Landscape agro-hydrological modeling: opportunities from remote sensing
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Transcript of 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
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
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
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
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
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
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
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)
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
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)
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)
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)
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
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
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
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
4. Moving to landscape agro-hydrological modeling• Performance assessment (Cai and Sharma, 2009; 2010)
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
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)
Thank you!
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