Meteoric waters move along pathways in the subsurface that differ as a function of lithology because of the effects of chemical and physical weathering. To explore how this affects stream chemistry, we investigated watersheds around an igneous intrusion in the Luquillo Mountains (Puerto Rico). We analyzed streams on 1) unmetamorphosed country rock (volcaniclastic sedimentary strata, VC) surrounding an igneous intrusion, 2) the quartz-diorite intrusion (QD), and 3) the metamorphosed aureole rock (hornfels-facies volcaniclastics, HF). These lithologies differ physically and chemically but weather under the same tropical rain forest conditions. The sedimentary VC lithology is pervasively fractured while the massive QD and HF lithologies are relatively unfractured. However, the QD fractures during weathering to produce spheroidally-weathered corestones surrounded by cm-thick rindlets of increasingly weathered rock. Meteoric waters flow pervasively through the network of already-fractured VC rock and the spheroidally weathered rindlets on the QD, but only access a limited fraction of the HF, explaining why streams draining HF are the most dilute in the mountains. This results in various thicknesses of regolith from thick (VC) to moderate (QD) to thin or nonexistent (HF). The pervasive fractures allow groundwater to flow deeply through the VC and then return to the mainstem river (Río Mameyes) at lower elevations. These “rock waters” drive concentrations of rock-derived solutes (silica, base cations, sulfate, phosphate) higher in the lower reaches of the stream. Water also flows through weathering-induced fractures on the QD at high elevations where rindletted corestones are present in stacks, and this water flux dissolves plagioclase and hornblende and oxidizes biotite. This “QD rock water” is not generated at lower elevations in the Río Icacos watershed, where stacks of corestones are absent, and contributions to stream solutes derive from weathering of feldspar- and hornblende-depleted saprolite. The stream chemistry in the QD-dominated watershed (Río Icacos) thus varies from concentrated QD-rock water at channel heads below steep ridgelines toward more diluted “saprolite water” downstream. These observations emphasize the importance of lithology and fracture patterns in dictating water flowpaths, stream chemistry, and regolith development in headwater catchments.
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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 (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‐SO4dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, and agricultural activities, to Ca‐HCO3water 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.
more » « less- NSF-PAR ID:
- 10382378
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 58
- Issue:
- 11
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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