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Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems

Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems. 1- Key Research Issues 2- Evapotranspiration through Remote Sensing 3- SEBAL Applications 4- Data Requirements and Way Forward. Mohsin Hafeez and Shahbaz Khan CSIRO Land and Water, Wagga Wagga.

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Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems

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  1. Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues2- Evapotranspiration through Remote Sensing3- SEBAL Applications4- Data Requirements and Way Forward Mohsin Hafeez and Shahbaz Khan CSIRO Land and Water, Wagga Wagga

  2. Water losses and gains are part of the water cycle ET is important at all scales loss crop farm irrigation area gain catchment surface groundwater Basin

  3. Murrumbidgee System Water Account (1991)

  4. Key Research Issues • ET is coupled mass/energy process, linking the energy and water cycles • Estimation of ET is critical for on-farm and regional models in irrigation systems • ET is the largest water balance component after rainfall and irrigation input • Water quantification (i.e. productive and non-productive use) is important for irrigated agriculture.

  5. Why determine spatial ET? • Classical methods will measure ET at the field scale. • Penman - Monteith (PM) method • Need to have accurate estimates of spatially distributed ET at multi-scales. • Remote sensing provide spatially distributed actual evapotranspiration • Accurate and cheap for large landscape systems • Many RS algorithms developed in last decades

  6. Current state-of-the-art Approaches for measuring ET • In-situ measurement (Bowen ratio tower, Lysimeters, etc.) • Air-borne measurement (fluxes) • Satellite measurement • High Spatial Resolution (ASTER and Landsat) • High Temporal Resolution (MODIS and NOAA-AVHRR) • Modelling Approaches (plant to catchment)

  7. Methods for Quantification of ET through RS • Empirical direct methods • - Characterizing crop water status through the cumulative temperature difference (Ts-Ta) • Residual methods of the energy budget • Combination of empirical relationship and physical modules (SEBAL, & SEBS) • Deterministic methods • Soil-Vegetation-Atmosphere Transfer models (SVAT) • Vegetation Index methods

  8. SEBAL Surface Energy Balance Algorithm for Land (SEBAL); thermodynamically based model, which partitions between sensible heat flux and latent heat of vaporization flux. The core of SEBAL is based on the assumption that at hot/dry pixels, all energy flux into the atmosphere is sensible heat and at cool/wet pixels all is latent heat. SEBAL robustly interpolates values at intermediate pixels but is very sensitive to the right choice and flux values at the extremes.

  9. Surface Energy Balance R n ET = R - G - H n ET is calculated as a “residual” of the energy balance (radiation from sun and sky) ET H (heat to air) Basic Truth: Evaporation consumes Energy The energy balance includes all major sources (Rn) and consumers (ET, G, H) of energy G (heat to ground) Adapted from IDAHAO

  10. SEBAL Derived Actual Evapotranspiration Energy Balance Equation Rn = Go + H+ λE Evaporative Fraction Daily ETa Seasonal ETa The energy balance components

  11. Emissivity Surface Temperature ALBEDO NDVI Pre-processing of satellite image Albedo

  12. Study Area (Lower Murrumbidgee)

  13. Land use Classification for Lower Murrumbidgee 24 October 1990

  14. Actual ET Distribution for Lower Murrumbidgee 24 October 1990

  15. ET from different land use classes using Landsat 5 TM sensor 24 October 1990

  16. Data Requirements • Ground based – temporal variation: • Micro-meteorology and fluxes • Calibration data (soil temperature, LAI, NDVI, LST, albedo, and net radiation) • Vegetation description and surface roughness • Airborne based – spatial variation: • Surface conditions - soil moisture, LST, NDVI, LAI, albedo, • Surface fluxes • Low flying over irrigation supply channels • Satellite based - model requirements: • NDVI, LAI, LST, albedo, emissivity, net radiation, surface roughness • Other data (rainfall, soil moisture, fluxes….)

  17. Way Forward • Uncertainty analysis of different input parameters for remote sensing based ET models • Validation of remote sensing derived ET by ground and airborne fluxes. • Customization of remote sensing based algorithms for ET estimation for Australian landscape. • Integration of spatial estimation of seasonal ET for water balance studies using system level approach • Flexible for any irrigation system

  18. Working across different scales with universities and other partners as one CSIRO

  19. Seasonal Evapotranspiration (ETseasonal) • Step 1: Decide the length of the season • Step 2: Determine period represented by each satellite image • Step 3: Compute the cumulative ETr for period represented by image. • Step 4: Compute the cumulative ET for each period (n = length of period in days) • Step 5: Compute the seasonal ET. ETseasonal =  ETperiod

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