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

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

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

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