One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena.

Hydroclimatic extremes, such as floods and droughts, affect aspects of our lives and the environment including energy, hydropower, agriculture, transportation, urban life, and human health and safety. Climate studies indicate that the risk of increased flooding and/or more severe droughts will be higher in the future than today, causing increased fatalities, environmental degradation, and economic losses. Using a suite of innovative approaches this book quantifies the changes in projected hydroclimatic extremes and illustrates their impacts in several locations in North America, Asia, and Europe.

Dynamically downscaled precipitation is often used for evaluating sub-daily precipitation behavior on a watershed-scale and for the input to hydrological modeling because of its increasing accuracy and spatiotemporal resolution. Despite these advantages, physical parameterizations in regional models and systematic biases due to the dataset used for boundary conditions greatly influence the quality of downscaled precipitation data. The present paper aims to evaluate the performance and the sensitivities of physical parameterizations of the Weather Research and Forecasting (WRF) model to simulate extreme precipitation associated with atmospheric rivers (ARs) over the Willamette watershed in Oregon. Also investigated was whether the optimized WRF configuration for extreme events can be used for long-term reconstruction using different boundary condition datasets. Three reanalysis datasets, the Twentieth Century Reanalysis version 2c (20CRv2c), the European Center for Medium-Range Weather Forecasts (ECMWF) twentieth century reanalysis (ERA20C), and the Climate Forecast System Reanalysis (CFSR), which have different spatial resolutions and dataset periods, were used to simulate precipitation at 4 km resolution. Sensitivity analyses showed that AR precipitation is most sensitive to the microphysics parameterization. Among 13 microphysics schemes investigated, the Goddard and the Stony-Brook University schemes performed the best regardless of the choice of reanalysis. Reconstructed historical precipitation with the optimized configuration showed better accuracies during the wet season than the dry season. With respect to simulations with CFSR, it was found that the optimized configuration for AR precipitation can be used for long-term reconstruction with small biases. However, systematic biases in the reanalysis datasets may still lead to uncertainties in downscaling precipitation in a different season with a single configuration.

Most extreme precipitation events that occur along the North American west coast are associated with winter atmospheric river (AR) events, causing flooding, landslides, extensive property damage, and loss of life. The studies contained within this dissertation use a combination of NCDC precipitation observations, NCEP-NCAR reanalysis, a 10-model ensemble of historical and future CMIP5 climate model simulations, and an NCEP-NCAR reanalysis driven regionally downscaled WRF model simulation to characterize the synoptic evolution of AR events along the North American west coast, the spatial variability of precipitation along the coast and inland, and changes in AR intensity and frequency that are expected by the end of the 21st century. Most regional flooding events are associated with precipitation periods of 24 hours or less, and two-day precipitation totals identify nearly all major events. Precipitation areas of major events are generally narrow, roughly 200 km in width, and most are associated with ARs. Composite evolutions indicate negative anomalies in sea-level pressure and upper-level height in the central Pacific, high-pressure anomalies over the southwest U.S., large positive 850-hPa temperature anomalies along the coast and offshore, and enhanced precipitable water and integrated water vapor fluxes in southwest- to northeast-oriented swaths. A small subset of extreme precipitation events over the southern portion of the domain is associated with a very different synoptic evolution: a sharp trough in northwesterly flow and post-cold-frontal convection. High precipitable water values are more frequent during the summer but are not associated with heavy precipitation because of upper-level ridging over the eastern Pacific and weak onshore flow that limits upward vertical velocities. Global climate models have sufficient resolution to simulate synoptic features associated with AR events, such as high values of vertically integrated vapor transport (IVT) approaching the coast. Ten CMIP5 simulations are used to identify changes in ARs impacting the west coast of North America between historical (1970-1999) and end-of-century (2070-2099) representative concentration pathway (RCP) 8.5 runs. The most extreme ARs are identified in both time periods by the 99th percentile of IVT days along a north-south transect offshore of the coast. Integrated water vapor (IWV) and IVT are predicted to increase, while lower-tropospheric winds change little. Winter-mean precipitation along the West Coast increases by 11-18% (4-6% °C[superscript -1]) while precipitation on extreme IVT days increases by 15-39% (5-19% °C[superscript -1]). The frequency of IVT days above the historical 99th percentile threshold increases as much as 290% by the end of this century. There appear to be only very slight changes in annual AR climatology from historical to future time periods when considering the most extreme events (99th percentile). However, when evaluating by the number of future days exceeding the historical threshold, there are significant increases in extreme IVT events in all months, especially when the majority of events take place. The peaks in historical and future frequency occur in similar months given the amount of model variability. Extreme IVT events appear to be occurring slightly earlier in the season, particularly in the northern part of the domain, and these results are similar to other studies. Spatially, 10-model mean historical composites of IVT reveal canonical AR conditions. At locations farther south, there is less model agreement on what AR events should look like, both in spatial extent and intensity; whereas farther north, the various models agree more. The future composites indicate very little spatial change. The models behave similarly in both the historical and future runs, suggesting little change in dynamics. The future-historical difference plots highlight the largest changes expected in the future, namely increases in IVT intensity which are primarily associated with thermodynamic changes related to future IWV increases due to warming. The dynamically downscaled NCEP-NCAR reanalysis-driven WRF model, run with a 36-km resolution outer domain and a 12-km nest, contains more realistic terrain than most GCMs and highlights the spatial precipitation distribution over the Pacific Northwest. Winter precipitation in the Pacific Northwest correlates well with offshore daily IVT (as high as &sim0.8) with spatial signatures indicative of frequent coastal mid-latitude cyclones impacting the coast. However, the most extreme AR events did not correlate as highly as expected with daily precipitation (as high as ~ 0.4), despite ARs accounting for 8% or more of the total winter precipitation. When wind direction was taken into account, the correlations were much higher (~.7-0.8), indicating wind direction is an important factor when extreme precipitation occurs along the coast.

Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.

Provides measurement, analysis and modeling methods for assessment of trends in extreme precipitation events, for academic researchers and professionals.