estimation of daily energy budget during the occurrence of ...€¦ · fyp final presentation,...

NUS Presentation Title 2001

FYP Final Presentation, AY2015/16

Kong ZiwenchangA0099562N

1

Estimation of Daily Energy Budget during the Occurrence of Floods and

Droughts in Illinois


2

Presentation Outline 1. Introduction and Research Objectives

2. Research Methodology1) Sources of Data for:

I. Meteorological ParametersII. Energy Flux Observations

2) Calculation Methods for:I. Radiation FluxesII. Heat Fluxes

3. Analysis and Results1) Energy Radiation Balances2) Earth Energy Budget3) Energy Integration4) Flux Behaviours under Drought and Flood Conditions

4. Discussion and Conclusion


• The balancing process of incoming and outgoing energy

• Directly influence the ecosystem

• Energy Radiation Balance𝑹𝒏 = 𝑹𝒏𝒔 −𝑹𝒏𝒍

• Energy Budget 𝑹𝒏 = 𝑮 + 𝝀𝑬 +𝑯

• Study Region: Illinois State, USA

• Research Objectives: 1. Find out reliable estimating methods

2. Understand flux sensibility to

controlling parameters

3. Flux behaviour under drought and

flood conditions 3

Trenberth et al. (2009)

Global Annual AverageFluxes (𝑾𝒎/𝟐) Symbol Land Ocean

Sensible Heat Flux 𝑯 27 12

Latent Heat Flux 𝑳𝒗𝑬𝑻 89 41

Ground Heat Flux 𝑮 0.9 -

Outgoing LongwaveRadiation 𝑳𝑾𝑹 ↑ 395.2

Down-welling LongwaveRadiation 𝑳𝑾𝑹 ↓ 200-400

Net Outgoing LongwaveRadiation 𝑹𝒏𝒍 79.6 57.5

Net Incoming Shortwave Radiation 𝑹𝒏𝒔 167.8 145.1

1. Introduction and Research Objectives


4




2) Calculation Methods for:I. Radiation Fluxes (𝑹𝒏𝒔,𝑹𝒏𝒍,𝑹𝒏)II. Heat Fluxes (𝑮, 𝝀𝑬, 𝑯)




5

2.1 Sources of Data• Meteorological parameters

• The Illinois State Water Survey (ISWS)

• Data of water, soil and climate• Available on daily and monthly basis• From 1992 to 2013• 19 stations across Illinois

• Energy Flux Observation

• The AmeriFlux Network• Data are acquired from filed sites

/derived further from modelling • Available on hourly basis• From 1997 to 2005• Bondville site

Parameter Symbol Unit

Max. Air Temperature 𝑻𝒎𝒂𝒙 °𝑪

Min. Air Temperature 𝑻𝒎𝒊𝒏 °𝑪

Avg. Relative Humidity RH %

Actual VapourPressure ea 𝒌𝑷𝒂

Precipitation P 𝒎𝒎

Avg. Wind Speed at 2m Altitude u 𝒎𝒔/𝟏

Soil Moisture SM 𝒎𝒎

(ISWS, 2002)(AmeriFlux, 2003)

Flux Symbol Unit

Outgoing LongwaveRadiation

𝑳𝑾𝑹 ↑

𝑾𝒎/𝟐

Down-welling LongwaveRadiation

𝑳𝑾𝑹 ↓

Net Radiation 𝑹𝒏

Latent Heat Flux LvET

Sensible Heat Flux H

Ground Heat Flux G


6

2.2 Calculation Methods

Flux (𝑾𝒎/𝟐) Method Controlling Parameters Unit

𝑹𝒏𝒍Modified Stefen-Boltzmann Law

Temperature (𝑻) 𝑲

Actual Vapour Pressure (𝒆𝒂) 𝒌𝑷𝒂

𝛔 ∗ 𝐓𝐦𝐚𝐱,𝑲𝟒J𝐓𝐦𝐢𝐧,𝑲𝟒𝟐

∗ (𝟎. 𝟑𝟒−𝟎. 𝟏𝟒 𝒆𝒂)(𝟏.𝟑𝟓(𝐑𝐬/𝐑𝐬𝟎) − 𝟎. 𝟑𝟓)

Relative ShortwaveRadiation (𝑹𝒔/𝑹𝒔𝟎)

-

𝐒𝐭𝐞𝐟𝐚𝐧−𝐁𝐨𝐥𝐭𝐳𝐦𝐚𝐧𝐧𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 (𝝈) 𝒎/𝟐𝑲/𝟒

𝑹𝒏𝒔 𝟏 − 𝜶 𝑹𝒔Albedo (𝜶) -

Total Incoming SolarRadiation (𝑹𝒔)

