The Potential Impact of Climate Change
on Agricultural in Puerto Rico
Dr. Eric W. Harmsen
Associate Professor, Dept. of Agricultural and
Biosystems Engineering
email: eharmsen@uprm.com
USDA
TSTAR
What might Puerto Rico’s agriculture
look like in the future?
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
Agenda
• 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 questions are routinely
addressed in engineering.
• What if the dam fails?
• What if the wind velocity reaches 150
mph?

What if the climate changes in
certain ways, how might agriculture
be affected?
METHODS
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).
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)
Adjuntas
Tmax
(oC)
NOAA
Climate
Division
Distance
to Coast
(km)
Tmean
(oC)
Tmin
(oC)
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
Evapotranspiration (ET)
Kc
Ks
Comparison of ETo from three
different methods
8
ETo (mm)
6
ETo pan
ETo Penman Monteith
ETo (PRET)
4
2
0
3/1/02
4/1/02
5/1/02
Date
6/1/02
Potential Evapotranspiration (ETo)
900 

0.408   Rn  G   
 u2  es  ea
T  273 

ETo 
    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.
Missing Parameters in the
Penman-Monteith Equation
• ea(Tdp): Tdp = Tmin + Kcorr (Harmsen et
al., 2002)
• u2: Historical averages for NOAA Climate
Divisions (Harmsen et al., 2002)
• Rnet: Hargreaves radiation equation
• G: Allen et al., 1998
RAINFALL DEFICIT (RFD)
RFD = (RAINFALL – ETo)
RFD < 0 MEANS THERE IS A DEFICIT
RFD > 0 MEANS THERE IS AN EXCESS
Yield Moisture Stress Relationship
ETcadj 

YR  Ky  1 
  100
ETc 
.

YR = Yield reduction (%)
Ky = Yield response factor
ETcadj = Adjusted (actual) crop ET
ETc = Kc ETo
ETo = potential or reference ET
Actual Evapotranspiration (ET)
ETcadj = Kc Ks ETo
Average Kc = 1.0
Based on 140 crops
(Allen et al., 1998)
Kc = crop coefficient
Ks = crop water stress factor
RAW = readily available water
TAW = Totally available water
YIELD RESPONSE FACTOR (Ky)
Alfalfa
1.1
Onion
1.1
Sunflower
0.95
Banana
1.2-1.35
Peas
1.15
Tomato
1.05
Beans
1.15
Pepper
1.1
Watermelon
1.1
Cabbage
0.95
Potato
1.1
Winter wheat
1.05
Citrus
1.1-1.3
Safflower
0.8
Cotton
0.85
Sorghum
0.9
Grape
0.85
Soybean
0.85
Groundnet
0.70
Spring Wheat
1.15
Maize
1.25
Sugarbeet
1.0
Onion
1.1
Sugarcane
1.2
WATER BALANCE
Si+1 = Ri – ETcadj,i – ROi – Rechi + Si
Si+1 is the depth of soil water in the beginning of
month i+1
Si is the depth of soil water in the profile at the
beginning of month i
Ri = rainfall during month i
ETi = Actual evapotranspiration during month i
ROi = Surface runoff during month i
Rechi = percolation or aquifer recharge during
month i
Surface Runoff (RO)
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
Aquifer Recharge (Rech)
Si+1 = Ri – ETcadj,i – ROi + Si
If Si+1 ≤FC then Rech = 0
If Si+1 > FC then Rech = Si+1 – FC
and Si+1 = FC
FC = Soil Field Capacity or Soil Water
Holding Capacity
AIR TEMPERATURE
RESULTS
Have we seen a warming trend in
the Caribbean?
28
53
1
14
5
Source: Ramirez-Beltran et al., 2007
Lajas, PR
35
o
Average Temperature ( C)
30
25
20
15
10
5
Lajas
Linear (Lajas)
y = 5E-06x + 24.93
0
1/1/61
11/6/67
9/10/74
SLOPE IS NOT STATISTICALLY
SIGNIFICANT
7/15/81
Date
5/19/88
3/24/95
Adjuntas, PR
35
o
AVERAGE TEMPERATURE ( C)
30
25
20
15
10
Adjuntas
Linear (Adjuntas)
5
y = 9E-05x + 18.521
0
1/1/70
6/24/75
SLOPE IS STATISTICALLY
SIGNIFICANT AT THE 5% LEVEL
12/14/80
6/6/86
DATE
11/27/91
5/19/97
Mayagüez, PR
AVERAGE TEMPERATURE (C)
35
30
25
20
15
10
5
Mayaguez
Linear (Mayaguez)
y = 8E-05x + 23.363
0
1/1/61
11/6/67
9/10/74
SLOPE IS STATISTICALLY
SIGNIFICANT AT THE 5% LEVEL
7/15/81
DATE
5/19/88
3/24/95
Downscaled Minimum, Mean and Maximum
Air Temperature (oC) for Lajas
Scenario A2
40
35
Temperature (C)
30
25
20
Tmin
15
Tmax
Tmean
Linear (Tmax)
Linear (Tmean)
Linear (Tmin)
10
2000
2020
2040
2060
YEAR
2080
2100
RAINFALL
RESULTS
Downscaled Rainfall (mm) for Lajas
Scenario A2
120
1000
100
RAINFALL (mm)
Rainfall (mm)
800
600
400
80
60
40
200
20
y = -0.0674x + 240.87
0
2000
2020
2040
2060
YEAR
2080
2100
Downscaled Rainfall at Lajas for Scenario A2
February and September
1200
February
September
RAINFALL (mm)
1000
Linear (September)
y = 1.5134x - 2808.1
Linear (February)
y = -0.1028x + 263.21
800
600
400
200
x + 240.87
80
2100
0
2000
2020
2040
2060
YEAR
2080
2100
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.”
RAINFALL (mm)
Rainfall Lajas B1
400
300
2000
2090
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
RAINFALL (mm)
Rainfall Lajas A2
400
300
2000
2090
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
RAINFALL (mm)
Rainfall Lajas A1fi
400
300
2000
2090
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
Daily Reference Evapotranspiration
(ETo) by Month at Lajas, PR
10
A2
A1fi
B1
Linear (A2)
Linear (B1)
Linear (A1fi)
ETo (mm)
8
6
4
2
2000
2020
2040
2060
YEAR
2080
2100
RAINFALL DEFICIT
RESULTS
RFD (mm)
RAINFALL DEFICIT LAJAS B1
200
150
100
50
0
-50
-100
-150
-200
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
2000
2090
RAINFALL DEFICIT LAJAS A2
RFD (mm)
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
-100
-200
DATE
Aug
Sep
Oct
Nov
Dec
2000
2090
RAINFALL DEFICIT LAJAS A1fi
200
RFD (mm)
100
0
-100
Jan
Feb
Mar
Apr
May
Jun
Jul
-200
-300
DATE
Aug
Sep
Oct
Nov
Dec
2000
2090
Relative Change in Rainfall Deficit
Change in Rainfall Deficit Relative to 2000 (mm)
February
September
Scenario
A1fi
A2
B1
Year
2000
2050
2090
2000
2050
2090
2000
2050
2090
Adjuntas Mayaguez Lajas Adjuntas Mayaguez Lajas
0.0
0.0
0.0
0.0
0.0
0.0
-19.3
-29.6
-17.6
-31.8
-24.9
-50.2
81.3
311.5
77.5
276.9
31.2
171.9
0.0
0.0
0.0
0.0
0.0
0.0
-65.5
-78.1
-54.9
-72.7
-45.8
-67.1
117.1
244.9
97.5
200.9
85.1
183.7
0.0
0.0
0.0
0.0
0.0
0.0
-35.6
-16.6
-34.3
-33.9
-33.9
-34.0
51.8
183.8
38.4
137.2
38.8
148.3
CROP YIELD
RESULTS
YIELD REDUCTION (%)
Yield Reduction Lajas B1
100
80
60
2000
2090
40
20
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
YIELD REDUCTION (%)
Yield Reduction Lajas A2
100
80
60
2000
2090
40
20
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
YEILD REDUCTION (%)
Yield Reduction Lajas A1fi
100
80
60
2000
2090
40
20
0
Jan
Feb
Mar
Apr
May
Jun
Jul
DATE
Aug
Sep
Oct
Nov
Dec
Disclaimer


