Impact of biofuel production on hydrology: A case study of Khlong Phlo Watershed

in Thailand

BIKESH SHRESTHAID108202(WEM/SET)

Committee Members: Dr. Mukand S. Babel (Chairperson) Dr. Sylvain R. Perret (Co-chairperson)

Dr. S. L. RanamukhaarachchiDr. Shahriar Md. Wahid

Presentation Outline

Introduction Study area Methodology Results and Discussion Conclusions & Recommendations

2

Rationale

3

Biofuel “as an alternative to fossil fuel”

57 billion L to reach151 billion L in 2017

Thailand: 5 billion L by 2022 Land use change for biofuel

production Water quantity and quality

impacts Impacts on the water resources

and hydrology not fully understood

Very few studies

Before After

4

Objectives

Analyze the impact of biofuel production on the water resource and hydrology of the Khlong Phlo watershed

Specific objectives:

1. Estimate water footprints of biofuel and biofuel energy

2. Evaluate impact on annual and seasonal water balance

3. Quantify impact on water quality

5

Scope

Review of global and Thailand’s biofuel status and plan

Collection of secondary data Estimation of green, blue and grey

water footprint Calibration and validation of SWAT

model Simulation of SWAT model for

several scenarios

6

Study Area

Location: Khlong Prasae

Rayong

12057’-13010’N

101035’-101045’E

Area :

202.8 km2

Rainfall :

1,734 mm

Temperature: 27 to 310

Humidity :

69 to 83%

Elevation :

13 to 723 msl

Land use :

Agri. (66%) Forest (33%)

Soils :

S – Cl - L S – L

7

Water footprint: Methodology

Climatic Parameters

Crop Coefficient

Effective Rainfall Reference crop ET

Crop ET Green WFCP

Irrigation required

Pollutant emissionAgreed water quality

Step 1: Water footprint of crops (WFCP)

Blue WFCP

Grey WFCP

Biofuel conversion rate

Green WFCP

Blue WFCP

Grey WFBGrey WFCP

Step 2: Water footprint of biofuel (WFB)

Green WFB

Blue WFB

Energyof biofuel

Green WFB

Blue WFB

Grey WFBEGrey WFB

Step 3: Water footprint of biofuel energy (WFBE)

Green WFBE

Blue WFBE

Formulae used for Water footprint (WF)

Green WF = min (Evapotranspiration, Effective Rain)

Blue WF = Irrigation requirement

Grey WF = max (Pollutant released/Permissible limit)

WFCP = Water use for crop production / crop yield

WFB = WFCP/ biofuel conversion rate

WFBE = WFB/ energy per liter biofuel

Energy /L biofuel

= HHV X density

8

9

Impact on water balance and water quality: Methodology (SWAT), Pre-processing Phase

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41

14

2

35

67

8

11

10

13

15

912

DEM Drainage

SoilLand use

Sub-watersheds

Hydrological Response Units

10

Impact on water balance and water quality: Methodology (SWAT), Processing Phase

Meteorological data

Model calibration and validation

Scenarios Simulation

Land use change scenarios

EvaluationWater balanceWater quality

Hydrological Response Units

Management data

Model Evaluation

11

Data Collected

Data Frequency Period SourceRainfall Daily 1984-2006 RID/TMDTemperature Daily 1984-2006 TMDWind speed Daily 1984-2006 TMDRelative Humidity Daily 1984-2006 TMDSunshine duration Daily 1984-2006 TMDDischarge Daily 1984-2006 RIDSediment load Daily 1997-2005 RID

Data Type SourceDEM 30 m resolution http://www.gdem.aster. or.jpLand use map 1:25,000 m LDDSoil map 1:100,000 m LDDDrainage map RID

Data SourceSoil properties LDD, www.iiasa.ac.atFertilizer use DOA, www.fao.org/ag/agl/fertistat/fst.fubc.en.asapCropping pattern Farmers , DOA of Thailand

Meterological data:

Spatial data:

Additional data:

12

Land use (2006)