𝑾𝒎/𝟐

I. Energy Radiation Fluxes


7


Flux (𝑾𝒎/𝟐) Method Controlling Parameter Unit

G

Empirical 𝑵𝒆𝒕𝑹𝒂𝒅𝒊𝒂𝒕𝒊𝒐𝒏(𝑹𝒏) 𝑾𝒎/𝟐−𝟐𝟏+ 𝟎.𝟑𝟓𝟔𝑹𝒏

Fourier's LawSoil Temperature (𝑻𝒔) °𝑪

Effective Soil Depth (𝜟𝒁) 𝒎𝒄𝒔× (𝑻𝒊−𝑻𝒊/𝟏)/∆𝒕 ×∆𝐳

H Monin-Obuhkov Similarity Theory

Air Temperature (𝑻) °𝑪

Wind speed (u) 𝒎/𝒔

𝟏𝟐𝟐.𝟒𝟒 ∗𝒑𝑻 ∗

𝒖∗∆𝑻

𝟎.𝟕𝟒 𝒍𝒏 𝒁𝟐𝒁𝟏

+ 𝟒. 𝟕/𝑳′(𝒁𝟐 − 𝒁𝟏)Height difference (𝒁𝟐 − 𝒁𝟏) 𝒎

Monin-Obuhkov Length (𝑳′) 𝒎

II. Heat Fluxes : G and H


8


Flux (𝑾𝒎/𝟐) Method Controlling Parameter Unit

LvET

Penman-Monteith

Temperature (𝑻) °𝑪Actual Vapour Pressure (𝒆𝒂) 𝒌𝑷𝒂

Wind Speed (u) 𝒎/𝒔𝝀 𝟎.𝟒𝟎𝟖×∆× 𝑹𝒏/𝑮 J𝟗𝟎𝟎×𝜸×𝒖𝟐× 𝒆𝒔/𝒆𝒂 / 𝑻J𝟐𝟕𝟑

∆J𝜸×(𝟏J𝟎.𝟑𝟒𝒖𝟐)latent heat of vaporization (𝝀) 𝑾𝒎/𝟐/ kg

Slope of Vapour Pressure (∆) 𝒌𝑷𝒂

Jensen-Haise Mean Air Temperature (𝑻𝒎) °𝑪

𝟎. 𝟒𝟏𝑹𝑺×(𝟎.𝟎𝟐𝟓𝑻𝒎 + 𝟎. 𝟎𝟕𝟖) Incoming Solar Radiation 𝑹𝒔 𝑾𝒎/𝟐

Priestley-TaylorPriestley-Taylor Constant (𝜶) -

Psychrometric constant 𝜸 𝑲𝑷𝒂/°𝑪𝜶×𝜟×(𝑹𝒏 − 𝑮)/(∆+ 𝜸) Slope of the Vapour Pressure (∆) 𝒌𝑷𝒂

Turc Mean Air Temperature (𝑻𝒎) °𝑪𝟎. 𝟑𝟏𝟑𝑻𝒎×(𝑹𝑺 + 𝟐. 𝟏)/(𝑻𝒎 + 𝟏𝟓) Incoming Solar Radiation (𝑹𝒔) 𝑾𝒎/𝟐

II. Heat Fluxes : LvET


9





3. Analysis and Results1) Energy Radiation Balances (𝑹𝒏 = 𝑹𝒏𝒔 − 𝑹𝒏𝒍)2) Earth Energy Budget3) Energy Integration4) Flux Behaviours under Drought and Flood Conditions



10

3.1.1 Net Incoming SW Radiation Rns

𝑹𝒏𝒔 = 𝟏− 𝜶 𝑹𝒔

• Albedo (𝜶) assumed as 0.23• 𝑹𝒏𝒔 highly related to the geometrical

relationship with the Sun • Smooth Trend

• Maxima in July 212.2 𝑾𝒎/𝟐

• Minima in December 53.7 𝑾𝒎/𝟐

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

300

Rns(W

m-2)

Time Series and Annual Cycle of Rns from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

300

Rns(W

m-2)

J F M A M J J A S O N DMonths

0

100

200

300

Rns(W

m-2)

Daily

Monthly

AnnualCycle


11

3.1.2 Net Outgoing LW Radiation Rnl

𝑹𝒏𝒍 = 𝛔 ∗𝑻𝒎𝒂𝒙𝟒 + 𝑻𝒎𝒊𝒏𝟒

𝟐∗

𝟎.𝟑𝟒 − 𝟎.𝟏𝟒 𝒆𝒂 ∗ (𝟏.𝟑𝟓(𝑹𝒔/𝑹𝒔𝟎)− 𝟎. 𝟑𝟓)

• Modified Stefan-Boltzmann Law• Relatively Bumpy Trend

• Maxima in September 51.9 𝑾𝒎/𝟐


Daily

Monthly

AnnualCycle

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

Rnl

(Wm

-2)

Time Series and Annual Cycle of Rnl from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

20

40

60

Rnl

(Wm

-2)