“Global and regional climate models have
not demonstrated skill at predicting
climate change and variability on multidecadal 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.”
(Nov. 17, 2006, Roger Pielke Sr. Weblog,
http://climatesci.atmos.colostate.edu)
Some sources of uncertainty
in climate modeling
•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
SUMMARY
• 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-Cont.
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.
 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.
 Significant Yield Reduction can be expected during the
months that receive less rainfall
 Yields improved during September for most scenarios
and locations.
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.
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.
Example Problem
Estimating Crop Water Requirements
and Net Irrigation Requirement

In this example input data for Ponce,
PR were used. Daily evapotranspiration
will be determined for a calabaza crop
starting on January 1st, 2007.
http://academic.uprm.edu/abe/PRAGWATER/
Average Average
ETo
Rainfall
(mm/mo) (mm/mo)
Month
January
February
March
April
May
June
July
August
September
October
November
December
Net Irrigation
Requirement
105.4
109.2
142.6
147.0
158.1
156.0
161.2
153.0
141.0
133.3
108.0
102.3
Net Irrigation
Requirement
(mm)
19.8
18.3
21.8
48.8
74.2
79.5
73.9
113.0
133.6
143.0
80.8
30.5
85.6
90.9
120.8
98.2
83.9
76.5
87.3
40.0
7.4
-9.7
27.2
71.8
Net Irrigation Requirement
140.0
120.0
ETo (mm/mo)
100.0
80.0
60.0
40.0
20.0
0.0
-20.0
1
2
3
4
5
6
7
Month
8
9
10
11
12
Daily Net Irrigation Requirement - Example Problem
Net Irrigation Requirement
Depth of Water (mm) .
5
Average Rainfall
Crop ET
4
3
2
1
zero
0
-1
-2
9/
4/
7
/0
7
/0
7
/0
7
/0
7
/0
07
26
3/
12
3/
26
2/
12
2/
29
1/
7
/0
07
15
1/
1/
1/
Days after Planting
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 USDATSTAR projects for their financial support.