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Large ensemble scenario discovery on global hydropower expansion and human well-being

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Abstract

Multiple societal challenges intensify risks and uncertainties posed to the human-Earth system. Scenario discovery is a computational approach to identify dynamics tied to critical societal outcomes using large scenario ensembles that cover a wide range of potential futures. Large ensemble scenario generation demands a detailed analysis of sensitivity to various parameters to guide the ensemble design and limit the computational burden. We present two scenario discovery efforts, each with a different theme, aimed at uncovering the drivers and uncertainties associated with global hydropower expansion and societal well-being. First, we develop two scenario ensembles using the Global Change Analysis Model (GCAM) to evaluate (1) climate change impacts on future global hydropower expansion and (2) future human well-being outcomes according to multiple metrics (e.g., food-energy-water security). GCAM is a leading global-scale integrated, multi-sector model that accounts for complex interactions and tradeoffs across multiple sectors. We then perform scenario discovery on both ensembles to identify the primary drivers of outcomes of interest from each ensemble. Ultimately, with the insights from scenario discovery, we explore how the uncertain future of global hydropower expansion and human well-being could unfold on diverse spatiotemporal scales.

Category
Model Uncertainties, Model Biases, and Fit-for-Purpose
Energy, Water, and Land System Transition
Metrics, Benchmarks and Credibility of model output and data for science and end users
Funding Program Area(s)