princeton university global evaluation of a modis based evapotranspiration product eric wood hongbo...
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![Page 1: Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d485503460f94a23384/html5/thumbnails/1.jpg)
Princeton Princeton UniversityUniversity
Global Evaluation of a MODIS based Evapotranspiration Product
Eric Wood Hongbo SuMatthew McCabe
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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.
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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
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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…
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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.
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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
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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…
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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!!!
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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
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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
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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
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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.
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Princeton Princeton UniversityUniversity
CEOP Tower DataGrassland Grassland Cropland
Old Aspen Forest Jack Pine Forest Rain Forest
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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?
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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
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Princeton Princeton UniversityUniversity
MODIS Retrievals
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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
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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
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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.
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Princeton Princeton UniversityUniversity
Global Evaluation of a MODIS based Evapotranspiration Product
Eric Wood Hongbo SuMatthew McCabe
![Page 21: Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe](https://reader036.vdocuments.site/reader036/viewer/2022062421/56649d485503460f94a23384/html5/thumbnails/21.jpg)
Princeton Princeton UniversityUniversity
Continental Studies
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Princeton Princeton UniversityUniversity
Model Sensitivity