Princeton Princeton UniversityUniversity

Global Evaluation of a MODIS based Evapotranspiration Product

Eric Wood Hongbo SuMatthew McCabe

Princeton Princeton UniversityUniversity

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 arehampered by our inability to close the water balance at any scale.

Princeton Princeton UniversityUniversity

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 Princeton UniversityUniversity

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 Princeton UniversityUniversity

SEBS Model Description

CEOP observations used to assess ET predictionsForcing data from validation tower sites supplemented with

MODIS data to produce estimates of surface fluxes.

Princeton Princeton UniversityUniversity

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 (Rn) – SW , α, ε, Ts, LW

Rn – G = H + LE

Rn = (1- α) SW + ε LW - εσ 4sT

The ground heat flux (G) is parameterized as a function of fractional cover – LAI/NDVI relationships

Princeton Princeton UniversityUniversity

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 Princeton UniversityUniversity

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 Princeton UniversityUniversity

Previous Investigations – SMACEX 02

Examining the spatial equivalence for corn and soybean

5 tower sites 3 tower sites

High resolution/quality data produces good quality estimates – examine model accuracy

Princeton Princeton UniversityUniversity

Previous Investigations – SMACEX 02

~ 1020 m Ê = 380.0 W/m2

σ = 35.7 W/m2

Ê = 392.3 W/m2

σ = 105.3 W/m2

~ 90 m

Ê = 367.5 W/m2

σ = 97.2 W/m2

~ 60 m

Princeton Princeton UniversityUniversity

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 Princeton UniversityUniversity

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 Princeton UniversityUniversity

CEOP Tower DataGrassland Grassland Cropland

Old Aspen Forest Jack Pine Forest Rain Forest

Princeton Princeton UniversityUniversity

CEOP Tower Data

Tower based results:

• ET estimates generally have RMS errors less than 50 W/m2 – for grassland sites these approach 20-30 W/m2

• 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 Princeton UniversityUniversity

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 Princeton UniversityUniversity

MODIS Retrievals

Princeton Princeton UniversityUniversity

MODIS Retrievals

Satellite based results:

• For the 3 study sites, ET estimates had RMS errors of 60 W/m2 for grassland and forested sites.

• Cropland site in Bondville significantly affected by uncertainty in land surface classification and parameterization – resulting in RMSE > 90 W/m2

• These errors were increased with operational forcings – although there are now improved products available

• Importance of identifying model sensitivities to input

Princeton Princeton UniversityUniversity

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 Princeton UniversityUniversity

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 Princeton UniversityUniversity

Global Evaluation of a MODIS based Evapotranspiration Product

Eric Wood Hongbo SuMatthew McCabe

Princeton Princeton UniversityUniversity

Continental Studies

Princeton Princeton UniversityUniversity

Model Sensitivity