Hydrological extremes associated with climate change are becoming an increasing concern all over the world. Frequent flooding, one of the extremes, needs to be analyzed while considering climate change to mitigate flood risk. This study forecasted streamflow and evaluated the risk of flooding in the Neuse River, North Carolina considering future climatic scenarios, and comparing them with an existing Federal Emergency Management Agency (FEMA) flood insurance study (FIS) report. The cumulative distribution function transformation (CDF-t) method was adopted for bias correction to reduce the uncertainty present in the Coupled Model Intercomparison Project Phase 6 (CMIP6) streamflow data. To calculate 100-year and 500-year flood discharges, the Generalized Extreme Value (GEV) (L-Moment) was utilized on bias-corrected multimodel ensemble data with different climate projections. The delta change method was applied for the quantification of flows, utilizing the future 100-year peak flow and FEMA 100-year peak flows. Out of all projections, shared socio-economic pathways (SSP)5-8.5 exhibited the maximum design streamflow, which was routed through a hydraulic model, the Hydrological Engineering Center’s River Analysis System (HEC-RAS), to generate flood inundation and risk maps. The result indicates an increase in flood inundation extent compared to the existing study, depicting a higher flood hazard and risk in the future. This study highlights the importance of forecasting future flood risk and utilizing the projected climate data to obtain essential information to determine effective strategic plans for future floodplain management.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18690号 / 工博第3968号 / 新制||工||1611(附属図書館) / 31623 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 中北 英一, 准教授 KIM Sunmin / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19976号 / 工博第4220号 / 新制||工||1653(附属図書館) / 33072 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 堀 智晴, 准教授 KIM SUNMIN / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20680号 / 工博第4377号 / 新制||工||1680(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 平石 哲也, 教授 中北 英一, 准教授 森 信人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Le système climatique génère un attracteur étrange, décrit par une distribution de probabilité, nommée la mesure SRB (Sinai-Ruelle-Bowen). Cette mesure décrit l'état et sa dynamique du système. Le but de cette thèse est d'une part de quantifier les modifications de cette mesure quand le climat change. Pour cela, la distance de Wasserstein venant de la théorie du transport optimal, permet de mesurer finement les différences entre distributions de probabilités. Appliquée à un modèle jouet de Lorenz non autonome, elle a permis de détecter et quantifier l'altération due à un forçage similaire à celui du forçage anthropique. La même méthodologie a été appliquée à des simulations de scénarios RCP du modèle de l'IPSL. Des résultats cohérents avec les différents scénarios ont été retrouvés. D'autre part, la théorie du transport optimal fournit un contexte théorique pour la correction de biais dans un contexte stationnaire : une méthode de correction de biais est équivalente à une loi jointe de probabilité. Une loi jointe particulière est sélectionnée grâce à la distance de Wasserstein (méthode Optimal Transport Correction, OTC). Cette approche étend les méthodes de corrections en dimension quelconque, corrigeant en particulier les dépendances spatiales et inter-variables. Une extension dans le cas non-stationnaire a également été proposée (méthode dynamical OTC, dOTC). Ces deux méthodes ont été testées dans un contexte idéalisé, basé sur un modèle de Lorenz, et sur des données climatiques (une simulation climatique régionale corrigée avec des ré-analyses SAFRAN). / The climate system generates a strange attractor, described by a probability distribution, called the SRB measure (Sinai-Ruelle-Bowen). This measure describes the state and dynamic of the system. The goal of this thesis is first, to quantify the modification of this measure when climate changes. For this, the Wasserstein distance, stemming from the optimal transport theory, allows us determine accurately the differences between probability distributions. Used on a non-autonomous Lorenz toy model, this metric allows us to detect and quantify the alteration due to a forcing similar to anthropogenic forcing. This methodology has been applied to simulation of RCP scenarios from the IPSL model. The results are coherent with different scenarios. Second, the optimal transport gives a theoretical context for stationary bias correction: a bias correction method is equivalent to a joint probability law. A specific joint law is selected with the Wasserstein distance (Optimal Transport Correction method, OTC). This approach allows us extending bias correction methods in any dimension, correcting spatial and inter-variables dependences. An extension in the non-stationary context has been also developed (dynamical OTC method, dOTC). Those two methods have been tested in an idealized case, based on a Lorenz model, and on climate dataset (a regional climate simulation corrected with respect to the SAFRAN reanalysis).

Background: Response bias can distort treatment effect estimates and inferences in clinical trials. Although prevention, quantification, and adjustments have been developed, current methods are not applicable when subject-level reliability is used as the measure of response bias. Thus, the objective of the current study is to develop, test, and recommend a series of bias correction strategies for use in these cases. Methods: Monte Carlo simulation and logistic regression modeling were used to develop the strategies, examining the collective impact of sample size (N), effect size (ES), reliability distribution, and response style on estimating the treatment effect size in a series of hypothetical clinical trials. The strategies included a linear (LW), quadratic (QW), or cubic weight (CW) applied to the subject-level reliability; a reliability threshold (%); or a combination of the two (W-%). Bias and percent relative root mean square error (RRMSE (%)) were calculated for each treatment effect estimate and RRMSE (%) was compared to inform the bias correction recommendations. Results: The following recommendations are made for each N and ES combination: N=200/ES=small: no adjustment, N=200/ES=medium: 40%-LW, N=200/ES=large: 40%-QW, N=2000/ES=small: 40%-LW, N=2000/ES=medium: 55%-CW, N=2000/ES=large: 75%-CW, N=20000/ES=small: 70%-CW, N=20000/ES=medium: 85%-CW, N=20000/ES=large: 95%-CW. Conclusion: Employing these bias correction strategies in clinical trials where subject-level reliability can be calculated will decrease error and increase accuracy of estimates and validity of inferences.

A changing climate can severely perturb regional hydrology and thereby affect human societies and life in general. To assess and simulate such potential hydrological climate change impacts, hydrological models require reliable meteorological variables for current and future climate conditions. Global climate models (GCMs) provide such information, but their spatial scale is too coarse for regional impact studies. Thus, GCM output needs to be downscaled to a finer scale either through statistical downscaling or through dynamic regional climate models (RCMs). However, even downscaled meteorological variables are often considerably biased and therefore not directly suitable for hydrological impact modeling. This doctoral thesis discusses biases and other challenges related to incorporating climate model output into hydrological studies and evaluates possible strategies to address them. An analysis of possible sources of uncertainty stressed the need for full ensembles approaches, which should become standard practice to obtain robust and meaningful hydrological projections under changing climate conditions. Furthermore, it was shown that substantial biases in current RCM simulations exist and that correcting them is an essential prerequisite for any subsequent impact simulation. Bias correction algorithms considerably improved RCM output and subsequent streamflow simulations under current conditions. In addition, differential split-sample testing was highlighted as a powerful tool for evaluating the transferability of bias correction algorithms to changed conditions. Finally, meaningful projections of future streamflow regimes could be realized by combining a full ensemble approach with bias correction of RCM output: Current flow regimes in Sweden with a snowmelt-driven spring flood in April will likely change to rather damped flow regimes that are dominated by large winter streamflows.

A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.

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Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network. For the first project, we consider the problem of jointly estimating Gaussian graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set. For the second one, we propose a method to jointly estimate the multilevel Gaussian graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using breast cancer patient data. / Ph. D.