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Abstract Accurately quantifying and predicting the reactive transport of nitrate () in hydrologic systems continues to be a challenge, due to the complex hydrological and biogeochemical interactions that underlie this transport. Recent advances related to time‐variant water age have led to a new method that probes water mixing and selection behaviors using StorAge Selection (SAS) functions. In this study, SAS functions were applied to investigate storage, water selection behaviors, and export regimes in a tile‐drained corn‐soybean field. The natural abundance stable nitrogen and oxygen isotopes of tile drainage were also measured to provide constraints on biogeochemical transformations. The SAS functions, calibrated using chloride measurements at tile drain outlets, revealed a strong young water preference during tile discharge generation. The use of a time‐variant SAS function for tile discharge generated unique water age dynamics that reveal an inverse storage effect driven by the activation of preferential flow paths and mechanically explain the observed variations in isotopes. Combining the water age estimates with isotope fingerprinting shed new light on export dynamics at the tile‐drain scale, where a large mixing volume and the lack of a strong vertical contrast in concentration resulted in chemostatic export regimes. For the first time, isotopes were embedded into a water age‐based transport model to model reactive transport under transient conditions. The results of this modeling study provided a proof‐of‐concept for the potential of coupling water age modeling with isotope analysis to elucidate the mechanisms driving reactive transport.
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Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments
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.
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- PAR ID:
- 10505295
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Water Research
- Volume:
- 244
- Issue:
- C
- ISSN:
- 0043-1354
- Page Range / eLocation ID:
- 120489
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.watres.2023.120489
