Patrick L. Gurian

The Schuylkill River Watershed in southeastern PA provides essential ecosystem services, including drinking water, power generation, recreation, transportation, irrigation, and habitats for aquatic life. The impact of changing climate and land use on these resources could negatively affect the ability of the watershed to continually provide these services. This study applies a hydrologic model to assess the impact of climate and land use change on water resources in the Schuylkill River Basin. A hydrologic model was created within the Structural Thinking Experiential Learning Laboratory with Animation (STELLA) modeling environment. Downscaled future climate change scenarios were generated using Localized Constructed Analogs (LOCA) from 2020 to 2040 for Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 emission scenarios. Three regional land use change scenarios were developed based on historical land use and land cover change trends. The calibrated model was then run under projected climate and land use scenarios to simulate daily streamflow, reservoir water levels, and investigate the availability of water resources in the basin. Historically, the streamflow objective for the Schuylkill was met 89.8% of the time. However, the model forecasts that this will drop to 67.2–76.9% of the time, depending on the climate models used. Streamflow forecasts varied little with changes in land use. The two greenhouse gas emission scenarios considered (high and medium emissions) also produced similar predictions for the frequency with which the streamflow target is met. Barring substantial changes in global greenhouse gas emissions, the region should prepare for substantially greater frequency of low flow conditions in the Schuylkill River.

Guanabara Bay has been known to be polluted with trash and sewage from the surrounding areas, but health risks from recreational contact with water in the basin have not been well characterized. In this paper, fecal indicator bacteria (FIB) monitoring data are used to predict risks in three different exposure scenarios: (1) bathing in freshwater rivers that discharge into Guanabara Bay, (2) wading in these freshwater rivers, and (3) sailing in Guanabara Bay. Concentrations of indicator bacteria in river samples were measured directly, and concentrations of indicator bacteria in bay samples were sourced from publicly available government data sets. Ratios between pathogens and fecal indicator concentrations were used to estimate risks for five selected pathogens based on the indicator concentrations. The median risk of disease estimated from E. coli indicator concentrations was 1.0, 9.9 × 10−1 and 8.2 × 10−4 for the swimming, wading, and sailing exposure pathways, respectively. Risks estimates based on concentrations of the enterococci indicator bacteria in the sailing exposure scenario were comparable, at 3.4 × 10−4. The sum of total risk estimated from the five selected pathogens was 5.9 × 10−1, 3.6 × 10−1, and 1.0 × 10−3 for the swimming, wading, and sailing exposure pathways, respectively. Estimated risks of swimming and wading in the rivers far exceeded risks associated with U.S. recreational contact standards, while estimated risks for sailing in the bay were well below these risk guidelines. The 95th percentile of the sailing risk was estimated to exceed the U.S. recreational contact risk level. This paper exemplifies an approach to conducting quantitative microbial risk assessments when only fecal indicator bacteria data are available. Context-specific data on the relevant exposure routes, exposure frequency, and site-specific indicator: pathogen ratios were lacking, which ultimately led to uncertainty in the model. This study is intended to provide a framework for estimating GI risk based on fecal indicator concentrations while acknowledging that the substantial variation in indicator:pathogen ratios make the results of such efforts uncertain.