20

40

60

Rnl

(Wm

-2)


12

3.1.3 Rnl – Relationships with controlling parameters


20

40

60

R nl (W

m-2)

Average Annual Cycle of Rnl , Tair , ea and Rs/Rs0 from 1992 to 2013


-50

0

50

T air(ºC

)


0

2

4

ea (k

Pa)


0.4

0.6

0.8

R s/Rs0

• Parametersincreasetowardssummeranddecreaseintowinter• SummerinIllinoishashighhumidity𝒆𝒂 andrelativeSWR𝑹𝒔/𝑹𝒔𝟎• 𝑹𝒏𝒍 drops in July due to increase in 𝒆𝒂• 𝑹𝒏𝒍 increases in September due to decrease in 𝒆𝒂 and high value of 𝑹𝒔/𝑹𝒔𝟎


1998 1999 2000 2001Years

-50

0

50

100

150

Rnl

(Wm

-2)

Daily Variation of Rnl

CalculatedFlux Tower

1998 1999Year 1998

-50

0

50

100

150

Rnl

(Wm

-2)

Daily Variation of Rnl


-50 0 50 100 150 200 250Observation (Flux Tower)

-50

0

50

100

150

200

250

Estim

atio

n fro

m E

quat

ion

Rnl (Wm-2)

R = 0.63

• Calculatedandobservedvaluesarecloseintermsofaverage,varianceandrootmeansquareerror.

• ReasonableCorrelationCoefficient• Limitations

• Cloudcoverdoesnotrepresenttypesofgasesandaerosolsinatmosphere

• Idealemissivityof1isassumed

13

3.1.4 Rnl - Correlation with Flux Tower Observation

ComparisonbetweenObservedandCalculatedValuesofEnergyFlux

FluxAverage(Wm-2) Variance RMS Correlation

CoefficientObservation Calculation Observation Calculation Observation Calculation

𝑹𝒏𝒍 28.7 26.2 1289.0 1246.6 46.0 44.0 0.6


14

3.1.5 Net Radiation Rn

𝑹𝒏 = 𝑹𝒏𝒔 − 𝑹𝒏𝒍• Controlled more significantly by 𝑹𝒏𝒔• Smooth Trend

• Maxima in July168.2 𝑾𝒎/𝟐


1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

Rn(Wm-2)

Time Series and Annual Cycle of Rn from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

Rn(Wm-2)


0

100

200

Rn(Wm-2)

Daily

Monthly

AnnualCycle


1998 1999Year 1998

-50

0

50

100

150

200

250

Rn(W

m-2

)

Daily Variation of Rn


15-100 -50 0 50 100 150 200 250 300Observation (Flux Tower)

-100

-50

0

50

100

150

200

250

300

Estim

atio

n fro

m E

quat

ion

Rn (Wm-2)

R = 0.76

1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-50

0

50

100

150

200

250

Rn(W

m-2

)

Daily Values of Rn from 1997 to 2005


3.1.6 Rn - Correlation with Flux Tower Observation




𝑹𝒏 76.3 91.0 3870.1 3617.3 98.4 109.0 0.8

• Calculatedvalueisslightlyaboveobservationintermofaveragevalue.

• HighCorrelationCoefficient• AssumptionofAlbedois

acceptable


16

3.1.7 Energy Radiation Balance

Annual Cycle of Radiation Fluxes from 1992 to 2013Radiation

Flux (Wm-2) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Average

𝑹𝒏𝒔 63.2 92.7 124.1 156.9 181.9 206.6 212.2 191.1 156.2 110.4 68.4 53.7 134.8

𝑹𝒏𝒍 35.7 40.2 43.0 45.2 42.8 48.9 44.0 44.1 51.9 49.6 39.2 34.0 45.1

𝑹𝒏 27.5 52.5 81.1 111.7 139.1 157.7 168.2 147.0 104.3 60.8 29.2 19.8 91.6


0

50

100

150

200

250

Ener

gy F

luxe

s (W

m-2

)

Annual Cycle for All Radiant Energy FLuxes RnRnlRns

• N𝐞𝐭𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧𝑹𝒏 islimitedby𝐯𝐚𝐥𝐮𝐞𝐨𝐟𝑹𝒏𝒔• Geometricalrelationshipwith

theSun• CloudCoverage

• Diminishedfurtherbyvalueof𝑹𝒏𝒍• Temperature• GroundEmissivity• AirHumidity• CloudCoverage

• 𝑹𝒏 isthetotalamountofenergyintheearthenergybudget


17





3. Analysis and Results1) Energy Radiation Balances2) Earth Energy Budget (𝑹𝒏 = 𝑮 + 𝝀𝑬+ 𝑯)3) Energy Integration4) Flux Behaviours under Drought and Flood Conditions



