No abstract

Pecan is a major crop in the lower Rio Grande Valley (LRGV), New Mexico. Currently, about 11,000 ha of pecan orchards at various stages of growth are consuming about 40% of irrigation water in the area. Pecan evapotranspiration (ET) varies with age, canopy cover, soil type and method of water management. There is a need for better quantification of pecan ET for the purpose of water rights adjudication, watershed management and agronomical practices. This paper describes a process where remote sensing information from Landsat-5 and Landsat-7 were combined with ground level measurements to estimate pecan ET and field scale actual crop coefficient (K c ) for the LRGV. The results showed that annual pecan water use for 279 fields ranged from 498 to 1,259 mm with an average water use of 1,054 mm. For fields with NDVI [ 0.6 (normalized difference vegetation index), which represented mature orchards (total of 232 fields), the annual water use ranged from 771 to 1,259 mm with an average water use of 1,077 mm. The results from remote sensing model compared reasonably well with ground level ET values determined by an eddy covariance system in a mature pecan orchard with an average error of 4% and the standard error of estimate (SEE) ranging from 0.91 to 1.06 mm/day. A small fraction (5%) of the pecan fields were within the range of maximum ET and K c .

Net radiation ͑R n ͒ϭkey variable in hydrological studies. Measured net radiation data are rarely available and are often subject to error due to equipment calibration or failure. In addition, point measurements of net radiation do not represent the diversity of the regional net radiation values which are needed for large scale evapotranspiration mapping. A procedure has been developed to estimate daily net radiation using canopy temperature, albedo, short wave radiation and air temperature. This procedure makes it possible to estimate R n by combining information from satellite and local weather stations. Three different methodologies are presented to estimate net radiation. Comparisons between net radiation using the three methods resulted in average error ranging from 1 to 30% and standard error of estimate ranging from 1.06 to 5.34 MJ/ m 2 / day.

No abstract

No abstract

This study presented the application of partial least squares regression (PLSR) in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks (ANN) models with the same input variables. The results showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models. modeling, daily pan evaporation, partial least squares regression, artificial neural networks, meteorological data Citation:Abudu S, Cui C L, King J P, et al. Modeling of daily pan evaporation using partial least squares regression.

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day -1 , respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day -1 , with a mean of 5.5 mm day -1 and standard deviation of 1.85 mm day -1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.

No abstract