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Evapotranspiration and Land Data Assimilation Wade T. Crow USDA Hydrology and Remote Sensing Laboratory Beltsville MD NASA/USDA ET Workshop, Silver Spring, MD, April 5, 2011. Components of a Land Data Assimilation System. Enhanced Analysis. Continuous LSM. Forcing Data.
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Evapotranspiration and Land Data Assimilation Wade T. Crow USDA Hydrology and Remote Sensing Laboratory Beltsville MD NASA/USDA ET Workshop, Silver Spring, MD, April 5, 2011
Components of a Land Data Assimilation System Enhanced Analysis Continuous LSM Forcing Data Intermittent State Observations • Two potential roles for ET in a land data assimilation system: • ET as analysis: Improving LSM ET predictions through data assimilation. • 2)ET as observation: Improving other types of LSM predictions by relating ET from diagnostic models to LSM states (and assimilating).
ET as analysis: Improving LSM ET predictions through assimilation of land surface state retrievals (radiometric surface temperature or soil moisture). • Assimilation of surface temperature: • a) Into a simple force-restore land surface temperature model: • Caparrini et al., J. Hydrometeor. 5, 145-159, 2004. • b) Into fully-complex land surface and boundary-layer models: • Reichle et al. J. Hydrometeor., 11, 1103–1122, 2010. • Margulis and Entekhabi, Mon. Wea. Rev., 131, 1272–1288, 2003. • Surface temperature assimilation issues: • Provide ET continuous predictions, but have not been shown to outperform solely diagnostic ET models based on surface temperature (Crow and Kustas, Bound. Layer Meteor., 2005). • Typically require pre-processing to account for dirunal biases in LSM surface temperature predictions. • Two-source surface temperature issues are often addressed simplistically in LSM’s.
ET as analysis: Improving LSM ET predictions through assimilation of land surface state retrievals (radiometric surface temperature or soil moisture). Assimilation of soil moisture: a) Extensive literature in the past 10 years but very little of it has focused on subsequent ET improvements. b) Assimilating remotely-sensed soil moisture to parameterize water stress in a Penman-Monteith ET product (GLEAMS). Mirrales et al., HESS, in press, 2011. • Soil moisture assimilation issues: • Uncertainty in LSM soil moisture/ET coupling. • Most data assimilation strategies do not explicitly balance water. • Requires modified filters (e.g. Pan and Wood, J. Hydrometeor., 7, 534–547, 2007). • Limited value as a viable downscaling tool (LSM’s generally have little skill below about 1-km).
ET as observation: Improving all LSM predictions (not just ET) by assimilating ET estimates from diagnostic models. • a) Assimilation of ET retrievals into water balance models: • Schuurmans et al., Adv. Water Resour., 26,151–159, 2003 • Pipunic et al., Remote Sens. Environ., 112,1295–1305, 2008 • b) Key assumption - ET/PET (fPET) estimates can provide accurate soil moisture proxy information. • Hain et al., J. Hydrometeor., 10, 665-683, 2009. • Opens up the possibility to enhance LSM state predictions using diagnostic ET observations. For example (Hain et al., JGR, in submission; Hain, Ph.D. Thesis): • Assimilation of ALEXI fPET and AMSR-E surface soil moisture products (VUA LPRM) into the NOAH LSM. • Evaluate impact on NOAH root-zone soil moisture over CONUS using a precipitation data-denial strategy.
Root-zone Soil Moisture (40-100 cm) Time-Averaged RMSD Microwave-based soil moisture contributes in lightly-vegetated areas but thermal-based ET information contributes more in heavily vegetated regions….
