1
Winter wheat 1.05
WATER BALANCEWATER BALANCE
SSi+1i+1 = R = Rii – ET – ETcadj,icadj,i – RO – ROii – Rech – Rechii + S + Sii
SSi+1i+1 is the depth of soil water in the beginning of is the depth of soil water in the beginning of month i+1month i+1
SSii is the depth of soil water in the profile at the is the depth of soil water in the profile at the beginning of month ibeginning of month i
RRii = rainfall during month i = rainfall during month i
ETETii = Actual evapotranspiration during month i = Actual evapotranspiration during month i
ROROii = Surface runoff during month i = Surface runoff during month i
RechRechii = percolation or aquifer recharge during = percolation or aquifer recharge during month imonth i
RO = C RRO = C R
R = monthly rainfallR = monthly rainfallC = monthly runoff coefficient = 0.3C = monthly runoff coefficient = 0.3
Long term values of Runoff CoefficientLong term values of Runoff Coefficient
AAñasco Watershed ñasco Watershed C = 0.33C = 0.33Guanajibo Watershed Guanajibo Watershed C = 0.2C = 0.2
Surface Runoff (RO)Surface Runoff (RO)
Aquifer Recharge (Rech)Aquifer Recharge (Rech)
SSi+1i+1 = R = Rii – ET – ETcadj,icadj,i – RO – ROii + S + Sii
If SIf Si+1 i+1 ≤FC then Rech = 0≤FC then Rech = 0
If SIf Si+1i+1 > FC then Rech = S > FC then Rech = Si+1i+1 – FC – FC
and Sand Si+1i+1 = FC = FC
FC = Soil Field Capacity or Soil Water FC = Soil Field Capacity or Soil Water Holding CapacityHolding Capacity
AIR TEMPERATURE AIR TEMPERATURE RESULTSRESULTS
Have we seen a warming trend in Have we seen a warming trend in the Caribbean?the Caribbean?
Source: Ramirez-Beltran et al., 2007
14
53
1
28
5
Lajas, PRLajas, PR
y = 5E-06x + 24.930
5
10
15
20
25
30
35
1/1/61 11/6/67 9/10/74 7/15/81 5/19/88 3/24/95
Date
Ave
rag
e T
emp
erat
ure
(oC
)
LajasLinear (Lajas)
SLOPE IS NOT STATISTICALLYSIGNIFICANT
Adjuntas, PRAdjuntas, PR
y = 9E-05x + 18.521
0
5
10
15
20
25
30
35
1/1/70 6/24/75 12/14/80 6/6/86 11/27/91 5/19/97
DATE
AV
ER
AG
E T
EM
PE
RA
TU
RE
(oC
)
Adjuntas
Linear (Adjuntas)SLOPE IS STATISTICALLYSIGNIFICANT AT THE 5% LEVEL
Mayagüez, PRMayagüez, PR
y = 8E-05x + 23.363
0
5
10
15
20
25
30
35
1/1/61 11/6/67 9/10/74 7/15/81 5/19/88 3/24/95
DATE
AV
ER
AG
E T
EM
PE
RA
TU
RE
(C
)
Mayaguez
Linear (Mayaguez)SLOPE IS STATISTICALLYSIGNIFICANT AT THE 5% LEVEL
10
15
20
25
30
35
40
2000 2020 2040 2060 2080 2100
YEAR
Tem
pe
ra
ture
(C
)
Tmin
Tmax
Tmean
Linear (Tmax)
Linear (Tmean)
Linear (Tmin)
Downscaled Minimum, Mean and Maximum Air Temperature (oC) for Lajas
Scenario A2
RAINFALL RAINFALL RESULTSRESULTS
y = -0.0674x + 240.870
200
400
600
800
1000
2000 2020 2040 2060 2080 2100
YEAR
Rai
nfa
ll (
mm
)
Downscaled Rainfall (mm) for LajasScenario A2
y = 1.5134x - 2808.1
y = -0.1028x + 263.21
0
200
400
600
800
1000
1200
2000 2020 2040 2060 2080 2100
YEAR
RA
INF
AL
L (
mm
)
February
September
Linear (September)
Linear (February)
Downscaled Rainfall at Lajas for Scenario A2 February and September
IPPC Report, Feb. 2007IPPC 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.”
