Estimation of nutrient (N and P) fluxes into Newark Bay, USA

Nie, Jing; Mirza, Sana; Viteritto, Michael; Li, Yuanyi; Witherell, B. B.; Yang, Daoguo; Yoo, Shinjae; Feng, Huan

doi:10.1016/j.marpolbul.2023.114832

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…In a forested watershed draining into Chesapeake Bay, Hurricane Irene in August 2011 caused an increase of total nitrogen on the rising limb of the storm compared to the 40 baseflow levels (Vidon et al, 2018). High discharge due to Hurricane Irene in 2011 also resulted in high nutrients loading to Newark Bay in northern New Jersey (Nie et al, 2023). The loss of vegetation, attributed to Hurricane Hugo, led to a 108-154% increase in exported nutrients primarily due to increased outflow during the hurricane (Wilson et al, 2006).…”

Section: Introduction 30mentioning

confidence: 99%

Short-term effects of hurricanes on nitrate-nitrogen runoff loading: a case study of Hurricane Ida using E3SM land model (v2.1)

Fang,

Tran,

Leung

2024

Preprint

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Abstract. When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region.

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Section: Introduction 30mentioning

confidence: 99%

Short-term effects of hurricanes on nitrate-nitrogen runoff loading: a case study of Hurricane Ida using E3SM land model (v2.1)

Fang,

Tran,

Leung

2024

Preprint

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“…Many efforts have been made to describe the major sources of N and P throughout the MARB and evaluate the effectiveness of nutrient reduction strategies [4]. Because of the randomness in the observations of nutrient loads [5], various statistical methods have been implemented to analyze trends in nutrient loads [6][7][8][9][10][11][12]. Linear regression was used to study yields throughout the MARB, considering various environmental impacts [13], and to identify the relative importance of sources and land use in nutrient delivery [14,15].…”

Section: Introductionmentioning

confidence: 99%

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

Zhen,

Feng,

Yoo

2023

Environments

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To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO3-NO2), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

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Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Zhen,

Feng,

Yoo

2024

Water

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Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

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Estimation of nutrient (N and P) fluxes into Newark Bay, USA

Cited by 3 publications

References 36 publications

Short-term effects of hurricanes on nitrate-nitrogen runoff loading: a case study of Hurricane Ida using E3SM land model (v2.1)

Short-term effects of hurricanes on nitrate-nitrogen runoff loading: a case study of Hurricane Ida using E3SM land model (v2.1)

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

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