USDA Forest Service 20220511 vector digital data For more methodological details, see: Wenger, S.J., C.H. Luce, A.F. Hamlet, D.J. Isaak, and H.M Neville. 2010. Macroscale hydrologic modeling of ecologically relevant flow metrics. Water Resources Research. 46: W09513. doi:10.1029/2009WR008839. This work was supported by the U.S. Forest Service Landscape Restoration & Ecosystem Services Research staff. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups listed in this paper for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This file represents modeled streamflow across the contiguous United States, for the percent change between the historical (1977-2006) and projected future end-of-century time period (2070–2099), based on gridded simulations of daily total runoff.The flow regime is of fundamental importance in determining the physical and ecological characteristics of a river or stream, but actual flow measurements are only available for a small minority of stream segments, mostly on large rivers. Flows for all other streams must be extrapolated or modeled. Modeling is also necessary to estimate flow regimes under future climate conditions. We modeled streamflow across the contiguous United States, for the historical period (1977–2006), and two projected future time periods, mid-century (2030–2059), and end-of-century (2070–2099). These are based on gridded simulations of daily total runoff. These use RCP 8.5 projections of temperature and precipitation, downscaled to a 1/8 degree (~12 km) cell size, which are used as inputs to the Variable Infiltration Capacity (VIC) macroscale hydrologic model. This dataset updates the previous Western U.S. Stream Flow Metric Dataset (Wenger et al., 2010) (a link to the old datasets is available on the project website: https://www.fs.fed.us/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml). It expands the spatial extent of this analysis, uses updated climate scenarios, and includes additional climate metrics. For each stream segment in the National Hydrography Dataset Plus Version 2 (NHDPlusV2) in the contiguous U.S. we calculated hydrographs for the three time periods. From these we calculated summary flow metrics to describe flow regimes for each stream segment and each time period and joined these to the NHD stream segments for visualization and analysis. These results allow scientists and managers to easily compare historical and projected flow patterns, including monthly, seasonal, and annual flow, flood and drought events, and timing of peak and low flows. Note: We recommend that line segments with an upstream area greater than 10,000 km2 be removed from the dataset for consideration of high flow metrics (using the field 'TotDASqKM'), since the downstream routing was simply an accumulation function. This is reasonable for larger watersheds at time scales of months and greater, but would be inaccurate for estimating floods at daily time scales on larger watersheds. Note also that the 10+ year flood models are not appropriate for use in engineering and design applications. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset. Modeled streamflow across the contiguous United States, for the percent change between the historical (1977-2006) and projected future end-of-century time period (2070–2099), based on gridded simulations of daily total runoff. 19771001 20990930 Contiguous United States, for the percent change between the historical (1977-2006) and projected future end-of-century time period (2070–2099), Complete None planned -124.724424 -66.988396 49.376613 25.124123 None USDA Forest Service None USFS None Rocky Mountain Research Station None RMRS None Office of Sustainability and Climate None OSC None Enterprise Data Warehouse None EDW None water None hydrology None streams None streamflow None stream flow None climate change None flood None low flow None drought None baseflow None national hydrography dataset None NHD None variable infiltration capacity None VIC ISO 19115 Topic Categories environment inlandWaters None Contiguous United States None USA None future None percent change None The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. USFS Chief Information Office, Enterprise Data Warehouse data@fs.fed.us USDA Forest Service Rocky Mountain Research Station and Office of Sustainability and Climate. For more details about the analysis methods, see: Wenger, S.J., C.H. Luce, A.F. Hamlet, D.J. Isaak, and H.M Neville. 2010. Macroscale hydrologic modeling of ecologically relevant flow metrics. Water Resources Research. 46: W09513. doi:10.1029/2009WR008839. Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595 Gridded input data were visually inspected for completeness and reasonable values for the location. The data were not extensively compared against observational data. Data are complete as of publication. Inaccuracies in the data can stem from several sources: 1) Input meteorological data - We used the VIC dataset from Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections at https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html#About. In previous versions of this analysis, predictions were worse for sites with strong groundwater influence, and some sites showed errors that may result from limitations in the forcing climate data. Higher resolution (1/16th degree) modeling provided small improvements over lower resolution (1/8th degree). Despite some limitations, the VIC model appears capable of representing several ecologically relevant hydrologic characteristics in streams. 2) Stream hydrography - The National Hydrography Dataset version 2 is the highest resolution national stream hydrography layer currently available. As technology improves this component will likely become more accurate and increase our ability to represent small scale features and processes. Flow metrics were calculated for National Hydrography Dataset (NHD) version 2 stream segments that had a COMID (unique identifier) in the value added attributes table (VAA). Since not all stream segments in the NHD flowline layer are in VAA, not all stream segments have flow metrics. Headwater catchments with no area in the NHD did not accumulate flow and therefore do not have flow metrics. Secondary divergences do not receive flow from upstream, therefore only secondary divergences with a local catchment area have flow metrics. We recommend that line segments with an upstream area greater than 10,000 km2 be removed from the dataset for consideration of high flow metrics, since the downstream routing was simply an accumulation function. This is reasonable for larger watersheds at time scales of months and greater, but would be inaccurate for estimating floods at daily time scales on larger watersheds. This can be done using the field ‘TotDASqKM.’ Note also that the 10+ year flood models are not appropriate for use in engineering and design applications. Gridded input data were visually inspected for completeness and reasonable values for the location. The data were not extensively compared against observational data. Gridded input data were visually inspected for completeness and reasonable values for the location. The data were not extensively compared against observational data. To calculate flow metrics for each stream segment, we started with gridded estimates of total runoff for the contiguous United States for three time periods: the historical period (1977-2006), mid-century (2030-2059), and end-of-century (2070-2099). We downloaded all of these from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive (Reclamation, 2014). Total runoff was calculated using the Variable Infiltration Capacity (VIC) hydrologic model, which uses climate and other inputs to simulate land-atmosphere fluxes, water balance, and hydrological processes (Liang et al., 1994). The climate inputs for this were downscaled using the Bias Correction and Spatial Disaggregation method (Wood et al., 2004), producing runoff data with output grid cells of 1/8 degree (~12 km) on a side. The projected future climate data use RCP 8.5 (a high emissions scenario—see Schwalm et al. (2020) for discussion of this choice). We used five CMIP5 global climate models (GCMs) (Lawrence Livermore National Laboratory, 2021), following the five used by the 2020 Forest Service 2020 RPA Assessment (Joyce & Coulson, 2020): • Least warm: MRI-CGCM3 (Meteorological Research Institute) • Hot: HadGEM2-ES (Met Office Hadley Centre / Instituto Nacional de Pesquisas Espaciais) • Dry: IPSL-CM5A-MR (Institut Pierre-Simon Laplace) • Wet: CNRM-CM5 (Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique) • Middle: NorESM1-M (Norwegian Climate Centre) To create stream hydrographs, we took the total runoff for each grid cell, and used this to calculate the total flow in each of the NHDPlusV2 catchments intersecting that cell (U.S. Geological Survey et al., 2012). We then applied these values to the stream segments associated with each catchment, applying a unit hydrograph to simulate the distribution of lag times required for runoff to work its way into the stream and then accumulated these flows downstream (Wenger et al., 2010); see Figure 2 for a visualization of this. These calculations resulted in three long-term time series of daily stream flow for each stream segment: one for the historical period, one for the middle of the 21st century, and one for the end of the century. For each