UC Irvine

Extreme precipitation models are crucial for understanding the characteristics of extreme weather, ensuring the risk of extreme events, and designing flood protection structures. Analytical relationships that link the risk of extreme precipitation with a specific duration at a given location (i.e. intensity-duration-frequency(IDF) relationships) are commonly used to characterize the extreme events. If homogeneous rainfall data with long records are available, reliable analytical relationships can be developed. Yet, this is often not the case and the gauge networks in many parts of the world are either short or sparse. As a result, uncertainties of the gauge-based analyses tend to be high. To alleviate these uncertainties, regional frequency analysis methods which pool the precipitation data from multiple sites with a homogeneous precipitation pattern are used for modeling extreme precipitations. Yet, defining these homogeneous regions requires making additional assumptions that in many cases are not met.

In this dissertation, I try to address the limitations of the conventional extreme precipitation modeling approaches by: 1) Employing remotely-sensed precipitation information, instead of gauge observations, to capture the spatiotemporal variability of extreme precipitation; and 2) Applying advanced statistical techniques that do not have the limitations of the conventional regional approaches and can accommodate additional information to enhance model's performance.

In the first part of this dissertation, suitability of remotely-sensed precipitation information for extreme precipitation modeling is explored. A bias-correction framework is introduced to adjust the biases in the satellite-based extreme precipitation estimates. It is shown that the proposed method can effectively reduce the biases in the remotely-sensed extreme rainfall data, especially at high elevation regions where ground-based observations are poor and satellite-based estimates are highly biased. Also, it is demonstrated that the satellite-based extreme precipitation estimates that are bias-adjusted with limited ground-based observations using the proposed method outperform the gauge interpolation estimates even at some densely-gauged regions. Results also demonstrate that the high quantile estimates from the bias-adjusted dataset are in agreement with the Frequency Atlas of the United States(NOAA Atlas 14) estimates across various durations and return periods.

In the next step of this dissertation, advanced statistical techniques are employed to model spatial and temporal characteristics of extreme precipitation from remotely-sensed information. A hierarchical Bayesian approach is used to model extreme precipitation and to generate spatially consistent quantile estimates with low estimation uncertainties. In addition to the Bayesian framework, Max-stable processes, as the generalization of the extreme value theory, are used to capture the marginal properties and tail dependence structure of the extreme precipitation data. The performances of the applied methods in modeling extreme precipitations are evaluated using various validation metrics. A comprehensive evaluation of the models' performances shows that the applied methods are capable of generating extreme return levels that are consistent with gauge-based observations.

Results of this dissertation demonstrate that the satellite-based precipitation information can be used for extreme precipitation modeling, especially at poorly instrumented regions. Also, this study serves as an introduction to the application of advanced statistical techniques for extreme precipitation modeling using remotely-sensed information. The applied methods resulted in outstanding performances in characterizing extreme precipitation from remotely-sensed information.