Code Land UseArea

Percentkm2

3 Rice 1.82 0.908 Cashew Nut 4.84 2.399 Cassava 9.88 4.87

21 Evergreen Forest 66.36 32.7327 Deciduous Forest 0.05 0.0341 Institutional Land 0.51 0.2543 Water bodies 0.89 0.4447 Residential 0.28 0.1457 Wet Land 0.01 0.0164 Orchard 27.96 13.7967 Oil Palm 1.12 0.5570 Rubber 85.12 41.9882 Range grass 1.83 0.9089 Sugarcane 2.11 1.04

Total 202.80 100.00

13

Land use change scenarios

A. Oil Palm expansion (Biodiesel)

Scenario A1-Orchard to oil Palm

- Oil palm <1 to 17%

Scenario A2- Rubber to oil Palm

- Oil palm <1 to 43%

Scenario A3- Orchard + rubber to

oil palm - Oil palm < 1 to 59%

Scenario A4- Forest to oil palm

- Oil palm <1 to 33%

B. Cassava expansion (Bio-ethanol)

Scenario B1-Orchard to cassava- Cassava 5 to 21%

Scenario B2- Rubber to cassava- Cassava 5 to 47%

Scenario B3- Orchard + rubber to

cassava- Cassava 5 to 63%

Scenario B4- Forest to cassava- Cassava 5 to 38%

C. Sugarcane expansion (Bio-ethanol)

Scenario C1-Orchard to sugarcane- Sugarcane 1 to 17%

Scenario C2- Rubber to sugarcane- Sugarcane 1 to 43%

Scenario C3- Orchard + rubber to

sugarcane- Sugarcane 1 to 59%

Scenario C4- Forest to sugarcane- Sugarcane 1 to 34%

Results and Discussion

15

Water footprint of crops (WFCP)

Oil Palm Cassava Sugarcane

775 m3/t

420 m3/t

85 m3/t

306 m3/t

106 m3/t

42 m3/t

142 m3/t

80 m3/t

12 m3/t

Sugarcane has low water footprint due to higher yield WFCP sensitive to yield

16

Water footprint of biofuel (WFB)

5800 L for oil palm = 1 L of biodiesel 2500 L for cassava and 3400L for sugarcane = 1 L of bio-

ethanol Grey water contributes 5-17% for cassava, 3-9% for sugarcane

and 3-12% for oil palm

Oil Palm Cassava Sugarcane0

1000

2000

3000

4000

5000

6000

Grey WFBlue WFGreen WF

L of

wate

r/ L

of

bio

fuel

5% 10% 15% 20%0

100

200

300

400

500

600

700

800

Oil Palm Cassava Sugarcane

Pollutant Loading to surface water

L of

Gre

y w

ate

r/L

of

bio

fuel

17

Water footprint of biofuel energy (WFBE)

177, 103 & 140 m3 for oil palm, cassava & sugarcane(5% scenario) 200, 120 & 150 m3 for oil palm, cassava & sugarcane (20%

scenario)

Oil Palm Cassava Sugarcane0

50

100

150

200Grey WF Blue WF Green WF

m3

/ G

J o

f e

ne

ry

18

Water footprint of biofuel energy (WFBE)

  Gerbens Leenes et al. (2008)

Crop

Green WFBE Blue WFBE Green WFBE Blue WFBE

m3/ GJ of Energy

m3/GJ of Energy

m3/GJ of Energy

m3/GJ of Energy

Cassava 72 25 79 8Sugarcane 87 49 64 55

WFBE comparison with a study by Gerbens-Leenes et al. (2008).

Sugar 13% and cassava 10% less Difference in crop water requirement (CWR) and yield CWR sensitive to climatic data and starting of growing period Nakhon Ratchasima for sugarcane and Chaing Mai for

cassava Yield 3 production years (2006-2008)(OAE) vs 5 production

years (1997-2001)(FAO) WF of biofuel sensitive to location

19

Irrigation required due to land use change

116 MCM

Present land use

Land use change scenario

Irrigation Required (MCM)

Oil palm 60Sugarcane 58

Cassava 29

Change in irrigation withdrawals under 58.2% land cover replacement scenario

Total water yield

20

Change in nitrogen application rate and pollutant loading to Surface water due to land use change

N= 57 kg/ha

6 kg/ha

Application rate

Pollutant loading

Present land use

LUCS Increase in Nitrogen application rate (%)

Oil palm 85

Cassava 76

Sugarcane 36

Under 58.2% land cover replacement scenario

21

Total average annual water yield (Baseline)

