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2 results on '"Brian Hinckley"'

1. Evaluating combinations of rainfall datasets and optimization techniques for improved hydrological predictions using the SWAT+ model

Author

Mahesh R. Tapas, Randall Etheridge, Thanh-Nhan-Duc Tran, Manh-Hung Le, Brian Hinckley, Van Tam Nguyen, and Venkataraman Lakshmi

Subjects
Hydrological Modeling, Soil and Water Assessment Tool Plus (SWAT+), Satellite Precipitation Products (SPPs), Autocalibration Techniques, Performance Indices, Physical geography, GB3-5030, Geology, QE1-996.5
Abstract

Study Region: This study focuses on the Cape Fear and Tar-Pamlico watersheds in North Carolina, which are characterized by diverse hydrological conditions, varied land use, soil types, and hydrological characteristics. Study Focus: The primary goal of this study is to examine the combined effects of three satellite precipitation products (SPPs) — ERA-5, gridMET, and GPM IMERG — along with three autocalibration techniques — DDS, GLUE, and LHS — on SWAT+ river flow predictions. Flow accuracy was assessed using three evaluation metrics: NSE, KGE, and R². New Hydrological Insights for the Region: Key findings revealed that five SWAT+ parameters (cn2, revap_co, flo_min, revap_min, and awc) were consistently sensitive across all SPPs and watersheds, with rainfall products exerting a greater influence on simulated river flow than optimization techniques. Among the SPPs, GPM IMERG performed the best, followed by ERA-5 and gridMET, while NSE was more responsive to changes in SPPs and calibration methods than KGE and R². For the Cape Fear and Tar-Pamlico watersheds, the study highlighted SWAT+ 's challenges in predicting base flow for groundwater-driven systems and demonstrated the potential of optimization techniques to improve flow simulations despite poor satellite-gauge rainfall correlation. The combination of the GPM IMERG dataset and the GLUE method proved most effective, offering valuable guidance for selecting optimal datasets and methods to enhance prediction accuracy in complex watersheds.

Published
2025
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2. Examining the impacts of increased corn production on groundwater quality using a coupled modeling system

Author

Ellen J. Cooter, Xiangnan Xing, Brian Hinckley, James L. Crooks, Mark Murphy, and Valerie Garcia

Subjects
Hydrology, Irrigation, Environmental Engineering, 010504 meteorology & atmospheric sciences, Biosphere modeling, Regression analysis, 010501 environmental sciences, Contamination, engineering.material, 01 natural sciences, Pollution, Article, Hydraulic conductivity, Groundwater quality, Regression modeling, Soil water, engineering, Environmental science, Environmental Chemistry, Fertilizer, Air quality index, Waste Management and Disposal, Groundwater, 0105 earth and related environmental sciences, Agricultural impacts
Abstract

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥ 5 mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additionally, the average number of animal feeding operations (AFOs) for these areas was nearly 5 times higher, and the mean N-fertilizer rate was 4 times higher. Finally, we found that areas prone to high groundwater nitrate-N concentrations attributable to the expansion scenario did not occur in new grid cells of irrigated grain-corn croplands, but were clustered around areas of existing corn crops. This application demonstrates the value of the coupled modeling system in developing spatially refined multi-variable models to provide information for geographic locations lacking complete observational data; and in projecting possible groundwater nitrate-N concentration outcomes under alternative future crop production scenarios., GRAPHICAL ABSTRACT

Published
2017
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