This study addresses the urgent need to understand the impacts of climate change on coastal ecosystems by demonstrating how to use the SWAT+ model to assess the effects of sea level rise (SLR) on agricultural nitrate export in a coastal watershed. Our framework for incorporating SLR in the SWAT+ model includes: (1) reclassifying current land uses to water for areas with elevations below 0.3 m based on SLR projections for mid-century; (2) creating new SLR-influenced land uses, SLR-influenced crop database, and hydrological response units for areas with elevations below 2.4 m; and (3) adjusting SWAT+ parameters for the SLR-influenced areas to simulate the effects of saltwater intrusion on processes such as plant yield and denitrification. We demonstrate this approach in the Tar-Pamlico River basin, a coastal watershed in eastern North Carolina, USA. We calibrated the model for monthly nitrate load at Washington, NC, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.61. Our findings show that SLR substantially alters nitrate delivery to the estuary, with increased nitrate loads observed in all seasons. Higher load increases were noted in winter and spring due to elevated flows, while higher percentage increases occurred in summer and fall, attributed to reduced plant uptake and disrupted nitrogen cycle transformations. Overall, we observed an increase in mean annual nitrate loads from 155,000 kg NO₃-N under baseline conditions to 157,000 kg NO₃-N under SLR scenarios, confirmed by a statistically significant paired t-test (p = 2.16 × 10^-10). This pioneering framework sets the stage for more sophisticated and accurate modeling of SLR impacts in diverse hydrological scenarios, offering a vital tool for hydrological modelers.

Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research gap by evaluating the impacts of using different satellite precipitation products—ERA5, IMERG, and gridMET—on flow and nitrate load simulations with the Soil and Water Assessment Tool Plus (SWAT+), using the Tar-Pamlico watershed as a case study. Although agricultural activities are higher in the summer, this study found the lowest nitrate load during this season due to reduced runoff. In contrast, the nitrate load was higher in the winter because of increased runoff, highlighting the dominance of water flow in driving riverine nitrate load. This study found that although IMERG predicts the highest annual average flow (120 m³/s in Pamlico Sound), it unexpectedly results in the lowest annual average nitrate load (1750 metric tons/year). In contrast, gridMET estimates significantly higher annual average nitrate loads (3850 metric tons/year). This discrepancy underscores the crucial impact of rainfall datasets on nitrate transport predictions and highlights how the choice of dataset can significantly influence nitrate load simulations.

Agriculture is a major contributor to nutrient pollution that drives eutrophication in aquatic ecosystems. This study integrates hydrological modeling with farmer behavioral analysis to assess the effectiveness of two agricultural conservation practices—cover crops and reduced nitrogen fertilizer application—in reducing nitrate loss from fields in the Tar-Pamlico River Basin of North Carolina. Survey responses from 279 farmers revealed widespread reluctance to adopt conservation practices, particularly strict fertilizer reductions. A hydrological model showed that applying each practice to 25 percent of agricultural land could substantially reduce nitrate export, with cover crops showing greater effectiveness than reduced fertilizer use. However, an integrated socio-hydrological model, which incorporated behavioral responses from farmers, predicted much smaller reductions in nitrate loss due to limited voluntary adoption. Specifically, nitrate reductions were overestimated by a factor of 8 for cover crops and by a factor of 25 for reduced fertilizer application when behavioral responses were excluded. This result highlights a critical limitation of traditional modeling approaches and underscores the importance of integrating human decision-making into environmental policy analysis. By linking policy incentives with both biophysical and social responses, this study offers a more realistic framework for designing cost-effective and impactful agricultural conservation strategies.