Predictive models of aboveground biomass of nonnative Tamarix ramosissima of various sizes were developed using destructive sampling techniques on 50 individuals and four 100-m² plots. Each sample was measured for average height (m) of stems and canopy area (m²) prior to cutting, drying, and weighing. Five competing regression models (P < 0.05) were developed to estimate aboveground biomass of T. ramosissima using average height and/or canopy area measurements and were evaluated using Akaike's Information Criterion corrected for small sample size (AIC_c). Our best model (AIC_c = −148.69, ΔAIC_c = 0) successfully predicted T. ramosissima aboveground biomass (R² = 0.97) and used average height and canopy area as predictors. Our 2nd-best model, using the same predictors, was also successful in predicting aboveground biomass (R² = 0.97, AIC_c = −131.71, ΔAIC_c = 16.98). A 3rd model demonstrated high correlation between only aboveground biomass and canopy area (R² = 0.95), while 2 additional models found high correlations between aboveground biomass and average height measurements only (R² = 0.90 and 0.70, respectively). These models illustrate how simple field measurements, such as height and canopy area, can be used in allometric relationships to accurately predict aboveground biomass of T. ramosissima. Although a correction factor may be necessary for predictions at larger scales, the models presented will prove useful for many research and management initiatives.