Mississippi River nitrate loads from high frequency sensor measurements and regression-based load estimation

Area of Science:

• Environmental Science
• Hydrology
• Water Quality

Background:

• Accurate nitrate (NO3-) load quantification from the Mississippi River is vital for predicting Gulf of Mexico hypoxia.
• Traditional regression-based models have been used for load estimation, but their accuracy at finer time scales is uncertain.

Purpose of the Study:

• To evaluate the performance of commonly used load estimation models against measured nitrate loads in the lower Mississippi River.
• To assess the impact of high-frequency nitrate sensor data on load quantification accuracy.

Main Methods:

• Utilized high-frequency nitrate sensors to measure NO3- concentrations in the lower Mississippi River over a two-year period.
• Compared measured daily and monthly NO3- loads with estimates from LOADEST, Composite Method, and WRTDS models.
• Analyzed concentration-discharge relationships and their variability, particularly during drought and flood conditions.

Main Results:

• Models showed a small overall underestimate (3.5%) across the study period but significant differences at shorter time scales.
• Discrepancies were most pronounced during the 2013 flood event, with models underestimating loads.
• Variability in concentration-discharge relationships was high, especially during extreme weather events like drought and flood.

Conclusions:

• High-frequency nitrate measurements provide more accurate and precise load estimates than traditional methods, especially for critical spring and early summer periods.
• Current models may underestimate Mississippi River nitrate loads, particularly during high-flow events, impacting hypoxia predictions.
• Advanced sensor technology offers benefits beyond load estimation, enabling a deeper understanding of nutrient dynamics.