PNW Fine Scale Hydroclimate Projections
A set of statistically downscaled climate and hydrologic projections for the Pacific Northwest, implemented at a daily temporal resolution and a spatial resolution of 30 arc-seconds (~800 m2). Fine-scale climate projections stem from the CMIP3 global model archive and incorporate both statistical and dynamical downscaling approaches. Hydrologic projections stem from a simplified set of climate projections over the states of Oregon and Washington.
Quick links to Oregon/Washington SWE dataset
Resource managers frequently require high resolution climate and hydrologic projections to inform planning. In the Pacific Northwest, numerous resources and infrastructure are strongly dependent on the influence and seasonality of snowpack, in particular due to its influence on both summer and winter hydrology. Unfortunately, observations of snowpack are sparsely distributed across the landscape and records often too short to reliably deduce climate trends. Managers would thus benefit from reliable estimates of historical and future snowpack, implemented at fine spatial scales. This dataset combines a new 30 arc-second (~800 m2) set of climate projections with (a) simulations of snowpack covering the states of Oregon and Washington (Figure 1), and (b) hydrologic simulations of evapotranspiration, soil moisture, runoff, and numerous other variables over select basins in Oregon and Washington (Figure 2).
This dataset was applied in a study undertaken by the US Forest Service and National Park Service to determine the vulnerability of infrastructure within the National Forests and Parks in the region. The assessment used the projected changes in flooding and snowpack to identify future risks to roads and trail systems. Figure 3 demonstrates the projected flooding risk at 1/16th degree resolution. Figure 4 shows the shift in snowmelt timing at the 30 arc-second resolution. The finer resolution of the new projections (Figure 4) allows park and forest managers to more accurately distinguish between areas of higher and lower risk.
Methods of generating historical data
The domain for this study covers nearly all of the states of Oregon and Washington (see Figure 1). Monthly historical (1915-2006) and future climate data (daily maximum temperature, minimum temperature, and precipitation) were obtained by using the Paremeter Regressions on Independent Slopes Model (PRISM; Daly et al., 2002) 30 arc-second climate averages. These were then used to bias-correct the daily 1/16th degree climate records generated previously by CIG (see chapter 3 of Hamlet et al., 2010) to obtain daily climate forcings at the new 30 arc-second resolution.
Methods of generating projected climate data
Due to the computational and storage limitations imposed by such a large number of grid cells, the future projections were limited to a set of three climate projections for the decade of the 2040s using the A1B greenhouse gas scenario (Nakicenovic et al., 2000). These projections were obtained from Littell et al. (2011), which used the "Modified Delta" method of downscaling applied to three different estimates of future climate: an average of the ten best-performing climate models and two bracketing runs, one from the PCM1 global climate model (which tends to be cooler than other models) and another from the MIROC 3.2 model (which tends to be warmer).
Hydrologic simulations were implemented using a modified version of the Variable Infiltration Capacity (VIC, Gao et al. 2010, Liang et al., 1994) macroscale model. Specifically, VIC was modified to include terrain slope and aspect in radiative calculations. With the exception of a few climate variables included in the soil files, which were taken directly from the 30 arc-second dataset, the daily wind data and vegetation and soil parameter files were obtained by linearly interpolating the values from the 1/16th degree files used in the Columbia Basin Climate Change Scenarios Project (CBCCSP, Hamlet et al., 2010). Finally, the snowband parameter file was generated to include a maximum of five subgrid elevation bands in each grid cell, defined based on a 2 arc-second (~60 m) resolution Digital Elevation Model (DEM). This latter file is particularly important for the present work, since it specifies the sub-grid elevation bands on which snow calculations are performed for each grid cell.
Simulations of snow water equivalent (SWE) were obtained for the entire states of Oregon and Washington. Additional hydrologic variables, following those defined in Chapter 8 of Hamlet et al. (2010), were obtained for the 58 study basins identified in Figure 3.
Availability of SWE data
Monthly summaries and annual statistics of SWE are archived for the historical and future simulations and made available for download. Specifically, the value of SWE is stored for the first day of each month for each grid cell in the simulation. In addition, a number of annual statistics are calculated (e.g., maximum swe, date of 10% accumulation, etc.). Summaries of the simulations are archived for the historical and future simulations. Daily results are available but not posted for download due to the size of the dataset. Contact firstname.lastname@example.org for the daily results. Due to the size of the dataset, these are not posted for download. Similarly, monthly summaries and raw daily output are archived for the 21 hydrologic variables simulated over the 58 study basins.
(Note that each data directory contains two subdirectories: one containing monthly summaries of the SWE simulations, and the other containing annual SWE statistics).
- Dataset README file
- Historical SWE simulations
- Future SWE simulations -- composite of the ten best climate models (A1B, 2040s)
- Future SWE simulations -- pcm1 (cooler) climate model (A1B, 2040s)
- Future SWE simulations -- miroc_3.2 (warmer) climate model (A1B, 2040s)
- 30 arc-sec climate data over PNW
- 30 arc-sec hydro simulations over the 58 study basins
Funding for this project was provided by a grant from the USDA US Forest Service Region 6 Pacific Northwest Research Station.
Mauger, G.S., 2010. Fine-scale climate and hydrologic projections for the Pacific Northwest. Climate Impacts Group, UW.
There are no updates to this project at this time.
Questions regarding this dataset can be directed to:
- Guillaume Mauger: (email@example.com)
Daly, C., W. P. Gibson, G.H. Taylor, G. L. Johnson, P. Pasteris. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research, 22, 99-113, 2002
Gao, Huilin, Qiuhong Tang, Xiaogang Shi, Chunmei Zhu, Ted Bohn, Fengge Su, Justin Sheffield, Ming Pan, Dennis Lettenmaier, and Eric F. Wood. "Water Budget Record from Variable Infiltration Capacity (VIC) Model Algorithm Theoretical Basis Document." Dept. Civil and Environmental Eng., Univ. Washington, Seattle, WA (2009): 09-18.
Hamlet A.F. et al., 2010: Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning studies. Chapter 4 in Final Project Report for the Columbia Basin Climate Change Scenarios Project.
Kalnay E. et al., 1996: The NCEP/NCAR 40-year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428.
Littell, J.S., M.M. Elsner, G. Mauger, E. Lutz, A.F. 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. DRAFT report available online here.
Nakicenovic, N et al. (2000). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change.