Root Zone

Shallow (unconfined) Aquifer

Vadose (unsaturated) Zone

Confining Layer

Deep (confined) Aquifer

PrecipitationEvaporation and Transpiration

Infiltration/plant uptake/ Soil moisture redistribution

Surface Runoff

Lateral Flow

Return FlowRevap from shallow aquifer

Percolation to shallow aquifer

Recharge to deep aquiferFlow out of watershed

207 mm

1734 mm

102 mm

289 mm

836 mm TWY = 597mm (S) vs 574mm (Ob)

48%

34%

18%Copyright: Dr. Jeff Arnold, USDA-ARS, Blacklands, Texas

0 100 200 300 400 5000

100

200

300

400

500

f(x) = 0.861284741204938 x + 13.2427125287581R² = 0.808849247383571

CalibrationObserved flow (mm)

Sim

ulat

ed f

low

(mm

)

1996/1

1996/2

1996/3

1996/4

1996/5

1996/6

1996/7

1996/8

1996/9

1996/10

1996/11

1996/12

1997/1

1997/2

1997/3

1997/4

1997/5

1997/6

1997/7

1997/8

1997/9

1997/10

1997/11

1997/12

1998/1

1998/2

1998/3

1998/4

1998/5

1998/6

1998/7

1998/8

1998/9

1998/10

1998/11

1998/12

1999/1

1999/2

1999/3

1999/4

1999/5

1999/6

1999/7

1999/8

1999/9

1999/10

1999/11

1999/12

2000/1

2000/2

2000/3

2000/4

2000/5

2000/6

2000/7

2000/8

2000/9

2000/10

2000/11

2000/12

020406080

100120140160180200 Observed

Simulated

Str

eam

flo

w (

mm

)

1986/11986/21986/31986/41986/51986/61986/71986/81986/91986/101986/111986/121987/11987/21987/31987/41987/51987/61987/71987/81987/91987/101987/111987/121988/11988/21988/31988/41988/51988/61988/71988/81988/91988/101988/111988/121989/11989/21989/31989/41989/51989/61989/71989/81989/91989/101989/111989/121990/11990/21990/31990/41990/51990/61990/71990/81990/91990/101990/111990/121991/11991/21991/31991/41991/51991/61991/71991/81991/91991/101991/111991/121992/11992/21992/31992/41992/51992/61992/71992/81992/91992/101992/111992/121993/11993/21993/31993/41993/51993/61993/71993/81993/91993/101993/111993/121994/11994/21994/31994/41994/51994/61994/71994/81994/91994/101994/111994/121995/11995/21995/31995/41995/51995/61995/71995/81995/91995/101995/111995/120

50100150200250300350400450 Observed

Simulated

Str

eam

flo

w (

mm

)

22

Monthly Flow calibration and validation

Calibration

Validation

Mean and SD < 10%, NS>0.5, R2>0.6

Mean and SD < 10%, NS>0.5, R2>0.6

0 50 100 150 2000

50

100

150

200

f(x) = 0.730914986737594 x + 8.89955018708061R² = 0.631104478546084

Validation Best fit lineObserved flow (mm)

Sim

ulat

ed fl

ow (m

m)

1997/1

1997/2

1997/3

1997/4

1997/5

1997/6

1997/7

1997/8

1997/9

1997/10

1997/11

1997/12

1998/1

1998/2

1998/3

1998/4

1998/5

1998/6

1998/7

1998/8

1998/9

1998/10

1998/11

1998/12

1999/1

1999/2

1999/3

1999/4

1999/5

1999/6

1999/7

1999/8

1999/9

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2000/1

2000/2

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2000/6

2000/7

2000/8

2000/9

2000/10

2000/11

2000/12

0.00

0.10

0.20

0.30

0.40

0.50 Observed Simulated

Sed

imen

t y

ield

(t/

ha)

23

Sediment yield calibration and validation

Total average annual sediment yield: Modeled with error 5.13% [0.60 t/ha (Sim) vs 0.57 t/ha (Obs)]

Monthly sediment yield:

