The Chesapeake Bay Total Maximum Daily Load (TMDL) set by the EPA in 2010 mandates that the Chesapeake Bay watershed partner states (New York, Pennsylvania, Delaware, West Virginia, Maryland, Virginia, and District of Columbia) must significantly reduce their respective nutrient loadings (NL) draining into the Bay by 2025, where NL are defined as Nitrogen (N), Phosphorus (P), and Total Suspended Solids (TSS) loadings. A key component of this reduction will be from non-point source pollution, defined as pollution that cannot be readily traced to a single source. Urban environments, due to large amounts of impervious surface, have been identified as a key non-point source contributor of NL into surrounding watersheds. Surges of NL, or NL “spikes”, into local water systems are more damaging than mean NL alone. Virginia’s Watershed Implementation Plan (WIP), which details how Virginia will meet the Bay TMDL’s NL goals, outlined the need to reduce urban NL. Mean NL and NL variability are expected to increase under climate change (CC). While there are many studies that outline cost-effective ways in which NL may be abated from urban environments, there are comparatively fewer studies that develop predictive frameworks for abating NL under CC. Thus, there is a lack of information regarding water quality policy and how effective it will be in controlling urban NL in the future. Policy makers and their advisors need to begin planning how to address this change. The urbanizing Difficult Run watershed in Fairfax County Virginia was chosen as a test-bed watershed for examining how CC may affect water quality policy in urban environments. There are multiple databases readily available for Difficult Run, and it is similar to many other urban sub-watersheds in Chesapeake Bay watershed. As often prescribed by the Chesapeake Bay Commission, EPA, Chesapeake Bay Program, and Virginia’s WIP, this study will utilize Best Management Practices (BMPs) to reduce NL stemming from the watershed. The NL focus is on Nitrogen. For the purpose of this study, urban BMPs are defined as a type of water pollution control that reduces nutrient export via a stormwater runoff control mechanism. Examples of relevant BMPs include, but are not limited to, bioretention practices, installing green roofs, cultivating urban forest management, restoring urban streams, and reducing shoreline erosion. This study uses mathematical programming to compare how the costs of achieving a given NL reduction will differ under differing climates. More specifically, the first objective is to compare the costs of achieving a given level of reduction in mean NL in the watershed based on historical conditions to those predicted under CC. The second objective is to evaluate effects of changes in variability of nutrient loadings under CC on the costs of achieving NL reductions. The first mathematical programming model is a cost minimization, linear programming model which minimizes the total cost of BMP placement subject to a user-defined reduction in mean NL. Constraints are placed on the number of BMPs that can be allocated to the watershed based on watershed characteristics. For example, the amount of stream buffers that can be installed is constrained by available stream frontage in the watershed. The second model is based on Qiu, Prato, and McCamley’s Safety-First model, which pulls its core construction from the Target Minimization of the Absolute Total Deviation (MOTAD) construction. The model contains two key parameters that can be adjusted by the user, the NL Target, and the probability of exceeding the Target. NL for the watershed were simulated using EPA’s Storm Water Management Model (SWMM) 5.1. Using historical data collected by three USGS water monitoring stations stationed in the watershed, NL were calibrated and validated to match historical NL for years 1971-1998. CC nutrient loading data were then simulated for years 2041-2068 where SWMM used input data generated from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) MM5I_cssm, which was modeled by Andrew Ross and Raymond Najjar of the Department of Meteorology at Penn State. Land constraints for the mathematical programming model were derived using a Geographic Information System (GIS). The data were compiled from sources including, but not limited to, Fairfax County Open GIS data base, USGS, ESRI, Virginia LIDAR, National Land Cover Database, and U.S. Soil Survey. Through analyzing these raster and vector data sets, geospatial information was derived regarding average slope percent, percent impervious surface, water table separation, and hydric soil grouping among others. The geospatial information was transferred into the mathematical programming models to limit the specific acreage for BMPs. Results Preliminary cost minimization results for N loading reduction show heavy favoritism for urban stream restoration, a commonly recommended and effective BMP. However, when taking into consideration the limited stream feet available for restoration due to Chesapeake Bay Protection Area land classifications, Low Impact Development (LID) practices become the principal BMPs chosen. Examples of LID include bioretention, bioswale, and permeable pavement. Percent N loading reductions were parametrically varied from zero to twenty-five percent in order to give policy makers additional information about the way in which costs behave for N loading abatement. From zero to roughly eighteen percent N loadings reduced, the costs rise in a linear fashion. From eighteen percent onward, the costs increase at an increasing rate forming a convex cost frontier. This behavior fits with most economic prediction where the marginal cost of pollution abatement is expected to increase at an increasing rate. Work is currently underway to evaluate the effects of climate change on costs of reducing mean NL and to evaluate costs of reducing NL under risk using the safety-first model. Preliminary results indicate that CC increases the variability of NL implying that costs of meeting water quality goals will be higher under CC when risk is considered. Discussion Potential Designing water quality policy that incorporates structural and land use change can no longer be based on historic conditions alone. CC is expected to alter many of the environmental variables which water quality and economic modelers utilize to construct policy recommendations. The public desires to improve water quality, yet resources to achieve goals are limited. Therefore, it is important that policy models incorporate the effects of CC, so water quality programs can be efficiently adapted to match these changing conditions. References: Qiu, Zeyuan, Tony Prato, and Francis McCamley. “Evaluating Environmental Risks Using Safety-First Constraints.” American Journal of Agricultural Economics 83(2)(May 2001): 402-413. Storm Water Management Model Reference Model Volume 1 – Hydrology (2015). Office of Research and Development: Water Supply and Water Resources Division. National Risk Management Laboratory. Environmental Protection Agency. Cincinnati, OH.