Extreme Learning Machine Predicts  High-Frequency Stream flow and  Nitrate-N Concentrations in a  Karst Agricultural Watershed

Abstract

Highlights A novel high-frequency dataset of streamflow, nitrate-N concentration, and soil moisture at depths throughout and below the root zone highlighted tight connectivity between soil moisture dynamics and nitrate-N concentrations spanning event to seasonal timescales. A TELM model successfully predicted both flow rate and nitrate exports from these complex systems because the time lag associated with the soil conditions was represented in model training. The inclusion of soil moisture and temperature data below the effective root zone was important to accurately capture the storm event hysteresis dynamics observed in the exported nitrate signals. Abstract. Efforts to reduce nitrogen contributions from karst agroecosystems have had variable success, in part due to an incomplete understanding of nitrogen source, fate, and transport dynamics in karst watersheds. Recent advancements in environmental sensors and data-driven artificial intelligence models may be useful in improving our understanding of system behavior and the linkages between soil hydrologic processes and karst nitrate loading dynamics. We collected 35 months of high-resolution streamflow, nitrate-N concentration, soil moisture and temperature (from 10-100 cm depths), and meteorological data in a karst agricultural watershed in the Inner-Bluegrass region of Central Kentucky. Two-layer extreme learning machine (TELM) models were developed to predict nitrate-N concentrations and flow rates as a function of meteorological and soil parameter inputs. Results suggest tight linkages between soil moisture gradients at different depths and nitrate-N concentrations at the watershed outlet. TELM modeling results supported visual observations from the high-frequency data and suggest that inclusion of both soil moisture and temperature parameters at all soil depths improved predictions of both flow rate and nitrate-N concentration (with optimal NSE values of 0.93 and 0.94, respectively, when all inputs were considered). Hysteresis analysis suggested that inclusion of the deepest soil layer (100 cm) was necessary to predict hysteresis observed during storm events. The findings of the study highlight the importance of variable activation of matrix waters in preferential flows throughout events and seasons and its subsequent impacts on nitrate-N concentrations. Results suggest that management models should incorporate vertical variability in soil hydrology to accurately characterize nitrate source and transport dynamics. Further, the results of hysteresis analysis underscore the importance of inclusion of hysteresis indices, in addition to typical model evaluation statistics, to ensure accurate representation of nutrient flow pathways. Keywords: Extreme learning machine, Karst agroecosystem, Nitrate, Water resources.

Impacts of Aquatic Vegetation Dynamics on Nitrate Removal in Karst Agricultural Streams: Insights from Unmanned Aircraft Systems and In Situ Sensing

“…As a result, the fate and transport of nitrogen are primarily impacted by transformations associated with aquatic vegetation. This watershed was chosen because of an extensive historical hydrologic and water quality dataset at the site (Ford et al, 2019), a previous study of algal and duckweed impacts on water quality (Bunnell et al, 2020), a multi-year in situ high-frequency dataset at the watershed outlet (McGill and Ford, 2024), and AI-based modeling of flow and nitrate concentrations in the watershed (McGill and Ford, 2024).…”

“…Specific conductance (µS/cm) and temperature (°C) were measured using an EXO conductivity and temperature sensor. For all parameters, data was collected continuously at a 15-minute interval, with some periodic gaps (McGill and Ford, 2024). Data was screened to check if points fell within maximum and minimum threshold values for the sensors per manufacturer specifications and were flagged if they fell outside of bounds.…”

“…The values of nitrate-nitrite were reported in milligrams of nitrogen per liter, mgN/L. Data was collected from September 2018-August 2021 at 15-minute intervals with some period gaps (McGill and Ford, 2024). Reference spectrum updates were generally carried out every four to five weeks per manufacturer specifications.…”

“…KGS follows EPA Method 9056A-Determination of Inorganic Anions by Ion Chromatography when determining the nitrate concentration of surface water samples (EPA, 2007). Linear regression of lab nitrate measurements vs. SUNA estimates (n = 80) yielded R-squared values of 0.98 and a slope of 0.96, indicating the strength of the optical measurement (McGill and Ford, 2024).…”

“…Nitrate concentration results were analyzed for diurnal variability to determine the impact of aquatic vegetation pools on N removal. First, days in which storm events occurred within 48 hours of the end of the day were excluded from analysis since concentrations may be more strongly impacted by hydrological processes as compared to in-stream biochemical processes (McGill and Ford, 2024). Differences in daily maxima and minima in nitrate concentrations, the timing of daily maxima, and the timing of daily minima were analyzed for each day in the filtered dataset.…”