stream segment and time period, we calculated the 23 flow metrics described below. We then averaged the results of the five models listed above to produce an ensemble projection; individual model results are available upon request. We removed stream segments without values for these flow metrics, as well as coastal line segments; versions of the streamflow metric datasets including all NHD segments are also available upon request. For project details, a user guide, and maps, see: http://www.fs.fed.us/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml This differs from previous versions of the flow metrics dataset in that: a) It covers the entire contiguous United States, rather than just the western states. b) This uses updated input data, with newer climate scenarios and models (CMIP5/RCP 8.5 instead of CMIP3/A1B). c) There are a variety of new flow metrics included in this version. d) In this version, flow metrics were calculated for each GCM and then these outputs were averaged. In the previous version, the gridded runoff data from 10 different GCMs were averaged together and then this average was used as the input for the flow metrics calculation. The reason for this change is that in previous versions of the models, future projections involved perturbing the historical time series: all models used the same historical time series, with flood events on the same days, just differences in their magnitudes, so all could be averaged together. In the new models, the data were modeled stochastically, not by perturbing the historical time series, so averaging the gridded output from the different models would have the effect of smoothing out the hydrographs, muting the magnitude of flood events. Calculating the flow metrics for each model separately and then averaging the results at the end resolves this issue. e) In the original version of the data, datasets were served as .dbf tables, rather than as geospatial data. 20210701 Vector String 2635472 8.98315284119521e-09 8.98315284119521e-09 Decimal Degrees D North American 1983 GRS 1980 6378137.0 298.257222101 S_USA.Hydro_FlowMet_2080sChgPct A collection of geographic features with the same geometry type (such as point, line, or polygon), the same attributes, and the same spatial reference. Esri GIS Dictionary OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. COMID Common identifier of the NHD feature NHD Data Dictionary Common identifier of the NHD feature GNIS_NAME Feature name from the Geographic Names Information System NHD Data Dictionary Feature name from the Geographic Names Information System LENGTHKM Feature length in kilometers NHD Data Dictionary Feature length in kilometers FTYPE NHD feature type (334: Connector, 336: Canal/Ditch, 428: Pipeline, 460: Stream/River, 558: Artificial Path, e.g., a line through the center of a lake) NHD Data Dictionary NHD feature type (334: Connector, 336: Canal/Ditch, 428: Pipeline, 460: Stream/River, 558: Artificial Path, e.g., a line through the center of a lake) PU_CODE The NHD region (2-digit hydrologic unit codes) or a subdivision of regions based on NHDPlus 'production units,' designated by letters appended to the region code, such as '10U' (the upper Missouri River basin); see Figure 1 in user guide (available here: http://www.fs.fed.us/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml) NHD Data Dictionary The NHD region (2-digit hydrologic unit codes) or a subdivision of regions based on NHDPlus 'production units,' designated by letters appended to the region code, such as '10U' (the upper Missouri River basin); see Figure 1 in user guide (available here: http://www.fs.fed.us/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml) TOTDASQKM The total upstream cumulative drainage area in square kilometers; when using this dataset for analysis, we recommend excluding areas with a cumulative area greater than 10,000 sq km. NHD Data Dictionary The total upstream cumulative drainage area in square kilometers; when using this dataset for analysis, we recommend excluding areas with a cumulative area greater than 10,000 sq km. TIDAL Indicates whether the stream is tidally influenced NHD Data Dictionary Indicates whether the stream is tidally influenced WBAREATYPE Indicates for artificial paths whether the line represents one of the following: 'Area of Complex Channels,' 'Canal/Ditch,' 'Lake/Pond,' 'Reservoir,' 'Stream/River,' or 'Wash.' NHD Data Dictionary Indicates for artificial paths whether the line represents one of the following: 'Area of Complex Channels,' 'Canal/Ditch,' 'Lake/Pond,' 'Reservoir,' 'Stream/River,' or 'Wash.' MA_P2080 Mean annual flow is calculated as the mean of the yearly cumulative discharge values USDA Forest