Monte Carlo simulation to characterize stormwater runoff uncertainty in a changing climate

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Climate change has the potential to intensify storm events which would affect design storms currently used in engineering that are based on historical, stationary data. Because the loss of stationarity due to climate change decreases the ability to accurately predict the magnitude of stormwater runoff due to extreme precipitation events, a framework for assessing the range of possibilities becomes necessary. The resulting change in design storms affects the anticipated amount of stormwater runoff and therefore the riskiness of existing hydraulic structures constructed to handle it. This paper presents a framework for assessing the risk associated with predicting stormwater runoff in the face of climate change. Historical and future climate scenarios in the Pacific Northwest were modeled using the Generalized Extreme Value (GEV) distribution, which was fit to the annual maximum 24-hour precipitation event for gridded data at 1/16 degree resolution using the method of L-moments. The intensity of the design storms for key return intervals was determined for the 1916-2006 historical climate and a number of future climate scenarios for the 2040s, which encompassed two SRES emissions scenarios, ten GCMs, and two downscaling methods. It was found that over the Pacific Northwest, on average the intensity of storm events was projected to increase. The less-frequent storms (such as the 100-year 24-hour storm) were found to increase in intensity proportionally more than more frequent storms. The study is focused on the largest runoff events due to large rainfall events during peak SWE season (around 1 April). In order to determine the posterior distribution of runoff volumes as a result of climate change, Markov chain Monte Carlo (MCMC) simulation coupled with the Variable Infiltration Capacity (VIC) macroscale hydrology model was employed over the Pacific Northwest at 1/2 degree resolution. For the Monte Carlo simulation equal probabilities were assigned to the occurrence of each emissions scenario and downscaling method. Each GCM was weighted by its ability to re-produce 20th century climate over the Pacific Northwest. Because of the sensitivity of the hydrologic model, snowpack and soil moisture conditions were simulated for each future climate scenario and fit to a normal distribution. For each return interval, a ``storm scenario'' based on the combined probability of the emissions scenario, GCM, downscaling method, antecedent soil moisture and antecedent snowpack was selected at random for a large number of realizations. The VIC model was then run with the corresponding scenario and a gridded runoff volume was produced. From the resulting number of realizations a confidence interval was constructed for each of the precipitation event return intervals over the entire domain. This distribution can be used for planners or engineers interested in evaluating stormwater designs in terms of their sensitivity to possible future climates.

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