Managing Water Resources

Water Resources Research

Hartford

2017

Hydropower infrastructure systems are complex, high consequence structures which must be operated safely to avoid catastrophic impacts to human life, the environment, and the economy. Dam safety practitioners must have an in‐depth understanding of how these systems function under various operating conditions in order to ensure the appropriate measures are taken to reduce system vulnerability. Simulation of system operating conditions allows modelers to investigate system performance from the beginning of an undesirable event to full system recovery. System dynamics simulation facilitates the modeling of dynamic interactions among complex arrangements of system components, providing outputs of system performance that can be used to quantify safety. This paper presents the framework for a modeling approach that can be used to simulate a range of potential operating conditions for a hydropower infrastructure system. Details of the generic hydropower infrastructure system simulation model are provided. A case study is used to evaluate system outcomes in response to a particular earthquake scenario, with two system safety performance measures shown. Results indicate that the simulation model is able to estimate potential measures of system safety which relate to flow conveyance and flow retention. A comparison of operational and upgrade strategies is shown to demonstrate the utility of the model for comparing various operational response strategies, capital upgrade alternatives, and maintenance regimes. Results show that seismic upgrades to the spillway gates provide the largest improvement in system performance for the system and scenario of interest.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using system dynamics simulation for assessment of hydropower system safety

King

Water Resources Research

Hartford

2017

“…A system can be generally defined as a collection of structural and non-structural elements that are connected and interact with each other to function as a whole [14,15]. This definition encompasses many types of systems that could be physical, organizational, social, or abstract.…”

Section: System Dynamics Simulation Approachmentioning

confidence: 99%

ANEMI3: An updated tool for global change analysis

Breach

2021

PLoS ONE

The ANEMI model is an integrated assessment model of global change that emphasizes the role of water resources. The model is based on the principles of system dynamics simulation to analyze changes in the Earth system using feedback processes. Securing water resources for the future is a key issue of global change, and ties into global systems of population growth, climate change, carbon cycle, hydrologic cycle, economy, energy production, land use and pollution generation. Here the third iteration of the model–ANEMI3 is described, along with the methods used for parameter estimation and model testing. The main differences between ANEMI3 and previous versions include: (i) implementation of the energy-economy system based on the principles of system dynamics simulation; (ii) incorporation of water supply as an additional sector in the global economy that parallels the production of energy; (iii) inclusion of climate change effects on land yield and potentially arable land for food production, and (iv) addition of nitrogen and phosphorus based nutrient cycles as indicators of global water quality, which affect the development of surface water supplies. The model is intended for analyzing long-term global feedbacks which drive global change. Because of this, there are limitations related to the spatial scale that is used. However, the model’s simplicity can be considered a strength, as it allows for the driving feedbacks to be more easily identified. The model in its current form allows for a variety of scenarios to be created to address global issues such as climate change from an integrated perspective, or to examine the change in one model sector on Earth system behaviour. The endogenous structure of the model allows for global change to be driven entirely by model structure rather than exogenous inputs. The new additions to the ANEMI3 model are found to capture long term trends associated with global change, while allowing for the development of water supplies to be represented using an integrated approach considering global economy and surface water quality.

“…System dynamics has since been applied across a variety of fields, including water resources engineering. Simonovic [14] presents simulation techniques that deal with water resources in general, with a particular focus on system dynamics simulation as a tool for water resources engineering. In system dynamics, a stock-and-flow model can be used to represent the system structure through feedback loops and delays.…”

Section: System Dynamics Simulation Model Developmentmentioning

confidence: 99%

A Deterministic Monte Carlo Simulation Framework for Dam Safety Flow Control Assessment

King

2020

Water

Simulation has become more widely applied for analysis of dam safety flow control in recent years. Stochastic simulation has proven to be a useful tool that allows for easy estimation of the overall probability of dam overtopping failure. However, it is difficult to analyze “uncommon combinations of events” with a stochastic approach given current computing abilities, because (a) the likelihood of these combinations of events is small, and (b) there may not be enough simulated instances of these rare scenarios to determine their criticality. In this research, a Deterministic Monte Carlo approach is presented, which uses an exhaustive list of possible combinations of events (scenarios) as a deterministic input. System dynamics simulation is used to model the dam system interactions so that low-level events within the system can be propagated through the model to determine high-level system outcomes. Monte Carlo iterations are performed for each input scenario. A case study is presented with results from a single example scenario to demonstrate how the simulation framework can be used to estimate the criticality parameters for each combination of events simulated. The approach can analyze these rare events in a thorough and systematic way, providing a better coverage of the possibility space as well as valuable insights into system vulnerabilities.