Geology topics

Kristen L. Underwood

Publications and source records attributed to Kristen L. Underwood.

3 recordsLinked to original sources

USGS2026

Reservoir releases and land cover interact to drive event-scale nitrate export in a large agricultural basin

Understanding the drivers of nitrate export in rivers is critical for developing effective nutrient management strategies. However, few studies have explored event-scale drivers of export in large river basins with human modifications like reservoirs. Here, we analyzed nitrate concentration-discharge (C-Q) relationships from 215 events at the outlet of the Kansas River Basin, USA (155,690 km 2 ) from 2014 to 2022 to (1) characterize event-scale nitrate export behaviors in a large agricultural basin, and (2) determine how different event characteristics are linked to these behaviors. We found that C-Q behaviors varied greatly, with 60% of events producing nitrate enrichment (n = 130) and 40% of events producing nitrate dilution (n = 85). These behaviors were correlated with complex spatial interlinkages between climate and land cover: across most of the basin, nitrate enrichment was correlated with drier antecedent conditions, but in wetter areas with higher proportions of urban and forested land cover, enrichment was more strongly correlated with precipitation magnitude/intensity. This difference in hydroclimatic controls on nitrate export might be related to differential distributions of nitrate sources within these land covers. Upstream of major reservoirs, however, neither variable was strongly correlated with C-Q behavior, suggesting that nitrate attenuation within reservoirs decouples event-scale concentration signals in upstream waters from those downstream. Reservoir outflows had variable impacts on C-Q behavior, reflecting reservoir-specific variations in nitrate attenuation efficiency. Together, these results identify specific complex interactions between hydroclimate, land cover, reservoir positioning, and individual reservoir properties that control event-scale nitrate export from large basins.

Colorado, Kansas, Nebraska↗

USGS2025

Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin

As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.

New York↗

USGS2024

Solute export patterns across the contiguous USA

Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large-scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS-Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long-term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments.

contiguous United States↗