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Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ETg) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ETg. An iterative approach is provided that calibrates GWM to ETg derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11-year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ETg to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM-grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first-order second moment analysis shows ETg to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ETg despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ETg appears to bias ETg during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness. [ABSTRACT FROM AUTHOR]

Escalating concerns about water supplies in the Great Basin have prompted numerous water budget studies focused on groundwater recharge and discharge. For many hydrographic areas ( HAs) in the Great Basin, most of the recharge is discharged by bare soil evaporation and evapotranspiration ( ET) from phreatophyte vegetation. Estimating recharge from precipitation in a given HA is difficult and often has significant uncertainty, therefore it is often quantified by estimating the natural discharge. As such, remote sensing applications for spatially distributing flux tower estimates of ET and groundwater ET ( ETg) across phreatophyte areas are becoming more common. We build on previous studies and develop a transferable empirical relationship with uncertainty bounds between flux tower estimates of ET and a remotely sensed vegetation index, Enhanced Vegetation Index ( EVI). Energy balance-corrected ET measured from 40 flux tower site-year combinations in the Great Basin was statistically correlated with EVI derived from Landsat imagery ( r2 = 0.97). Application of the relationship to estimate mean-annual ETg from four HAs in western and eastern Nevada is highlighted and results are compared with previous estimates. Uncertainty bounds about the estimated mean ETg allow investigators to evaluate if independent groundwater discharge estimates are 'believable' and will ultimately assist local, state, and federal agencies to evaluate expert witness reports of ETg, along with providing new first-order estimates of ETg. [ABSTRACT FROM AUTHOR]

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration ( METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin-wide water budgets. Each METRIC evapotranspiration ( ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first-order ET estimates that are similar to time intensive manual efforts with less time investment. [ABSTRACT FROM AUTHOR]