USGS Science⌕ Search

USGS · 70024288

Effect of flood-induced chemical load on filtrate quality at bank filtration sites

Abstract

Riparian municipal wells, that are located on riverbanks, are specifically designed to capture a portion of the river water through induced infiltration. Runoff from agricultural watersheds is found to carry enormous amounts of pesticides and nitrate. While the risk of contamination for a vast majority of sites with small-capacity vertical wells is low, potential exists for medium to large capacity collector wells to capture a fraction of the surface water contaminants during flood. Prior monitoring and current modeling results indicate that a small-capacity (peak pumpage 0.0315 m3/s) vertical bank filtration well may not be affected by river water nitrate and atrazine even during flood periods. For a medium capacity (0.0875-0.175 m3/s) hypothetical collector well at the same site, potential exists for a portion of the river water nitrate and atrazine to enter the well during flood periods. Various combinations of hydraulic conductivity of the riverbed or bank material were used. For nitrate, it was assumed either no denitrification occurred during the period of simulation or a half-life of 2 years. Equilibrium controlled sorption (organic carbon partition coefficient of 52 ml/g) and a half-life of between 7.5 and 15 weeks were considered for atrazine. Combinations of these parameters were used in various simulations. Peak concentrations of atrazine or nitrate in pumped water could vary from less than 1% to as high as 90% of that in the river. It was found that a combination of river stage, pumping rates, hydraulic properties of the riverbed and bank, and soil/pesticide properties could affect contaminant entry from river water to any of these wells. If the hydraulic conductivity of the bed and bank material were low, atrazine would not reach the pumping well with or without sorption and degradation. However, for moderately low permeable bank and bed materials, some atrazine from river water could enter a hypothetical collector well while pumping at 0.0875 m3/s. It was interesting to note that doubling the pumpage of this collector well would bring in more ground water from the aquifer (with no atrazine) and thus have a lower concentration of atrazine in the filtrate. For highly conductive banks, it is possible to find some atrazine at a vertical well for a sustained pumpage rate of 0.0125 m3/s if the effect of sorption is neglected. However, with equilibrium sorption, the concentration would be below the detection limit. On the other hand, if a collector well of capacity 0.0875 m3/s is used at the place of the vertical well with highly conductive banks, atrazine concentration in the filtrate would be about 80% of river water even assuming equilibrium sorption and a half-life of 7.5 weeks. Remediation of river water contamination of the aquifer using 'scavenger' wells between the river and the pumping well(s) was not a feasible option due to the contact of the aquifer with a highly conductive bank at the site. However, moving the existing pumping well(s) 100 m upstream would have negligible impact from the bank-stored water. ?? 2002 Elsevier Science B.V. All rights reserved.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C. Ray, T.W. Soong, Y.Q. Lian, G.S. Roadcap. 2002. Effect of flood-induced chemical load on filtrate quality at bank filtration sites. https://doi.org/10.1016/s0022-1694(02)00168-3

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↗