Rosas Cabello, Carlos2025-12-192025-12-192025-12-19http://hdl.handle.net/10393/51198https://doi.org/10.20381/ruor-31634Accurate prediction of hydroclimatic extremes, such as droughts, floods, and water availability, hinges on understanding how remote oceanic forcings modulate continental precipitation. Conventional teleconnection analyses face two critical limitations: (i) their reliance on linear, stationary assumptions, which conceal regime shifts and erode predictive skill, and (ii) their vulnerability to short or fragmented records, which amplifies uncertainty. Although traditional methods can estimate degrees of ocean-land influence, a clear need remains for approaches that link both processes under nonlinear, nonstationary conditions while retaining strong physical interpretability for forecasting and decision-making. This dissertation advances teleconnection and hydroclimate analysis by: (a) formulating the Point-Slope (PS) similarity metric, which blends Bayesian changepoint detection with segment-specific trend estimation to capture evolving, potentially nonlinear ocean-land connections; (b) generating station-specific Oceanic Regions of Influence (ROIs) that locate where low-frequency climate modes exert maximal control on local rainfall; (c) designing a geographically flexible PS-driven clustering framework that produces hydrologically homogeneous, physically interpretable regions; (d) benchmarking PS against Spearman correlation and Empirical Orthogonal Functions (EOFs) to quantify improvements in signal detection, spatial coherence, and resilience to missing data; and (e) testing PS sensitivity to confirm computational efficiency and methodological stability. The findings demonstrate that PS (i) uncovers dynamic teleconnections overlooked by conventional metrics; (ii) scales linearly with record length, yielding 0-1 values that directly represent the joint probability of synchronized changepoints and aligned trends; (iii) generates ROI maps that expose fine-scale oceanic zones governing variability at individual rain gauges; (iv) forms clusters with markedly greater monthly-precipitation homogeneity than those produced with Spearman correlation, especially for key modes such as Niño-3.4; and (v) simultaneously delineates oceanic regions of influence across the full predictor field, and their corresponding terrestrial clusters, more accurately than traditional EOF analysis. Together, these advances provide a scalable, reproducible, and transparent framework for incorporating teleconnection insights into regional water-management planning and next-generation predictive systems under a changing climate. Looking ahead, the PS framework is not intended to replace established methodologies but rather to complement and enhance them. Its probabilistic and segment-based structure provides a foundation that can be readily combined with emerging technologies such as artificial intelligence, machine learning, and high-performance computing. Such integration can improve both the detection and interpretation of teleconnections, enabling hybrid approaches that fuse statistical rigor with computational adaptability. This opens promising avenues for advancing predictive accuracy, refining early-warning systems, and strengthening the scientific basis of climate-informed water-management strategies.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/TeleconnectionsHydrologyChangepointsSimilarity MeasuresThesis