IDEAS home Printed from https://ideas.repec.org/p/ags/aaea16/235685.html
   My bibliography  Save this paper

Costs of Meeting Water Quality Goals under Climate Change in Urbanizing Watersheds: The Case of Difficult Run, Virginia

Author

Listed:
  • Giuffria, Jonathon M.
  • Bosch, Darrell J.
  • Taylor, Dan B.
  • Alamdari, Nasrin

Abstract

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.

Suggested Citation

  • Giuffria, Jonathon M. & Bosch, Darrell J. & Taylor, Dan B. & Alamdari, Nasrin, 2016. "Costs of Meeting Water Quality Goals under Climate Change in Urbanizing Watersheds: The Case of Difficult Run, Virginia," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235685, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea16:235685
    as

    Download full text from publisher

    File URL: http://ageconsearch.umn.edu/record/235685/files/AAEA2016_Poster%20-Giuffria%20et%20al.pdf
    Download Restriction: no

    More about this item

    Keywords

    Environmental Economics and Policy; Risk and Uncertainty;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea16:235685. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.