Juan José Leitón Montero

Used Bayesian hierarchical modeling to analyze the seasonal temperature and precipitation projections of regions covered by the PRUDENCE projects, focusing on CH2018 climate scenarios and the RCP8.5 warming scenario. This model implementation expands the work by Kerkhoff, Tay, and Künsch by evaluating temperature and precipitation variables for every combination of regions and seasons.

I used posterior distributions to estimate parameters associated with bias and assumption coefficients, climatological means, interannual variables, and additive bias, in addition to calculating climate change estimates for five different time horizons. Also, I found a generalized variation temperature pattern of all analyzed region and season combinations and identified season- and region-dependent patterns for precipitation.

By comparing bias associated with the regional climate and general circulation model chains and their drivers, I evaluated the additive bias reduction from dynamic scaling. Finally, I assessed the results accounting for a potential 20% component reduction and classified the combinations of regions, seasons, and chains based on this value.

Modeled hydroclimatic time series using autoregressive models, Gaussian processes, and wavelets to quantify the national electricity system's energy portfolio uncertainty.

I utilized five different modeling approaches to reproduce the stochastic characteristics of a set of hydroclimatic time series. The modeled time series were evaluated based on the parent distribution, distribution of extremes, and multivariate and temporal dependencies.