Estimation of Daytime Net Radiation Over Well‐Watered Grass

Dong, Allen; Grattan, S. R.; Carroll, John J.; Prashar, C. R. K.

doi:10.1061/(asce)0733-9437(1992)118:3(466)

Cited by 51 publications

(40 citation statements)

References 7 publications

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“…Statistics of total population by county for 2002 were obtained from the U.S. Federal Census [see ''2002 Data Profiles'' on the U.S. Census Bureau Web site (http://www.census.gov/acs/www/ Products/index.htm)]. These data were used to classify the temperature records as coming from either urban or rural stations in order to minimize biases introduced by urbanization (Goodridge 1992 Dong et al 1992;Monteith 1973]. Net radiation is then R n hour converted from units of watts per meter squared to millimeters per hour using the latent heat of vaporization according to Eq.…”

Section: Data a Meteorological Recordsmentioning

confidence: 99%

“…(10). The CIMIS version of the Penman hourly evapotranspiration (ETo hour ) equation is (Penman 1948;Pruitt and Doorenbos 1977;Dong et al 1992;Satterlund 1979;Monteith 1973; CIMIS Web site) …”

Section: Data a Meteorological Recordsmentioning

confidence: 99%

“…(9) and (11)-(16) can be found in Dong et al (1992). In these equations, ␣ is the surface albedo over well-watered Potential evapotranspiration ETo formulations based on the Penman equation sometimes consider a measured or estimated ground heat flux (G), which is subtracted from R n , in order to account for heat transference at the air-soil interface (Monteith 1965).…”

Section: Data a Meteorological Recordsmentioning

confidence: 99%

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Sources of Variability of Evapotranspiration in California

Hidalgo

Cayan

Dettinger

2005

Journal of Hydrometeorology

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The variability (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002) of potential evapotranspiration estimates (ETo) and related meteorological variables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data from the National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were used to validate the results. The objective is to determine the characteristics of climatological ETo and to identify factors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strongly correlated with net radiation (R n ) anomalies, relative humidity (RH), and cloud cover, and less with average daily temperature (T avg ). The highest intraseasonal variability of ETo daily anomalies occurs during the spring, mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulation pattern is associated with anomalies of ETo and its driving meteorological inputs, R n , RH, and T avg , at daily to seasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z 700 ) anomalies over a region off the west coast of North America, approximately between 32Њ and 44Њ latitude, referred to as the California Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the lowheight (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skies maintained through anomalously high Z 700 anomalies offshore of the North American coast. Spring CPA, cloudiness, maximum temperature (T max ), pan evaporation (E pan ), and ETo conditions have not trended significantly or consistently during the second half of the twentieth century in California. Because it is not known how cloud cover and humidity will respond to climate change, the response of ETo in California to increased greenhousegas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a decline of R n by about 6% would be required to compensate for a warming of ϩ3ЊC.

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Section: Data a Meteorological Recordsmentioning

confidence: 99%

Section: Data a Meteorological Recordsmentioning

confidence: 99%

Section: Data a Meteorological Recordsmentioning

confidence: 99%

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Sources of Variability of Evapotranspiration in California

Hidalgo

Cayan

Dettinger

2005

Journal of Hydrometeorology

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“…The 0 to 100 W m −2 R s range, on the other hand, usually occurs during early morning and late afternoon (assuming clear-sky conditions), at times when the solar energy hits the surface at very low solar altitude angles. Under these conditions, surface albedo is high (Dong et al, 1992) and a large portion of R s is consequently reflected from the surface, having a reduced impact on the energy balance of the surface.…”

Section: Effect Of Solar Radiationmentioning

confidence: 99%

“…These factors were not included in the analysis because they are difficult to obtain since they are not commonly measured. However, it is recognized that they change during the day and during the growing season (Payero et al, 2003;Dong et al, 1992) and could affect (T c − T a ).…”

Section: Effect Of Other Variablesmentioning

confidence: 99%

Non-Water-Stressed Baselines for Calculating Crop Water Stress Index (Cwsi) for Alfalfa and Tall Fescue Grass

Payero¹,

Neale²,

Wright³

2005

Transactions of the ASAE

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ABSTRACT. The lack of transferability of the Cropanopy temperature measured with infrared thermometers is often promoted as a basis for irrigation scheduling (Geiser et al., 1982;Stanghellini and Lorenzi, 1994;Wanjura and Upchurch, 1996;Wanjura and Upchurch, 2000;Wanjura et al., 2003;Bockhold et al., 2003;Colaizzi et al., 2003). Although this technology has a long history of development, it is yet to be adopted by farmers to schedule irrigations. Wolpert (1962) was among the first to study the factors affecting canopy temperature, using a theoretical mathematical representation of all the variables important to the heat balance of a plant leaf. Gates (1964) and Linacre (1964) recognized that transpiration was an important factor controlling leaf temperature, as it acts as a cooling mechanism. At that time, canopy temperature was measured using thermocouples embedded in the leaves, which was not very practical for general use. The use of infrared thermometers to measure canopy temperature, however, was becoming feasible (Conaway and van Bavel, 1967;Fuchs and Tanner, 1966). Carlson et al. (1972) recognized that canopy temperature provided a measure of the plant response to its environment and suggested that the factors affecting canopy temperature were the same ones affecting evapotranspiration. These factors included wind speed, solar radiation, air temperature, vapor pressure deficit, and soil moisture.Linking canopy temperature to soil moisture was particularly important since the potential of using canopy temperature as an indicator of crop water stress and as a tool for irrigation scheduling was then recognized. The basic assumption was that transpiration cools the leaves and as available soil moisture decreases, transpiration is reduced and, therefore, the temperature of the leaves increases. Considerable research followed, trying to use canopy temperature as a tool for irrigation scheduling, and many indexes were developed to relate canopy temperature to crop water stress Jackson et al., 1977;Blad et al., 1981). Ehrler (1973) made the seminal observation that canopy minus air temperature (T c − T a ) was lineally related to air vapor pressure deficit (VPD) and Ehrler et al. (1978) demonstrated that (T c − T a ) was a reliable indicator of plant water stress by relating it to measured plant water potential. Idso et al. (1981) realized that lower and upper baselines could be established empirically for both non-water-stressed and for non-transpiring crop conditions, respectively. They used these baselines to calculate what they called the Crop C

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References

2012

Unsaturated Soil Mechanics in Engineering Practice

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Estimation of Daytime Net Radiation Over Well‐Watered Grass

Cited by 51 publications

References 7 publications

Sources of Variability of Evapotranspiration in California

Sources of Variability of Evapotranspiration in California

Non-Water-Stressed Baselines for Calculating Crop Water Stress Index (Cwsi) for Alfalfa and Tall Fescue Grass

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