Spatial Stream Network Object for Penobscot River, Maine, USA Fine Resolution Stream Temperature Model

High resolution spatial stream network (SSN) models are needed to predict stream temperature distributions across large basins at a fine scale, to identify thermal refuge areas for conservation and protection, and to predict the effects of weather variation and management actions on coldwater habitat. EPA has been working with the Penobscot tribe and Maine Temperature Monitoring Working Group to plan development of a fine scale temperature model for the Penobscot River basin in Maine. This suite of datasets with supporting Python code provides calibration and prediction covariates for a fine resolution SSN model for the Penosbscot. Included are an SSN object with catchment covariates and associated Python code and metadata. At this point model development has not been initiated.高分辨率空间河网（spatial stream network, SSN）模型可用于在精细尺度下预测大型流域的河流水温分布，识别可供保育与保护的水温避难区域，并预测天气变化与管理措施对冷水生境的影响。美国环境保护署（Environmental Protection Agency, EPA）正与彭诺布斯科特印第安部落及缅因州水温监测工作组合作，规划开发针对缅因州彭诺布斯科特河流域的精细尺度水温模型。本配套数据集及辅助Python代码，可为该流域的高分辨率SSN模型提供校准与预测协变量。数据集包含带有集水区协变量的SSN对象、配套Python代码及元数据。截至目前，该模型的开发工作尚未启动。