Geology Reports⌕ Search

USGS · 70000095

Sources and fate of nitrate in the Illinois River Basin, Illinois

Abstract

We conducted a two-year investigation into the sources and fate of nitrate (NO3-) in the Illinois River from the Chicago area to the river's confluence with the Mississippi River. Samples from waterways in the Chicago area (Des Plaines River and the Sanitary and Ship Canal) had relatively high concentrations of nitrogen (N) species and NO3- isotopic compositions indicative of treated wastewater (TWW). Downstream of the Brandon Road Lock and Dam, NO3- in tributaries discharging to the Illinois River primarily comes from tiles draining row crops. Nitrate isotopic signatures from these tributaries as well as drain tiles were indicative of synthetic fertilizer and/or soil organic matter (SOM) at various stages of denitrification. Nitrate-N concentrations generally decreased in the Illinois River with distance from the Chicago area primarily due to dilution. The decrease in NO3-N concentrations was especially conspicuous during the summer, when there is minimal discharge from drain tiles and NO3-N concentrations in the tributaries were low. In August 2005, when conditions were very dry, NO3-N concentrations decreased from 7.4 mg/L in the Chicago area to less than 1 mg/L near where the Illinois River discharges to the Mississippi River. The isotopic composition of NO3- in water samples from the Illinois River were a mixture of three end members: (1) fertilizer and/or SOM in drain tile water, typically showing the least amount of denitrification, (2) fertilizer and/or SOM in deeper ground water, showing the highest degree of denitrification, and (3) TWW. There was seasonal variability, depending on the volume of water flowing in the Illinois River. During high flow periods, river water samples plotted closest to those of tile drain samples; during low flow periods, a greater influence of TWW was observed in the isotopic composition. A subset of summer samples from the Chicago waterways had isotopic values plotting near and within the domain that characterizes manure and sewage. Nitrate in the Chicago area is primarily derived from TWW, with its isotopic signature evident downstream at least as far as Pekin during most of the year and all the way to the Mississippi River during periods of low flow. Denitrification occurs predominantly in groundwater between and away from drain tiles, although there is evidence that in-stream denitrification and/or biological uptake of NO3- occurs in the Peoria Lake reach of the Illinois River, at least during periods of low flow in the summer. We calculated that the river was losing about half of its NO3-N load in Peoria Lake in August 2005 (a period of very low flow), at a rate of about 7500 kg/day.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S.V. Panno, W.R. Kelly, Keith C. Hackley, H.-H. Hwang, A.T. Martinsek. 2008. Sources and fate of nitrate in the Illinois River Basin, Illinois. https://doi.org/10.1016/j.jhydrol.2008.06.027

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

KEEP EXPLORING

Related USGS reports

Inverse modeling for post-wildfire hydrologic process understanding over snow-dominated watersheds

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Colorado, Montana, New Mexico↗

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↗

Deep learning error post-processing improves stochastic watershed modeling

Hydrologic extremes, including floods and droughts, pose substantial societal risks that are expected to intensify with climate change. Deterministic watershed models (DWMs) remain a mainstay for modeling these extremes, but lack explicit representation of uncertainty, limiting their utility for risk-informed planning. Stochastic watershed models (SWMs) address this limitation by generating ensembles of streamflow via models of observed DWM residuals. However, most SWMs struggle with the complex dependence between DWM residuals and the underlying hydrologic state, which can complicate stochastic simulations under nonstationary climates. Deep learning (DL) models, whether used as standalone models or post-processors for process-based DWMs, offer a pathway to address this challenge by reducing conditional dependence. In this study, we evaluate SWMs applied to seven models: three process-based models (PRMS, Hymod, and HBV), their hybrid process-DL counterparts, and a pure DL DWM, focusing on daily simulations and extremes under both historical conditions and synthetic climate change scenarios. Results for a case study watershed in Massachusetts show that SWMs applied to hybrid or pure DL DWMs consistently outperform those applied to process-based DWMs. However, an SWM applied to the pure DL model exhibits weaknesses at low flows for this study basin, underscoring the value of hybrid approaches. Extending the analysis across 73 additional basins demonstrates that these improvements are robust and generalizable statewide. This work highlights the potential of a DL-enhanced stochastic watershed modeling framework to advance hydrologic risk prediction under changing climate conditions, offering a scalable methodology for integrating uncertainty into watershed modeling for long-term planning.

Journal of Hydrology↗