Regional Model Projections of Heavy Precipitation for use in Stormwater Planning

Full Title

Regional Model Projections of Heavy Precipitation for use in Stormwater Planning

Project Overview

Historical trends (e.g., Mass et al. 2011) and climate projections (e.g., Warner et al. 2015) suggest that climate change will lead to more frequent heavy precipitation and consequently more intense hydrologic extremes in the Pacific Northwest, especially in fall and early winter. Past work has emphasized the need for physically based – as opposed to statistically based – methods for “downscaling” coarse-scale global model projections to scales that can capture the interactions of weather systems with the complex terrain of the Pacific Northwest (e.g., Salathé et al. 2014).

Dynamically downscaled projections – in which Regional Climate Models (RCMs) are used to simulate local scale changes in climate – are still relatively rare, especially at the scales needed to assess changes in stormwater risk. The primary aim of this project is to produce two new RCM simulations and deliver post-processed model projections that are tailored to the needs of stormwater modeling and Combined Sewer Outflow planning. In addition, this research explores the possibility of using the RCM simulations to identify statistical relationships between large-scale weather conditions and local variations in extreme precipitation.

Approach

The approach is described in detail in the reports listed below. In brief, the project included the following elements:

  • Global Climate Model scenarios: Global models are needed to drive the RCM simulations. The two global climate model scenarios were obtained from the Climate Model Intercomparison Project, phase 5 (CMIP5; Taylor et al., 2012). Projections were obtained for both a low- and a high-end greenhouse gas scenario (Representative Concentration Pathway (RCP) 4.5 and 8.5, respectively; van Vuuren et al. 2011) and a low-end RCP 4.5 greenhouse gas scenario. Global models were evaluated based on their ability to accurately simulated the climate of the Pacific Northwest. Of those that performed best, a pair of models was selected that span the range of projected changes in regional climate.
  • Regional Climate Model projections: RCM simulations were performed using the Weather Research and Forecasting (WRF, Skamarock et al. 2005) community mesoscale model. Building on extensive UW experience with this model, WRF was configured using the same approach used in previous work (Salathé et al. 2010, 2014). WRF simulations extended from 1970-2099. In order to facilitate stormwater analyses, results were saved at an hourly time step.
  • Evaluate link to large-scale weather: WRF simulations were evaluated to identify the large-scale conditions that drive variations in heavy precipitation. In this exploratory work we found that the distribution of extreme precipitation in western Washington is affected by small variations in the direction of winds, the stability of the lower atmosphere, and the speed of vertical winds, or convection (Lorente et al., in review).
  • Develop products to support stormwater planning: WRF projections were bias-corrected to match rain gauge observations for wide range of federally and locally-operated weather stations. Results were used to evaluate the change in the frequency, intensity, and duration of precipitation events. Changes were summarized as a function of duration (1 to 360 hours) and exceedance probability, for three future time periods: the 2030s, 2050s, and 2080s, relative to the recent past (e.g.: 1980-2009). Result are made available for download and are summarized in an interactive web tool.

Application

Results are designed to support both municipal and rural stormwater planning. The online tool allows users to evaluate projections as a function of precipitation intensity, duration, and frequency. Similarly, output files are designed for use in both urban stormwater and rural hydrologic modeling applications.

Anticipated Project Outputs

Key Personnel

  • Guillaume Mauger, (Principal Investigator), University of Washington*
  • Jason Won, University of Washington*
  • Katherine Hegewisch, U. Idaho Department of Geography
  • Cary Lynch, Joint Global Change Research Institute
  • Raquel Lorente, University of Murcia, Spain
  • Rick Steed, University of Washington
  • Eric Salathé, University of Washington Bothell

 * Indicates CIG Personnel or CIG Affiliate(s)

Funder(s)

The primary source of funding for this work was the King County Department of Natural Resources and Parks. Additional funding was provided by the Washington State Department of Ecology, City of Everett, Thurston County, and the Port Gamble S’Klallam tribe.

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