The recent extreme hydrological extremes over the globe highlight the importance of understanding the role of atmospheric dynamics and climate variability on the occurrence of these extreme events and the associated temporal and spatial characteristics of sequences of the precipitation events. Most of the studies have been focusing on overall average impacts of long-term global climate change on extremes. Majority are driven largely by considering the changes of the moisture holding capacity as a function of temperature, as indicated by the Clausius-Clapeyron equation. Given the complex dynamical structure of the atmosphere, one needs to also consider the attendant atmospheric circulation and moisture transport mechanisms that lead to extreme precipitation and subsequent floods as evidenced in the recent major floods. This study first develops insights into the causative climatic factors associated with precipitation induced regional floods events and understand the roles of Atmospheric Rivers (AR) or Tropical Moisture Exports (TME) and atmospheric circulation patterns associated with the frequency and/or persistency of such events in the midlatitudes. The second part explores the spatiotemporal relationship between climate variability and global extreme precipitation occurrence using a graph based approach based upon the concept of reciprocity to investigated the linkages and influences of the slowly changing boundary conditions on the development or propagation of atmospheric circulations, to assess the predictability of global precipitation extremes given the leading modes of identified climate dipole networks. A multi-timescale statistical, climate informed, stochastic streamflow forecast model serves as the bridge linking the first two parts to the application in the third part: application on water resources management by developing a multi-timescale climate informed stochastic hybrid stimulation-optimization model for multi-purpose reservoir systems, which enables the utilization of the streamflow forecast. The novel reservoir operation model attempts to change the game of water resources management from its conservative, rigid rule-following scheme to a robust, market-based, reliable water allocation strategy. Part I. Tropical Moisture Exports, Extreme Precipitation and Major Flood Atmospheric Rivers are being increasingly identified as associated with some extreme floods. More generally, such floods may be associated with tropical moisture exports that exhibit relatively robust teleconnections between moisture source regions and flood regions. First, a large-scale flood event that persisted over Western Europe in January 1995 is studied. During the last ten days of the month, two rare flooding events, associated with heaviest rainfall in 150 years, occurred in two places, one over Brittany (West of France), and the second in the France-Germany border region and parts of neighboring countries. In this study, we explore the month-long evolution of tropical moisture exports (TME) and their connection to the precipitation events that led to the Brittany event. The persistent large-scale atmospheric circulation patterns that led to the birth, death and evolution of these TME as atmospheric rivers with landfalls in Western Europe are identified, and the relationship of daily extreme precipitation to these patterns is examined. Singular value decomposition (SVD) analysis and a generalized linear model (GLM) are used to assess whether knowledge of the atmospheric circulation patterns from the prior record is useful for explaining the occurrence of their rare events. The analysis establishes the importance of both global and regional atmospheric circulation modes for the occurrence of such persistent events and the hydrologic importance of diagnosing global atmospheric moisture pathways. Part II. Seasonal to Interannual Variability of Tropical Moisture Exports, Extremes and ENSO A statistically and physically based framework is put forward that investigates the relationship between Tropical Moisture Exports (TMEs), Extreme Precipitation and Floods. TMEs is the more general phenomena than Atmospheric Rivers (ARs) in terms of (1) facilitates the poleward transport of warm and moist air masses from low latitudes, primarily tropical oceanic areas, to higher latitudes; (2) contributes to the global climatology precipitation and its extremes; (3) closely relates to floods events, especially in the midlatitudes. The TMEs itself has seasonal and interannual variability that is regulated by slowly changing boundary conditions and climate variability, such El Niño Southern Oscillation (ENSO), while the trajectories and movements are presumably led by atmospheric circulations patterns driven by the balance of global energy and water budgets. In this study, we take Northwest US (NE US) to show how the TMEs is related to extreme precipitation and then floods, and the results of the variability of TMEs, coupled with atmospheric circulation patterns, on the extremes. Historical large floods events in NE US in different seasons are studied for their link to the TMEs. Major moisture sources of TMEs that contributes to precipitation, extremes and floods in NE US are identified, together with the sources' seasonally and interannually varying characterizes in terms of both birth and entrance to the NE US, with the consideration of large scale climate regulations and atmospheric circulation patterns. Part III. Correlation Networks for Identifying