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Winter storm Uri brought severe cold to the southern United States in February 2021, causing a cascading failure of interdependent systems in Texas where infrastructure was not adequately prepared for such cold. In particular, the failure of interconnected energy systems restricted electricity supply just as demand for heating spiked, leaving millions of Texans without heat or electricity, many for several days. This motivates the question: did historical storms suggest that such temperatures were known to occur, and if so with what frequency? We compute a temperature-based proxy for heating demand and use this metric to answer the question “what would the aggregate demand for heating have been had historic cold snaps occurred with today’s population?”. We find that local temperatures and the inferred demand for heating per capita across the region served by the Texas Interconnection were more severe during a storm in December 1989 than during February 2021, and that cold snaps in 1951 and 1983 were nearly as severe. Given anticipated population growth, future storms may lead to even greater infrastructure failures if adaptive investments are not made. Further, electricity system managers should prepare for trends in electrification of heating to drive peak annual loads on the Texas Interconnection during severe winter storms.
The property damaged and the lives disrupted by recent hurricanes, floods, droughts, and water quality violations highlight the inadequacy of water infrastructure in the United States and around the world. Decisions about managing these infrastructure systems are strongly informed by societal perceptions of risk, which in turn are shaped through narratives of high-impact events in academic, governmental, commercial, and popular media. In recent years, post hoc analyses of high-impact water and climate disasters have increasingly focused on the role of anthropogenic climate change (ACC). This is a welcome development that helps to build support for much-needed mitigation of global greenhouse gas emissions and pushes companies, governments, and aid agencies to prepare for a changing environment. Yet climate impacts require a confluence of physical hazards and societal vulnerabilities, and so narratives centered only on the role of ACC can neglect the aging infrastructure, increasing development with exposure to climate risks, and inadequate maintenance that set the stage for meteorological and hydrological events to become humanitarian disasters. The fatalistic narratives that emerge, which often imply that because an event was exacerbated by climate change its consequences could not have been averted, discourage adaptive planning.
The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: while historical and paleo climate records emphasize modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi-periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N-year historical record. The uncertainty of IPCC-like future scenarios is considered through an equivalent sample size N. The relative importance of estimating the short- or long-term risk extremes depends on the investment life M. Shorter design lives are preferred for situations where inter-annual to decadal variability can be successfully identified and predicted, suggesting the importance of sequential investment strategies for adaptation.
Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multi-variable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multi-variable zero-inflated beta models reduce the 90% prediction intervals for Hurricane Harvey building structure loss estimates on average by 78% (totalling US$ 3.8 billion) compared to commonly used models.
During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.
General circulation models (GCMs) have been demonstrated to produce estimates of precipitation, including the frequency of extreme precipitation, with substantial bias and uncertainty relative to their representation of other fields. Thus, while theory predicts changes in the hydrologic cycle under anthropogenic warming, there is generally low confidence in future projections of extreme precipitation frequency for specific river basins. In this paper, we explore whether a GCM simulates large-scale atmospheric circulation indices that are associated with regional extreme precipitation (REP) days more accurately than it simulates REP days themselves, and thus whether conditional simulation of the precipitation events based on the circulation indices may improve the simulation of REP events. We show that a coupled Geophysical Fluid Dynamics Laboratory GCM simulates too many springtime REP days in the Ohio River Basin in historical (1950–2005) simulations. The GCM, however, does credibly simulate the distributional and persistence properties of several indices (which represent the large-scale atmospheric pressure features, local atmospheric moisture content, and local vertical velocity) that are shown to modulate the likelihood of REP occurrence in the reanalysis/observational record. We show that simulation of REP events based on the GCM-based atmospheric indices greatly reduces the bias of GCM REP frequency relative to the observed record. The simulation is conducted via a Bayesian regression model by imposing the empirical relationship between observed REP occurrence and the reanalysis-based atmospheric indices. Application of this model to future (2006–2100) representative concentration pathway 8.5 scenario suggests an increasing trend in springtime REP incidence in the study region. The proposed approach of simulating precipitation events of interest, particularly those poorly represented in GCMs, with a statistical model based on climate indices that are reasonably simulated by GCMs could be applied to subseasonal to seasonal forecasts as well as future projections.
During the past two decades, government efforts to provide water access to rural communities in Brazil’s semiarid Northeast region have focused on building systems to capture and store rainwater, most importantly through the One Million Cisterns Program (P1MC). This article presents an analytic model based on daily precipitation data to evaluate the sustainability of rainwater capture. Application of this model to analysis of the P1MC reveals the heterogeneous climate in this region causes large spatial variability in the effectiveness of this program. In addition, the size of the area of capture, the run-off coefficient of the roofs, and the amount of first-flush diversion also have important effects. This analysis demonstrates while rainwater capture can offer sufficient water for drinking, as a stand-alone solution it cannot meet P1MC objectives of guaranteeing sustainable and universal access to water for drinking, cooking, and basic hygiene in all regions and years.
Papers in preparation and in press
Projections of future climate risks can vary considerably from one source to another, posing considerable communication and decision-analytical challenges. One such challenge is how to present trade-offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or invoking Laplace’s principle of insufficient reason to justify a simple average. These approaches can underestimate risks, hide deep uncertainties, and provide little insight into which assumptions drive decision-relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade-offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.
