Identifying the spatial pattern and importance of hydro‐geomorphic drainage impairments on unpaved roads in the northeastern USA

Roads have been widely studied as sources of runoff and sediment and identified as pollutant production sources to receiving waters. Despite the wealth of research on logging roads in forested, upland settings, little work has been conducted to examine the role of extensive networks of rural, low‐volume, unpaved roads on water quality degradation at the catchment scale. We studied a network of municipal unpaved roads in the northeastern US to identify the type and spatial extent of ‘hydro‐geomorphic impairments’ to water quality. We mapped erosional and depositional features on roads to develop an estimate of pollutant production. We also mapped the type and location of design interventions or best management practices (BMPs) used to improve road drainage and mitigate water quality impairment. We used statistical analyses to identify key controls on the frequency and magnitude of erosional features on the road network, and GIS to scale up from the survey results to the catchment scale to identify the likely importance of unpaved roads as a pollutant source in this setting. An average of 21 hydro‐geomorphic impairments were mapped per kilometer of road, averaging 0.3 m³ in volume. Road gradient and slope position were key controls on the occurrence of these features. The presence of BMPs effectively reduced erosion frequency. Scaled up to the watershed and using a conservative estimate of road–stream connectivity, our results for the Winooski River watershed in the northeastern US suggest that roughly 16% and 6% of the average annual sediment and phosphorus flux, respectively, of the Winooski River may be derived from unpaved roads. Our study identifies an under‐appreciated source of water quality degradation in rural watersheds, provides insights into identifying ‘hot spots’ of pollutant production associated with these networks, and points to effectiveness of design interventions in mitigating these adverse impacts on water quality. Copyright © 2017 John Wiley & Sons, Ltd.     

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study

The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g., oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater tetrachloroethylene (TCE) plume that underlies the Tooele Army Depot‐North (TEAD‐N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF‐based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well‐calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging‐based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation.