The Priestley-Taylor parameter α for the Amazon forest

Viswanadham, Y.; Filho, Virginio; André, Romísio Geraldo Bouhid

doi:10.1016/0378-1127(91)90143-j

Cited by 38 publications

(24 citation statements)

References 28 publications

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“…Figure 9 summarizes the comparison between selected potential ET and ET a * for the three validated surface types (needleleaf forest, broadleaf forest, cropland). The Priestley and Taylor model (equation (4)) has been applied successfully in the past at hourly time-scales (De Bruin and Keijman 1979, Stannard 1993, Kim and Entekhabi 1997) over various land types (McNaughton and Black 1973, Thompson 1975, Jarvis and McNaughton 1986, Singh and Tailefer 1986, Viswanadham et al 1991, Stannard 1993, Kim and Entekhabi 1997. This model was used here in conjunction with the reference a values obtained through actual measurements by Choudhury et al (1994).…”

Section: Selection Of the Validation Dataset And Comparison With Potementioning

confidence: 99%

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

Han

Viau

Anctil

2010

International Journal of Remote Sensing

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In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semiempirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sunsynchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R 2 ¼ 0.88; root mean square (rms) error ¼ 2.21 C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed -a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various landcover types. The standard error of this coefficient estimate was 0.002 mm h -1 K -1 for both time periods. A strong correlation (R 2 . 0.87; rms. error ,0.17 mm h -1 ) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.

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Section: Selection Of the Validation Dataset And Comparison With Potementioning

confidence: 99%

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

Han

Viau

Anctil

2010

International Journal of Remote Sensing

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“…However, empirical studies have highlighted the more conservative nature of tree stomata, often resulting in lower rates of potential evaporation in forested areas (Shuttleworth and Calder, 1979;Kelliher et al, 1993;Teuling et al, 2010). Therefore, the α for tall vegetation is defined following the findings by McNaughton and Black (1973), Shuttleworth et al (1984), Viswanadham et al (1991), Diawara et al (1991), and Eaton et al (2001), which report an average value of 0.97 (with a 0.08 standard deviation over the different studies) for various forests during unstressed and precipitation-free periods (i.e. no rainfall interception).…”

Section: Rainfall Interception Modelmentioning

confidence: 99%

GLEAM v3: satellite-based land evaporation and root-zone soil moisture

et al. 2017

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Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented.

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“…This value is lower than that (1.26), suggested by Priestley and Taylor (1972), obtained for conditions of minimum advection and large saturated surface. However, in the tropical forest of Manaus in the Brazilian state of Amazonas, Viswanadham et al (1991) found that ␣ e varied from 0.6 to 3.12, and, over a transitional forest in the Brazilian state of Mato Grosso, Vourlitis et al (2002) found ␣ e values between 0.57 and 1.07.…”

Section: ϫ2mentioning

confidence: 99%

Control of Dry Season Evapotranspiration over the Amazonian Forest as Inferred from Observations at a Southern Amazon Forest Site

et al. 2007

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The extent to which soil water storage can support an average dry season evapotranspiration (ET) is investigated using observations from the Rebio Jarú site for the period of 2000 to 2002. During the dry season, when total rainfall is less than 100 mm, the soil moisture storage available to root uptake in the top 3-m layer is sufficient to maintain the ET rate, which is equal to or higher than that in the wet season. With a normal or less-than-normal dry season rainfall, more than 75% of the ET is supplied by soil water below 1 m, whereas during a rainier dry season, about 50% of ET is provided by soil water from below 1 m. Soil moisture below 1-m depth is recharged by rainfall during the previous wet season: dry season rainfall rarely infiltrates to this depth. These results suggest that, even near the southern edge of the Amazon forest, seasonal and moderate interannual rainfall deficits can be mitigated by an increase in root uptake from deeper soil.How dry season ET varies geographically within the Amazon and what might control its geographic distribution are examined by comparing in situ observations from 10 sites from different areas of Amazonia reported during the last two decades. Results show that the average dry season ET varies less than 1 mm day Ϫ1 or 30% from the driest to nearly the wettest parts of Amazonia and is largely correlated with the change of surface net radiation of 25% and 30%. Thus the geographic variation of the average dry season ET appears to be mainly determined by the surface radiation.

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The Priestley-Taylor parameter α for the Amazon forest

Cited by 38 publications

References 28 publications

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

GLEAM v3: satellite-based land evaporation and root-zone soil moisture

Control of Dry Season Evapotranspiration over the Amazonian Forest as Inferred from Observations at a Southern Amazon Forest Site

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