Introduction

1. Keep Buy Keep Buy Not Buy Sell Not Buy Sell Immediate vicinity land use type Distance to shoreline Distance to primary roads Fig 1: Function: Water Quality Source: National Image Library Fig 2: Function: Wildlife Habitat Source: National Image Library Distance to population centers Fig 5: CA Model Illustration T = 1 T = 2 High SLR (P0) Buy Low SLR (1- P0) Decision High SLR/Undeveloped (P1) Low SLR/Undeveloped (P2) Not Buy High SLR/Developed (P3) Low SLR/Developed (1-P1-P2-P3) Fig 6: Two stage decision process Introduction Wetland conservation is a major environmental concern in the Chesapeake Bay region. Substantial losses due to land development and other factors have had profound impacts on the Bay’s aquatic resources. Major wetland functions include: habitat provision, water quality improvement, flood protection, bank stabilization, and sediment control. Current conservation efforts fail to account for the impacts of climate change on sea level, which can affect the success of conservation efforts. • Cellular Automaton (CA) Model • CA examines changes taking place purely as a function of what happens in the immediate vicinity of any particular cell. The land use data is mapped into cells, as shown in Figure 5. • We identify four major drivers that influence the development possibility for each undeveloped land cell. • We assign different weight sets to the four major drivers to reflect three different future land use scenarios: compact development, dispersed development, and nodal development. Designing Wetland Conservation Strategies under Climate ChangeJiayi Li, Elizabeth Marshall, James Shortle, Richard Ready, Carl HershnerDepartment of Agricultural Economics and Rural SociologyVirginia Institute of Marine Sciences Objective This study develops a methodology for evaluating public wetlands conservation investments that takes climate change into account. We demonstrate the methodology for the Elizabeth River watershed in Virginia under plausible sea-level rise and land use scenarios. We consider a 30-year time period • Discrete Stochastic Sequential Programming (DSSP) • We consider two types of uncertain events that may affect decisions in our DSSP model. - Acquisition of new information about high or low sea-level rise (SLR). - Knowing the likelihood that an undeveloped land parcel would become developed. • Figure 6 shows how these uncertain events are included in a 2-stage decision process. Fig 3: Elizabeth River Watershed, Virginia • Methods • Cost-effective analysis is used to compare two wetland conservations strategies: - Strategy 1: Preserve high-elevation undeveloped land adjacent to existing wetland. Fig 4: Wetland Migration (Titus, 1990) • - Strategy 2: Relocate wetland to suitable areas where land prices are low. • The cellular automaton (CA) model is used to construct a development vulnerability index and to project land use changes for the study area. • The discrete stochastic sequential programming (DSSP) technique is used to minimize the costs of implementing each wetland conservation strategy. Acknowledgement: 1. Support is provided by the Global Change Research Program, Office of Research and Development, U.S. Environmental Protection Agency (Cooperative Agreement R-83053301). 2. Steve Graham, Penn State and Tamia Rudnicky, Virginia Institute of Marine Science (VIMS) provided GIS data and analysis assistance. 3. Marcia Bermen, Walter Priest and Dan Schatt, VIMS gave valuable suggestions.