CSIRO LAND and WATER 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

CSIRO LAND and WATER Water losses and gains are part of the water cycle gain loss ET is important at all scales

CSIRO LAND and WATER Murrumbidgee System Water Account (1991)

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

CSIRO LAND and WATER Why determine spatial ET? l Classical methods will measure ET at the field scale. – Penman - Monteith (PM) method l 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

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

CSIRO LAND and WATER 1. Empirical direct methods -Characterizing crop water status through the cumulative temperature difference (T s -T a ) 2. Residual methods of the energy budget –Combination of empirical relationship and physical modules (SEBAL, & SEBS) 3. Deterministic methods –Soil-Vegetation-Atmosphere Transfer models (SVAT) 4. Vegetation Index methods Methods for Quantification of ET through RS

CSIRO LAND and WATER MethodsAdvantageDisadvantage Simplified Relationship Operational from local to regional scale Spatial variation of coefficients Inference models (Kc f(NDVI)) Operational if combined with ground measurements Requires calibration for each crop type; Kc varies according to water stress Empirical- physical (SEBAL,….) Operational, low cost, need no additional climatic data. Requires presence of wet and dry pixels. Some empirical relationship Deterministic (SVAT,….) Estimation of intermediate variables (LAI), links with climate, hydrological models, assimilation to find some parameters Requires more parameters ± easy to estimate. Requires accurate remote sensing data models (PBL, …) Estimation of climatic data, lateral exchange accounted, possible to stimulate landuse modification Complex and high cost for CPU, only short simulation for high spatial resolution

CSIRO LAND and WATER 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. SEBAL

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

CSIRO LAND and WATER The energy balance components Energy Balance Equation R n = G o + H + λE Evaporative Fraction Daily ETa Seasonal ETa SEBAL Derived Actual Evapotranspiration

CSIRO LAND and WATER ALBEDONDVI Surface Temperature Emissivity Albedo Pre-processing of satellite image

CSIRO LAND and WATER Study Area (Lower Murrumbidgee)

CSIRO LAND and WATER 24 October 1990 Land use Classification for Lower Murrumbidgee

CSIRO LAND and WATER Actual ET Distribution for Lower Murrumbidgee 24 October 1990

CSIRO LAND and WATER 24 October 1990 ET from different land use classes using Landsat 5 TM sensor

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

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

CSIRO LAND and WATER Working across different scales with universities and other partners as one CSIRO

CSIRO LAND and WATER Seasonal Evapotranspiration (ET seasonal ) l Step 1: Decide the length of the season l Step 2: Determine period represented by each satellite image l Step 3: Compute the cumulative ET r for period represented by image. l Step 4: Compute the cumulative ET for each period (n = length of period in days) l Step 5: Compute the seasonal ET. ET seasonal =  ET period