Probabilistic Climate Change Analysis for Stormwater Runoff In the Pacific Northwest
Gregory S. Karlovits, now with USACEJennifer C. Adam (presenting), Washington State University
AMS 25th Conference on HydrologySeattle, WAJanuary 25, 2011
Climate Change in the PNW
2045
From Mote and Salathé (2010), University of Washington Climate Impacts Group
Temperature Relative to 1970-1999
Precipitation Relative to 1970-1999
Larger agreement among GCMs for annual temperature than for annual precipitation
However, seasonality and extreme events also important
Sources of Uncertainty in Predicting Stormwater Runoff under Climate Change Future Meteorological Conditions
Future Greenhouse Gas (GHG) emissions Global Climate Model (GCM) structure and
parameterization Downscaling to relevant scale for hydrologic
modeling Hydrologic Modeling
Hydrologic model structure, parameterization, and scale
Antecedent (Initial) Conditions▪ Soil moisture▪ Snowpack / Snow Water Equivalent (SWE)
Objectives
At the regional scale, how will stormwater runoff from key design storms change due to climate change?
What is the range of uncertainty in this prediction and what are the major sources of this uncertainty?
How can we make these forecasts useful to planners and engineers?
General Methodology
For key design storms, find changes in storm intensities for different emission scenarios/GCMs
Use a hydrology model to compare future projected storm runoff to historical
Use a probabilistic method to assess range and sources of uncertainty
Design Storms
24-hour design storms with average return intervals of 2, 25, 50 and 100 years
Statistical modeling using Generalized Extreme Value (GEV) using method of L-Moments (Rosenberg et al., 2010)
Meteorological data: from Elsner et al. (2010): gridded at 1/16th degree over PNW Historical: 92 years of data (1915-2006) Future: 92 realizations of 2045 climate, hybrid delta
statistical downscaling
VIC Macroscale Hydrology Model
Variable Infiltration Capacity (VIC) Model
• Process-based, distributed model run at 1/2-degree resolution
• Sub-grid variability (vegetation, elevation, infiltration) handled with statistical distribution
• Resolves energy and water budgets at every time step
• Routing not performed for this studyGao et al. (2010), Andreadis et al. (2009),
Cherkauer & Lettenmaier (1999), Liang et al. (1994)
Monte Carlo Framework
Random Sampling from: Future Meteorological Conditions▪ Future Greenhouse Gas (GHG) emissions▪ Global Climate Model (GCM) structure and
parameterization▪ Downscaling to relevant scale for hydrologic
modeling Hydrologic Modeling▪ Hydrologic model structure, parameterization,
and scale▪ Antecedent (Initial) Conditions▪ Soil moisture▪ Snowpack
Modeled in VIC, fit to discrete normal
distribution
Monte Carlo Framework, cont’d For each return interval, 5000 combinations were
selected for VIC simulation GCM weighted by backcasting ability as quantified
by Mote and Salathé (2010) Approach based on Wilby and Harris, 2006, WRR
Monte Carlo Results (Average of 5000 Simulations)
Historical 50-year stormRandom selection of soil
moisture and SWE
Future 50-year stormRandom selection of emission
scenario, GCM, soil moisture and SWE
Historical Future
Monte Carlo Results, Continued
Percent change, historical to future runoff due to 50-year
storm
Coefficient of variation for runoff for 5000 simulations of 50-year
storm
Isolation of Uncertainty due to GCM
Coefficient of variation due to selection of GCM only (50-year
storm)
Coefficient of variation for runoff for 5000 simulations of 50-year
storm
All SourcesGCM only
Conclusions
Runoff is projected to increase for many places in the Pacific Northwest Largest increases related to most uncertainty
Range and sources of uncertainty highly variable across the PNW
Probabilistic methods can improve forecasts and isolate sources of uncertainties enables us a better understanding on where to
focus resources for improved prediction Need for more comprehensive uncertainty
assessment and higher resolution studies
Questions?
Chehalis, WAPhoto: Bruce Ely (AP) via http://www.darkroastedblend.com/2008/06/floods.html
Outline
1. Introduction: 1. Pacific Northwest (PNW) climate change2. Sources of uncertainty in predicting
hydrologic impacts2. Data, model and methods
1. Climate data2. Design storms3. Hydrologic model4. Monte Carlo simulation
3. Results and uncertainty analysis
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