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**AMS 25th Conference on Hydrology**

Seattle, WA January 25, 2011 Probabilistic Climate Change Analysis for Stormwater Runoff In the Pacific Northwest Gregory S. Karlovits, now with USACE Jennifer C. Adam (presenting), Washington State University

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Introduction

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**Climate Change in the PNW**

2045 Temperature Relative to Larger agreement among GCMs for annual temperature than for annual precipitation However, seasonality and extreme events also important Precipitation Relative to LOWESS (locally-weighted scatterplot smoothing) Smoothed traces in temperature (top) and precipitation (bottom) for the twentieth and twenty-first century model simulations for the PNW, relative to the 1970–1999 mean. The heavy smooth curve for each scenario is the REA value, calculated for each year and then smoothed using loess. The top and bottom bounds of the shaded area are the 5th and 95th percentiles of the annual values (in a running 10-year window) from the ∼20 simulations, smoothed in the same manner as the mean value. Mean warming rates for the twenty-first century differ substantially between the two SRES scenarios after 2020, whereas for precipitation the range is much wider than the trend and there is little difference between scenarios From Mote and Salathé (2010), University of Washington Climate Impacts Group

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**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) Snowpack and soil moisture are two examples of how moisture storage may moderate the effects of climate change, but there are other sources of uncertainty as well

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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?

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Data and Methods

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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

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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 ( ) Future: 92 realizations of 2045 climate, hybrid delta statistical downscaling

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**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 study Gao et al. (2010), Andreadis et al. (2009), Cherkauer & Lettenmaier (1999), Liang et al. (1994)

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**Modeled in VIC, fit to discrete normal distribution**

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 Snowpack and soil moisture are two examples of how moisture storage may moderate the effects of climate change, but there are other sources of uncertainty as well Modeled in VIC, fit to discrete normal distribution

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**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

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**Results and Conclusions**

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**Monte Carlo Results (Average of 5000 Simulations)**

Historical Future Similar pattern historical to future. Historical 50-year storm Random selection of soil moisture and SWE Future 50-year storm Random selection of emission scenario, GCM, soil moisture and SWE

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**Monte Carlo Results, Continued**

In general, increasing in intensity, some areas decreasing with intensity. Negative areas: could be partially because of decreases in SWE with climate change. CV is stdev of 5000 realizations over mean of 5000 realizations. Gives estimate of overall uncertainty. Largest CVs in areas with most intense storm depths (Even when normalized by the mean) Percent change, historical to future runoff due to 50-year storm Coefficient of variation for runoff for 5000 simulations of 50-year storm

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**Isolation of Uncertainty due to GCM**

All Sources GCM only Coefficient of variation due to selection of GCM only (50-year storm) Coefficient of variation for runoff for 5000 simulations of 50-year storm

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**Uncertainty Estimation for Individual Grid Cells**

Canada Washington State Palouse Watershed Oregon

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**Cumulative Density Functions**

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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

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Questions? Chehalis, WA Photo: Bruce Ely (AP) via

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**Outline Introduction: Data, model and methods**

Pacific Northwest (PNW) climate change Sources of uncertainty in predicting hydrologic impacts Data, model and methods Climate data Design storms Hydrologic model Monte Carlo simulation Results and uncertainty analysis

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**Isolation of Uncertainty: Emission Scenario**

Absolute Difference (A1B – B1) As a Percentage of Historical On left: absolute difference: largest differences in areas of large storms intensities. On right: expressed as a percent of historical, showing more even distribution of effects between A1B and B1. Reason for pattern on left: A1b and b1 effects polar and opposite along the coasts. Resulting in the strong polarity there. Why?? Absolute difference in runoff due to emissions scenario (A1B – B1) (mm) Difference (A1B – B1) as a percentage of historical (%)

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