Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA
Understanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from >100 monitoring stations collected over 60 years...
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| Published in: | Water resources research Vol. 58; no. 11 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Washington
John Wiley & Sons, Inc
01-11-2022
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Understanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from >100 monitoring stations collected over 60 years (1958–2018) across the Colorado River Watershed in Texas (103,000 km2). We paired this river chemistry data with complementary lithology, land use, climate, and stream discharge information. Machine learning techniques were used to produce new insights on controls of stream water chemical behavior, which were validated using traditional multivariate analyses. Studies on stream flow and chemistry in the American west and globally have shown strong relationships between major ion chemical composition, climate, and lithology which hold true for the Colorado River basin in this study. Reactive minerals, including carbonates and evaporites, dominate major ion chemistry across the upper, low‐precipitation regions of the watershed. Upstream and middle reaches of the Colorado River showed shifts from Na‐Cl‐SO4 dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, and agricultural activities, to Ca‐HCO3 water types controlled by carbonate dissolution. In the lower portion of the watershed multiple analyses demonstrate that stream chemistry is more influenced by greater precipitation and the presence of silicate minerals than the middle and upstream reaches. This study demonstrates the power of applying machine learning approaches to publicly available long term water chemistry data sets to improve the understanding of watershed interactions with surficial lithology, salinity sources, and anthropogenic influences of water quality.
Plain Language Summary
Across the United States public and private users rely on large rivers for access to water, making water quality of crucial concern. Water quality measurements are widely available but require intensive pre‐processing due to their irregular collection across space and time. Here, publicly available water quality measurements from the Colorado River basin were analyzed using machine learning techniques to understand the influence of land use, geologic, and climate factors on water quality. This is especially important for the Colorado River as it runs across a range of rock types, land uses, and precipitation regimes and therefore displays complex interactions with the land surface that produce changes in water quality. We found that the upper Colorado River is dominated by Na+, Cl−, and SO42− which are derived from multiple sources including agriculture, and salt dissolution in shallow groundwater. The middle reaches are dominated by Ca2+ and HCO3− which are mainly contributed by the large areas of limestone bedrock. Downstream reaches of the river show more inputs from precipitation as well as potential seawater mixing in coastal areas. Overall, these machine learning techniques were effective in demonstrating large scale trends across this watershed and could be improved with more detailed data sets.
Key Points
The distribution of reactive minerals (e.g., evaporites and carbonates) dictates the chemical behavior of the Colorado River
Climate factors act as a secondary control, with increasing precipitation leading to decreased overall concentrations and dilution behavior
Agriculture, urban development, reservoirs, and oil and gas wells lead to noise in chemical measurements and obscures natural signals |
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| ISSN: | 0043-1397 1944-7973 |
| DOI: | 10.1029/2022WR032155 |