Chapter 10: Streamflow Metric Projections

Dataset Overview

This dataset includes historical and projected future streamflow metrics for stream segments throughout the western U.S., produced from daily runoff and baseflow outputs from the Variable Infiltration Capacity (VIC) hydrologic model (see chapter glossary for definitions of terms). Streamflow projections are based on an ensemble of global climate models. The dataset is available for stream segments in the National Hydrography Dataset Plus Version 2 (NHDPlusV2) database (U.S. Environmental Protection Agency and U.S. Geological Survey, 2012). Available streamflow metrics in the dataset include mean annual flow, mean summer flow (figure 10.1), mean August flow, winter exceedance statistics (number of daily flows exceeding the 95th percentile of daily flows), the 1.5-, 10-, 25-year, and maximum modeled flood statistics, the center of flow timing, and the baseflow index. These streamflow metrics are available for historical, mid-century, and end-of-century time periods; absolute and percent changes are also available.

Data Access:

Figure 10.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • The set of streamflow metric projections in this dataset can be used to evaluate potential climate-driven habitat changes on a species-by-species basis. In stream ecosystems of the Pacific Northwest, different species of fish and other aquatic organisms have different streamflow metrics that are ecologically important to their life cycles. For example, fall-spawning fish may benefit from infrequent winter flooding, whereas spring-spawning fish may benefit from infrequent summer flooding. Streamflow metrics such as mean summer flow may also affect other components of habitat quality, such as stream temperatures. As a result, use of this dataset to inform aquatic conservation requires consideration of both the magnitude of projected change over time in a given streamflow metric and of the nature of that change relative to life cycles and habitat requirements of species of conservation concern.

Applicable scales for detailed spatial assessments:

  • For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
  • At the scale of: a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g. the North Cascades), a single state (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest or in western North America).

Applicable scales for assessing general patterns:

  • Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
  • At the scale of: a small (< 1 km2) nature preserve, a state park or state wildlife area, a local watershed (12-digit hydrologic unit code [HUC-12]).

Use of the dataset in conservation applications may be limited by the following considerations:

  • For some aquatic species, changes in high and low flows during summer may be especially important, however, these metrics were not well predicted by the VIC model in producing this dataset. In addition, streamflow metrics were not well predicted in groundwater-dominated streams, suggesting that use of this dataset to guide conservation decisions in such streams could be problematic. Finally, considerations of geographic scale may be important in applying this dataset for conservation purposes. The VIC predictions that produced this dataset may exhibit bias at fine geographic scales (for individual stream reaches or small catchments) that becomes less important when integrated across larger geographic scales.

Past or current conservation applications:

  • This dataset has been the basis for climate-change vulnerability assessments on several national forests in Oregon and Washington (USDA Forest Service Region 6) as well as Regions 1, 4, and 5. Example conservation applications are available from This dataset was also the basis for niche modeling to project fish responses to climate change (Wenger et al. 2011a, 2011b)

Dataset citation:

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.

Dataset documentation links: (open access) (open access)

Data access:

The dataset can be downloaded and viewed from:

Metadata access:

A comprehensive user guide and additional metadata are available from:

Dataset corresponding author:
Charles Luce
USDA Forest Service
[email protected]

Data type category (as defined in the Introduction to this guidebook):  Hydrology

Species or ecosystems represented:  This dataset represents stream and river ecosystems. No individual species are represented.

Units of mapped values:  

For mean-annual flow, mean-summer flow, mean August flow, and 1.5-, 10-, 25-year, and maximum modeled flood: cubic feet per second (ft3/s).
For winter exceedance statistics: number of days
For center of flow timing: day of the water year (or day of calendar year)
Baseflow index: ratio

Range of mapped values: Ranges of mapped values vary by streamflow metric.

Spatial data type: Line vector data stored as feature data in file geodatabase

Data file format(s): File geodatabase

Spatial resolution: Stream segments from the NHDPlusV2 datasets

Geographic coordinate system: North American Datum of 1983

Projected coordinate system: Unprojected

Spatial extent: Regional

Dataset truncation: The dataset is defined by hydrologic boundaries and thus is not truncated at any non-ecological borders.

Time period represented: Historical (1977-2006); future (later than 2020)

Future time period(s) represented:  Mid-century (2030-2059), end-of-century (2070-2099).

In addition to historical and future time periods, absolute and percent changes were calculated between the historical and the future time periods.

Baseline time period (against which future conditions were compared): 1977-2006.

Methods overview:

Historical and projected future streamflow metrics were estimated using daily runoff and baseflow outputs from the Variable Infiltration Capacity (VIC) macroscale hydrologic model. The VIC model is a physically based model that accounts for surface energy and water fluxes. In models that created this dataset, pixels were 1/16th degree (or 1/8th degree in the Great Basin processing unit). For historical simulations, input meteorological data came from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset (Daly et al. 1994). For future simulations, meteorological data came from an ensemble of 10 global climate models (see Littell et al., 2011). Simulations were performed on a daily time step. For each stream segment in the NHDPlusV2 dataset, hydrographs were produced using the model for the historical period, mid-century, and end-of-century. From these hydrographs, summary statistics (streamflow metrics) were calculated, then absolute and percent differences were calculated between the historical and future time periods. These were joined to the NHDPlusV2 dataset (September 2012 snapshot) for each region and were then merged together into a single comprehensive dataset for each time period.

For more information, please consult the dataset citation listed in section 2 of this chapter.

