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In dry summer months, stream baseflow sourced from groundwater is essential to support aquatic ecosystems and anthropogenic water use. Hydrologic signatures, or metrics describing unique features of streamflow timeseries, are useful for quantifying and predicting these valuable baseflow and groundwater storage resources across continental scales. Hydrologic signatures can be predicted based on catchment attributes summarising climate and landscape and can be used to characterise baseflow and groundwater processes that cannot be directly measured. While past watershed‐scale studies suggest that landscape attributes are important controls on baseflow and storage processes, recent regional‐to‐global scale modelling studies have instead found that landscape attributes have weaker relationships with hydrologic signatures of these processes than expected compared to climate attributes. In this study, we quantify two landscape attributes, average geologic age and the proportion of catchment area covered by wetlands. We investigate if incorporating these additional predictors into existing large‐sample attribute datasets strengthens continental‐scale, empirical relationships between landscape attributes and hydrologic signatures. We quantify 14 hydrologic signatures related to baseflow and groundwater processes in catchments across the contiguous United States, evaluate the relationships between the new catchment attributes and hydrologic signatures with correlation analysis and use the new attributes to predict hydrologic signatures with random forest models. We found that the average geologic age of catchments was a highly influential predictor of hydrologic signatures, especially for signatures describing baseflow magnitude in catchments, and had greater importance than existing attributes of the subsurface. In contrast, we found that the proportion of wetlands in catchments had limited influence on our hydrologic signature predictions. We recommend incorporating catchment geologic age into large‐sample catchment datasets to improve predictions of baseflow and storage hydrologic signatures and processes across continental scales.
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Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions
Abstract 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.
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- PAR ID:
- 10473644
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Groundwater
- ISSN:
- 0017-467X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/gwat.13354
