James Randall Etheridge

A natural wetland receiving drainage from a 24-ha urbanized catchment in the Falls Lake Watershed of North Carolina was evaluated to determine if it was providing ecosystem services with regards to phosphate and Escherichia coli (E. coli) attenuation. Inflow and outflow characteristics including nutrient and bacteria concentrations along with physicochemical properties (discharge, pH, oxidation reduction potential, temperature, and specific conductance) were assessed approximately monthly for over 2 years. The median exports of phosphate (0.03 mg/s) and E. coli (5807 MPN/s) leaving the wetland were 85% and 57% lower, respectively, relative to inflow loadings, and the differences were statistically significant (p < 0.05). Hydraulic head readings from three piezometers installed at different depths revealed the wetland was a recharge area. Phosphate and E. coli concentrations were significantly greater in the shallowest piezometer relative to the deepest one, suggesting treatment occurred during infiltration. However, severe erosion of the outlets is threatening the stability of the wetland. Upstream drainageway modifications were implemented to slow runoff, and septic system repairs and maintenance activities were implemented to improve water quality reaching the wetland and Lick Creek. However, more work will be needed to conserve the ecosystem services provided by the wetland.

Accurate flow prediction is a primary goal of hydrological modeling studies, which can be affected by the use of varying rainfall datasets, autocalibration methods, and performance indices. The combined effect of three rainfall datasets — Fifth generation of European ReAnalysis (ERA-5), Gridded meteorological data (gridMET), Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG) — and three autocalibration techniques — Dynamically Dimensioned Search (DDS), Generalized Likelihood Uncertainty Estimation (GLUE), Latin Hypercube Sampling (LHS) — on SWAT+ river flow prediction was measured using three evaluation metrics — Nash Sutcliffe Efficiency (NSE), Kling Gupta Efficiency (KGE) and coefficient of determination (R ² ) — for two watersheds in North Carolina (Cape Fear, Tar Pamlico) using the Soil Water Assessment Tool Plus (SWAT+) model. Five parameters in the SWAT+ model, cn2, revap_co, flo_min, revap_min, and awc, were found to be significantly sensitive under all combinations for both watersheds. Simulated flow varied more with the change in rainfall than the calibration technique used. We discovered that GPM IMERG gave the best results of the rainfall datasets, followed by ERA-5 and gridMET. We observed that the NSE score is more sensitive to different combinations of rainfall datasets and calibration techniques than the KGE scores. SWAT+ underperformed in the prediction of base flow for the groundwater-driven watershed. Overall, we recommend using the GPM IMERG rainfall dataset with the GLUE optimization technique and KGE performance index for optimal flow simulations. The results from this study will help hydrological modelers choose an optimal combination of rainfall dataset, autocalibration technique, and performance index depending on watershed characteristics.

Image-based stage and discharge measuring systems are among the most promising new non-contact technologies available for long-term hydrological monitoring. This article evaluates and reports the long-term performance of the GaugeCam (www.gaugecam.org) image-based stage measuring system in situ. For this we installed and evaluated the system over several months in a tidal marsh to obtain a good stratification of the measured stages. Our evaluation shows that the GaugeCam system was able to measure within about ±5 mm for a 90% confidence interval over a range of about 1 m in a tidal creek in a remote location of North Carolina, USA. Our results show that the GaugeCam system nearly performed to the desired design of ±3 mm accuracy around 70% of the time. The system uses a dedicated target background for calibration and geometrical perspective correction of images, as well as auto-correction to compensate for camera movement. The correction systems performed well overall, although our results show a ‘croissant-shaped’ mean error (-1 to +4 mm,) varying with water stage. We attribute this to the small, yet present, ‘fish-eye’ effect embedded in images, for which our system did not entirely correct in the tested version, and which might affect all image-based water level measurement systems.

Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis. One particular data imputation challenge is filling large data gaps when the only related data comes from the same sensor station. In this paper, we propose a framework to improve the popular multivariate imputation by chained equations (MICE) method for dealing with missing data. One key strategy we use to improve model accuracy is to reshape the original sensor data to leverage the correlation between the missing data and the observed data. We demonstrate our framework using data from continuous water quality monitoring stations in Vermont. Because of possible irregularly spaced peaks throughout the time series, the reshaped data is split into extreme and normal values and two MICE models are built. We also recommend that sensor-collected data should be transformed to meet the machine learning model assumptions. According to our experimental results, these strategies can improve MICE data imputation model accuracy at least 23% for large data gaps based on $\text {R}^{{2}}$ values and are promising to be applied for other data imputation algorithms.

Private groundwater wells have the potential to be an unmonitored source of contaminants that can harm human health for millions of people throughout the United States. Developing models that predict potential exposure to contaminants, such as nitrate, could guide sampling efforts and allow the residents to take action to reduce their risk. Machine learning models have been successful in predicting nitrate contamination using geospatial information such as proximity to nitrate sources or soil type, but previous models have not considered meteorological factors that change temporally. In this study, we test random forest (regression and classification) and linear regression models to predict nitrate contamination of wells using rainfall and temperature records over the previous 180-days. We trained and tested models for (1) all of North Carolina, (2) each geographic region in North Carolina, (3) a three-county region with high density animal agriculture, and (4) a three-county region with a low density of animal agriculture. All regression models had poor predictive performance (R2 = 0.04) for all areas tested. The random forest classification model for the coastal plain region showed fair agreement (Cohen’s kappa = 0.23) when trying to predict whether contamination occurred. All other classification models had slight or poor predictive performance. Our results show that temporal changes in rainfall and temperature alone are not enough to predict nitrate contamination in most areas of North Carolina but show potential in the coastal plain region.

Wastewater contains elevated concentrations of fecal indicator bacteria (FIB). The type of wastewater treatment technology and septic system density may influence the FIB concentration and exports at the watershed scale. The goal of this study was to gain a better understanding of FIB concentrations and exports from watersheds served by conventional septic (CS) systems, sand filter (SF) septic systems, and a municipal sewer (SEW) system. Seven watersheds (3 CS, 3 SF, and 1 SEW) were monitored to quantify FIB concentration and export monthly from April 2015 to March 2016. The type of wastewater treatment did not yield significant differences in FIB concentration or exports when pooling watersheds using similar wastewater treatment. Watersheds with the highest septic densities (approximately 0.4 systems ha⁻¹) contained greater FIB concentrations and exports than watersheds with the lowest (approximately 0.1–0.2 systems ha⁻¹), but only FIB concentrations significantly differed. These findings suggest that when the septic system density exceeds 0.4 systems ha⁻¹, water quality degradation from septic leachate may be observable at the watershed scale, especially in watersheds dominated by residential development. More research is recommended to determine if this density threshold is similar for other water pollutants and/or in watersheds with differing hydrogeological, land use, and wastewater characteristics.

Public participation in groundwater projects is increasing, however, the efficacy of the data collected in such studies, is not well‐documented in the literature. In this study, the authors describe a citizen science project focused on measuring and recording groundwater levels in an aquifer and evaluate whether the groundwater data collected by the participants are trustworthy. A total of 31 participants were initially recruited to measure and record groundwater levels from 29 monitoring wells on a barrier island. Following recruitment, the authors provided training to the citizen scientists by introducing groundwater concepts, and showing the participants how to measure, record and report groundwater level data (over an 81‐day period) with an electronic water level meter. The water level data recorded by the citizen scientists (i.e., 35 time series datasets with over 450 unique measurements) were then compared to high frequency data recorded by automated water level loggers that were already deployed in the groundwater monitoring wells to assess the trustworthiness of the data. Trustworthiness was evaluated using measures of reliability (i.e., consistency in measuring the same thing), validity (i.e., degree to which results are truthful), and other standard graphical and statistical techniques. The results suggest that with proper training, guidance, and motivation, citizen scientists can collect trustworthy groundwater level data that could be useful for monitoring the sustainability of aquifers and managing of groundwater levels. It is noted however, that such positive outcomes require significant investments of time and effort on the part of the project managers.