Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe

Princeton University Knowledge of the Hydrological Cycle Knowledge of water balances and their response to climate variations at different scales are of critical importance: Drought and flood prediction Future climate states Water resource management Determining trends and spatial patterns in the terrestrial water cycle are hampered by our inability to close the water balance at any scale.

Princeton University Importance of Evapotranspiration Evapotranspiration (ET) provides the link between the energy and water budgets at the land surface. Developing a globally robust algorithm for the prediction of surface energy fluxes is a significant challenge The purpose of this analysis was to evaluate the adaptability of the SEBS model to different climatic conditions and land cover classifications – using both tower based and remote sensing data Also, what is the potential for using operational products in achieving routine prediction of evapotranspiration

Princeton University NASA MOD-16 Evapotranspiration  Princeton University funded to research a MODIS based ET product (July, 2004)  Based primarily on the SEBS model, although other approaches are being explored – (can one model work in all environments/all conditions)  Global product – but locally validated – hence need for thorough evaluation – CEOP sites!!!  Princeton is keen to partner with other groups to investigate the best means of forwarding the planned MODIS product – model intercomparison, field experiments etc…

Princeton University SEBS Model Description CEOP observations used to assess ET predictions Forcing data from validation tower sites supplemented with MODIS data to produce estimates of surface fluxes.

Princeton University Use the Surface Energy Balance Model (SEBS) to determine daily/10-daily ET predictions (limited by surface temperature). SEBS Model Description Components of the radiation balance are used to determine the net radiation (R n ) – SW , α, ε, T s, LW  R n – G = H + LE Rn = (1- α) SW  + ε LW  - εσ The ground heat flux (G) is parameterized as a function of fractional cover – LAI/NDVI relationships

Princeton University SEBS Model Description Wind, air temperature, humidity (aerodynamic roughness, thermal dynamic roughness) SEBS calculates H using similarity theory: Various sub-modules for calculating needed components…

Princeton University Evaluating SEBS Model Predictions Issues of measurement accuracy, frequency, type… Intensive field experiments offer excellent detail, but are temporally limited Continuous measurements are usually spatially sparse… What is the best / most efficient combination of these. Global product – but locally validated Predictions are only as good as the evaluation data!!!

Princeton University Previous Investigations – SMACEX 02 Examining the spatial equivalence for corn and soybean 5 tower sites3 tower sites High resolution/quality data produces good quality estimates – examine model accuracy

Princeton University Previous Investigations – SMACEX 02 ~ 1020 m Ê = W/m 2 σ = 35.7 W/m 2 Ê = W/m 2 σ = W/m 2 ~ 90 m Ê = W/m 2 σ = 97.2 W/m 2 ~ 60 m

Princeton University Global Evaluation - CEOP Data Coordinated Enhanced Observation Period provides globally distributed data sets from which estimates of ET can be produced. Located over a variety of landscapes and hydro- climatologies they offer: Data to assess global scale application Allow comparison of different model output Offer a continuous source of data to examine seasonality

Princeton University ET Predictions with CEOP Tower Data CEOP-1 data extending from July 1 – September 30, 2001 Tower based results: estimated as daily averages, calculated between 5 a.m. and 6 p.m. from hourly observations. 6 sites were chosen – distributed across 5 countries and 3 continents Each represents a unique climate classification, allowing broad scale assessment of SEBS.

Princeton University CEOP Tower Data Grassland Cropland Old Aspen Forest Jack Pine Forest Rain Forest

Princeton University CEOP Tower Data Tower based results: ET estimates generally have RMS errors less than 50 W/m 2 – for grassland sites these approach W/m 2 Cropland site in Bondville exhibits most error – due to uncertainty in land surface classification (corn/soybean) Compared with SMACEX results – CEOP towers exhibit a greater degree of variability How accurate are in-situ measurements?

Princeton University MODIS Retrievals with CEOP Data Use MODIS based estimates of the surface temperature to predict ET How do predictions compare with in-situ observations? Does operational meteorology offer an alternative to tower based forcing? Examine grassland/cropland/forested sites Is data availability (LST) sufficient to offer routine prediction

Princeton University MODIS Retrievals

Princeton University MODIS Retrievals Satellite based results: For the 3 study sites, ET estimates had RMS errors of 60 W/m 2 for grassland and forested sites. Cropland site in Bondville significantly affected by uncertainty in land surface classification and parameterization – resulting in RMSE > 90 W/m 2 These errors were increased with operational forcings – although there are now improved products available Importance of identifying model sensitivities to input

Princeton University Problems and Questions There are major issues associated with predicting ET: o Temporal sampling – instantaneous / time averaged o Seasonality – intensive campaigns / continuous monitoring o Resolution – point / pixel scale disparity o Equivalence between measured / modeled variable o Validation / calibration / evaluation – different needs?? o How accurate do we need to be? Uncertainty analysis! o How well do we predict the other variables in the water balance – holistic or component modelling

Princeton University Problems and Questions There are major issues associated with product evaluation: How can we do this better??? Scintillometry, model comparison, multi-objective approaches (see whether predictions agree with other water balance components) Wealth of “pattern based” information in remote sensing data Can we use the data better??? Techniques used in rainfall analysis – statistical equivalence / organisation – scale decomposition – wavelet transformation. Ground based networks are not ‘truth’ – what is the ‘best’ estimate.

Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe

Princeton University Continental Studies

Princeton University Model Sensitivity