CIG Datasets

Effect of Climate Change on the Hydrology of the Chehalis Basin


Recent flooding in the Chehalis basin has led managers, residents, and others to begin evaluating options for managing flood risk in the future.

Climate change is expected to both increase the risk of flooding and result in decreased summer low flows – with implications for human communities and ecosystems alike. This page summarizes the results of a study aimed at quantifying the impacts of climate change on streamflow in the Chehalis River basin.

Example Applications

The specific objectives of this work were to:

  1. Refine projections of changing hydrology in the Chehalis River Basin,
  2. Supply the larger project team with new inputs for hydraulic and ecosystem models, and
  3. Evaluate the potential for climate change to alter the proportion of runoff originating above the proposed dam during flood events.

About the Dataset

This section provides a brief overview of the provenance of the streamflow projections. For additional information, please see the project report.

A primary motivation for this study was the need to reconcile disparate estimates of climate change induced changes in flood risk. In order to provide multiple estimates for comparison, projections were made using two downscaling approaches – one statistical, and one dynamical – and two hydrologic models.

The two downscaled climate datasets used in this study are:

  1. Statistically downscaled projections: Multivariate Adaptive Constructed Analogues (MACA, Abatzoglou and Brown 2011, Projections are based on 10 global models included in the CMIP5 (Coupled Model Intercomparison Project Phase 5, Taylor et al. 2012) experiment. The 10 GCMs were selected from the larger set of CMIP5 simulations based on their ability to accurately represent the climate of the Pacific Northwest (Rupp et al. 2011). Projections span from 1950-2099, and include both a low (RCP 4.5) and a high (RCP 8.5) greenhouse gas scenario
  2. Dynamically downscaled projections: Weather Research and Forecasting (WRF) Mesoscale Model (; Skamarock et al. 2005). Projections are based on 2 global models included in the CMIP5 (Coupled Model Intercomparison Project Phase 3, Meehl et al. 2007) experiment. Projections span from 1970-2069 and are based on a moderate (SRES A1B) greenhouse gas scenario.

Both downscaled climate datasets are produced at 0.0625-degree resolution (about 5 km x 7 km). The MACA projections use a bias-adjusted version of the Livneh et al. (2013) gridded observational dataset as a basis for the statistical downscaling. For comparison with the MACA results, two versions of the WRF projections are included: (1) “RawWRF”, in which the daily weather fields from the WRF model are simply bilinearly interpolated onto the 0.0625-degree grid, and (2) “bcWRF”, in which an additional bias-correction is applied to match the statistics of the Livneh et al. dataset.

The downscaled climate projections are used as input to both hydrologic models to produce streamflow estimates. The two hydrologic models used in this study are:

  1. The Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model (Gao et al. 2010). VIC is a physically based macro-scale hydrologic model which simulates all aspects of the hydrology affecting surface and shallow groundwater. We used VIC model version 4.1.2 implemented with inputs developed by Hamlet et al. (2013).
  2. The Distributed Hydrology and Soil Vegetation Model (DHSVM, Wigmosta et al. 1994). DHSVM is a gridded, spatially distributed hydrology model that represents physical characteristics of the watershed, using characteristics such as topography, land cover, soil type, soil thickness, and a streamflow network. For this study, we implemented DHSVM at 150 m resolution.


The map below links to historical and future streamflow projections for 58 sites chosen in tandem with project partners. Out of all of the sites, 11 had sufficient USGS streamflow observations (at least 30 years) for bias correction. These were bias-corrected using a newly-developed approach, in which the statistics of the daily streamflow estimates are adjusted to match the observations. This is likely to result in improved estimates of extremes (flooding, low flows).

For those interested in downloading the full archive, the figure below shows how the results are organized.

Figure 1
Figure 1. Data structure for streamflow results.


Funding for this project was provided by the Washington State Recreation and Conservation Office (RCO #15-1479) via a sub-contract with Anchor QEA, LLC.


Mauger, G.S., S.-Y. Lee, C. Bandaragoda, Y. Serra, J.S. Won, 2016. Refined Estimates of Climate Change Affected Hydrology in the Chehalis basin. Report prepared for Anchor QEA, LLC. Climate Impacts Group, University of Washington, Seattle. doi:10.7915/CIG53F4MH

Download the report here.


Please contact Guillaume Mauger ( with any questions about this project.

Reports & References

Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32(5), 772-780.

Gao, H., Tang, Q., Shi, X., Zhu, C., Bohn, T. J., Su, F., Sheffield, J., Pan, M., Lettenmaier, D. P., and Wood, E. F. (2010). Water budget record from Variable Infiltration Capacity (VIC) model. Algorithm Theoretical Basis Document for Terrestrial Water Cycle Data Records.

Hamlet, A.F., M.M. Elsner, G.S. Mauger, S-Y. Lee, I. Tohver, and R.A. Norheim. 2013. An overview of the Columbia Basin Climate Change Scenarios Project: Approach, methods, and summary of key results. Atmosphere-Ocean 51(4):392-415, doi: 10.1080/07055900.2013.819555.

Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., … & Brekke, L. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Scientific data, 2.

Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: A new era in climate change research,Bulletin of the American Meteorological Society, 88, 1383-1394.

Rupp, D. E., Abatzoglou, J. T., Hegewisch, K. C., & Mote, P. W. (2013). Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research: Atmospheres118(19).

Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., & Powers, J. G. (2005). A description of the advanced research WRF version 2 (No. NCAR/TN-468+ STR). National Center For Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div.

Taylor, K. E. et al., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498, doi:10.1175/BAMS-D-11-00094.1

Wigmosta, M. S., Vail, L. W., & Lettenmaier, D. P. (1994). A distributed hydrology‐vegetation model for complex terrain. Water resources research,30(6), 1665-1679.