ABSTRACT:

In land surface models, the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the earth’s surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface on effective watershed-scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed-scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the continental US for which sufficient streamflow data are available (n=1561). Then, we use machine learning methods to model these properties across the continental. Model validation results in high confidence for estimates of log(K) (r2 > 0.89; 1% < bias < 9%) and reasonable confidence for S (r2 > 0.83; -70% < bias < -18%). Our estimates of effective K are, on average, two orders of magnitude higher than comparable soils-texture based estimates of average K, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effective S compare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5-3355mm). Because estimates of S are much lower than the global maximums generally used in land surface models (e.g., 5000mm in Noah-MP), they may serve both to limit model spin-up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed-scale effective hydraulic variables that are necessary for the implementation of land surface models for the entire continental US.