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Sections 305(b) and 314 of the Clean Water Act (CWA) require states, territories, and authorized tribes to provide biennial reports to EPA on the condition of waters within their boundaries. EPA regulations at 40 CFR 130.7 require states to provide biennial submissions of impaired waters lists. EPA provides guidance on these reports in a way that supports the Agency's strategy for achieving a broad-scale, national inventory of water quality conditions. The guidance is from EPA to states, territories, authorized tribes, and interstate commissions ("jurisdictions") to help states prepare and submit Section 305(b) reports to EPA. Use of the integrated report (IR) format provides jurisdictions a recommended reporting format and suggested content to be used in developing a single document that integrates the reporting requirements of Sections 303(d), 305(b), and 314. This format allows jurisdictions to report on the water quality standards attained for all waters, document the availability of data and information for each segment, identify certain trends in water quality conditions, and set priorities for protecting and restoring the health of the nation's aquatic resources. Section 305(b) of the Clean Water Act requires each state to conduct water quality surveys to determine a water body's overall health, including whether or not basic uses are being met. States, tribes, and other jurisdictions define appropriate uses for a waterbody and incorporate these uses into water quality standards that are approved by EPA. Water body uses include aquatic life protection, fish and shellfish production, drinking water supply, swimming, boating, fishing, and agricultural irrigation, among others. 2022 Final ID 305B Streams and Waterbodies.
Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010–2039, 2040–2069, 2070–2099) during this century to a baseline period (1980–2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), heat zones (HeatZone), and cumulative drought severity (CDSI). These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al. (2007) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Monthly precipitation and temperature downscaled to 30 arc-seconds (~800 meters) by Daly et al. (2008) for the period 1980–2015 and Thrasher et al. (2013) for the period 2016–2099, were aggregated to a 10 square kilometer grid and used to calculate a self-calibrated palmer drought severity index that was then aggregated into 30-year cumulative drought severity index values. Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 13 (CDSI) or 14 (GDD, HeatZone, PHZ) individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: one [two for nonCDSI] baseline file (1980–2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes.
This dataset updates the Wigington et al. (2013) hydrologic landscape (HL) approach for Oregon to make it more broadly applicable and applies the revised approach to the Pacific Northwest (PNW; i.e., Oregon, Washington, and Idaho). Specific changes incorporated are the use of assessment units based on National Hydrography Dataset Plus V2 catchments, a modified snowmelt model validated over a broader area, an aquifer permeability index that does not require preexisting aquifer permeability maps, and aquifer and soil permeability classes based on uniform criteria. Polygon features in this dataset were created by aggregating (dissolving boundaries between) adjacent, similarly-coded hydrologic assessment units.