This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination ( R2 ) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN an...

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the spring-summer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN+ANN. The results indicated that the hybrid TFN+ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN+ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous...

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.

Agricultural soils of southern New Mexico, especially sand and sandy loams receiving numerous nitrogen (N) fertilizations, are prone to leaching large amounts of N to shallow groundwater. Adsorption and leaching experiments were conducted to investigate the use of clinoptilolite zeolite (CZ) as an amendment to sandy soils to increase N retention and reduce nitrate (NO3−) leaching. Urea-ammonium-nitrate (UAN 32) fertilizer was applied to four soil treatments to simulate crop irrigation. The treatments were composed of 100% CZ, 100% loamy sand (LS), a mixture of 80∶20% (LS:CZ), and a mixture 60∶40% (LS:CZ) by mass, respectively. Results from the experiments showed an inverse relationship between nitrate-nitrogen (NO3−-N) adsorption and the amount of CZ added to soil caused by anion exclusion and a direct relationship between ammonium-nitrogen (NH4+-N) adsorption and the amount of CZ mixed with LS due to NH4+-N entrapment by the CZ molecules. It is therefore recommended to use other types of fertiliz...

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R² of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.