Stochastic estimation of plant-available soil water under fluctuating water table depths

Transpiration coefficients for three Great Basin shrubs

“…where ETr i is mean reference ET for day i (Or & Groeneveld, 1994), n is number of species, and LAI jmax is mid-summer LAI for species j. In Eqn 5, Ta is a linear function of maximum LAI or cover for the growing season (approximately at the summer solstice).…”

“…ETr i estimates are determined using a modified Penman equation (Pruitt & Doorenbos, 1977). In order to derive predictions of transpiration using Eqn 1, it is necessary to forecast likely ETr i ; therefore, mean daily ETr was derived by fitting a Fourier series model to ETr i measured during a 10-year period (Or & Groeneveld, 1994). …”

“…Few examples exist, however, of application of this concept to native vegetation ET (Wight et al, 1986;Or & Groeneveld, 1994). The technique can be joined easily to measures of vegetation abundance and thus potentially could be applied to estimate transpiration over large areas (Hanks, 1985).…”

Water balance for Great Basin phreatophytes derived from eddy covariance, soil water, and water table measurements

“…In environments such as the Owens Valley where the water table fluctuates due to groundwater pumping, the relative contribution of groundwater uptake to ET would be expected to vary as the vegetation is forced to rely more heavily on retained soil water than on recharge from the water table (Sorenson et al, 1991). Or and Groeneveld (1994) developed the only soil water model specifically for Great Basin phreatophytes over a fluctuating water table, but that study and others Snyder and Williams, 2000) suggested partitioning transpiration between soil water and groundwater for native phreatophytes was still poorly understood. The objective of this study was to quantify partitioning of plant water uptake between soil water and groundwater during the growing season for different water table depths and phreatophyte assemblages.…”

Estimating Soil Carbon Levels Using an Ensemble Kalman Filter

“…When the model is linear and errors are Gaussian and time independent, procedures are relatively straightforward. This approach has been applied to a number of problems in agriculture and forestry (Or and Groeneveld, 1994;Tani et al, 1992;Wendroth et al, 1999). Extensions to the Kalman filter have been developed to overcome difficulties associated with non-linear models (e.g., Albiol et al, 1993;Graham, 2002).…”