Monte Carlo Simulation to Characterize Stormwater Runoff Uncertainty in a Changing Climate

G.S. Karlovits, J.C. Adam, Washington State University2010 AGU Fall Meeting, San Francisco, CA

Outline

1. Climate change and uncertainty in the Pacific Northwest

2. Data, model and methods1. Climate data2. Design storms3. VIC4. Monte Carlo simulation

3. Results and uncertainty analysis

Climate Change in the PNW

95th percentile (10-year moving average)

5th percentile (10-year moving average)

LOWESS-smoothed21-model ensemble averages

Modeled historical (with bounds)

2045

From Mote and Salathé (2010)

Uncertainty Projections for future climate based on many

assumptions Greenhouse gas emissions scenario Global climate model (GCM) Downscaling of climate data

Effects of changing temperature and precipitation on hydrology uncertain as well Effects on moisture storage (moderation or

enhancement)▪ Snowpack▪ Soil moisture

Other sources of uncertainty in forecasting hydrology▪ Hydrologic model structure▪ Model calibration parameters

Objectives/Motivation

How much uncertainty is there in forecasting future runoff in the Pacific Northwest due to climate change?

What causes this uncertainty?

Can we improve our forecast for runoff in the future so planners and engineers have a tool to help prepare for climate change?

General Methodology

Find change in 2, 25, 50, 100-year 24-hour storm intensities for different emissions scenarios/GCMs

Use a hydrology model to compare future projected storm runoff to historical

Use a probabilistic method to isolate uncertainty and improve forecast

Design Storms

Storms with an average return interval of 2, 25, 50 and 100 years from extreme value distribution Annual probability of exceedance = 0.50, 0.96,

0.98, 0.99 Historical: 92 years of data (1915-2006) Future: 92 realizations of 2045 climate

Hybrid delta downscaling method▪ Delta method with bias correction

Historical and future data aggregated from data in Elsner et al. (2010)

VIC Hydrology Model Need to take changes in precipitation and

temperature and turn them into changes in runoffVariable Infiltration Capacity

Model• Process-based, distributed

model run at 1/2-degree resolution

• Sub-grid variability (soil, vegetation, elevation) handled with statistical distribution

• Gridded results for fluxes and states

• No interaction between grid cells

Gao et al. (2010), Liang et al. (1994)

Monte Carlo Simulation

Modeling random combination for met data and hydrologic model parameters Emissions scenario (equal probability) GCM (weighted by hindcasting ability)▪ GCMs with higher bias in recreating 1970-1999

PNW climate given lower selection probability Snowpack Soil moisture

Modeled in VIC, fit to discrete normal distribution

Monte Carlo Simulation For each return interval, 5000 combinations of emissions

scenario, GCM, soil moisture and snowpack quantile were made

(Pseudo-)random numbers generated using the Mersenne Twister algorithm (Matsumoto and Nishimura 1998)

Monte Carlo Results

Historical 50-year stormRandom selection of soil moisture

and SWE

Future 50-year stormRandom selection of emissions

scenario, GCM, soil moisture and SWE

Monte Carlo Results

Percent change, historical to future runoff due to 50-year storm

Coefficient of variation for runoff for 5000 simulations of 50-year storm

Emissions Scenario/GCM

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

Coefficient of variation due to selection of GCM (50-year storm)

CDFs

-2 -1 0 1 2 3 4 5 6 7 80

0.2

0.4

0.6

0.8

1Palouse (Cell 335)

Historical FutureDifference

Runoff (mm)

Cum

ulat

ive

Prob

abili

ty0 25 50 75 10

012

515

017

520

00

0.20.40.60.8

1Queets (Cell 291)

Historical FutureDifference

Runoff (mm)

Cum

ulat

ive

Prob

abili

ty

Conclusions

Runoff is projected to increase for many places in the Pacific Northwest Largest increases related to most

uncertainty Uncertainty in emissions scenario is

a factor in all future projections Even A1FI scenario low for 21st century

Probabilistic methods can improve forecasts and isolate uncertainties

Questions?

Chehalis, WAPhoto: Bruce Ely (AP) via http://www.darkroastedblend.com/2008/06/floods.html

Contact me:

Gregory KarlovitsWSUgskarlov@wsu.edu

Jennifer AdamWSUjcadam@wsu.edu