Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions

Jackson S. Swilley, Danielle Tijerina-Kreuzer, Hoang V. Tran, Jun Zhang, Chen Yang, Laura E. Condon, Reed M. Maxwell

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three-dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations. R-squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically-informed estimates. The results of this work are used to inform a follow-on study that tests additional data-driven approaches in multiple basins within the contiguous United States.

Original languageEnglish (US)
Pages (from-to)75-92
Number of pages18
Issue number1
StatePublished - Jan 1 2024

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Computers in Earth Sciences


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