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University of California (2022)

Changes in Hydrologic Extremes : Impacts of Nonstationarity on Water Resource Management

Wang, Kimberly

Titre : Changes in Hydrologic Extremes : Impacts of Nonstationarity on Water Resource Management

Auteur : Wang, Kimberly

Université de soutenance : University of California

Grade : Doctor Philosophy (PhD) in Civil Engineering 2022

Résumé
Climate change brings about several new hydrological changes including changes in intensity and frequency of extreme events such as drought and extreme precipitation. For a more accurate understanding of the impact of these extreme events on water resources, work is being done to move towards methods capable of accounting for nonstationarity. In this work, I examine how the presence of spatial nonstationarity and temporal nonstationarity can affect water resource management in two contexts. First, I consider drought in California linked to warming winter temperatures. The spatial nonstationarity present in California temperature records complicate attempts to create gridded datasets from station observations, and ability of the interpolation method to account for spatial nonstationarity greatly impacts the final products. I examine five gridded datasets to find substantial differences between winter temperature trend magnitudes and spatial patterns largely due to the interpolation method selected as well as to differences in station lists and bias correction methods. The differences in temperature also strongly impact modeled changes in Snow Water Equivalent (SWE) over the last century, which are a vital component of water resources in California. Second, I examine extreme precipitation using Monte Carlo simulations representing events typical in the US to determine the bias and variance in error associated with using a temporally stationary versus temporally nonstationary model. In comparing the performance between a stationary and nonstationary version of the generalized extreme value distribution, I find the classic bias-variance tradeoff in modeling limits the usefulness of using a nonstationary distribution, which should be applied when record length is long, the coefficient of variation of the data is low, and the behavior of temporal nonstationarity is high (large increases in the mean and standard deviation of the data with time). This result complicates the reality of transferring from a stationary method of analysis to a nonstationary, revealing that for many stations in the US, record lengths are too short (fifty years and under) for the nonstationary application to be definitively useful. Présentation et version intégrale

Page publiée le 29 novembre 2022