Estimated effects of climate change on flood vulnerability of U.S. bridges

We assessed the potential impacts of increased river flooding from climate change on bridges in the continental United States. Daily precipitation statistics from four climate models and three greenhouse gas (GHG) emissions scenarios (A2, A1B, and B1) were used to capture a range of potential changes in climate. Using changes in maximum daily precipitation, we estimated changes to the peak flow rates for the 100-year return period for 2,097 watersheds. These estimates were then combined with information from the National Bridge Inventory database to estimate changes to bridge scour vulnerability. The results indicate that there may be significant potential risks to bridges in the United States from increased precipitation intensities. Approximately 129,000 bridges were found to be currently deficient. Tens of thousands to more than 100,000 bridges could be vulnerable to increased river flows. Results by region vary considerably. In general, more bridges in eastern areas are vulnerable than those in western areas. The highest GHG emissions scenarios result in the largest number of bridges being at risk. The costs of adapting vulnerable bridges to avoid increased damage associated with climate change vary from approximately $140 to$250 billion through the 21st century. If these costs were spread out evenly over the century, the annual costs would be several billion dollars. The costs of protecting the bridges against climate change risks could be reduced by approximately 30% if existing deficient bridges are improved with riprap.United States. Environmental Protection Agency. Office of Atmospheric Programs (Contract #EP-W-07-072

Virus Fate and Transport in Groundwater : Organic matter, uncertainty, and cold climate

Water managers must balance the need for clean and safe drinking water with ever-increasing amounts of waste-water. A technique for treating and storing surface water called “managed aquifer recharge” (MAR) is frequently used to help maintain this balance. When MAR is used to produce drinking water, water managers must ensure that disease-causing microbial contaminants are removed from the water prior to its distribution. This thesis examined the processes responsible for removing a specific class of microbial contaminants called “enteric viruses” during MAR. Viruses are naturally removed in groundwater through adsorption and inactivation mechanisms. This thesis investigated how these virus removal mechanisms were affected by ionic strength (IS), dissolved organic carbon (DOC), and the age of the sand used in a MAR infiltration basin. This was done using batch and flow-through column experiments designed to mimic conditions characteristic of a basin infiltration MAR scheme in Uppsala, Sweden. Bacteriophage MS2 was used as a proxy for enteric viruses. All of the experiments were conducted at 4°C. Experimental data were modeled to describe the fate and transport of viruses in the infiltrated groundwater. Conventional least-squares optimization and generalized likelihood uncertainty estimation (GLUE) were compared as model fitting-approaches in order to determine how data uncertainty affects parameter estimates and model predictions. Results showed that the sand used in the infiltration basins accumulates adsorbed organic matter as it is exposed to infiltrating surface waters. This reduced the amount of MS2 that was removed due to adsorption and inactivation. Results from GLUE indicated that MS2 is more likely to inactivate in a time-dependent manner when in the presence of sand with high concentrations of organic matter. Both model fitting techniques indicated that virus attachment rates were significantly lower for sand with high organic carbon content. Neither methodology was capable of adequately capturing the kinetics of virus adsorption. Uncertainties in the experimental data had a large effect on the conclusions that could be drawn from fitted models. This study showed that the presence of natural organic matter reduces the value of the infiltration basin as a microbial barrier

The effects of ionic strength and organic matter on virus inactivation at low temperatures : general likelihood uncertainty estimation (GLUE) as an alternative to least-squares parameter optimization for the fitting of virus inactivation models

This study examined how the inactivation of bacteriophage MS2 in water was affected by ionic strength (IS) and dissolved organic carbon (DOC) using static batch inactivation experiments at 4 A degrees C conducted over a period of 2 months. Experimental conditions were characteristic of an operational managed aquifer recharge (MAR) scheme in Uppsala, Sweden. Experimental data were fit with constant and time-dependent inactivation models using two methods: (1) traditional linear and nonlinear least-squares techniques; and (2) a Monte-Carlo based parameter estimation technique called generalized likelihood uncertainty estimation (GLUE). The least-squares and GLUE methodologies gave very similar estimates of the model parameters and their uncertainty. This demonstrates that GLUE can be used as a viable alternative to traditional least-squares parameter estimation techniques for fitting of virus inactivation models. Results showed a slight increase in constant inactivation rates following an increase in the DOC concentrations, suggesting that the presence of organic carbon enhanced the inactivation of MS2. The experiment with a high IS and a low DOC was the only experiment which showed that MS2 inactivation may have been time-dependent. However, results from the GLUE methodology indicated that models of constant inactivation were able to describe all of the experiments. This suggested that inactivation time-series longer than 2 months were needed in order to provide concrete conclusions regarding the time-dependency of MS2 inactivation at 4 A degrees C under these experimental conditions