Treffer: Spatially Mapped Statewide Estimated Potential Evapotranspiration using an Efficient Surface Interpolation Method: A Case Study of South Carolina

Potential evapotranspiration (PET) exhibits substantial spatial and temporal variability across large landscapes, necessitating site-specific estimation for accurate environmental and water resource assessments. However, obtaining PET or ET data for specific locations across an entire state remains challenging due to the limited number of weather stations and associated environmental datasets. This study aimed to develop an automated geospatial modeling framework to map PET distribution across South Carolina, USA, using PET estimated by the temperature-based Hargreaves–Samani (H–S) method with daily weather data from 59 NOAA stations. Because the accuracy of spatial interpolation depends on both the target variable and the desired spatial resolution, we focused on high-resolution (1 m) surface mapping of PET. Four interpolation algorithms—Inverse Distance Weighting (IDW), Spline, Kriging, and Bayesian Kriging—were evaluated for their performance in mapping H–S PET. The models were assessed both visually and statistically using regional datasets on land cover, elevation, and precipitation across five ecoregions (Blue Ridge, Piedmont, Southeastern Plains, Middle Atlantic Plains, and Southern Coastal Plains). Multivariate regression analyses of 200 randomly sampled points indicated that the IDW method outperformed the other approaches, yielding higher R² values and lower standard errors. In addition to this comparative evaluation, the study presents a review of interpolation techniques, discussing their theoretical foundations, advantages, limitations, and potential environmental applications. The final PET maps were produced through automated geospatial models developed in ArcGIS ModelBuilder using Python scripts. These models, available in Toolbox (*.tbx) and script formats upon request, provide a practical framework for researchers and land managers to efficiently generate PET and other environmental variable maps for site-specific planning and analysis.