Dr. Eric W. Harmsen Associate Professor, Dept. of Agricultural and Biosystems Engineering email: [email protected] The Potential Impact of Climate Change

Dr. Eric W. HarmsenDr. Eric W. Harmsen

Associate Professor, Dept. of Agricultural and Associate Professor, Dept. of Agricultural and Biosystems EngineeringBiosystems Engineering

email: [email protected]: [email protected]

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


DATE

RA

INF

AL

L (

mm

)

2000

2090

Rainfall Lajas A1fi

0

100

200

300

400


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


DATE

RF

D (

mm

)

2000

2090

RAINFALL DEFICIT LAJAS A2

-200

-100

0

100

200


DATE

RF

D (

mm

)

2000

2090

RAINFALL DEFICIT LAJAS A1fi

-300

-200

-100

0

100

200


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


DATE

YIE

LD

RE

DU

CT

ION

(%

)

2000

2090

Yield Reduction Lajas A2

0

20

40

60

80

100


DATE

YIE

LD

RE

DU

CT

ION

(%

)

2000

2090

Yield Reduction Lajas A1fi

0

20

40

60

80

100


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)


Significant Yield Reduction can be expected during the months that receive less rainfall

Yields improved during September for most scenarios and locations.


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)


(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)

.


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.

Documents

Dr. Eric W. Harmsen Associate Professor, Dept. of Agricultural and Biosystems Engineering email: [email protected] The Potential Impact of Climate Change