18

3.2.1 Ground Heat Flux G – Fourier’s Law

𝐆 = 𝒄𝒔× (𝑻𝒊−𝑻𝒊/𝟏)/∆𝒕 ×∆𝐳

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

-50

0

50

G (W

m-2

)

Time Series and Annual Cycle of G by Fourier's Law from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

-5

0

5

G (W

m-2

)


-2

-1

0

1

G (W

m-2

)

• Soil temperature gradient and effective depth• Maxima in March 1.0 𝑾𝒎/𝟐

• Minima in November -1.1 𝑾𝒎/𝟐

Daily

Monthly

AnnualCycle


19

3.2.1 Ground Heat Flux G – Empirical Method

𝑮 = −𝟐𝟏 + 𝟎. 𝟑𝟓𝟔𝑹𝒏

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

-50

0

50

G(W

m-2)

Time Series and Annual Cycle of G by Empirical Method from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

-50

0

50

G(W

m-2)


-50

0

50

G(W

m-2)

• Linear Regression with 𝑹𝒏• Maxima in July 26.7 𝑾𝒎/𝟐

• Minima in December -21.4 𝑾𝒎/𝟐

Daily

Monthly

AnnualCycle


1998 1999Year 1998

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

G (W

m-2

)

Daily Variation of G by Empirical Method CalculatedFlux Tower

1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-40

-20

0

20

40

60

G (W

m-2

)

Daily Values of G by Empirical Method from 1997 to 2005CalculatedFlux Tower

1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-40

-20

0

20

40

60

G (W

m-2

)

Daily Values of G by Fourier's Law from 1997 to 2005CalculatedFlux Tower

1998 1999Year 1998

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

G (W

m-2

)

Daily Variation of G by Fourier's Law CalculatedFlux Tower

20

3.2.1 G - Correlation with Flux Tower ObservationComparisonbetweenObservedandCalculatedValuesofEnergyFlux



G Empirical 1.41.4

266.2410.0

16.420.3 0.5

Fourier'sLaw 0.0 281.8 16.8 0.1

• Empiricalmethod:calculatedandobservedvaluesarecloseintermofaveragevalue

• Fourier’slaw:calculatedandobservedvaluesarecloseinvarianceandRMS

• However,EmpiricalmethodyieldsmuchhigherCorrelationCoefficient

• UseEmpiricalmethod


21

3.2.2 Sensible Heat Flux H –Monin-Obuhkov Similarity Theory (MOST)

𝑯 = 𝟏𝟐𝟐.𝟒𝟒 ∗𝒑𝑻 ∗

𝒖∗∆𝑻

𝟎. 𝟕𝟒𝒍𝒏 𝒁𝟐𝒁𝟏

+ 𝟒.𝟕/𝑳′(𝒁𝟐 − 𝒁𝟏)

• Controlled by humidity, wind speed 𝒖,temperature 𝑻 and height 𝒁• Maxima in May 83.8 𝑾𝒎/𝟐

• Minima in November 41.8 𝑾𝒎/𝟐

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

H(W

m-2)

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

20406080100

H(W

m-2)


20406080100

H(W

m-2)

Time Series and Annual Cycle of H from 1992 to 2013

Daily

Monthly

AnnualCycle


22

3.2.2 H – Relationships with controlling parameters


40

60

80

H (W

m-2)

Average Annual Cycle of H , Tair , u and SM from 1992 to 2013


-50

0

50

T air(W

m-2)


2

4

6

u (W

m-2)


650

700

750

SM (m

m)

• TheincreaseinH inspringisinlinewiththatofairtemperatureandwindspeed

• However,H dropsinJulyandreachesitsminimainAugustdespiteincreaseintemperatureandwindspeed

• The drop is affected by positive fluctuation in soil moisture.


1998 1999 2000 2001 2002 2003 2004 2005 2006Years

0

50

100

150

H (W

m-2

)

Daily Values of H from 1997 to 2005CalculatedFlux Tower

1998 1999Year 1998

-50

0

50

100

150

H (W

m-2

)

Daily Variation of HCalculatedFlux Tower

-100 -50 0 50 100 150Observation (Flux Tower)

-100

-50

0

50

100

150

200

Estim

atio

n fro

m E

quat

ion

H by MOST (Wm-2)

R = 0.82

23

3.2.2 H (MOST) -Correlation with Flux Tower Observation


FluxAverage(𝑾𝒎/𝟐) Variance RMS Correlation


H 20.2 31.4 2739.3 3208.9 56.1 64.8 0.8

• Generaloverestimation• HighervarianceandRMScompared

toobservation• Sourceoferror:

