The Experts below are selected from a list of 4227 Experts worldwide ranked by ideXlab platform
Ali Rahimi Khoob - One of the best experts on this subject based on the ideXlab platform.
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comparative study of hargreaves s and artificial neural network s methodologies in estimating reference evapotranspiration in a semiarid environment
Irrigation Science, 2008Co-Authors: Ali Rahimi KhoobAbstract:The Penman–Monteith Equation (PM) is widely recommended because of its detailed theoretical base. This method is recommended by FAO as the sole method to calculate reference evapotranspiration (ETo) and for evaluating other methods. However, the detailed climatological data required by the Penman–Monteith Equation are not often available especially in developing nations. Hargreaves Equation (HG) has been successfully used in some locations for estimating ETo where sufficient data were not available to use PM method. The HG Equation requires only maximum and minimum air temperature data that are usually available at most weather stations worldwide. Another method used to estimate ETo is the artificial neural network (ANN). Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to compare HG and ANN methods for estimating ETo only on the basis of the temperature data. The 12 weather stations selected for this study are located in Khuzestan plain (southwest of Iran). The HG method mostly underestimated or overestimated ETo obtained by the PM method. The ANN method predicted ETo better than HG method at all sites.
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Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment
Irrigation Science, 2007Co-Authors: Ali Rahimi KhoobAbstract:The Penman–Monteith Equation (PM) is widely recommended because of its detailed theoretical base. This method is recommended by FAO as the sole method to calculate reference evapotranspiration (ETo) and for evaluating other methods. However, the detailed climatological data required by the Penman–Monteith Equation are not often available especially in developing nations. Hargreaves Equation (HG) has been successfully used in some locations for estimating ETo where sufficient data were not available to use PM method. The HG Equation requires only maximum and minimum air temperature data that are usually available at most weather stations worldwide. Another method used to estimate ETo is the artificial neural network (ANN). Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to compare HG and ANN methods for estimating ETo only on the basis of the temperature data. The 12 weather stations selected for this study are located in Khuzestan plain (southwest of Iran). The HG method mostly underestimated or overestimated ETo obtained by the PM method. The ANN method predicted ETo better than HG method at all sites.
Jiří Kučera - One of the best experts on this subject based on the ideXlab platform.
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Direct Penman–Monteith parameterization for estimating stomatal conductance and modeling sap flow
Trees, 2016Co-Authors: Jiří Kučera, Patricia Brito, María Soledad Jiménez, Josef UrbanAbstract:The novel approach for direct parameterization of the Penman–Monteith Equation was developed to compute diurnal courses of stand canopy conductance from sap flow. The Penman–Monteith Equation of evaporation is often combined with sap flow measurements to describe canopy transpiration and stomatal conductance. The traditional approach involves a two-step calculation. In the first step, stomatal conductance is computed using an inverted form of Penman–Monteith Equation. The second step correlates these values with environmental factors. In this work, we present an improved approach for direct parameterization of the Penman–Monteith Equation developed to compute diurnal courses of stand canopy conductance (g c) from sap flow. The main advantages of this proposed approach versus using the classical approach are: (1) the calculation process is faster and involves fewer steps, (2) parameterization provides realistic values of canopy conductance, including conditions of low atmospheric vapor pressure deficit (D), whereas the traditional approach tends to yield unrealistic values for low D and (3) the new calculation method does not require enveloping curves to describe dependence of g c on D and thus avoids subjective data selection but it still allows to visualize separable responses of g c to environmental drivers (i.e., global radiation and vapor pressure deficit). The proposed approach was tested to calculate g c and to model the sap flow of a high mountain Pinus canariensis forest. The new calculation method permitted us to describe the stand canopy conductance and stand sap flow in sub-hour resolution for both day and night conditions. Direct parameterization of the Penman–Monteith approach as implemented in this study proved sufficiently sensitive for detecting diurnal variation in g c and for predicting sap flow from environmental variables under various atmospheric evapotranspirative demands and differing levels of soil water availability.
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Assessment of transpiration estimates for Picea abies trees during a growing season
Trees, 1992Co-Authors: Emil Cienciala, Jan-erik Hällgren, Anders Lindroth, Jan Cermak, Jiří KučeraAbstract:The tree-trunk heat balance method with internal heating and sensing of temperature was used to estimate sap-flow rate of spruce trees in a stand in southern Sweden. Sap-flow rate values were scaled up to stand transpiration and utilised for calculation of canopy conductance. The calculated values provided the basis for a function relating canopy conductance to vapour pressure deficit, which was implemented in the Penman-Monteith Equation. The stand was mostly growing in non-limiting soil water conditions (irrigation regime applied during dry periods). The whole-season transpiration was assessed by two different approaches and then compared: the sap-flow rate measurements were scaled to stand transpiration and the adapted Penman-Monteith estimate. They gave similar results: the transpiration totals differed by 3% and the coefficient of determination of the linear regression was r^2 = 0.89. Similarly good was the assessment for a set of rainy days. The Penman-Monteith estimate adapted in this way proved to be reasonably precise and reliable in this forest stand and usable even in wet conditions. The seasonal transpiration of the spruce stand was 392 mm according to the adapted Penman-Monteith Equation. Mean daily transpiration was 1.8 mm and daily maximum transpiration was 4.8–4.9 mm as estimated by sap-flow rate measurements.
