Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

Park, Daeryong; Um, Myoung-Jin; Markus, Momcilo; Jung, Kwang-Mo; Keefer, Laura; Verma, Siddhartha

doi:10.3390/su13137508

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…The flow-weighted average concentration ratio method and error correction techniques were found to improve load estimate accuracy. The accuracy of regression-based methods, such as WRTDS (Weighted Regression on Time, Discharge, and Season) and LOADEST (LOAD ESTimator), for estimating nitrate loads from sparse measurements in agricultural watersheds was investigated [4,5] and compared to that of the physical-based SWAT (Soil and Water Assessment Tool) model and simple linear interpolation. The bias factor method was evaluated for addressing missing extreme concentrations in aquatic exposure assessments when sampling frequency is less than daily, using a dataset of 69 near-daily sampled site-years and various sampling designs [6].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the numerous studies focused on improving nutrient load estimation techniques and addressing their uncertainties [2][3][4][5][7][8][9][10][11][12], there is still a need for further research on the influence of sampling frequency and estimation methods on the uncertainty of nutrient load estimates under different seasonal and hydrological conditions, especially during high-flow seasons and extreme precipitation events. This study aims to address the knowledge gap by focusing on an agricultural watershed dominated by non-point source pollution in the Yiluo River Basin, China.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Zhang,

Xu,

et al. 2024

Water

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Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (<10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Zhang,

Xu,

et al. 2024

Water

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Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season

Fang,

Caers,

Maher

2024

Front. Water

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The temporal dynamics of solute export from catchments are challenging to quantify and model due to confounding hydrological and biogeochemical processes and sparse measurements. Conventionally, the concentration-discharge relationship (C-Q) and statistical approaches to describe it, such as the Weighted Regressions on Time, Discharge and Seasons (WRTDS), have been widely used. Recently, deep learning (DL) approaches, especially Long-Short-Term-Memory (LSTM) models, have shown predictive capability for discharge, temperature, and dissolved oxygen. However, it is not clear if such advances can be expanded to water quality variables driven by complex subsurface biogeochemical processes. This work evaluates the performance of LSTM and WRTDS for 20 water quality variables across ~500 catchments in the continental US. We find that LSTM does not markedly outperform WRTDS in our dataset, potentially limited by the current measurement capabilities of water quality across CONUS. Both models present similar performance patterns across water quality variables, with the LSTM displaying better performance for nutrients compared to weathering-derived solutes. Additionally, the LSTM does not benefit from flexibility in the inputs. For example, incorporation of climate data that constrains streamflow generation, does not significantly improve the LSTM performance. We also find that data availability is not a straightforward predictor of LSTM model performance, although higher availability tends to stabilize performance. To fully assess the potential of the LSTM model, it may be necessary to use a higher frequency dataset across the CONUS, which does not exist today. To evaluate the dynamics of C-Q patterns relative to model performance, we introduce a “simplicity index” considering both the seasonality in the concentration pattern and the linearity in the C-Q relationship, or the C-Q-t pattern. The simplicity index is strongly correlated with model performance and differentiates the underlying controls on water quality dynamics. Further DL experiments and model-intercomparison highlight the strengths and deficiencies of existing frameworks, pointing to the need for further hydrogeochemical theories that are amenable to complex basins and solutes.

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Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

Cited by 2 publications

References 39 publications

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season

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