• Useofsoilsurfacetemperatureduetoabsenceofairtemperatureatthe2nd layer

• However,highcorrelationcoefficient


24

3.2.3 Latent Heat Flux LvET–Penman-Monteith Method

𝑷𝑬𝑻 =𝟎. 𝟒𝟎𝟖×∆× 𝑹𝒏−𝑮 + 𝟗𝟎𝟎×𝛄×𝒖 𝒆𝒔 − 𝒆𝒂

𝐓 + 𝟐𝟕𝟑∆ + 𝛄× 𝟏 + 𝟎.𝟑𝟒𝒖

• Controlled by humidity𝒆𝒂 , wind speed 𝒖,temperature 𝑻 and available energy 𝑹𝒏 −𝑮• Maxima in July 131.7 𝑾𝒎/𝟐

• Minima in January 21.8 𝑾𝒎/𝟐

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)

Time Series and Annual Cycle of LvET by Penman-Monteith Method from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)


0

50

100

150

LvET

(Wm-2)

Daily

Monthly

AnnualCycle


25

3.2.3 Latent Heat Flux LvET–Jensen-Haise Method

𝑷𝑬𝑻 = 𝟎. 𝟒𝟏𝟒𝑹𝑺×(𝟎.𝟎𝟐𝟓𝑻𝒎 + 𝟎. 𝟎𝟕𝟖)• Controlled by temperature 𝑻 and total

incoming solar r𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧𝑹𝑺• Maxima in July 125.4 𝑾𝒎/𝟐


1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

LvET

(Wm-2)

Time Series and Annual Cycle of LvET by Jensen-Haise Method from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

100

200

LvET

(Wm-2)


0

100

200

LvET

(Wm-2)

Daily

Monthly

AnnualCycle


26

3.2.3 Latent Heat Flux LvET–Priestley-Taylor Method

𝑷𝑬𝑻 = 𝜶×𝜟×(𝑹𝒏 − 𝑮)/(∆ + 𝜸)• Controlled by slope of vapour pressure ∆ and

available energy 𝑹𝒏− 𝑮• Maxima in July149.4 𝑾𝒎/𝟐


1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)

Time Series and Annual Cycle of LvET by Priestley-Taylor Method from 1992 to 2013

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)


0

50

100

150

LvET

(Wm-2)

Daily

Monthly

AnnualCycle


27

3.2.3 Latent Heat Flux LvET–Turc Method

𝑷𝑬𝑻 = 𝟎.𝟑𝟏𝟑𝑻𝒎×(𝑹𝑺 + 𝟐. 𝟏)/(𝑻𝒎 + 𝟏𝟓)• Controlled by temperature 𝑻 and total

incoming SW r𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧𝑹𝑺• Maxima in March 131.2 𝑾𝒎/𝟐


1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)


0

50

100

150

LvET

(Wm-2)

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

50

100

150

LvET

(Wm-2)

Time Series and Annual Cycle of LvET by Turc Method from 1992 to 2013

Daily

Monthly

AnnualCycle


1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-50

0

50

100

150

200

LvET

(Wm

-2)

Daily Values of LvET from 1997 to 2005CalculatedFlux Tower

1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-50

0

50

100

150

200

LvET

(Wm

-2)


1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-50

0

50

100

150

200

LvET

(Wm

-2)

Daily Values of LvET fro 1997 to 2005CalculatedFlux Tower

1998 1999 2000 2001 2002 2003 2004 2005 2006Years

-50

0

50

100

150

200

LvET

(Wm

-2)


28

3.2.3 Latent Heat Flux LvET–Correlation with Flux Tower Observation


FluxAverage(𝑾𝒎/𝟐) Variance RMS Correlation


LvET

Penman-Monteith

25.1

36.3

1466.0

2632.1

45.8

62.9 0.7

Jensen-Haise 29.0 2397.4 56.9 0.6

Priestley-Taylor 40.0 2960.0 67.5 0.6

Turc 22.4 1403.2 43.6 0.8

• TurcMethodyieldsvaluesclosesttotheobservationinallterms• Therefore,useTurcmethod1998 1999

Year 1998-50

0

50

100

150

200

LvET

(Wm

-2)

Daily Variation of LvET CalculatedFlux Tower

1998 1999Year 1998

-50

0

50

100

150

200

LvET

(Wm

-2)


1998 1999Year 1998

-50

0

50

100

150

200

LvET

(Wm

-2)


1998 1999Year 1998

-50

0

50

100

150

200

LvET

(Wm

-2)



29J F M A M J J A S O N DMonths

-150

-100

-50

0

50

100

150

200

Flux

es (W

m-2

)

Comparison between Rn and the Heat FluxesLvETHGRnDifference between Rn and Sum of Heat Fluxes

Annual Cycle of Heat Fluxes from 1992 to 2013Heat Flux (𝑾𝒎/𝟐) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Average𝑳𝒗𝑬𝑻 2.9 8.2 29.0 65.1 93.1 121.1 131.2 114.3 83.3 47.3 16.5 4.0 59.7