This dataset relied on the following general types of models:  Hydrologic models

This dataset employed the following specific models:  Variable infiltration capacity (VIC) model (Liang et al., 1994)

Major input data sources for this dataset included:

Historical climate observations or models, future climate projections, streamflow data or other hydrologic data

This dataset used the following general circulation models (GCMs): ECHAM5, BCCR, HadCM, MIROC3.2, MIROC3.2-HI, HadGEM1, ECHO-G, PCM1, CNRM-CM3, CSIRO-Mk3.5

An ensemble across GCMs is provided; individual files for individual GCMs are not available. More information about climate models is available in appendix 1. Detailed information about climate models, including model evaluation and comparison among models, is available from Randall et al. (2007) and Rupp et al. (2013).

This dataset used the following greenhouse-gas scenarios: SRES A1B

More information about greenhouse-gas scenarios is available in appendix 2 and from Knutti and Sedláček (2013).

Creation of this dataset involved the following methods to change the spatial resolution of climate models (e.g. to downscale or resample climate models):  GCM simulations were downscaled using a spatially explicit delta method (Littell et al., 2011).

The mapped values of the dataset may be interpreted as follows:

Interpretation of mapped values depends on the streamflow metric. The data show historical and future values for each metric, as well as both absolute and percent change. Stream segments that show a greater magnitude or percent change may be interpreted as streams that are projected to have greater hydrologic alteration due to climate change. For example, if mean summer flow from the historical period is compared to mean summer flow for the 2080s, streams that show the greatest decrease are anticipated to experience the most dramatic declines in summer streamflow.

Representations of key concepts in climate-change ecology:

This dataset primarily represents climate-change exposure (the magnitude of projected change in climate drivers of streamflow) and climate sensitivity of various streamflow metrics for various streams. Taken together, stream segments that show the greatest projected change across a variety of streamflow metrics could be considered especially vulnerable to climate change from a hydrologic perspective.

Changes in stream hydrology have the potential to affect aquatic communities in a variety of ways. Some species may be directly affected by changes in the timing or magnitude of streamflow. In addition, changes in streamflow can affect other characteristics of aquatic ecosystems, such as stream temperatures, water quality (e.g., dissolved oxygen and sediment loads), and connectivity of aquatic habitat. Thus, climate-driven changes to stream hydrology have the potential to produce complex, multifaceted changes in overall habitat suitability for a variety of species. These potential changes can be considered along with species-level sensitivities to changing stream hydrology (and its cascading effects) and aquatic species’ abilities to cope with or adapt to changing environmental conditions.

This dataset involves the following assumptions, simplifications, and caveats:

The VIC model does not explicitly model water fluxes into and out of deep subsurface reservoirs. The study that produced this dataset found evidence that streams with strong groundwater influence were poorly predicted by the model. Thus, model results may have low accuracy for strongly groundwater-dominated streams. Additionally, model estimates for the center of flow timing were biased early for snow-dominated streams and biased late for rainfall-dominated streams. Because the VIC model poorly predicted summer high- and low-flow metrics, historical and future projections for these metrics should be interpreted with caution.

Quantification of uncertainty:

Uncertainty resulting from choices of greenhouse-gas scenario or global climate model were not quantified. However, a comparison was conducted between two resolutions (grid sizes) of the VIC hydrologic model, which showed modest improvement in accuracy at the finer resolution (1/8th degree) relative to the coarser resolution (1/16th degree).

Field verification:
The VIC model used to produce this dataset was validated using data from 55 U.S. Geological Survey gaging stations in the Pacific Northwest, by comparing streamflow metrics calculated from observed daily hydrographs to those calculated from VIC model outputs. This comparison enabled an assessment of accuracy and bias for each modeled streamflow metric (see Wenger et al. 2010). In general, mean flows, winter high flows, center of flow timing, and hydrologic regime (rain-dominated or snow-dominated) were reasonably well predicted. However, summer high- and low-flow metrics were poorly predicted. Baseflow indices and flood levels greater than the 1.5-year flood were not assessed.

Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).

Daly, C., R. Neilson, and D. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33:140–158.

Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.

Liang, X., D. P. Lettenmaier, E. F. Wood, and J. Burges. 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research 99:14415–14428.

Littell, J., M. Elsner, G. Mauger, E. Lutz, A. Hamlet, and E. Salathé. 2011. Regional climate and hydrologic change in the Northern US Rockies and Pacific Northwest: internally consistent projections of future climate for resource management. Project report for USFS JVA 09-JV-11015600-039. Climate Impacts Group, University of Washington, Seattle, WA.

Randall, D., R. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, and et al. 2007. Climate models and their evaluation. in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. Miller, editors. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, NY.

Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research 118: 884–907.

U.S. Environmental Protection Agency and U.S. Geological Survey. 2012. National Hydrography Dataset Plus – NHDPlus version 2.10. U.S. Environmental Protection Agency, Washington, DC.

Wenger, S. J., D. J. Isaak, J. B. Dunham, K. D. Fausch, C. H. Luce, H. M. Neville, B. E. Rieman, M. K. Young, D. E. Nagel, D. L. Horan, and G. L. Chandler. 2011a. Role of climate and invasive species in structuring trout distributions in the interior Columbia River. Canadian Journal of Fisheries and Aquatic Sciences 68:988–1008.

Wenger, S. J., D. J. Isaak, C. H. Luce, H. M. Neville, K. D. Fausch, J. B. Dunham, D. C. Dauwalter, M. K. Young, M. M. Elsner, B. E. Rieman, A. F. Hamlet, and J. E. Williams. 2011b. Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change. PNAS 108:14175–14180.

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.