Using a multicentury reconstruction of drought, we investigate how rare the 2012–2015 California drought is. A Bayesian approach to a nonstationary, bivariate probabilistic model, including the ...estimation of copula parameters is used to assess the time‐varying return period of the current drought. Both the duration and severity of drought exhibit similar multicentury trends. The period from 800 to 1200 A.D. was perhaps more similar to the recent period than the period from 1200 to 1800 A.D. The median return period of the recent drought accounting for both duration and severity, varies from approximately 667–2652 years, if the model parameters from the different time periods are considered. However, we find that the recent California drought is of unprecedented severity, especially given the relatively modest duration of the drought. The return period of the severity of the recent drought given its 4 year duration is estimated to be nearly 21,000 years.
Key Points
A Bayesian approach to a nonstationary, bivariate probabilistic model, including the estimation of copula parameters is developed
The return period of the recent drought varies from approximately 667 to 2652 years under nonstationary assumption.
The return period of the severity of the recent drought given its 4 year duration is estimated to be nearly 21,000 years.
Significant groundwater depletion in regions where grains are procured for public distribution is a primary sustainability challenge in India. We identify specific changes in the Indian Government's ...Procurement & Distribution System as a primary solution lever. Irrigation, using groundwater, facilitated by subsidized electricity, is seen as vital for meeting India's food security goals. Using over a century of daily climate data and recent spatially detailed economic, crop yield, and related parameters, we use an optimization model to show that by shifting the geographies where crops are procured from and grown, the government's procurement targets could be met on average even without irrigation, while increasing net farm income and arresting groundwater depletion. Allowing irrigation increases the average net farm income by 30%. The associated reduction in electricity subsidies in areas with significant groundwater depletion can help offset the needed spatial re-distribution of farm income, a key political obstacle to changes in the procurement system.
The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural ...network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.
Abstract
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.
Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio ...risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.
Plain Language Summary
It is important to know why past floods occurred in different parts of the United States in the same year and how they were connected to each other. This knowledge helps determine how likely it is for major annual floods to occur all across the country in the future. Consequently, an understanding of what drives such total annual flood losses each year and their relation to climate conditions is of strategic importance. Large‐scale climate patterns can influence where, how much, and how often it floods in a given year. The coincidence of many such events in a year across the country is an example of a compound flood hazard, whose aggregate loss is of interest. This study uses a stochastic model for simulating multiple floods across the United States while preserving information as to their co‐occurrence. The model also calculates the likelihood of combined flood losses, along with its uncertainty. The key patterns in the ocean and the atmosphere that lead to these widespread losses are then identified. The broader impact of this study is to establish a foundation that any country or region can use to evaluate potential consequences of compound risk from joint extreme events.
Key Points
A non‐parametric copula‐based stochastic framework is forwarded to assess the risk of multiple peak floods across a region
Peak floods in the US exhibit significant co‐occurrence of extreme events, leading to compound flood hazard with high aggregate losses
The joint occurrence of extreme flood events across the country is a signature of large‐scale climate teleconnections
The lower Brahmaputra River in Bangladesh and Northeast India often floods during the monsoon season, with catastrophic consequences for people throughout the region. While most climate models ...predict an intensified monsoon and increase in flood risk with warming, robust baseline estimates of natural climate variability in the basin are limited by the short observational record. Here we use a new seven-century (1309–2004 C.E) tree-ring reconstruction of monsoon season Brahmaputra discharge to demonstrate that the early instrumental period (1956–1986 C.E.) ranks amongst the driest of the past seven centuries (13th percentile). Further, flood hazard inferred from the recurrence frequency of high discharge years is severely underestimated by 24–38% in the instrumental record compared to previous centuries and climate model projections. A focus on only recent observations will therefore be insufficient to accurately characterise flood hazard risk in the region, both in the context of natural variability and climate change.
Storage and controlled distribution of water have been key elements of a human strategy to overcome the space and time variability of water, which have been marked by catastrophic droughts and floods ...throughout the course of civilization. In the United States, the peak of dam building occurred in the mid‐20th century with knowledge limited to the scientific understanding and hydrologic records of the time. Ecological impacts were considered differently than current legislative and regulatory controls would potentially dictate. Additionally, future costs such as maintenance or removal beyond the economic design life were not fully considered. The converging risks associated with aging water storage infrastructure and uncertainty in climate in addition to the continuing need for water storage, flood protection, and hydropower result in a pressing need to address the state of dam infrastructure across the nation. Decisions regarding the future of dams in the United States may, in turn, influence regional water futures through groundwater outcomes, economic productivity, migration, and urban growth. We advocate for a comprehensive national water assessment and a formal analysis of the role dams play in our water future. We emphasize the urgent need for environmentally and economically sound strategies to integrate surface and groundwater storage infrastructure in local, regional, and national water planning considerations. A research agenda is proposed to assess dam failure impacts and the design, operation, and need for dams considering both paleo and future climate, utilization of groundwater resources, and the changing societal values toward the environment.
Plain Language Summary
Water storage and control have been key elements of a human strategy to overcome differences between water availability and water needs. The future promises changes to when and where water will be available and many regions in the USA will likely see an increase in the imbalance between existing water storage and evolving demands for water. This indicates the need for more storage or new dams to meet human and ecological needs. The current trend for removal of old, hazardous or unpopular dams now and into the future may impact regional groundwater outcomes, food and energy production, migration, and urban growth. We advocate for a formal analysis of the role dams play in the future of the USA's water landscape. We also stress the need for national water planning considerations to develop environmentally and economically sound strategies to integrate the management of surface and groundwater storage infrastructure in the USA.
Key Points:
Climate change projections suggest more hydrologic extremes. Are more dams subsequently needed?
Most US dams now exceed their economic design life and represent a need for infrastructure investment and recognition of associated risks
A national water assessment is needed to examine dam removal and modified storage provision options considering hydroclimatic risk exposure
Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading ...failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. In this model, the critical tolerance threshold is originally proposed to indicate the vulnerability of network to cascading failures. In addition, some key parameters that are important to network vulnerability are identified and quantified through numerical simulation. Results show that cascading failures can be prevented when the values of tolerance parameter are above a critical tolerance threshold. Otherwise interdependent networks collapse after attacking a critical fraction of power nodes. Interdependent networks become more vulnerable with the increase in interdependence strength, which implies the importance of protecting those interconnected nodes to reduce the consequences of cascading failures. Interdependent networks are most vulnerable under high-load attack, which shows the significance of protecting high-load nodes.
•Precipitation was investigated through pressure change event (PCE).•PCE was related to probability of precipitation (POP) and precipitation depth (PD).•PCE occurrence was associated to average ...monthly temperature (AMT).•The associations between POP and PD with PCE and AMT were explored.
Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed.
In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and ...longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns.
Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.
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