𝑯 46.4 47.8 59.4 60.6 63.8 59.3 48.6 45.2 46.8 46.8 41.8 45.6 51.0

𝑮 -19.2 -12.4 -1.5 7.4 16.4 24.8 26.7 19.3 6.9 -9.2 -19.1 -21.5 1.6

𝑹𝒏 27.5 52.5 81.1 111.7 139.1 157.7 168.2 147.0 104.3 60.8 29.2 19.8 91.6

𝚺(𝑯𝒆𝒂𝒕𝑭𝒍𝒖𝒙) 30.1 43.5 86.8 133.1 173.4 205.3 206.5 178.8 137.0 84.8 39.3 28.1 112.2

3.2.4 The Earth Energy Budget

• Fromannualaverage,𝑳𝒗𝑬𝑻 is the most prominent heat flux, followed by 𝑯

• However,𝑮 isimportantwhenenergyavailabilityislowinwinter.

• Generally,sumofheatfluxishigherthan𝑹𝒏• largelyduetooverestimationof𝑯• mostsignificantinsummer


30








31

3.3.1 Energy Partition (Heat flux/𝑅¦)

• 𝑹𝒏 isdissipatedmainlybyevapotranspiration(LvET)

• However,whengroundisdryortemperatureislow,evapotranspirationslowsdown,andmoreenergyisconvertedintosensibleheatH

• G istheenergymediatorassoilprovidesenergystorage• Inwinter,groundemitsenergybackto

ecosystem

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecG/𝑹𝒏 0.3 0.2 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.3

LvET/𝑹𝒏 0.0 0.1 0.3 0.5 0.5 0.6 0.7 0.6 0.6 0.5 0.2 0.1H/𝑹𝒏 0.7 0.7 0.7 0.5 0.4 0.3 0.2 0.3 0.3 0.5 0.5 0.6

1.55,1%

51.01,46%59.65,

53%

Annual

G

H

LvETUnit: 〖𝑾𝒎


1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014-5

0

5

10

15

20

25

30

35

Tem

pera

ture

(ºC

)

Temperature

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Soil

Moi

stur

e (m

m)

Comparison between Change in Monthly Temperature and Bowen Ratio from 92 to 13SM

32

3.3.2 Bowen Ratio (𝜷= H/LvET)Annual Cycle of Heat Fluxes from 1992 to 2013

Heat Flux (𝑾𝒎/𝟐) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

AverageT 1.0 3.6 10.4 17.4 22.9 27.6 29.1 28.2 25.2 18.4 10.5 3.1 16.4

SM 729.7 690.1 713.1 727.8 712.4 729.3 695.7 718.0 694.4 705.9 745.7 706.9 714.1

𝜷 16.3 5.9 2.1 0.9 0.7 0.5 0.4 0.4 0.6 1.0 2.5 11.5 0.9

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014550

600

650

700

750

800

Soil

Moi

stur

e (m

m)

Comparison Between Change in Monthly Soil Moisture and Bowen Ratio from 92 to 13Soil Moisture

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Bow

en R

atio

(H/L

vET)

Bowen Ratio• 𝜷islargelyrelatedtotemperatureT

• Temperature threshold for evapotranspiration to take place

• Itisalsocontrolledbywateravailabilityinsoil• Whenenoughenergyisavailable

• 𝜷ishighinwinteranddropdrasticallyinlatespring

• PlantstakeupSMingrowseason,resultinhigher𝜷 in spring compared to autumn

• Annual average of 0.86 suggest surface type of temperate forests and grasslands


33

3.3.3 Energy Budget Closure

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Years

-100

-50

0

50

100

150

200

250

Ener

gy F

lux

(Wm

-2)

Comparison between Rn and the Sum of Heat Fluxes from 1992 to 2013RnSum of Heat FluxesRn-LvET-H-G

• Generalalignmentbetweensumofheatfluxesand𝑹𝒏

• However,sumofheatfluxesfluctuatemorerigorouslythan𝑹𝒏

• Average maximum difference in 22 years• 47.6 𝑾𝒎/𝟐 in June

𝑹𝒏 = 𝑮 + 𝝀𝑬+ 𝑯


34








35

3.4 Energy Fluxes under Drought and Flood Conditions

• SeveredroughtoccurredinIllinoisin2005.• The timing of the dryness is during spring and summer.• Decreaseintheagriculturalproduction,whichexertsnegativesocio-

economicimpact.

• FloodoccurredinIllinoisinJune2008.• Constant large amount of precipitation in spring.• Residentssufferedfromfacilitydamagesandfinancialloss.


36

3.4.1 Drought Year in Illinois - 2005

• DepletionofwaterhasdirectimpactonairhumidityRHandsoilmoistureSM.Bothofthemarebelowaverage.

