[1] This review surveys the basic theories, observational methods, satellite algorithms, and land surface models for terrestrial evapotranspiration, E (or λE, i.e., latent heat flux), including a long-term variability and trends perspective. The basic theories used to estimate E are the Monin-Obukhov similarity theory (MOST), the Bowen ratio method, and the Penman-Monteith equation. The latter two theoretical expressions combine MOST with surface energy balance. Estimates of E can differ substantially between these three approaches because of their use of different input data. Surface and satellite-based measurement systems can provide accurate estimates of diurnal, daily, and annual variability of E. But their estimation of longer time variability is largely not established. A reasonable estimate of E as a global mean can be obtained from a surface water budget method, but its regional distribution is still rather uncertain. Current land surface models provide widely different ratios of the transpiration by vegetation to total E. This source of uncertainty therefore limits the capability of models to provide the sensitivities of E to precipitation deficits and land cover change.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2011RG000373

A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability

A Review of Global Terrestrial Evapotranspiration: Observation, Modeling, Climatology, and Climatic Variability

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

/pdf/generalized-ocean-color-inversion-model-for-retrieving-4v1p2kp2s1.pdf

Generalized ocean color inversion model for retrieving marine inherent optical properties

Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China

A theoretical model was developed estimating the scattering by seawater that are due to concentration fluctuation. Combining with the model proposed for density fluctuation (Optics Express, 17, 1671, 2009), we evaluated the overall effect of sea salts on the scattering. The variation of seawater scattering with the salinity is a combination of two factors: decreasing contribution due to density fluctuation and increasing contribution due to concentration fluctuation, with the latter effect dominating. The trend is, however, slightly non-linear and the linear adjustment of scattering with salinity that is frequently used would lead to an underestimate by an average of 2%. The results estimated at S = 38.4‰ agree with the measurements by Morel (Cahiers Oceanographiques, 20, 157, 1968) with an average difference of 1%, well within his experimental error of 2%. The spectral signature also varies with salinity, with the power-law slope increasing from -4.286 to -4.306 for salinity from 0 to 40‰.

Scattering by pure seawater: Effect of salinity

Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea

The use of density derivative of the refractive index from the classic Lorentz-Lorenz equation or its variations performed poorly in estimating the scattering by water, leading to the alternative use of pressure derivative instead, which however has been scarcely measured due to its extremely low sensitivity Recently, density derivative has been deduced directly from theoretical models Three characterizations of density derivative of the refractive index were evaluated and scattering of water thus calculated converge with each other within 35% and agree with the measurement by Morel (Cahiers Oceanographiques, 20, 157, 1968) within 2% (with depolarization ratio = 0039), all improving over the earlier estimates based on either density or pressure derivatives Taking into account of uncertainty associated with the depolarization ratio, the prediction based on the model by Proutiere et al (J Phy Chem, 96, 3485, 1992) still agrees with the measurement within the experimental errors (2%)

Estimating scattering of pure water from density fluctuation of the refractive index.

A new model for seawater scattering was developed, in which Gibbs function was used exclusively to derive the thermodynamic parameters that are associated with density fluctuation. Because Gibbs function was determined empirically from highly accurate measurements of a group of thermodynamic variables and is valid for S(A) up to 120 g kg(-1) (Deep-Sea Research I, 55, 1639, 2008), we expect the model is also valid over the extended range of salinity. The model agrees with the measurements by Morel (Cahiers Oceanographiques, 20, 157, 1968) with an average difference of -0.6% for S = 0 and 2.7% for S = 38.4. The scattering by seawater as predicted increases with salinity in a non-linear fashion, and linear extrapolation of scattering based on Morel's measurements would overestimate by up to 30%. The extrapolation of ZHH09 model (Optics Express, 17, 5698, 2009), which is valid for S(A) up to 40 g kg(-1), however, agrees with the prediction within +/- 2.5% over the entire range of salinity. Even though there are no measurements available for validation, the results suggested that the uncertainty is limited in using the newly developed model in estimating the scattering by seawater of high salinity.

Lianbo Hu

Papers

Scattering by pure seawater: Effect of salinity

Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea

Estimating scattering of pure water from density fluctuation of the refractive index.

Scattering by pure seawater at high salinity

On the remote estimation of Ulva prolifera areal coverage and biomass