Donald Payne - One of the best experts on this subject based on the ideXlab platform.
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Estimation of transpiration by single trees: comparison of sap flow measurements with a combination Equation
Agricultural and Forest Meteorology, 1997Co-Authors: Heping Zhang, L.p. Simmonds, James I. L. Morison, Donald PayneAbstract:Abstract Sap flow estimates for whole trees (scaled from measurements on selected branches using the heat balance method) were compared with estimates of transpiration based on porometry in a study of poplar trees in an agroforestry system in the south of the UK. Sap flow showed good agreement with the transpiration rate estimated using the Penman-Monteith Equation with measured stomatal conductance (R2 = 0.886) on six selected days during the season. The dominant environmental variable influencing transpiration was the vapour pressure deficit, as the “aerodynamic term” in the Penman-Monteith Equation accounted for more than 70% of daily total transpiration, with the rest due to the “radiation component”. Stomatal conductance, estimated by inverting the Penman-Monteith Equation from continuous measurements of sap flow over 55 days, was used to determine the parameters for a multiplicative stomatal conductance model. For an independent data set there was better agreement between measured sap flow and transpiration predicted from the stomatal conductance (R2 = 0.90) than for calculated and predicted stomatal conductance (R2 = 0.51).
Bhaskar J Choudhury - One of the best experts on this subject based on the ideXlab platform.
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global pattern of potential evaporation calculated from the penman monteith Equation using satellite and assimilated data
Remote Sensing of Environment, 1997Co-Authors: Bhaskar J ChoudhuryAbstract:Abstract Potential evaporation has been used to provide a reference level for actual evaporation in many studies of land surface heat and water balance. While Thornthwaite's formula has been used in many regional and global studies, the Penman-Monteith Equation has been shown to provide an accurate estimate of this evaporation. Thus, global pattern of potential evaporation has been caleulated from the Penman-Monteith Equation using satellite and assimilated data for a 24-month period, January 1987 to December 1988. The albedo and surface resistance have been taken to be, respectively, 0.23 and 70 s m −1 , which are considered to be representative values for actively growing well-watered grass covering the ground. Satellite observations have been used to obtain spatially representative monthly values of solar radiation, fractional cloud cover, air temperature, and vapor pressure, while aerodynamic resistance has been calculated using four-dimensional data assimilation results. Meteorologic data derived from satellite observations are compared with the surface (station) measurements. The calculated potential evaporation values are compared with lysimeter observations for evaporation from well-watered grass at 35 widely distributed locations in different climatic regimes to quantify the accuracy of the calculated values. The evaporation values have been archived for distribution. http://hydro4.gsfc.nasa.gov/STAFF/ChoudhuryBj/pmpotevap.html.
Josef Urban - One of the best experts on this subject based on the ideXlab platform.
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Direct Penman–Monteith parameterization for estimating stomatal conductance and modeling sap flow
Trees, 2016Co-Authors: Jiří Kučera, Patricia Brito, María Soledad Jiménez, Josef UrbanAbstract:The novel approach for direct parameterization of the Penman–Monteith Equation was developed to compute diurnal courses of stand canopy conductance from sap flow. The Penman–Monteith Equation of evaporation is often combined with sap flow measurements to describe canopy transpiration and stomatal conductance. The traditional approach involves a two-step calculation. In the first step, stomatal conductance is computed using an inverted form of Penman–Monteith Equation. The second step correlates these values with environmental factors. In this work, we present an improved approach for direct parameterization of the Penman–Monteith Equation developed to compute diurnal courses of stand canopy conductance (g c) from sap flow. The main advantages of this proposed approach versus using the classical approach are: (1) the calculation process is faster and involves fewer steps, (2) parameterization provides realistic values of canopy conductance, including conditions of low atmospheric vapor pressure deficit (D), whereas the traditional approach tends to yield unrealistic values for low D and (3) the new calculation method does not require enveloping curves to describe dependence of g c on D and thus avoids subjective data selection but it still allows to visualize separable responses of g c to environmental drivers (i.e., global radiation and vapor pressure deficit). The proposed approach was tested to calculate g c and to model the sap flow of a high mountain Pinus canariensis forest. The new calculation method permitted us to describe the stand canopy conductance and stand sap flow in sub-hour resolution for both day and night conditions. Direct parameterization of the Penman–Monteith approach as implemented in this study proved sufficiently sensitive for detecting diurnal variation in g c and for predicting sap flow from environmental variables under various atmospheric evapotranspirative demands and differing levels of soil water availability.