Search Results (6)

Amid the growing energy–water nexus crisis, large dams are being reconsidered as viable solutions despite significant environmental concerns. A critical and enduring issue with large dams is the threat they pose to downstream communities and infrastructure in the event of structural failure. The Oroville Dam spillway incident, where inadequate maintenance led to uplift forces that exceeded the structural capacity of a chute slab, causing severe damage, has renewed the focus on the structural stability of spillway components. This study argues that conventional methods, which rely on averaged values and empirical coefficients, may be inadequate for accurately capturing the dynamical stresses on spillway chutes induced by turbulent flow conditions. We propose a novel approach using stochastic simulation schemes to generate synthetic time series of velocity, which are then applied to a differential equation governing the chute slab oscillations. Through a hypothetical case study inspired by the Oroville incident, we demonstrate two key issues: first, that the conventional approach significantly underestimates the maximum stresses experienced by chute slabs under dynamic uplift pressures; and second, that the stochastic structure of the velocity, particularly the variance and persistence, plays a major role in determining the maximum stress. Full article

Since early 2018 the “Natural Hazards” Section of Geosciences journal has aimed to publish pure, experimental, or applied research that is focused on advancing methodologies, technologies, expertise, and capabilities to detect, characterize, monitor, and model natural hazards and assess their associated risks. This stream of geoscientific research has reached a high degree of specialization and represents a multi-disciplinary research realm. To inaugurate this section, the Special Issue “Key Topics and Future Perspectives in Natural Hazards Research” was launched. After a year and half since the call for papers was initially opened, the special issue is now completed with the editorial introducing the collection of 10 selected papers covering the following hot topics of natural hazards research: (i) trends in publications and research directions at international level; (ii) the role of Big Data in natural disaster management; (iii) assessment of seismic risk through the understanding and quantification of its three components (i.e., hazard, vulnerability and exposure/impact); (iv) climatic/hydro-meteorological hazards (i.e., drought, hurricanes); and (v) scientific analysis of past incidents and disaster forensics (i.e., the Oroville Dam 2017 spillway incident). The present editorial provides a summary of each paper of the collection within the current context of scientific research on natural hazards, pointing out the salient results and key messages. Full article

Unprecedented flooding events put dams and downstream communities at risk, as evidenced by the recent cases of the Oroville and Whaley bridge dams. Empirical models may describe expected ‘normal’ dam behaviour, but they do not account for changes due to recurring extreme weather events. Numerical modelling provides insights into this, but results are affected by the chosen material properties. Long-term field monitoring data can help with understanding the mechanical behaviour of earthfill dams and how this is affected by the environment over decades. We analyse the recorded settlements for one of the largest earthfill dams in Europe. We compare the evolution of these settlements to the reservoir level, rainfall, and the occurrence of earthquakes for a period of 31 years after first impoundment. We find that the clay core responds to the reservoir fluctuations with an increasing (from 0 to 6 months) time delay. This is the first time that a change in the behaviour of a central clay core dam, as observed from field data, is reported in the international literature. Seepage rates, as recorded within the drainage galleries, are directly affected by cumulative rainfall depths exceeding 67 mm per fortnight. Full article

In February 2017, a failure occurring in Oroville Dam’s main spillway risked causing severe damages downstream. A unique aspect of this incident was the fact that it happened during a flood scenario well within its design and operational procedures, prompting research into its causes and determining methods to prevent similar events from reoccurring. In this study, a hydroclimatic analysis of Oroville Dam’s catchment is conducted, along with a review of related design and operational manuals. The data available allows for the comparison of older flood-frequency analyses to new alternative methods proposed in this paper and relevant literature. Based on summary characteristics of the 2017 floods, possible causes of the incident are outlined, in order to understand which factors contributed more significantly. It turns out that the event was most likely the result of a structural problem in the dam’s main spillway and detrimental geological conditions, but analysis of surface level data also reveals operational issues that were not present during previous larger floods, promoting a discussion about flood control design methods, specifications, and dam inspection procedures, and how these can be improved to prevent a similar event from occurring in the future. Full article

Existing analyses of the February 2017 Oroville Dam Crisis identify maintenance failures and engineering shortcomings as the root cause of a nearly catastrophic failure of the tallest dam in the United States. However, the focus on technical shortcomings largely overlooks the role of adaptive decision-making that eventually averted the crisis. Understanding the decisions that both created the circumstances leading up to the crisis and saved the dam from collapse requires that risk analysis be complemented by a resilience perspective. This paper presents a case study on the Oroville Dam Crisis to develop a timeline of failures and successes in the resilience processes of sensing, anticipation, adapting, and learning throughout the history of the dam. Three factors that drove poor operational and engineering decisions include: (1) misalignment of technical nomenclature and design requirements for the ungated spillway, (2) overconfidence in original engineering designs, and (3) conflicting pressure from diverse stakeholders. These factors contributed to unrealistic perceptions of dam capabilities and promoted decisions to forgo maintenance and design efforts that may have mitigated the danger. Full article

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km ${}^2$ network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. Full article