Monte Carlo Simulation to Characterize Stormwater Runoff

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AMS 25th Conference on Hydrology
Seattle, WA
January 25, 2011
Gregory S. Karlovits, now with USACE
Jennifer C. Adam (presenting), Washington State
University
Temperature Relative to 1970-1999
Precipitation Relative to 1970-1999
2045
Larger agreement
among GCMs for
annual
temperature than
for annual
precipitation
 However,
seasonality and
extreme events
also important

From Mote and Salathé (2010), University of Washington Climate Impacts Group

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)

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?



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



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
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
study
Gao et al. (2010), Andreadis et al. (2009),
Cherkauer & Lettenmaier (1999), Liang et al. (1994)

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
Modeled in VIC, fit to
▪ Soil moisture
discrete normal
▪ Snowpack
distribution
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

Historical
Historical 50-year storm
Random selection of soil moisture and
SWE
Future
Future 50-year storm
Random selection of emission scenario,
GCM, soil moisture and SWE
Percent change, historical to future
runoff due to 50-year storm
Coefficient of variation for runoff for
5000 simulations of 50-year storm
GCM only
Coefficient of variation due to selection
of GCM only (50-year storm)
All Sources
Coefficient of variation for runoff for
5000 simulations of 50-year storm
Canada
Washington State
Palouse Watershed
Oregon
Palouse
-2
-1
0
1
2
3
4
Runoff (mm)
Historical
Future
5
6
7
8

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
Chehalis, WA
Photo: Bruce Ely (AP) via http://www.darkroastedblend.com/2008/06/floods.html
1.
Introduction:
1. Pacific Northwest (PNW) climate change
2. Sources of uncertainty in predicting hydrologic
impacts
2.
Data, model and methods
1.
2.
3.
4.
3.
Climate data
Design storms
Hydrologic model
Monte Carlo simulation
Results and uncertainty analysis
Absolute Difference (A1B – B1) As a Percentage of Historical
Absolute difference in runoff due to
emissions scenario (A1B – B1) (mm)
Difference (A1B – B1) as a percentage of
historical (%)
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