Service Mean annual flow is calculated as the mean of the yearly cumulative discharge values MJAN_P2080 Mean flow for January; the monthly values can be used to derive seasonal estimates for different definitions of seasons for different purposes USDA Forest Service Mean flow for January; the monthly values can be used to derive seasonal estimates for different definitions of seasons for different purposes MFEB_P2080 Mean flow for February USDA Forest Service Mean flow for February MMAR_P2080 Mean flow for March USDA Forest Service Mean flow for March MAPR_P2080 Mean flow for April USDA Forest Service Mean flow for April MMAY_P2080 Mean flow for May USDA Forest Service Mean flow for May MJUN_P2080 Mean flow for June USDA Forest Service Mean flow for June MJUL_P2080 Mean flow for July USDA Forest Service Mean flow for July MAUG_P2080 Mean flow for August USDA Forest Service Mean flow for August MSEP_P2080 Mean flow for September USDA Forest Service Mean flow for September MOCT_P2080 Mean flow for October USDA Forest Service Mean flow for October MNOV_P2080 Mean flow for November USDA Forest Service Mean flow for November MDEC_P2080 Mean flow for December USDA Forest Service Mean flow for December MDJF_P2080 Mean flow for winter (December/January/February) USDA Forest Service Mean flow for winter (December/January/February) MMAM_P2080 Mean flow for spring (March/April/May) USDA Forest Service Mean flow for spring (March/April/May) MJJA_P2080 Mean flow for summer (June/July/August) USDA Forest Service Mean flow for summer (June/July/August) MSON_P2080 Mean flow for fall (September/October/November) USDA Forest Service Mean flow for fall (September/October/November) HIQ1_5_P2080 The 1.5-year flood is calculated by first finding the greatest daily flow from each year. The 33rd percentile of the annual maximum series defines the flow that occurs every 1.5 years, on average. USDA Forest Service The 1.5-year flood is calculated by first finding the greatest daily flow from each year. The 33rd percentile of the annual maximum series defines the flow that occurs every 1.5 years, on average. HIQ10_P2080 The 10-year flood (90th percentile of the annual maximum series) USDA Forest Service The 10-year flood (90th percentile of the annual maximum series) HIQ25_P2080 The 25-year flood (96th percentile of the annual maximum series) USDA Forest Service The 25-year flood (96th percentile of the annual maximum series) LO7Q1_P2080 Average date of center of lowest 7-day flow, calculated using either January–December or June–May, whichever has a lower standard deviation in the date of the low-flow week; done to prevent erroneous results when the drought season crosses the break between years (e.g., drought on day 365 of the first year and day 1 of the second year: average day number = 183) USDA Forest Service Average date of center of lowest 7-day flow, calculated using either January–December or June–May, whichever has a lower standard deviation in the date of the low-flow week; done to prevent erroneous results when the drought season crosses the break between years (e.g., drought on day 365 of the first year and day 1 of the second year: average day number = 183) LO7Q10_P2080 Average lowest 7-day flow during a decade (calculated as the 10th percentile of the annual minimum weekly flows [Lo7Q1]) USDA Forest Service Average lowest 7-day flow during a decade (calculated as the 10th percentile of the annual minimum weekly flows [Lo7Q1]) BFI_P2080 The baseflow index is the ratio of the average daily flow during the lowest 7-day flow of the year to the average daily flow during the year overall USDA Forest Service The baseflow index is the ratio of the average daily flow during the lowest 7-day flow of the year to the average daily flow during the year overall W95_P2080 The average number of daily flows between December 1 and March 31 which exceed the 95th percentile of daily flows across the entire year USDA Forest Service The average number of daily flows between December 1 and March 31 which exceed the 95th percentile of daily flows across the entire year SHAPE Feature geometry. Esri Coordinates defining the features. SHAPE.LEN USFS Chief Information Office, Enterprise Data Warehouse data@fs.fed.us The U.S. Forest Service makes no warranty, express or implied, nor assumes any liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data or for the improper or incorrect use of those data. The data are dynamic and may change over time. The user is responsible for verifying the limitations of the geospatial data and for using the data accordingly. 20220511 USFS Chief Information Office, Enterprise Data Warehouse FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.