Gregory S. Karlovits, now with USACE Jennifer C. Adam (presenting), Washington State University AMS 25 th Conference on Hydrology Seattle, WA January 25,

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

Introduction

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?

Data and Methods

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

Results and Conclusions

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

Uncertainty Estimation for Individual Grid Cells

Washington State

Palouse Watershed

Canada

Oregon

Cumulative Density Functions

-2 -1 0 1 2 3 4 5 6 7 8

Palouse

Historical Future

Runoff (mm)

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

Isolation of Uncertainty: Emission Scenario

Absolute difference in runoff due to emissions scenario (A1B – B1)

(mm)

Difference (A1B – B1) as a percentage of historical (%)

Absolute Difference (A1B – B1)As a Percentage of Historical