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Abstract Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well‐established for high‐relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low‐relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low‐relief wetland‐rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low‐relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment‐scale studies for low‐relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low‐relief wetland‐rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes.
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New Predictors for Hydrologic Signatures: Wetlands and Geologic Age Across Continental Scales
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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- Award ID(s):
- 2124923
- PAR ID:
- 10623745
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Hydrological Processes
- Volume:
- 39
- Issue:
- 2
- ISSN:
- 0885-6087
- Subject(s) / Keyword(s):
- baseflow catchment attributes hydrologic signatures machine learning metrics random forest
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/hyp.70080
