Regression Modeling of Evapotranspiration Variability across the United States

Antoine Girard^*
*Correspondence: Antoine Girard, Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA, Email:

Introduction

Evapo Transpiration (ET) is a fundamental component of the water cycle, representing the combined processes of evaporation from land and water surfaces and transpiration from vegetation. Accurately estimating ET is crucial for assessing water availability, managing agricultural irrigation, forecasting droughts and modeling climate systems. However, ET is influenced by multiple interacting factors such as climate conditions, vegetation type and land cover, making it difficult to measure or estimate across large regions using traditional methods. To address this challenge, regression modeling offers a promising approach by correlating ET with measurable environmental variables. In their influential study, Sanford and Selnick (2013) applied regression techniques to estimate ET across the conterminous United States, using a combination of climate data and land-cover information. Their work demonstrates how statistical modeling can be effectively used to generate spatially comprehensive ET estimates and uncover patterns of variability shaped by regional climatic and ecological conditions [1].

Description

The methodology developed by Sanford and Selnick involved using long-term streamflow data from relatively undisturbed watersheds to compute actual evapotranspiration as the residual between precipitation and stream discharge. These ET estimates, grounded in observed hydrologic data, served as the foundation for developing regression models. The researchers incorporated key climate variables such as annual precipitation and mean temperature, along with land-cover classifications derived from national datasets, as independent variables in their regression analysis. By applying these models to spatial data layers, they were able to produce detailed maps showing ET estimates across the entire continental U.S. The strength of this regression-based approach lies in its ability to synthesize large volumes of hydrologic and environmental data, yielding spatially continuous results without the need for extensive ground-based ET measurements, which are often sparse or unavailable.

The regression models also revealed valuable insights into the geographic variability of ET across different regions. For instance, the highest ET values were found in the humid Southeast and Pacific Northwest, where abundant rainfall and dense vegetation lead to substantial water loss through transpiration. In contrast, the arid Southwest exhibited much lower ET due to limited precipitation and sparse vegetation cover. The models showed that precipitation was generally the strongest predictor of ET, though temperature and vegetation type also played important roles in shaping local and regional patterns. These findings not only highlight the complex interactions between climate and land cover but also support the development of improved water resource models and land-use planning tools. Moreover, the regression-based ET estimates provide a valuable input for national-scale hydrologic assessments, including groundwater recharge studies, drought monitoring systems and ecosystem service evaluations [2].

Conclusion

Regression modeling has proven to be a powerful method for estimating evapotranspiration variability across the United States, bridging the gap between localized hydrologic observations and national-scale water management needs. The work of Sanford and Selnick underscores the potential of combining empirical data with environmental predictors to generate accurate and scalable ET datasets. As climate change and land-use transformation continue to reshape water availability, regression-based ET modeling offers a practical and scientifically robust foundation for future research and policy development. Ultimately, such models enhance our ability to monitor and manage one of the most vital fluxes in the hydrologic cycle with greater precision and confidence.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Barlow, Paul M., William L. Cunningham, Tong Zhai and Mark Gray. “US Geological Survey groundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0): User guide for estimation of base flow, runoff and groundwater recharge from streamflow data.” US Geol Surv

Google Scholar Cross Ref Indexed at

2. Sanford, Ward E. and David L. Selnick. "Estimation of evapotranspiration across the conterminous United States using a regression with climate and landâ?cover data 1." J Am Water Resour Assoc49 (2013): 217-230.

Google Scholar Cross Ref Indexed at