Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality

Golden, Heather E.; Evenson, Grey R.; Christensen, Jay R.; Lane, Charles R.

doi:10.1021/acs.est.2c06983

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…In the last decade, it has increasingly been shown that landscape N accumulation within soils, sediments, and groundwater of intensively farmed landscapes can lead to multi-decadal time lags in achieving water quality goals (Van Meter and Basu 2017, Van Meter et al 2018, Ilampooranan et al 2019, Chang et al 2021, Liu et al 2021. With this understanding, there has been a call for new modeling approaches that can better capture the time lags associated with subsurface legacy N accumulation (Vero et al 2018, Lutz et al 2022, Golden et al 2023. However, without robust data-driven estimates of groundwater N accumulation, we are unable to reliably calibrate watershed legacy N models and to refine our predictions of watershed time lags.…”

Section: Predictions Of Groundwater Nitrate-n Accumulation Magnitudesmentioning

confidence: 99%

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Meter

Schultz

Chang

2023

Environ. Res. Lett.

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Increases in nitrogen (N) fertilizer application, livestock densities, and human population over the last century have led to substantial increases in nitrate contamination. While increases in riverine N loads are well-documented, the total magnitude of N accumulation in groundwater remains unknown. Here we provide a first data-driven estimate of N mass accumulation in groundwater within the Upper Mississippi River Basin (UMRB), an area of intensive row-crop agriculture and the primary contributor to Gulf of Mexico hypoxia. Using approximately 49,000 well nitrate concentration values and a suite of geospatial predictors, we developed a Random Forest model to produce gridded predictions of depth-varying nitrate concentrations. Our results suggest that approximately 15 Tg of N (328 ± 167 kg-N ha-1) is currently stored in UMRB groundwater recharged over the last 50 years. For context, we compare these predictions to those from a lumped statistical model, which predicts accumulation of 387 ± 133 kg-N ha-1, as well as to a simple N mass balance model of the UMRB, which puts an upper bound on accumulation of approximately 1,000 kg-N ha-1 (1967-2017). These findings highlight the importance of considering legacy N when forecasting future water quality, as N in the subsurface will continue to impair drinking water quality and elevate surface water N concentrations for decades to come.

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Section: Predictions Of Groundwater Nitrate-n Accumulation Magnitudesmentioning

confidence: 99%

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Meter

Schultz

Chang

2023

Environ. Res. Lett.

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“…Both the Global NEWS and WorldQual currently overlook the in uence of legacy nutrient dynamics. While the IMAGE-GNM considers the contribution of historical nutrient inputs to nutrient transport by surface runoff (Beusen et al 2015), the associated lag time (years-to-decades) and how legacy nutrient dynamics interact with land management practices requires further clari cation (Golden et al 2023). Furthermore, other model improvements require quantitative information on hydrological processes and the transport/emission of other pollutants/sediments in the basin to better understand the changing trends of global riverine nutrient emissions (Tang et al 2018).…”

Section: Applicable Conditions Of the Three Global Modelsmentioning

confidence: 99%

Evaluation of three prevalent global riverine nutrient transport models

Jiao,

Zhou,

et al. 2023

Environ Sci Pollut Res

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Global nitrogen (N) and phosphorus (P) transport models provide a quantitative assessment of riverine nutrient source, transport, accumulation and depletion processes. By conducting a meta-analysis of the modeled results and accuracy metrics from previous studies, this study evaluated the accuracies and their in uencing factors of three prevalent global riverine N and P transport models (Global NEWS, IMAGE-GNM and WorldQual). The Global NEWS model exhibited higher accuracies in predicting riverine dissolved organic nitrogen (DON; R 2 = 0.58, NSE = 0.57) and dissolved organic phosphorus (DOP; R 2 = 0.59, NSE = 0.59) yields compared to riverine dissolved inorganic nitrogen (DIN; R 2 = 0.56, NSE=-0.80) and dissolved inorganic phosphorus (DIP; R 2 = 0.33, NSE=-0.12) yields. The DIN and DIP sub-models of Global NEWS were applicable for basins with areas greater than 2.2×10 4 km 2 and 3.2×10 4 km 2 , respectively. The IMAGE-GNM model demonstrated satisfactory accuracies in predicting riverine total nitrogen (TN; R 2 = 0.56, NSE = 0.53) and total phosphorus (TP; R 2 = 0.59, NSE = 0.48) concentrations, particularly in European basins. The IMAGE-GNM model performed better for simulation of riverine TN concentration when data set was longer than 21 years and for regions above 54°N, and for simulation of riverine TP concentration when data set was longer than 22 years and for regions above 55°N. The WorldQual model demonstrated relatively poor performance in simulating riverine TN (R 2 = 0.76, NSE = 0.34) and TP (R 2 = 0.71, NSE=-0.25) concentrations. For model improvements in future, the Global NEWS and WorldQual would bene t from more detailed in-stream nutrient retention/release and transformation modules, while improved chemical weathering dynamics could further enhance the Global NEWS. For the IMAGE-GNM, modi cation of the soil erosion module is warranted to enhance e ciency in basins outside Europe.Consideration of legacy effects is required to improve these three models. The results of this study provide valuable guidance for the model selecting and improvement for speci c needs.

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“…Cropland is the foundation of food security and human health, with the largest N flux on earth (Cui et al., 2023). N loss from the soil system on cropland not only reduces soil fertility and crop yield, but also adversely affects the environmental quality (Golden et al., 2023). Nitrate is the main form of N loss (Sun et al., 2023), the loss of nitrate from soil to streams and lakes through runoff and other means can lead to eutrophication, causing excessive growth of aquatic algae, and damaging water quality (Miller et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Response Mechanisms of Elevated CO₂ Concentration on Various Forms of Nitrogen Losses in the Golden Corn Belt

Zhang,

Han,

Wen

et al. 2024

Water Resources Research

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Nitrogen (N) loss is a significant source of water quality pollution in alluvial watersheds. However, the mechanisms linking N loss and elevated CO2 concentration (eCO2) are not well recognized. In this study, we comprehensively calibrated the SWAT model equipped with a dynamic CO2 input and response module to investigate the response mechanisms between multiform N losses and eCO2 in a representative large‐scale watershed. Results revealed nitrate loss under eCO2 exceeding 100% in some upstream zones under the SSP5‐8.5 scenario (P < 0.05) compared to the constant CO2 concentration. This was directly related to the great increase in hydrological variables, which were the carriers of N losses, and the intensive inputs of N fertilizer. Results also found that nitrate leaching was greater than the other two processes for future periods, peaking at 309.3%, as compared to the baseline period. The findings suggested reducing fertilizer inputs by 10%–20% was promising, especially for reducing nitrate loss through runoff and leaching by up to 17.7% and 12.2%. This study explored the mechanisms of increased N loss in response to eCO2 and provided scientific evidence for early warning and making decisions to improve water quality at a large watershed scale.

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Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality

Cited by 17 publications

References 43 publications

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Evaluation of three prevalent global riverine nutrient transport models

Assessing the Response Mechanisms of Elevated CO₂ Concentration on Various Forms of Nitrogen Losses in the Golden Corn Belt

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Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality

Cited by 17 publications

References 43 publications

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Evaluation of three prevalent global riverine nutrient transport models

Assessing the Response Mechanisms of Elevated CO2 Concentration on Various Forms of Nitrogen Losses in the Golden Corn Belt

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Assessing the Response Mechanisms of Elevated CO₂ Concentration on Various Forms of Nitrogen Losses in the Golden Corn Belt