Calibration Validation

Calibration: Mean and SD < 10%, NS>0.5, R2>0.6

Validation: Mean > 10% and SD < 10%, NS< 0.5, R2<0.6

0.00 0.10 0.20 0.30 0.40 0.500.00

0.10

0.20

0.30

0.40

0.50

f(x) = 0.722861676819684 x − 0.00221311085701534R² = 0.536548342848488

f(x) = 0.780391896423481 x + 0.00880805557839847R² = 0.685949118739294

Calibation Linear (Calibation)Validation Linear (Validation)

Observed sediment yield (t/ha)

Sim

ulat

ed s

edim

ent y

ield

(t/h

a)

24

Effect of land use change on annual water balance

Differences in annual water balance from land use change scenarios to baseline: Oil palm

Differences in annual water balance from land use change scenarios to baseline: Cassava

Differences in annual water balance from land use change scenarios to baseline: Sugarcane

Oil palm o forest removal increase

runoff Cassava and sugarcane

o effect all components Cassava

o runoff high Sugarcane

o base flow high

SR BF TWYLD ET

-10

-5

0

5

10

15

Scenario A1 Scenario A2 Scenario A3 Scenario A4

Diff

ere

nce

fro

m b

ase

line

(%)

SR BF TWYLD ET

-20

-10

0

10

20

30

40

Scenario B1 Scenario B2 Scenario B3 Scenario B4

Diff

ere

nce

fro

m b

ase

line

(%

)

SR BF TWYLD ET

-20

-10

0

10

20

30

40

Scenario C1 Scenario C2 Scenario C3 Scenario C4

Diff

ere

nce

fro

m b

ase

line

(%)

25

Effect of land use change on monthly water yield

Differences in monthly water yield from land use change scenarios to baseline: Max land

use

Differences in monthly water yield from land use change scenarios to baseline: Rubber

replace

Differences in monthly water yield from land use change scenarios to baseline: Forest

replace

Max land use: less water o Oil palm during Jan - Oct o Cassava in Deco Sugarcane over Nov – Dec

Forest replace: water yieldo Oil palm for seven months (J, M - Jul and S)o Cassava and Sugarcane for all

except Nov - Dec

J F M A M J J A S O N D-5

0

5

10

15

20

Scenario A3 Scenario B3

Diff

ere

nce

fro

m b

ase

line

(mm

)

J F M A M J J A S O N D-4

0

4

8

12

16

Scenario A2 Scenario B2

Diff

ere

nce

fr

om

base

line

(mm

)

J F M A M J J A S O N D-6

-3

0

3

6

9

12

Scenario A4 Scenario B4

Diff

ere

nce

fro

m b

ase

line

(mm

)

26

Effect of land use change on water quality

Differences in NPS pollutants from land use change scenarios to baseline: Oil palm

Differences in NPS pollutants from land use change scenarios to baseline: Cassava

Differences in NPS pollutants from land use change scenarios to baseline: Sugarcane

Replace forest o increases pollutant loading

Cassava o high soil loss, nitrate, total

phosphorus

NO3-N loss Total P loss Sediment loss

-10-505

1015202530

Scenario A1 Scenario A2 Scenario A3 Scenario A4

Diff

ere

nce

fro

m b

ase

line

(%

)

NO3-N loss Total P loss Sediment loss0

1020304050607080

Scenario B1 Scenario B2 Scenario B3 Scenario B4

Diff

ere

nce

fro

m b

ase

line

(%

)

NO3-N loss Total P loss Sediment loss-10

0

10

20

30

40

50

60

Scenario C1 Scenario C2 Scenario C3 Scenario C4

Diff

ere

nce

fro

m b

ase

line

(%

)

Conclusions and Recommendations

28

Conclusions

Cassava, the most water efficient crop to produce biofuel

Bio-ethanol production will affect the water balance

Biodiesel no impact on water balanceo Forest conversion will affect the water balance

Bio-ethanol production will have impact on water quality

Biodiesel production will also effect the water quality due to increased nitrate loading o Conversion of orchard showed less water quality

impact Biofuel production will have negative impact on

the environment

29

Recommendations

Cassava to be promoted in water scarce areas but the environmental impacts must be considered

Supports the policy to promote biodiesel replacing orchard

Conversion of rubber no impact on water balance but will affect water quality

For Government of Thailand:

For further study: A research at a large scale at basin level Study effective BMPs Climate change and land use change for biofuel

production

THANK YOU