Rainfall Lajas B1
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RA
INF
AL
L (
mm
)
2000
2090
Rainfall Lajas A2
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RA
INF
AL
L (
mm
)
2000
2090
Rainfall Lajas A1fi
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RA
INF
AL
L (
mm
)
2000
2090
2
4
6
8
10
2000 2020 2040 2060 2080 2100
YEAR
ET
o (
mm
)
A2
A1fi
B1
Linear (A2)
Linear (B1)
Linear (A1fi)
Daily Reference Evapotranspiration(ETo) by Month at Lajas, PR
RAINFALL DEFICIT RAINFALL DEFICIT RESULTSRESULTS
RAINFALL DEFICIT LAJAS B1
-200-150-100-50
050
100150200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RF
D (
mm
)
2000
2090
RAINFALL DEFICIT LAJAS A2
-200
-100
0
100
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RF
D (
mm
)
2000
2090
RAINFALL DEFICIT LAJAS A1fi
-300
-200
-100
0
100
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
RF
D (
mm
)
2000
2090
Relative Change in Rainfall DeficitRelative Change in Rainfall Deficit
Scenario Year Adjuntas Mayaguez Lajas AdjuntasMayaguez Lajas2000 0.0 0.0 0.0 0.0 0.0 0.02050 -19.3 -17.6 -24.9 81.3 77.5 31.2
2090 -29.6 -31.8 -50.2 311.5 276.9 171.9
2000 0.0 0.0 0.0 0.0 0.0 0.02050 -65.5 -54.9 -45.8 117.1 97.5 85.1
2090 -78.1 -72.7 -67.1 244.9 200.9 183.7
2000 0.0 0.0 0.0 0.0 0.0 0.02050 -35.6 -34.3 -33.9 51.8 38.4 38.8
2090 -16.6 -33.9 -34.0 183.8 137.2 148.3
A2
B1
Change in Rainfall Deficit Relative to 2000 (mm)February September
A1fi
CROP YIELD CROP YIELD RESULTSRESULTS
Yield Reduction Lajas B1
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
YIE
LD
RE
DU
CT
ION
(%
)
2000
2090
Yield Reduction Lajas A2
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
YIE
LD
RE
DU
CT
ION
(%
)
2000
2090
Yield Reduction Lajas A1fi
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DATE
YE
ILD
RE
DU
CT
ION
(%
)
2000
2090
DisclaimerDisclaimer
““Global and regional climate models have Global and regional climate models have not demonstrated skill at predicting not demonstrated skill at predicting climate change and variability on multi-climate change and variability on multi-decadal time scales.” decadal time scales.”
““Beyond some time period, our ability to Beyond some time period, our ability to provide reliable quantitative and detailed provide reliable quantitative and detailed projections of climate must deteriorate to projections of climate must deteriorate to a level that no longer provides useful a level that no longer provides useful information to policymakers.” information to policymakers.” (Nov. 17, 2006, Roger Pielke Sr. Weblog, (Nov. 17, 2006, Roger Pielke Sr. Weblog,
http://climatesci.atmos.colostate.edu)http://climatesci.atmos.colostate.edu)
•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
• 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. •
SUMMARYSUMMARY
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.SUMMARY-Cont.
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.SUMMARY-Cont.
Significant Yield Reduction can be expected during the months that receive less rainfall
Yields improved during September for most scenarios and locations.
SUMMARY-Cont.SUMMARY-Cont.
Conclusions and Conclusions and RecommendationsRecommendations
With increasing rainfall deficits during the With increasing rainfall deficits during the dry months, the agricultural sector’s dry months, the agricultural sector’s demand for water will increase, which may demand for water will increase, which may lead to conflicts in water use. lead to conflicts in water use.
The results indicate that the wettest The results indicate that the wettest month (September) will become month (September) will become significantly wetter. The excess water can significantly wetter. The excess water can possibly be captured in reservoirs to offset possibly be captured in reservoirs to offset the higher irrigation requirements during the higher irrigation requirements during the drier months. the drier months.
Example ProblemExample ProblemEstimating Crop Water Requirements Estimating Crop Water Requirements
and Net Irrigation Requirementand Net Irrigation Requirement
In this example input data for Ponce, PR In this example input data for Ponce, PR were used. Daily evapotranspiration will were used. Daily evapotranspiration will be determined for a be determined for a calabazacalabaza crop crop starting on January 1starting on January 1stst, 2007., 2007.
http://academic.uprm.edu/abe/PRAGWATER/
Net Irrigation Requirement
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
1 2 3 4 5 6 7 8 9 10 11 12
Month
ET
o (
mm
/mo
)Net Irrigation
Requirement
Month
Average ETo
(mm/mo)
Average Rainfall
(mm/mo)
Net Irrigation Requirement
(mm)
January 105.4 19.8 85.6
February 109.2 18.3 90.9
March 142.6 21.8 120.8
April 147.0 48.8 98.2
May 158.1 74.2 83.9
June 156.0 79.5 76.5
July 161.2 73.9 87.3
August 153.0 113.0 40.0
September 141.0 133.6 7.4
October 133.3 143.0 -9.7
November 108.0 80.8 27.2December 102.3 30.5 71.8
Daily Net Irrigation Requirement - Example Problem
-2
-1
0
1
2
3
4
5
1/1/07
1/15/07
1/29/07
2/12/07
2/26/07
3/12/07
3/26/07
4/9/07
Days after Planting
Dep
th o
f W
ate
r (m
m)
.
Net Irrigation Requirement
Average Rainfall
Crop ET
zero
AcknowledgementsAcknowledgements
Norman L. Miller, Atmosphere and Ocean Sciences Norman L. Miller, Atmosphere and Ocean Sciences Group, Earth Sciences Division, Berkeley National Group, Earth Sciences Division, Berkeley National Laboratory. Laboratory.
Nicole J. Schlegel, Department of Earth and Nicole J. Schlegel, Department of Earth and Planetary Science, University of California, Planetary Science, University of California, Berkeley Berkeley
Jorge E. Gonzalez, Santa Clara UniversityJorge E. Gonzalez, Santa Clara University
I would like to thank the NASA-EPSCoR and USDA-I would like to thank the NASA-EPSCoR and USDA-TSTAR projects for their financial support. TSTAR projects for their financial support.
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