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Landscape agro-hydrological modeling: opportunities from remote sensing

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

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

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  • Forecast: weather, crop, soil, water
  • SEBAL complicated Clarification and validation impossible without remote sensing.
  • Conditions monitoring and yield forecast Spatial details
  • Only about two key messages: in a low inflow situation, storage provides supply by effective reuses
  • Not only about crops but also livestock, fisheries, agroforestry. And reduce vulnerability to risks. Surface water includes river, reservior, and farm ponds
  • Model: processes and senario analysis Remote sensing: calibration and validation, spatial extroplation
  • Transcript

    • 1. Landscape agro-hydrological modeling: opportunities from remote sensing Xueliang Cai (IWMI) 19 April, 2010, IWMI Seminar, Battaramulla, Sri Lanka
    • 2. Outline
      • Background – agrohydrology
      • Agro-hydrological modeling at field scale
      • Integrate agro-hydrological modeling with remote sensing at irrigation scheme level
      • Moving to landscape agro-hydrological modeling
      • Conclusions
      Photo Credit: Xueliang Cai
    • 3. 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
    • 4. 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
    • 5.
      • 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
    • 6. 2. Agro-hydrological modeling at field scale
      • Water cycling processes limited to field level
      Key limitations:
      • Crop growth conditions vary
      Source: Xueliang Cai (IWMI), 2007 LAI Biomass Yield ET The physical world is different from one point to another
    • 7. 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level
        • Yangtze river
      Zhanghe irrigation system (ZIS) : Layout in OASIS Source: LANDSAT
        • Hilly to plain landscape
        • “ Melon-on-the-vine” irrigation system
        • Paddy rice – rapeseed rotation
        • Rainfall ≈ 1000 mm
      Legend Irri. Canal Drain. Canal Irri. Unit Reservoir Diversion Drainage
    • 8. 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level a. Land cover and crop dynamics 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: 16,000 Remote sensing: 21,000 RS provides objective and accurate information on area and distribution “ ” “ ” NDVI (Cai and Cui, 2009b) Single crop (cotton, wheat) Water Settlement Forest Rice – wheat Rice – rapeseed Double crop Legend
    • 9. 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level b. ET estimate (Cai and Cui, 2009a) SSEB: Land use map ET a map from RS IU boundary ET values of each land use in each simulation unit Model calibration
    • 10. 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level c. Crop growth conditions and yields (1) In ZIS, Doorenbos – Kasam model: (2) In Central Asia, biophysical modeling: (Cai et al., 2008; Cai et al., 2009) Cotton yield Yield cotton = 5.156*IRS NDVI - 0.964 R 2 = 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 WBM cotton = 71.18*(IRS TBVI32) 3.96 R 2 = 0.834 0 2 4 6 8 10 12 0.1 0.3 0.5 0.7 IRS TBVI32 2006 2007 Cotton Leaf Area Index LAI cotton = 10.37*(IRS TBVI31) 1.915 R 2 = 0.725 0 1 2 3 4 5 0.1 0.2 0.3 0.4 0.5 0.6 IRS TBVI31 2006 2007
    • 11. 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level d. Small storages spatial hydrological modeling Irrigation canal Average connection : 4.75 Number: 2795 Capacity (million m 3 ): 4.47 (Cai et al., 2007; Roost et al., 2008a) Tuanlin Pond ID return ratio Pond efficiency   % % 1 26.2 54.4 2 16.7 50.5 3 13.0 32.0 Average 18.6 45.6
    • 12. IU2 Rainfall (mm) Canal supply (million m 3 ) 3. Integrate agro-hydrological modeling and remote sensing at irrigation scheme level (Cai, 2007; Roost et al., 2008b) year rainfall Canal supply dainage inflow GW inflow paddy irrigaiton local storage supply drainage outflow field percolation ET yield paddy 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
    • 13. 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
    • 14.
      • 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.
      Necessary tradeoff between explicit agro-hydrological modeling and the need of understanding on intervention – water – agriculture. Opportunities from RS/GIS: 4. Moving to landscape agro-hydrological modeling key issue: Global products New sensor New computational power Distributed model RS/GIS Validation Extrapolation
    • 15. 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
    • 16.
      • Water accounting
      Day of year 8-day ET (mm) Basin average Limpopo Province - Rainfed Downstream - XaiXai ET a /ET p Rainfall Olifants - irrigated 4. Moving to landscape agro-hydrological modeling Source: IWMI, 2009
    • 17.
      • Performance assessment
      4. Moving to landscape agro-hydrological modeling (Cai and Sharma, 2009; 2010)
    • 18. 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
    • 19. 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)
    • 20. Thank you! www.iwmi.org