Theses and Dissertations
Conference papers and presentations
The February 2021 Texas Freeze highlighted the vulnerability of energy systems and their co-dependent infrastructure systems on climatic factors. As Texas continues to be one of the leaders in the adoption of renewable wind and solar energy, the importance of understanding the joint dependence of the availability of these resources, and of the climate sensitive demand associated with heating and cooling becomes critical. We present a novel space-time simulator based on a generalized k-nearest neighbor method that can generate spatially distributed daily time step simulations of wind power, solar radiation and cooling (CDD) and heating (HDD) degree days. The simulations preserve the cross-field dependence in space and across times in our applications to a 40 year long historical climate re-analysis data from Texas, including the spatial structure of the principal components of the data and their associated temporal spectra. We use the simulations to identify annual maxima of HDD and CDD for different block lengths (e.g., 1, 3, 7, and 14 days) over the entire ERCOT and identify the corresponding available wind and solar resource also over ERCOT. These are then used to identify the joint probability distributions of climate sensitive energy supply and demand, and their associated uncertainty. The estimated multivariate return periods can be used to inform reliable system design. A diagnosis of the atmospheric circulation parameters that lead to the extremes was also conducted for physical insights as to the mechanisms.
Ensuring the reliable distribution of safe drinking water and proper disposal of wastewater is critical to the well-being of modern society. Water treatment plants (WTPs) and wastewater treatment plants (WWTPs) tend to be located near large water bodies for cost-effective pumping of raw water and discharging of sewage downstream. Additionally, most WWTPs are built in low elevation areas to take advantage of gravity flows. Many WTPs and WWTPs are increasingly vulnerable to extreme precipitation events that occur more frequently due to global climate change. Hybrid systems that combine the centralized infrastructure and distributed wastewater treatment units with direct potable reuse (DPR) are an adaptation option for utilities. We investigate the benefit of such hybrid systems in terms of improving resilience to extreme precipitation events with a small city in the U.S., the City of Lumberton, North Carolina. Lumberton was hit by Hurricane Matthew in 2016 and Hurricane Florence in 2018 with different extents of water and wastewater service failure. We build a quantitative model for the water system of Lumberton that enables many-query analysis. We examine the performance of a hybrid system, which has three distributed DPR sites at three existing tank stations that are located either outside 500-year flood hazard zones or in 500-year flood hazard zones with reduced risk, against two hazard scenarios relative to the original system. The first hazard scenario is the flooding of the WTP and the pump station, which reside in the 100-year flood hazard zone. The second is a biological invasion (Escherichia coli) from sewer overflow. We show that the hybrid system has a higher capacity to withstand flooding under certain levels. It can maintain reliable water supply for more than 72 hours if the DPR quantity is maximized, while the original system can only provide less than 24 hours of supply from tanks’ storage. Moreover, the hybrid system contains much-retained impacted areas in case of contaminant invasions as the distributed DPR sites form relatively independent pressure zones. We demonstrate that this modeling framework can provide decision-makers with quantitative data for what-if scenarios with new adaptation alternatives and their synergies (e.g., integrated water and wastewater systems, expanded power backup).
Winter storm Uri brought severe cold to the southern United States in February ...
Over the last two decades, many investigators have developed seasonal climate f...
The prediction and simulation of streamflow extremes across a river basin has s...
AGU Fall Meeting 2019, Abstract H11G-07. Presented 9 December 2019.
In this study we identify the atmospheric conditions that precede and accompany regional extreme precipitation events with the potential to cause flooding. We begin by identifying a coherent space-time structure in the record of extreme precipitation within the Ohio River \ldots
Regional-scale extreme rainfall and flooding are temporally and spatially associated with the occurrence of tropical moisture exports (TMEs) in the Ohio River Basin (ORB). TMEs are related to but not synonymous with atmospheric rivers, which refer to specific filiamentary organizational processes. TMEs to the ORB may be driven by strong, persistent ridging over the Eastern United States and troughing over the Central United States, creating favorable conditions for southerly flow and moisture transport from the Gulf of Mexico and Caribbean Sea. However, the strong inter-annual variation in TME activity over the ORB suggests dependence on global-scale features of the atmospheric circulation. We suggest that this synoptic dipole pattern may be viewed as the passage of one or more high-wavenumber, transient Rossby waves. We build a multi-level hierarchical Bayesian model in which the probability distribution of TME entering the ORB is a function of the phase and amplitude of the traveling waves. In turn, the joint distribution of the phase and amplitude of this wave is modulated by hemispheric-scale features of the atmospheric and oceanic circulation, and the amplitude and synchronization of quasi-stationary Rossby waves with wavenumber 1-4. Our approach bridges information about different features of the atmospheric circulation which inform the predictability of TME at multiple time scales and develops existing understanding of the atmospheric drivers of TMEs beyond existing composite and EOF studies.
Climate model projections are commonly used for water resources management and planning under nonstationarity, but they do not reliably reproduce intense short-term precipitation and are instead more skilled at broader spatial scales. To provide a credible estimate of flood trend that reflects climate uncertainty, we present a framework that exploits the connections between synoptic-scale oceanic and atmospheric patterns and local-scale flood-producing meteorological events to develop long-term flood hazard projections. We \ldots