Variability in the Environmental Factors Driving Evapotranspiration from a Grazed Rangeland during Severe Drought Conditions

Alfieri, Joseph G.; Blanken, Peter D.; Yates, David; Steffen, Konrad

doi:10.1175/jhm569.1

Cited by 31 publications

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some regression models such as principal component regression can obtain more consistent results for ET 0 estimation (Alfieri et al, 2007), but convenient linear regression is more often used to estimate ET 0 . In addition to the ANNs, MLR was also used to estimate ET 0 from the climate variables in the present work.…”

Section: Comparison Of the Anns And Mlrsmentioning

confidence: 99%

Artificial neural network models for reference evapotranspiration in an arid area of northwest China

Huo

Feng

Kang

et al. 2012

Journal of Arid Environments

View full text Add to dashboard Cite

Section: Comparison Of the Anns And Mlrsmentioning

confidence: 99%

Artificial neural network models for reference evapotranspiration in an arid area of northwest China

Huo

Feng

Kang

et al. 2012

Journal of Arid Environments

View full text Add to dashboard Cite

“…Finally, surface conditions were characterized approximately every 10 days using measurements of normalized difference vegetation index (NDVI), greenness fraction (F g ), and leaf area index (LAI). The greenness fraction was determined following Gutman and Ignatov (1997) using the constants for minimum and maximum NDVI derived for the IHOP_2002 domain reported in Alfieri et al (2007).…”

Section: Surface Datamentioning

confidence: 99%

“…By standardizing these variables, i.e. rescaling them to range between zero and one, prior to conducting the regression, it is possible to eliminate the effects of scale and use the regression coefficients to estimate the relative influence of each of the predictor variables on G (Alfieri et al 2007). The relative influence is expressed as a percentage and is defined as…”

Section: Analysis Of Surface Datamentioning

confidence: 99%

Quantifying the Spatial Variability of Surface Fluxes Using Data from the 2002 International H2O Project

Alfieri

Niyogi

Zhang

et al. 2009

Boundary-Layer Meteorol

Self Cite

View full text Add to dashboard Cite

Spatial variability in the exchange of energy and moisture is a key control on numerous atmospheric, hydrologic, and environmental processes. Using observations made on fair weather days during the 2002 International H2O Project, four methods for quantifying the spatial variability of surface fluxes are investigated. The first two methods utilize applied statistical techniques to describe the spatial variability of the surface fluxes, while the third method is a geostatistical technique rooted in variography. Typically, the methods yield similar results, with median values of horizontal variability consistent to within 5%. The geostatistical technique, however, provides much more information than the other statistical methods; it not only provides an estimate of the spatial variability, but also provides estimates of the total variability, the non-spatial variability due to measurement error, and the range of spatial correlation among the data points. The fourth method is based on the relationship between the components of the surface energy budget. This method describes the variability in the fluxes in terms of the slope of the best-fit line relating the time-averaged latent and sensible heat fluxes from different locations along the flight path. The meaning of the slopes can also be interpreted in terms of the spatial variability in the available energy. For four of the five days analyzed, the key control on the spatial variability in the turbulent heat fluxes was horizontal variability in the soil heat flux. In turn, the soil heat flux varied as a function of surface properties including surface temperature, soil moisture content, and leaf area index. On the remaining day, 25 May, the primary control was the variability in net radiation.

show abstract

“…As one component of a complex network of interconnected processes, evapotranspiration (ET) is influenced by numerous factors such as available energy, soil moisture, vegetation density, and humidity (Farquhar and Sharkey, 1982; Van de Griend and Owe, 1994;Alves and Pereira, 2000;Alfieri et al, 2007). For example, the amount of energy available to drive ET depends on atmospheric properties, such as humidity and aerosol content, which influence atmospheric transmissivity (Brutsaert, 1975;Bird and Riordan, 1986).…”

Section: Introductionmentioning

confidence: 99%

Effect of the revisit interval and temporal upscaling methods on the accuracy of remotely sensed evapotranspiration estimates

Alfieri

Anderson

Kustas

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Accurate spatially distributed estimates of actual evapotranspiration (ET) derived from remotely sensed data are critical to a broad range of practical and operational applications. However, due to lengthy return intervals and cloud cover, data acquisition is not continuous over time, particularly for satellite sensors operating at medium ( ∼ 100 m) or finer resolutions. To fill the data gaps between clear-sky data acquisitions, interpolation methods that take advantage of the relationship between ET and other environmental properties that can be continuously monitored are often used. This study sought to evaluate the accuracy of this approach, which is commonly referred to as temporal upscaling, as a function of satellite revisit interval. Using data collected at 20 Ameriflux sites distributed throughout the contiguous United States and representing four distinct land cover types (cropland, grassland, forest, and open-canopy) as a proxy for perfect retrievals on satellite overpass dates, this study assesses daily ET estimates derived using five different reference quantities (incident solar radiation, net radiation, available energy, reference ET, and equilibrium latent heat flux) and three different interpolation methods (linear, cubic spline, and Hermite spline). Not only did the analyses find that the temporal autocorrelation, i.e., persistence, of all of the reference quantities was short, it also found that those land cover types with the greatest ET exhibited the least persistence. This carries over to the error associated with both the various scaled quantities and flux estimates. In terms of both the root mean square error (RMSE) and mean absolute error (MAE), the errors increased rapidly with increasing return interval following a logarithmic relationship. Again, those land cover types with the greatest ET showed the largest errors. Moreover, using a threshold of 20 % relative error, this study indicates that a return interval of no more than 5 days is necessary for accurate daily ET estimates. It also found that the spline interpolation methods performed erratically for long return intervals and should be avoided.

show abstract

Variability in the Environmental Factors Driving Evapotranspiration from a Grazed Rangeland during Severe Drought Conditions

Cited by 31 publications

References 36 publications

Artificial neural network models for reference evapotranspiration in an arid area of northwest China

Artificial neural network models for reference evapotranspiration in an arid area of northwest China

Quantifying the Spatial Variability of Surface Fluxes Using Data from the 2002 International H2O Project

Effect of the revisit interval and temporal upscaling methods on the accuracy of remotely sensed evapotranspiration estimates

Contact Info

Product

Resources

About