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Record W2550962109 · doi:10.1002/2016wr019551

Comparison of static and dynamic resilience for a multipurpose reservoir operation

2016· article· en· W2550962109 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Resources Research · 2016
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaBC Hydro
KeywordsInflowResilience (materials science)Reservoir modelingReservoir simulationReliability engineeringEnvironmental scienceComputer sciencePetroleum engineeringRisk analysis (engineering)EngineeringGeology

Abstract

fetched live from OpenAlex

Abstract Reliability, resilience, and vulnerability are the traditional risk measures used to assess the performance of a reservoir system. Among these measures, resilience is used to assess the ability of a reservoir system to recover from a failure event. However, the time‐independent static resilience does not consider the system characteristics, interaction of various individual components and does not provide much insight into reservoir performance from the beginning of the failure event until the full performance recovery. Knowledge of dynamic reservoir behavior under the disturbance offers opportunities for proactive and/or reactive adaptive response that can be selected to maximize reservoir resilience. A novel measure is required to provide insight into the dynamics of reservoir performance based on the reservoir system characteristics and its adaptive capacity. The reservoir system characteristics include, among others, reservoir storage curve, reservoir inflow, reservoir outflow capacity, and reservoir operating rules. The reservoir adaptive capacity can be expressed using various impacts of reservoir performance under the disturbance (like reservoir release for meeting a particular demand, socioeconomic consequences of reservoir performance, or resulting environmental state of the river upstream and downstream from the reservoir). Another way of expressing reservoir adaptive capacity to a disturbing event may include aggregated measures like reservoir robustness, redundancy, resourcefulness, and rapidity. A novel measure that combines reservoir performance and its adaptive capacity is proposed in this paper and named “dynamic resilience.” The paper also proposes a generic simulation methodology for quantifying reservoir resilience as a function of time. The proposed resilience measure is applied to a single multipurpose reservoir operation and tested for a set of failure scenarios. The dynamic behavior of reservoir resilience is captured using the system dynamics simulation approach, a feedback‐based object‐oriented method, very effective for modeling complex systems. The results of dynamic resilience are compared with the traditional performance measures in order to identify advantages of the proposed measure. The results confirm that the dynamic resilience is a powerful tool for selecting proactive and reactive adaptive response of a multipurpose reservoir to a disturbing event that cannot be achieved using traditional measures. The generic quantification approach proposed in the paper allows for easy use of dynamic resilience for planning and operations of various civil infrastructure systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.348
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it