How Data Uncertainty Influences Estimates of Root Zone Water Storage Capacity

Abstract

Abstract Root zone water storage capacity () sets the lower limit on the water available to plants and is critical for modeling terrestrial water and carbon fluxes. Due to subsurface heterogeneity in soils, roots, and geology, in‐situ measurements of are infeasible at regional to global scales. Instead, is often estimated using a water balance approach—calculating the difference between precipitation and evapotranspiration to approximate water deficits. This method can be applied using diverse data sets, including those from eddy covariance, satellite remote sensing, reanalysis, and land surface model outputs. However, each data product contains its own uncertainties, and the influence of data set choice on estimates has not been quantified. In this study, we assess the uncertainty in estimates across 90 FLUXNET sites in the continental United States with at least 5 years of data, comparing results from six different precipitation and evapotranspiration data sets. We find substantial variation in estimates across data sets, sites, and land cover types, with particularly high uncertainty for values exceeding 500 mm. Much of this uncertainty can be attributed to differences among evapotranspiration products rather than random errors. Our analysis shows that single data set approaches to quantifying underestimate the variability that emerges when multiple data sets are considered, especially in the Western US. These results underscore the need to account for data‐driven uncertainty when estimating subsurface water storage and provide a foundation for improving model parameterizations of ecosystem drought response and land‐atmosphere interactions.