This module introduces the application of stable water isotopes (δ¹⁸O and δD) to quantify the origin of precipitation and hydrological inputs under varying climatic conditions. Using Bayesian mixing models implemented in MixSIAR (R package), participants will learn to: Prepare and structure isotopic datasets from precipitation, potential water sources, and discrimination factors. Apply a Bayesian mixing model to estimate the relative contributions of different hydrological sources (rainfall, snowmelt, groundwater). Evaluate model outputs through statistical summaries and convergence diagnostics (e.g., MCMC trace plots, Gelman–Rubin statistics) to ensure robustness. Visualize estimated source proportions and their posterior distributions, enabling clear interpretation in both climatic and hydrological studies. By the end of the module, participants will be able to trace water fluxes within a watershed, assess the climatic influence on precipitation sources, and communicate their findings effectively using results derived from Bayesian analysis. In this training context, simulated datasets are employed to provide order-of-magnitude examples and hands-on practice with MixSIAR. δ¹⁸O (delta-O-18): The ratio of oxygen-18 (¹⁸O) to oxygen-16 (¹⁶O) in water. δD (δ²H, delta-Deuterium): The ratio of hydrogen-2 (²H, deuterium) to hydrogen-1 (¹H) in water.

This is a Dashboard that I created with RStudio and Quarto. The data are from gapminder R package consisting of GDP per capita, Population and Life Expectancy for countries in Africa, Asia, Europe and Americas. In the R package data are in 5 year distances from 1952 to 2007.

Análisis de la distribución geográfica de los accesos a internet de la AUI y de las empresas Claro, Personal y Movistar en barrios populares registrados por Renabap en la ciudad capital de San Luis. Datos extraídos de Enacom, Renabap, DatAr y el Ministerio de Ciencia y Tecnología de la provincia de San Luis