• CloudcoverislowfromMarchtoJune,whichallowmoresolarradiationtoentertheEarth

• Wind speed 𝒖 ismuchhigherthantheaveragewhiletemperatureTisaboutthesameasaverage

Mar Apr May JunMonths

30

40

50

60

70

80

90

100

110

P(mm)

Trends of Key Parameters from March to June in Year 20052005Average


0

5

10

15

20

25

T(ºC)


56

58

60

62

64

66

68

70

72

74

76

RH


0.65

0.7

0.75

0.8

Rs/Rso


640

660

680

700

720

740

760

SM(mm)


4.8

4.9

5

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

u(m/s)


37


120

140

160

180

200

220

240

Rns(Wm-2)

Trends of Fluxes from March to June in Year 2005


42

44

46

48

50

52

54

56

58

60

62

Rnl(W

m-2)


80

90

100

110

120

130

140

150

160

170

Rn(W

m-2)


-5

0

5

10

15

20

25

30

G(W

m-2)


55

60

65

70

75

80

H(Wm-2)


20

40

60

80

100

120

140

LvET

(Wm-2)

3.4.1 Drought Year in Illinois - 2005

• Duetoitsrelationwithcloudcover,𝑹𝒏𝒔 ismoderatelyaffectedbythecondition.

• 𝑹𝒏𝒍 issignificantlyraisedduringthedrought,duetodecreaseinhumidityandcloudcover.

• 𝑹𝒏 isaboveaverage,indicateslargeramountofenergyunderdroughtcondition.

• Consequentially, all heat fluxes are above average.• Sensible heat flux H increases drastically in March , as plant growth worsens the

water depletion• Increase in latent heat flux LvET slows down from April.


38

3.4.2 Flood Year in Illinois - 2008

• WaterabundancehasdirectimpactonairhumidityRHandsoilmoistureSM. Bothofthemareaboveaverage.

• CloudcoverishigherthanaveragevaluefromMarchtoJune.

• Wind speed 𝒖 ismuchlowerthantheaverage.

• TemperatureTisaboutthesteadilybelowaverage.


60

70

80

90

100

110

120

130

P(mm)

Trends of Key Parameters from March to June in Year 20082008Average


-5

0

5

10

15

20

25

T(ºC)


68

70

72

74

76

78

80

82

84

RH


0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

0.76

Rs/Rso


710

720

730

740

750

760

770

SM(mm)


4

4.2

4.4

4.6

4.8

5

5.2

5.4

u(m/s)


39


80

100

120

140

160

180

200

220

Rns(Wm-2)

Trends of Fluxes from March to June in Year 2008


36

38

40

42

44

46

48

50

Rnl(W

m-2)


40

60

80

100

120

140

160

Rn(W

m-2)


-15

-10

-5

0

5

10

15

20

25

G(W

m-2)


40

45

50

55

60

65

H(Wm-2)


0

20

40

60

80

100

120

140

LvET

(Wm-2)

3.4.2 Flood Year in Illinois - 2008

• Duetoitsrelationwithcloudcover,𝑹𝒏𝒔 issignificantlysmallerthanaverage.

• 𝑹𝒏𝒍 issignificantlyloweredduringwithhigherprecipitation,duetoincreaseinairhumidity

andcloudcover.

• 𝑹𝒏 isbelowaverage,indicatessmalleramountofenergyunderfloodcondition.

• Consequentially, all heat fluxes are below average.• Sensible heat flux H decreases drastically in April• Increase in latent heat flux LvET is steady and complementary to change in H.• Temperaturelimitstheamountofevapotranspiration,resultsinaccumulationofwater.


40








41


HeatFlux

(𝑾𝒎/𝟐)Method

GEmpirical

−𝟐𝟏 +𝟎. 𝟑𝟓𝟔𝑹𝒏

H Monin-Obuhkov Similarity Theory

𝟏𝟐𝟐.𝟒𝟒∗𝒑𝑻∗

𝒖∗∆𝑻

𝟎.𝟕𝟒 𝒍𝒏 𝒁𝟐𝒁𝟏

+ 𝟒.𝟕/𝑳′(𝒁𝟐 − 𝒁𝟏)

LvET Turc𝟎. 𝟑𝟏𝟑𝑻𝒎×(𝑹𝑺 + 𝟐. 𝟏)/(𝑻𝒎+𝟏𝟓)

Energy Flux

(𝑾𝒎/𝟐)Method

𝑹𝒏𝒍

Modified Stefen-Boltzmann Law

𝛔 ∗ 𝐓𝐦𝐚𝐱,𝑲𝟒J𝐓𝐦𝐢𝐧,𝑲𝟒𝟐 ∗ (𝟎.𝟑𝟒 −

𝟎. 𝟏𝟒 𝒆𝒂)(𝟏.𝟑𝟓(𝐑𝐬/𝐑𝐬𝟎)− 𝟎.𝟑𝟓)

𝑹𝒏𝒔 𝟏 −𝜶 𝑹𝒔

• Estimation Methods


42


• Net Radiation (𝑹𝒏) is controlled by • Total incoming solar energy 𝑹𝒔• Temperature

• Humidity

• Cloud cover

• Latent Heat Flux (LvET) takes up most of 𝑹𝒏 followed by Sensible heat flux (H).

