Landscape agro-hydrological modeling: opportunities from remote sensing

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Landscape agro-hydrological modeling: opportunities from remote sensing Xueliang Cai (IWMI) 19 April, 2010, IWMI Seminar, Battaramulla, Sri Lanka

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Landscape agro-hydrological modeling: opportunities from remote sensing - Xueliang Cai, International Water Management Institute (IWMI)

Transcript of Landscape agro-hydrological modeling: opportunities from remote sensing

Page 1: Landscape agro-hydrological modeling: opportunities from remote sensing

Landscape agro-hydrological modeling: opportunities from remote sensing

Xueliang Cai (IWMI)

19 April, 2010, IWMI Seminar, Battaramulla, Sri Lanka

Page 2: Landscape agro-hydrological modeling: opportunities from remote sensing

Outline

1. Background – agrohydrology2. Agro-hydrological modeling at field scale3. Integrate agro-hydrological modeling with remote

sensing at irrigation scheme level4. Moving to landscape agro-hydrological modeling5. Conclusions

Photo Credit: Xueliang Cai

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1. Background – agrohydrology

• Hydrology in agricultural areas;• Linking water distribution, movement, and quality to

agricultural systems;• Process based simulation help understand human

intervention – water – agric. responses;• Irrigation & drainage, and farming practices have

significant impact on hydrology.

Photo Credit: Xueliang Cai

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2. Agro-hydrological modeling at field scale

• SWAP, ORYZA2000, DSSAT, AquaCrop…• Soil-Plant-Atmosphere Continuum• Crop growth simulation• Irrigation demand and supply analysis• conjunctive management of soil, water and fertilizer

Agro-hydrological modeling is better developed at field scale

Photo Credit: Xueliang Cai

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DSS for irrigation forecasting and optimal water allocation (Cai et al., 2003)

2. Agro-hydrological modeling at field scale

Weather forecast input

Crop ET forecast

Soil moisture balance

Irrigation forecast

calibration

calibration

Irrigation forecast module

Water depth Rainfall Irrigation

Water balance of simulated paddy field

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2. Agro-hydrological modeling at field scale

• Water cycling processes limited to field level

Key limitations:

• Crop growth conditions vary

LAI Biomass Yield ET

The physical world is different from one point to another

Source: Xueliang Cai (IWMI), 2007

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3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

Hilly to plain landscape “Melon-on-the-vine” irrigation system

Paddy rice – rapeseed rotation Rainfall ≈ 1000 mm

1

YSD

1

2

12

2

3

11

111

13

41

42

43

101

102

81

82

112

103

52

51

131

6

3

FQ

1217

491

8

42

41

91

92

5

53

122

21

P1

P2P3

P5

P4

P6

pumping

(virtualsegment)

underground pipe

pumping

Legend

1

YSD

1

2

12

2

3111

13

41

42

43

101

81

82

52

51

6

3

FQ

1217

8

42

41

91

92

5

53

P1

P2P3

P5

P4

P6

pumping

(virtualsegment)

underground pipe

pumping

Irri. CanalDrain. CanalIrri. Unit

ReservoirDiversionDrainage

Yangtze river

Zhanghe irrigation system (ZIS):Layout in OASIS

Source: LANDSAT

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a. Land cover and crop dynamics

Single crop (cotton, wheat)

WaterSettlementForest

Rice – wheat Rice – rapeseed Double crop

Legend

Crop dynamics and the impact on hydrology are far more significant than land use change

Irrigated area of the third main canal (ha):Design: 68, 000 Gov. statistics: 29,000 ZIS: 16,000Remote sensing: 21,000

RS provides objective and accurate information on area and distribution

“”

“”

3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

NDVI

(Cai and Cui, 2009b)

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b. ET estimate

3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

Land use mapETa map from RSIU boundary

ET values of each land use in each simulation unit

Model calibration(Cai and Cui, 2009a)

SSEB:

CH

xHf TT

TTET

fpa ETETET

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c. Crop growth conditions and yields

)1(1m

ay

m

a

ET

ETK

Y

Y(1) In ZIS, Doorenbos – Kasam model:

(2) In Central Asia, biophysical modeling:

Cotton yield

Yieldcotton = 5.156*IRS NDVI - 0.964

R2 = 0.753

0.0

0.5

1.0

1.5

2.0

2.5

0.25 0.3 0.35 0.4 0.45 0.5 0.55

IRS-NDVI (Sept 4, 2007)

2007

Cotton biomass

WBMcotton = 71.18*(IRS TBVI32)3.96

R2 = 0.834

0

2

4

6

8

10

12

0.1 0.3 0.5 0.7IRS TBVI32

2006

2007

Cotton Leaf Area Index

LAIcotton = 10.37*(IRS TBVI31) 1.915

R2 = 0.725

0

1

2

3

4

5

0.1 0.2 0.3 0.4 0.5 0.6

IRS TBVI31

2006

2007

3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

(Cai et al., 2008; Cai et al., 2009)

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d. Small storages spatial hydrological modeling

Tuanlin

Irrigation canal

Average connection : 4.75Number: 2795 Capacity (million m3 ): 4.47

3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

Pond ID return ratio

Pond efficiency

% % 1 26.2 54.42 16.7 50.53 13.0 32.0

Average 18.6 45.6

(Cai et al., 2007; Roost et al., 2008a)

