Evaporation from Nonvegetated Surfaces: Surface Aridity Methods and Passive Microwave Remote Sensing

A novel method is presented to analytically resolve the terrestrial latent heat flux (lE) and conductances (boundary layer g B and surface g S ) using net radiation (R N ), ground heat flux (G), air temperature (T a ), and relative humidity (RH). This method consists of set of equations where the two unknown internal state variables (g B and g S ) were expressed in terms of the known core variables, combining diffusion equations, the Penman-Monteith equation, the Priestley-Taylor equation, and Bouchet's complementary hypothesis. Estimated lE is validated with the independent eddy covariance lE observations over Soil Moisture Experiment 2002 (SMEX-02); the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) selected sites from FLUXNET and tropics eddy flux, representing four climate zones (tropics, subtropics, temperate, and cold); and multiple biomes. The authors find a RMSE of 23.8-54.6 W m 22 for hourly lE over SMEX-02 and GCIP and 23.8-29.0 W m 22 for monthly lE over the FLUXNET and tropics. Observational and modeled evidence in the reduction in annual evaporation (E) pattern on the order of 33% from 1999 to 2006 was found in central Amazonia. Retrieved g S responded to vapor pressure deficit, measured lE, and gross photosynthesis in a theoretically robust behavior. However, the current scheme [Penman-Monteith-Bouchet-Lhomme (PMBL)] showed some overestimation of lE in limited soil moisture regimes. PMBL provides similar results when compared with another Priestley-Taylor-based lE estimation approach [Priestley-Taylor-Jet Propulsion Laboratory (PT-JPL)] but with the advantage of having the conductances analytically recovered.

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“…Equation (6) resembles the a-b formulations of lE (Noilhan and Planton 1989;Lee and Pielke 1992;Cahill et al 1999). From Eqs.…”

Section: A Expression For G Bmentioning

confidence: 99%

Latent Heat Flux and Canopy Conductance Based on Penman–Monteith, Priestley–Taylor Equation, and Bouchet’s Complementary Hypothesis

Mallick

Jarvis

Fisher

et al. 2013

Journal of Hydrometeorology

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“…[] and Cahill et al . []. The form of α , β , r ss or LE max is obtained either physically or empirically.…”

Section: Introductionmentioning

confidence: 99%

“…Although many different formulations have been developed since the 60's, there is still no consensus on the best way to parameterize evaporation over large areas [ Desborough et al ., ; Sakaguchi and Zeng , ]. Nevertheless the literature has indicated that (1) existing θ ‐based formulations differ in four main aspects: the θ lower and upper threshold values, the nonlinearity of the relationship between evaporation and θ , the required input data other than θ , and the sensing depth of θ data, (2) simple empirical expressions may provide better evaporation simulations than physically derived formulations [ Dekic et al ., ; Mihailovic et al ., ; Yang et al ., ], (3) the β formulation seems to be more robust than the α one [ Cahill et al ., ; Van den Hurk et al ., ], and (4) very little work has been done to evaluate the above formulations with observations over a range of soil and atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach

Merlin

Stefan

Amazirh

et al. 2016

Water Resources Research

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International audienceA meta-analysis data-driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02-0.56, a sand fraction range of 0.05-0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance ($r_{ss}$) formulation based on surface soil moisture ($\theta$) and two resistance parameters $r_{ss,ref}$ and $\theta_{efolding}$. The data-driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut-off soil moisture value $\theta_{1/2}$ at which SEE=0.5, and first derivative of SEE at $\theta_{1/2}$, named $\Delta\theta_{1/2}^{-1}$. An analytical relationship between $(r_{ss,ref};\theta_{efolding})$ and $(\theta_{1/2};\Delta\theta_{1/2}^{-1})$ is first built by running a soil energy balance model for two extreme conditions with $r_{ss} = 0$ and $r_{ss}\sim\infty$ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair $(\theta_{1/2} ; \Delta\theta_{1/2}^{-1})$ either from the time series of SEE and $\theta$ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear $\text{SEE}(\theta)$ relationships and potentially large random deviations of observed SEE from the mean observed $\text{SEE}(\theta)$. The second method parameterizes $\theta_{1/2}$ as a multi-linear regression of clay and sand percentages, and sets $\Delta\theta_{1/2}^{-1}$ to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol-Biosph\`{e}re-Atmosph\`{e}re), H-TESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land-surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data

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“…The first part is straightforward and intuitive; that it occurs in nature is indisputable. The second part, the increase of evaporation following a soil wetting, is also intuitive and is directly supported by various local evaporation meaurements [e.g., Cahill et al , 1999]. It is indirectly supported by the presence of negative precipitation‐temperature correlations that span much of the United States [ Huang and Van den Dool , 1993], the argument being that wet soil induced by high precipitation leads to a higher surface latent heat flux (evapotranspiration) at the expense of the surface sensible heat flux.…”

Section: Introductionmentioning

confidence: 99%

Observational evidence that soil moisture variations affect precipitation

Koster

Suárez

Higgins

et al. 2003

Geophysical Research Letters

238

205

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Land‐atmosphere feedback, by which precipitation‐induced soil moisture anomalies affect subsequent precipitation, may be an important element of Earth's climate system, but its very existence has never been demonstrated conclusively at regional to continental scales. Evidence for the feedback is sought in a 50‐yearobservational precipitation dataset covering the United States. The precipitation variance and autocorrelation fields are characterized by features that agree (in structure, though not in magnitude) with those produced by an atmospheric general circulation model (AGCM). Because the model‐generated features are known to result from land‐atmosphere feedback alone, the observed features are suggestive of the existence of feedback in nature.

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Evaporation from Nonvegetated Surfaces: Surface Aridity Methods and Passive Microwave Remote Sensing

Cited by 20 publications

References 18 publications

Latent Heat Flux and Canopy Conductance Based on Penman–Monteith, Priestley–Taylor Equation, and Bouchet’s Complementary Hypothesis

Latent Heat Flux and Canopy Conductance Based on Penman–Monteith, Priestley–Taylor Equation, and Bouchet’s Complementary Hypothesis

Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach

Observational evidence that soil moisture variations affect precipitation

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