Predictors for Extended Range Forecasts for Extreme Precipitation Correlation networks identified from financial, genomic, ecological, epidemiological, social and climate data are being used to provide useful topological insights into the structure of high dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship between climate and extreme global precipitation is explored using a graph based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad sea surface temperatures (SST) anomalies and pentad sea level pressure (SLP) anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. We explore the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a Principal Component logistic regression on the strong global dipoles found between SST and SLP. Unprecedented success of the predictive skill under cross validation for 30 days precipitation higher than the 90th percentile is indicated for selected global regions for each wet season considered. Part IV. Applications of Climate Informed Streamflow Forecasts for Water Management Streamflow forecasts at multiple time scales (e.g., season and year ahead) provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such instruments in lieu of a traditional operation and allocation. A water allocation process that enables multiple contracts with different durations, to facilitate participatory management of the reservoir by users and system operators, is presented here. Since these contracts are based on a verifiable reliability they may in turn be insurable. A Multi-timescale climate informed Stochastic Hybrid Simulation - Optimization Model (McISH) is developed, featuring (1) dynamic flood control storage allocation at a specified risk level; (2) multiple duration energy/water contracts with user specified reliability and prices; and (3) contract sizing and updating to reflect changes in both demands and supplies. The model incorporates multi-timescale (annual and seasonal) streamflow forecasts, and addresses uncertainties across both timescales. The intended use is as part of an interaction between users and water operators to arrive at a set of short-term and long term contracts through disclosure of demand or needs and the value placed on reliability and contract duration. An application is considered using data for the Bhakra Dam, India. The issues of forecast skill and contract performance given a set of parameters are examined to illustrate the approach. Prospects for the application in a general setting are discussed.

Storm induced flooding presents a multifaceted threat to coastal communities across the world.With climate change and sea level rise this danger is expected to increase. As coastal communities become exposed to more frequent and more severe flooding, the need for protective measures will increase. To know how to optimally protect against coastal flooding requires an understanding of future flood risk, storms, and storm surge. These are challenging to estimate due to many sources of uncertainty. In this thesis I present a methodology to forecast this future flood risk. I combine multiple computational, physics and statistical models to accurately describe the fluid dynamics of flooding, the cyclones that drive surge, and how climate change will influence these different components in the future. These computational models must be fast so that they can be embedded into an optimization framework that makes many evaluations. To find an optimal protective measure I employ stochastic and derivative free optimization methods. A complete study is conducted on New York City and optimal protective strategies are found for minimizing the total cost from storm surge subject to different budget constraints.

The very concept of sustainable development calls for a holistic understanding of socio-economic and natural systems in order to help achieve greater sustainability. The complexity characterizing such systems, however, makes it likely impossible for quantified approaches of even isolated problems to account for all relevant factors in a single, robust and deterministic representation of reality – an inherent feature which largely motivates the use of statistical models applied to empirical data. On three independent examples with significant socio-economic and environmental importance, the present dissertation illustrates how econometrical models applied to real-life environmental data can be fruitfully deployed to facilitate the identification and motivation of innovative policies to achieve greater sustainability. Specifically, the first chapter explores the extent to which large-scale irrigation affects local climate by inducing cooler temperatures in areas located downwind from irrigated land, an externality with positive economic consequences quantified in terms of improved crop yields and reduced human mortality. The second chapter illustrates the benefits offered by a family of new differencing estimators (as theoretically derived from a generalization of existing techniques found in the literature) on the example of the nonparametric estimation of error variance in streamflow measurements - a step that is critical for the accurate prediction by hydrological models of extreme flood events. The third chapter investigates the joint effect of traffic speed and acceleration on urban air quality in order to help anticipate the consequences of innovative traffic regulation on the concentration of key air pollutants with detrimental consequences on human health and the economy.