Frontiers in assessing septic systems vulnerability in coastal Georgia, USA: Modeling approach and management implications.

Threats to public health and environmental quality from septic systems are more prevalent in areas with poorly draining soils, high water tables, or frequent flooding. Significant research gaps exist in assessing these systems' vulnerability and evaluating factors associated with higher rates of septic systems replacement and repair. We developed a novel GIS-based framework for assessing septic system vulnerability using a database of known septic system specifications and a modified Soil Topographic Index (STI) that incorporates seasonal high groundwater elevation to assess risks posed to septic systems in coastal Georgia. We tested the hypothesis that both the modified STI and septic system specifications such as tank capacity per bedroom and drainfield type would explain most of the variance in septic system repair and replacement using classification inference tree and generalized logistic regression models. Our modeling results indicate that drainfield type (level vs. mounded) is the most significant variable (p-value < 0.001) in predicting septic systems functionality followed by septic tank capacity per bedroom (p-value < 0.01). These show the importance of septic system design regulations such as a minimum requirement for horizontal separation distance between the bottom of trenches and seasonal water table, and adequate tank capacity design. However, for septic systems with a mounded drainfield and a larger tank capacity per bedroom, the modified STI representing hydrological characteristics of septic system location is a significant predictor of a high septic system repair and replacement rate. The methodology developed in this study can have important implications for managing existing septic systems and planning for future development in coastal areas, especially in an environment of rapid climatic change.

Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: successes and challenges.

Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders - a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools.

Green infrastructure and its catchment-scale effects: an emerging science.

Urbanizing environments alter the hydrological cycle by redirecting stream networks for stormwater and wastewater transmission and increasing impermeable surfaces. These changes thereby accelerate the runoff of water and its constituents following precipitation events, alter evapotranspiration processes, and indirectly modify surface precipitation patterns. Green infrastructure, or low-impact development (LID), can be used as a standalone practice or in concert with gray infrastructure (traditional stormwater management approaches) for cost-efficient, decentralized stormwater management. The growth in LID over the past several decades has resulted in a concomitant increase in research evaluating LID efficiency and effectiveness, but mostly at localized scales. There is a clear research need to quantify how LID practices affect water quantity (i.e., runoff and discharge) and quality at the scale of catchments. In this overview, we present the state of the science of LID research at the local scale, considerations for scaling this research to catchments, recent advances and findings in scaling the effects of LID practices on water quality and quantity at catchment scales, and the use of models as novel tools for these scaling efforts.

Cumulative Effects of Low Impact Development on Watershed Hydrology in a Mixed Land-Cover System.

Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover. The results showed overall increases in shallow subsurface runoff and infiltration, as well as evapotranspiration, and decreases in peak flows and surface runoff across all types and configurations of LID. Among individual LID practices, rain gardens had the greatest influence on each component of the overall watershed water balance. As anticipated, the combination of LID practices at the highest implementation level resulted in the most substantial changes to the overall watershed hydrology. It is notable that all hydrological changes from the LID implementation, ranging from 0.01 to 0.06 km2 across the study watershed, were modest, which suggests a potentially limited efficacy of LID practices in mixed land cover watersheds.

Modeling the Effects of Onsite Wastewater Treatment Systems on Nitrate Loads Using SWAT in an Urban Watershed of Metropolitan Atlanta.

Onsite wastewater treatment systems (OWTSs) can be a source of nitrogen (N) pollution in both surface and ground waters. In metropolitan Atlanta, GA, >26% of homes are on OWTSs. In a previous article, we used the Soil Water Assessment Tool to model the effect of OWTSs on stream flow in the Big Haynes Creek Watershed in metropolitan Atlanta. The objective of this study was to estimate the effect of OWTSs, including failing systems, on nitrate as N (NO-N) load in the same watershed. Big Haynes Creek has a drainage area of 44 km with mainly urban land use (67%), and most of the homes use OWTSs. A USGS gauge station where stream flow was measured daily and NO-N concentrations were measured monthly was used as the outlet. The model was simulated for 12 yr. Overall, the model showed satisfactory daily stream flow and NO-N loads with Nash-Sutcliffe coefficients of 0.62 and 0.58 for the calibration period and 0.67 and 0.33 for the validation period at the outlet of the Big Haynes Watershed. Onsite wastewater treatment systems caused an average increase in NO-N load of 23% at the watershed scale and 29% at the outlet of a subbasin with the highest density of OWTSs. Failing OWTSs were estimated to be 1% of the total systems and did not have a large impact on stream flow or NO-N load. The NO-N load was 74% of the total N load in the watershed, indicating the important effect of OWTSs on stream loads in this urban watershed.

Confirmation of the Impact of Onsite Wastewater Treatment Systems on Stream Base-Flow Nitrogen Concentrations in Urban Watersheds of Metropolitan Atlanta, GA.

Wastewater and lawn fertilizer potentially contribute to degraded water quality in urban watersheds. Previously we described a study from 2011 to 2012 in which we examined the effect of the density of onsite wastewater treatment systems (OWTS) on nitrogen concentrations in 24 small streams in metropolitan Atlanta. Our objective in this study was to confirm that the impact on water quality that we observed was due to OWTS and not lawn fertilizer. We sampled the same 24 streams again in 2013 and 2014, representing watersheds ranging in area from 0.18 to 8.8 km. We conducted regression analysis of the effect of OWTS and season, used dual-isotope analysis (nitrogen and oxygen in nitrate) to identify sources and determine the effect of denitrification and mixing, and conducted stream walks to identify areas where animals had access to the streams. Twelve streams were characterized as high-density (HD, more than 75 systems km) OWTS and 12 as low-density (LD, less than 75 systems km) OWTS. Water samples were collected three times a year under base-flow conditions, from November 2011 to July 2014, and analyzed for nitrate (NO-N), ammonium (NH-N), and total Kjeldahl nitrogen. Total nitrogen and NO-N concentrations increased linearly with increasing OWTS density above a threshold of about 75 OWTS km. Dual-isotope analysis of NO showed that stream NO originated predominantly from OWTS in HD watersheds and from a combination of animal waste and perhaps organic N in LD watersheds. Stream walks showed that livestock had access to some of the LD streams with high N concentrations. Our results confirm that HD OWTS can significantly degrade water quality at the watershed scale.