Preliminary analysis of the hydrologic and geochemical controls on acid‐neutralizing capacity in two acidic seepage lakes in Florida

Curtis D. Pollman; T. M. Lee; William J. Andrews; L. A. Sacks; S.A. Gherini; R.K. Munson

doi:10.1029/91wr01435

Home/Archive/USGS

USGS · 70216796

Preliminary analysis of the hydrologic and geochemical controls on acid‐neutralizing capacity in two acidic seepage lakes in Florida

Curtis D. Pollman·T. M. Lee·William J. Andrews·L. A. Sacks·S.A. Gherini·R.K. Munson

View original source ↗Export metadata ↓

Abstract

In late 1988, parallel studies of Lake Five‐O ( p H 5.14) in the Florida panhandle and Lake Barco ( p H 4.50) in north central Florida were initiated to develop hydrologic and major ion budgets of these lakes as part of an overall effort to improve understanding of the hydrologic, depositional, and biogeochemical factors that control acid‐neutralizing capacity (ANC) in seepage lakes. Preliminary findings from these studies indicate that earlier perceptions of lake hydrology and mechanisms of ANC regulation in Florida seepage lakes may have to be revised. The traditional perspective of seepage lakes in the Florida panhandle views these systems as dominated by precipitation inputs and that ANC regulation is due largely to in‐lake processes. Our results for Lake Five‐O show modest to steep hydraulic gradients almost entirely around the lake. In addition, the horizontal hydraulic conductivity of the surficial aquifer is high (8–74 m day −1 ), indicating that large quantities of groundwater flow into Lake Five‐O. Calculations of net groundwater flow from hydrologic budgets also indicate that groundwater may comprise at least 38 to 46% of the total inflow. For Lake Barco, net flow estimates of the minimum groundwater inflow range from 5 to 14% of total inflow. Enrichment factor and ion flux calculations for Lake Five‐O and Lake Barco indicate that terrestrial as well as in‐lake processes contribute significantly to ANC regulation. The extent that terrestrial processes contribute to ANC generation is directly related to the magnitude of groundwater inflow as well as the degree of ion enrichment or depletion that occurs in the surficial aquifer. Net ANC generation in both study lakes was dominated by anion retention (NO 3 − and SO 4 2− ). Where previous studies concluded that in‐lake reduction was the primary sink for SO 4 2− , our preliminary calculations show that adsorption of SO 4 2− within the watershed is perhaps twice as important as in‐lake reduction as a source of ANC. Net base cation enrichment in both lakes was negligible.

Explore related subjects

Water Resources Research Article

Source-reported bounding extent: 24.546468° to 31.000682° latitude; -87.634938° to -80.031362° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Curtis D. Pollman, T. M. Lee, William J. Andrews, L. A. Sacks, S.A. Gherini, R.K. Munson. 2008-01-08. Preliminary analysis of the hydrologic and geochemical controls on acid‐neutralizing capacity in two acidic seepage lakes in Florida. https://doi.org/10.1029/91wr01435

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions

Predictive stream temperature models are commonly used to address spatial and temporal gaps in observational data because stream temperature information is essential for environmental management decisions. National models capture broad patterns driven by climate, topography, and seasonality, but often lack the spatial resolution to simulate fine-scale variation in groundwater discharge, riparian shading, or tributary inflows. In contrast, watershed models may simulate finer-resolution patterns but miss regional drivers or be limited by scarcity of observations. To generate watershed-extent, fine-resolution stream temperature predictions, we developed 18 multiscale machine-learning models that leverage (a) a broad-extent, coarse-resolution stream temperature model and (b) finer-resolution observations and predictor data. The coarse-resolution model simulates daily stream temperature across the conterminous United States on the National Hydrologic Geospatial Fabric (13.7 km median reach length; 865,364 km total). The fine-resolution models simulate daily stream temperature on the National Hydrography Dataset Plus version 2 (1.5 km median; 5,192,808 km total). We compared multiscale models to seven purely broad- (national) or watershed-extent models, testing them in eight watersheds (436–985 km 2 ) with differing hydrologic settings and under realistic data availability scenarios. Especially when observations within a watershed were scarce, multiscale models generated more accurate predictions than national or watershed-specific models. A recurrent graph convolution neural network pretrained on coarse predictions and fine-tuned on watershed-specific observations was most accurate across watersheds and data scenarios. These results highlight that leveraging both coarse, national results and watershed-specific data can increase model accuracies, particularly when observations within a watershed are spatially or temporally limited.

Water Resources Research↗

USGS2026

Landscape controls river continuum dissolved organic carbon in northeastern Alaska

Rivers draining continuous permafrost landscapes on the North Slope of Alaska span from the mountains to the coast, integrating gradients in vegetation, soil organic matter, and hydrologic flow. We investigated how these landscape transitions influence dissolved organic carbon (DOC) concentrations, radiocarbon ages, and export along the river continuum over seasonal timeframes in northeastern Alaska. Water samples were collected from the Canning River and its tributaries from the spring freshet through late summer via multiple campaigns during 2011–2024. A simulation from the Permafrost Water Balance Model (PWBM) estimated terrain-specific contributions to freshwater and DOC export. River DOC concentrations peaked during the spring and declined in summer, with episodic increases linked to summer rainfall. Summer river DOC concentrations from the Brooks Range remained low (0.41–0.64 mg C L −1 ) but increased downstream (0.5–3.0 mg C L −1 ), becoming younger from the mountains (805 yBP) to the river delta (525 yBP) as flow from DOC-enriched streams draining the foothills (6–18 mg C L −1 ; Modern yBP) entered the Canning. The PWBM simulation revealed that while the mountains dominate the Canning's annual (90%) and spring-summer (88%–93%) freshwater export, the foothills disproportionately influence its annual (56%) and spring-summer (44%–58%) DOC export. DOC yields from the coastal plain were also high but had little influence due to its small contributing area. Our findings highlight the outsized role of the foothills in modulating river-integrated DOC export to coastal ecosystems. Considering the impact of these landscapes on carbon source and runoff will aid in anticipating future DOC fluxes from Arctic rivers.

Alaska↗

USGS2026

Predicting Minnesota lake ice phenology with deep learning, explainable methods, and a physically based benchmark, 1980-2018

Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased toward few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational record, but these approaches produce large errors when estimating ice phenology for individual lakes. Accurate, lake-specific estimates of ice formation and breakup through time are important for estimating variability in ice loss across heterogenous lakes and for understanding broader implications. This work explores machine learning (ML) approaches for hindcasting ice phenology using observations from 1980 to 2018 across 625 Minnesota lakes, covering 4359 lake-years of record. We used daily weather and static lake attributes to develop 60 neural networks for hindcasting lake ice time series. We considered LSTMs and attention-based transformers of varying size and initial parameters. We found that the largest LSTM performed most accurately on withheld data, and on a test set of unseen years and lakes, it outperformed a state-of-the-art physically based model in daily accuracy (97% vs. 95%), year-level metrics (RMSE for ice formation = 6.9 vs. 13.2 days; ice breakup = 7.5 vs. 12.2 days; ice duration = 9.6 vs. 13.7 days), and estimating loss of ice cover from 1980 to 2018 (observed = 8.4 days, LSTM = 7.2 days, GLM = 3.4 days). This demonstrates that ML can estimate historical lake ice formation and breakup dates within a week of observed phenology across heterogeneous lakes and recreate broad scale trends in ice cover phenology with limited long-term ice records.

Minnesota↗