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Abstract Quantifying nitrate leaching in agricultural fields is often complicated by inability to capture all water draining through a specific area. We designed and tested undisturbed soil monoliths (termed “soil block mesocosms”) to achieve complete collection of drainage. Each mesocosm measures 1.5 m × 1.5 m × 1.2 m and is enclosed by steel on the sides and bottom with a single outlet to collect drainage. We compared measurements from replicate mesocosms planted to corn (Zea maysL.) with a nearby field experiment with tile‐drained plots (“drainage plots”), and with drainage from nearby watersheds from 2020 through 2022 under drought conditions. Annual mesocosm drainage volumes were 6.5–24.6 cm greater than from the drainage plots, likely because the mesocosms were isolated from the subsoil and could not store groundwater below the drain depth, whereas the drainage plots accumulated infiltration as groundwater. Thus, we obtained consistent nitrate leaching measurements from the mesocosms even when some drainage plots yielded no water. Despite drainage volume differences, mean flow‐weighted nitrate concentrations were similar between mesocosms and drainage plots in 2 of 3 years. Mesocosm annual drainage volume was 8.7 cm lower to 16.7 cm higher than watershed drainage, likely due to lagged influences of groundwater. Corn yields were lower in mesocosms than drainage plots in 2020, but with irrigation, yields were similar in subsequent years. Mean 2020 surface soil moisture and temperature were similar between the mesocosms and nearby fields. Based on these comparisons, the mesocosms provide a robust method to measure nitrate leaching with lower variability than field plots. 

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70%-75%) and NO3-N loading (77%-82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies. 

Abstract Although the hypoxia formation in the Gulf of Mexico is predominantly driven by increased riverine nitrogen (N) export from the Mississippi-Atchafalaya River basin, it remains unclear how hydroclimate extremes affect downstream N loads. Using a process-based hydro-ecological model, we reveal that over 60% of the land area of the Basin has experienced increasing extreme precipitation since 2000, and this area yields over 80% of N leaching loss across the region. Despite occurring in ~9 days year −1 , extreme precipitation events contribute ~1/3 of annual precipitation, and ~1/3 of total N yield on average. Both USGS monitoring and our modeling estimates demonstrate an approximately 30% higher annual N load in the years with extreme river flow than the long-term median. Our model suggests that N load could be reduced by up to 16% merely by modifying fertilizer application timing but increasing contribution of extreme precipitation is shown to diminish this potential. 

Abstract While spatial heterogeneity of riverine nitrogen (N) loading is predominantly driven by the magnitude of basin‐wide anthropogenic N input, the temporal dynamics of N loading are closely related to the amount and timing of precipitation. However, existing studies do not disentangle the contributions of heavy precipitation versus non‐heavy precipitation predicted by future climate scenarios. Here, we explore the potential responses of N loading from the Mississippi Atchafalaya River Basin to precipitation changes using a well‐calibrated hydro‐ecological model and Coupled Model Intercomparison Project Phase 5 climate projections under two representative concentration pathway (RCP) scenarios. With present agricultural production and management practices, N loading could increase up to 30% by the end of the 21st century under future climate scenarios, half of which would be driven by heavy precipitation. Particularly, the RCP8.5 scenario, in which heavy precipitation and drought events become more frequent, would increase N loading disproportionately to projected increases in river discharge. N loading in spring would contribute 41% and 51% of annual N loading increase under the RCP4.5 and RCP8.5 scenarios, respectively, most of which is related to higher N yield due to increases in heavy precipitation. Anthropogenic N inputs would be increasingly susceptible to leaching loss in the Midwest and the Mississippi Alluvial Plain regions. Our results imply that future climate change alone, including more frequent and intense precipitation extremes, would increase N loading and intensify the eutrophication of the Gulf of Mexico over this coming century. More effective nutrient management interventions are needed to reverse this trend.