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year rainfall Canal supply

dainage inflow

GW inflow

paddy irrigaiton

local storage supply

drainage outflow

field percolation

ET

yieldpaddy forest/

upland other

2001 274.1 220 0 95 244.9 174 255 63.5 449.8 88.2 70 6645

2002 382.5 29 0 4 181.6 181 146 67.9 444.3 82.7 71 6924

2003 480.5 33 0 16 128.5 128 281 74.7 409.6 78.4 74 7274

2004 627.8 51 0 30 78.9 85 379 93.9 377.5 71.5 87 7942

IU2Rainfall (mm)

Cana

l sup

ply

(mill

ion

m3 )

3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level

(Cai, 2007; Roost et al., 2008b)

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4. Moving to landscape agro-hydrological modeling

• The need to satisfy multiple uses;• The need to conjunctively manage all water resources:

surface water, groundwater, and soil water;• Multi-systems upstream – downstream water demand –

supply analysis;• Agrohydrology for equal water uses and the poorest;• Improving water productivity needs to manage externalities.

Photo Credit: Xueliang Cai

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Necessary tradeoff between explicit agro-hydrological modeling and the need of understanding on intervention – water – agriculture.

Opportunities from RS/GIS:

• Land use / land cover and the dynamics• Evapotranspiration• Water resources (rainfall, surface storages, soil moisture, groundwater);• Crop biophysical parameters• Spatial hydrological modeling• Performance assessment and causes analysis.

4. Moving to landscape agro-hydrological modeling

key issue:

Global productsNew sensor New computational power

Distributed model RS/GIS

Validation

Extrapolation

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Outflow from the system: 12% Irrigation water efficiency reported by ZIS: 42%

Percentages of evapotranspiration and outflow to gross inflow:

4. Moving to landscape agro-hydrological modeling

(Cai, 2007)

• Water accounting, strengthened by remote sensing, avoids uncertainties from process based modeling while providing useful information

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Day of year

8-day ET (mm)

Basin average

Limpopo Province - Rainfed

Downstream - XaiXai

ETa/ETp

Rainfall Olifants - irrigated

4. Moving to landscape agro-hydrological modeling• Water accounting

Source: IWMI, 2009

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4. Moving to landscape agro-hydrological modeling• Performance assessment (Cai and Sharma, 2009; 2010)

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5. Conclusions

• Agro-hydrological modeling helps to analyze hydrological processes in agricultural areas and the consequences;

• Agro-hydrological modeling needs to scale up to basin extent;

• Water accounting and performance assessment helps to clear the big picture and reduce model uncertainties.

• RS* provides good opportunities for monitoring and modeling;

• Landscape modeling to include all water users of all water resources;

Photo Credit: Xueliang Cai*RS: Remote Sensing

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References• Cai, X.L., Sharma, B.R., 2010. Integrating remote sensing, census and weather data for an assessment of rice yield, water

consumption and water productivity in the Indo-Gangetic river basin. Agricultural Water Management, 97(2): 309-316.• Cai, X.L., Thenkabail, P.S., Biradar, C., Platonov, A., Gumma, M., Dheeravath, V., Cohen, Y., Goldshlager, N., Eyal Ben-Dor,

Victor Alchanatis, Vithanage, J.V., Markandu, A., 2009. Water productivity mapping using remote sensing data of various resolutions to support “more crop per drop”. Journal of Applied Remote Sensing, 3, 033557.

• Cai, Xueliang, Sharma, Bharat, 2009. Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic Basin. Science in China Series E: Technological Sciences. 52(11): 3300-3308.

• Cai Xue-liang, Cui Yuan-lai, 2009a. A simplified ET mapping algorithm and the application in Zhanghe Irrigation District. Journal of Irrigation and Drainage. 28(2): 51-54. (In Chinese with English abstract)

• Cai Xue-liang, Cui Yuan-lai, 2009b. Cropping patterns extraction using multi-sensor and multi-temporal remotely sensed data. Transactions of the Chinese Society of Agricultural Engineering, 25(8): 124-130. (In Chinese with English abstract)

• Roost, N., X.L., Cai, Turral, H., D. Molden, YL. Cui. 2008. An assessment of distributed, small-scale storage in the Zhanghe Irrigation System, China. Part I: Storage capacities and basic hydrological properties. Agricultural Water Management (ISI). 95: 698-706

• Roost, N., X.L., Cai, Turral, H., D. Molden, YL. Cui. 2008. An assessment of distributed, small-scale storage in the Zhanghe Irrigation System, China. Part II: Impacts on the system water balance and productivity. Agricultural Water Management. 95: 685-697

• Cai, X.L., Thenkabail, P.S., Platonov, A., 2008. Biophysical and yield modeling for benchmarking cotton water use and productivity using very high resolution satellite sensor data. Paper published at proceedings of Asia conference of remote sensing 2008, November 11-14, 2008, Colombo, Sri Lanka.

• CAI Xue-liang, CUI Yuan-lai, DAI Jun-feng, 2007. Small Storage Based Return Flows Estimation and Evaluation in Melon-on-the-Vine Irrigation System. Journal of Wuhan University (Engineering edition), 40(2): 46-50. (In Chinese with English abstract)

• Cai Xueliang, 2007. Strategy analysis on integrated irrigation water management using agro-hydrological model and RS/GIS. PhD thesis. Wuhan University. China.

• CAI Xue-liang, CUI YL, SONG Zq, WANG Lx, WU L. 2003. Study on Real-time Irrigation Forecasting in Doushan Irrigation Scheme, Journal of Irrigation and Drainage, Vol.22, No.3, 33-36 (In Chinese with English abstract)

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Thank you!

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