Jacob D. Nelson, Benjamin D. Bowes, Linnea Saby, Majid Shafiee-Jood, Jonathan L. Goodall
{"title":"估算全州水质信贷需求的地理信息系统方法:应用于弗吉尼亚州规划的交通项目","authors":"Jacob D. Nelson, Benjamin D. Bowes, Linnea Saby, Majid Shafiee-Jood, Jonathan L. Goodall","doi":"10.1111/1752-1688.13195","DOIUrl":null,"url":null,"abstract":"<p>Uncertainty in water quality trading (WQT) markets is frequently cited as a deterrent for participation, with few studies focusing on uncertainty in future water quality credit needs. To reduce this uncertainty, we present a geographic information system (GIS)-based methodology for estimating an upper bound of water quality credit needs for a set of spatially referenced planned construction projects over a large geographic region. We demonstrate the methodology by applying it to estimate future credit needs for the Virginia Department of Transportation's (VDOT) 6-year improvement program. The results show that 25% of the state's 6-digit hydrologic unit code (HUC) basins lack sufficient current credit supply to meet the estimated future credit need from VDOT's planned projects alone. Furthermore, while 70% of the 8-digit HUCs containing planned projects have a sufficient current credit supply to meet VDOT credit needs, this is true for only 20% of the 10-digit HUCs. Finally, nearly 25% of the planned transportation projects, representing potentially $9 million in credit purchases at current market rates, will be constructed in catchments with impaired water bodies. State regulations will initially limit these projects to trade with credit banks collocated at the 12-digit HUC level. This application demonstrates how the GIS-based methodology can be applied to reduce uncertainty about future WQT credit needs and how needs are aligned with current credit supply.</p>","PeriodicalId":17234,"journal":{"name":"Journal of The American Water Resources Association","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1752-1688.13195","citationCount":"0","resultStr":"{\"title\":\"A geographic information system approach for estimating state-wide water quality credit need: Application for planned transportation projects in Virginia\",\"authors\":\"Jacob D. Nelson, Benjamin D. Bowes, Linnea Saby, Majid Shafiee-Jood, Jonathan L. Goodall\",\"doi\":\"10.1111/1752-1688.13195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Uncertainty in water quality trading (WQT) markets is frequently cited as a deterrent for participation, with few studies focusing on uncertainty in future water quality credit needs. To reduce this uncertainty, we present a geographic information system (GIS)-based methodology for estimating an upper bound of water quality credit needs for a set of spatially referenced planned construction projects over a large geographic region. We demonstrate the methodology by applying it to estimate future credit needs for the Virginia Department of Transportation's (VDOT) 6-year improvement program. The results show that 25% of the state's 6-digit hydrologic unit code (HUC) basins lack sufficient current credit supply to meet the estimated future credit need from VDOT's planned projects alone. Furthermore, while 70% of the 8-digit HUCs containing planned projects have a sufficient current credit supply to meet VDOT credit needs, this is true for only 20% of the 10-digit HUCs. Finally, nearly 25% of the planned transportation projects, representing potentially $9 million in credit purchases at current market rates, will be constructed in catchments with impaired water bodies. State regulations will initially limit these projects to trade with credit banks collocated at the 12-digit HUC level. 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A geographic information system approach for estimating state-wide water quality credit need: Application for planned transportation projects in Virginia
Uncertainty in water quality trading (WQT) markets is frequently cited as a deterrent for participation, with few studies focusing on uncertainty in future water quality credit needs. To reduce this uncertainty, we present a geographic information system (GIS)-based methodology for estimating an upper bound of water quality credit needs for a set of spatially referenced planned construction projects over a large geographic region. We demonstrate the methodology by applying it to estimate future credit needs for the Virginia Department of Transportation's (VDOT) 6-year improvement program. The results show that 25% of the state's 6-digit hydrologic unit code (HUC) basins lack sufficient current credit supply to meet the estimated future credit need from VDOT's planned projects alone. Furthermore, while 70% of the 8-digit HUCs containing planned projects have a sufficient current credit supply to meet VDOT credit needs, this is true for only 20% of the 10-digit HUCs. Finally, nearly 25% of the planned transportation projects, representing potentially $9 million in credit purchases at current market rates, will be constructed in catchments with impaired water bodies. State regulations will initially limit these projects to trade with credit banks collocated at the 12-digit HUC level. This application demonstrates how the GIS-based methodology can be applied to reduce uncertainty about future WQT credit needs and how needs are aligned with current credit supply.
期刊介绍:
JAWRA seeks to be the preeminent scholarly publication on multidisciplinary water resources issues. JAWRA papers present ideas derived from multiple disciplines woven together to give insight into a critical water issue, or are based primarily upon a single discipline with important applications to other disciplines. Papers often cover the topics of recent AWRA conferences such as riparian ecology, geographic information systems, adaptive management, and water policy.
JAWRA authors present work within their disciplinary fields to a broader audience. Our Associate Editors and reviewers reflect this diversity to ensure a knowledgeable and fair review of a broad range of topics. We particularly encourage submissions of papers which impart a ''take home message'' our readers can use.