• LvET and H are sensitive to both temperature and water availability.

• A highly complementary relationship between H and LvET is found.

• Important Findings


43

4. Discussion and Conclusion• Ground heat flux (G) is not very significant in general, but becomes

important when energy input is low during winter.

• Under drought and flood conditions• 𝑹𝒏𝒍 is more affected than 𝑹𝒏𝒔.

• Large amount of 𝑹𝒏 (above average) is absorbed by ecosystem during drought.

• Small amount of 𝑹𝒏(below average) is available during the build-up process of

flooding.

• All heat fluxes, G, H and LvET, are above average during drought, and below

average during flood formation.

• Fluctuation in H is the greatest among all, LvET and G has steady trends.

• Energy availability is the greatest factor in determining Fluxes’ behaviours.

• Other factors include: precipitation, soil moisture, wind speed etc.


1. Allen, R.G.; Pereira, L.S.; Raes, D. & Smith, M. (1998). Crop Evaporation-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, N° 56, FAO, Rome.

2. Betts, A., Ball, J., & Viterbo, P. (1999). Basin-scale surface water and energy budgets for the mississippi from the ECMWF reanalysis. Journal of Geophysical Research. D. Atmospheres, 104(D16), 19.

3. DeHeer-Amissah, A., H gstr m, U., & Smedman-H gstr m, A. (1981). Calculation of sensible and latent heat fluxes, and surface resistance from profile data. Boundary-Layer Meteorology, 20(1), 35-49. doi:10.1007/BF00119923

4. Gallego-Elvira, B., C.M. Taylor, P. P. Harris, D. Ghent, K. L. Veal, and S. S. Folwell (2016), Global observational diagnosis of soil moisture control on the land surface energy balance, Geophys. Res. Lett., 43, doi:10.1002/2016GL068178.

5. Jones, M.A. Douglas (1966). Variability of Evapotranspiration in Illinois. Illinois State Water Survey, Urbana.

6. Kandel, R. (2012). Understanding and measuring earth's energy budget: From fourier, humboldt, and tyndall to CERES and beyond. Surveys in Geophysics, 33(3-4), 337. doi:10.1007/s10712-011-9162-y

7. Tabari, H., & Talaee, P. H. (2014). Sensitivity of evapotranspiration to climatic change in different climates. Global and Planetary Change, 115, 16. doi:10.1016/j. gloplacha. 2014.01.006

8. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). earth's global energy budget. Bulletin of the American Meteorological Society, 90(3), 311-323. doi:10.1175/2008BAMS2634.1

9. Trenberth, K. E., & Fasullo, J. T. (2012;2011;). Tracking Earth’s energy: From el niño to global warming. Surveys in Geophysics, 33(3), 413-426. doi:10.1007/s10712-011-9150-2

10. Trenberth, K. E., & Shea, D. J. (2005). Relationships between precipitation and surface temperature. Geophysical Research Letters, 32(14), L14703. doi:10.1029/2005GL022760

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References


11. Tukimat, N.N.A., S. Harun & S. Shahid (2012). Potential evapotranspiration model for muda irrigation project, malaysia.Water Resource Management, 23(1), 57-69. doi:10.1007/s11269-008-9264-6

12. Wang, K., & Dickinson, R. E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2), np-np. doi:10.1029/2011RG000373

13. Yamada, K., Hayasaka, T., & Iwabuchi, H. (2012). Contributing factors to downward longwave radiation at the earth's surface. Sola, 8, 94-97. doi:10.2151/sola.2012-024

14. Brutseart, W. (1982). Evaporation into the atmosphere: theory, history, and applications. Kluwer Academy Publishers. First edition. Dordrecht, The Netherlands. 129p.

15. Miller, D. H. (1981). Energy at the surface of the earth: An introduction to the energetics of ecosystems. New York: Academic Press.

16. Anderson, R. C. (1970). Prairies in the prairie state. Transactions of the Illinois State Academy of Science 63: 214—221.

17. Cho Thanda Nyunt (2011). Examining the sensitivity of Potential Evapotranspiration to Climate Change.

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45

References


Thank you !

46


47

AnnualCycleofRadiationFluxesfrom1992to2013Radiation

Flux(𝑾𝒎/𝟐)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AnnualAverage

𝑹𝒏𝒔 63.2 92.7 124.1 156.9 181.9 206.6 212.2 191.1 156.2 110.4 68.4 53.7 134.8

𝑹𝒏𝒍 35.7 40.2 43.0 45.2 42.8 48.9 44.0 44.1 51.9 49.6 39.2 34.0 45.1𝑹𝒏 27.5 52.5 81.1 111.7 139.1 157.7 168.2 147.0 104.3 60.8 29.2 19.8 91.6

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