Probable Vegetation Groundwater Dependent Ecosystems - Central Tablelands

The purpose of this project is to create a data set that identifies vegetation communities that have a probability of being a groundwater dependent ecosystem within NSW. Probability categories are high, medium, low and non GDE. The data is required to meet legislative requirements for water sharing plans and water resource plans. This is a base data layer that can be used to aid research projects, assessment of state significant development applications, licence applications and compliance issues. For ease of data modelling and management, the dataset was divided into the following catchment management areas; Border Rivers-Gwydir, Central Tablelands, Central West, Hawkesbury-Nepean, Hunter-Central Rivers, Lachlan, Lower Murray Darling, Murray, Murrumbidgee, Namoi, Northern Rivers, Southern Rivers, Southern Tablelands, Sydney Metro and Western Division (Far West and Western MDBA). \r\n\r\nPlease note: Groundwater Dependent Ecosystem mapping used the NSW DPE Environment and Heritage vegetation mapping product as the underlying data source. NSW DPE Water activities such as bore applications that are examined at a site specific scale may need subsequent verification to confirm.\r\n\r\nMethod report is available via the DoI Water website - https://water.dpie.nsw.gov.au/__data/assets/pdf_file/0010/151894/High-Probability-GDE-method-report.pdf

本项目旨在构建一个数据集，用于识别新南威尔士州（NSW）内具有地下水依赖型生态系统（Groundwater Dependent Ecosystem, GDE）可能性的植被群落。可能性类别分为高、中、低及非GDE四类。该数据需满足水资源共享计划与水资源规划的立法要求，是一个基础数据层，可用于辅助研究项目、州级重大开发申请评估、许可申请及合规性问题处理。为便于数据建模与管理，数据集按以下流域管理区域划分：Border Rivers-Gwydir、Central Tablelands、Central West、Hawkesbury-Nepean、Hunter-Central Rivers、Lachlan、Lower Murray Darling、Murray、Murrumbidgee、Namoi、Northern Rivers、Southern Rivers、Southern Tablelands、Sydney Metro及Western Division（远西及Western MDBA）。请注意：地下水依赖型生态系统制图以新南威尔士州规划、工业与环境部（NSW DPE）环境与遗产局的植被制图产品为基础数据源。NSW DPE水务活动（如在特定场地尺度审查的钻井申请）可能需后续验证以确认。方法报告可通过DoI水务网站获取：https://water.dpie.nsw.gov.au/__data/assets/pdf_file/0010/151894/High-Probability-GDE-method-report.pdf