Day 1 : Module Details

Module 1 : Introduction to Hydroclimatic Extremes and R Programming Basics

Concepts Covered:

Overview of Hydroclimatic Extremes:

• Introduction to extreme hydroclimatic events (floods, droughts, heavy rainfall).
• Importance of studying extremes in the context of climate change and water resource management.
• Common data sources for hydroclimatic extremes (e.g., precipitation, streamflow).

Introduction to R for Data Analysis:

• Basics of R programming.
• Overview of libraries for data analysis and visualization (tidyverse, ggplot2, etc.)

Learning Outcomes:

Basic Data Manipulation and Visualization

Module 2 : Extreme Value Analysis (EVA) for Hydroclimatic Events

Concepts Covered:

Introduction to Extreme Value Analysis (EVA):

• Basics of EVA and its applications in hydrology (flood frequency analysis, extreme rainfall events).
• Block maxima and peak-over-threshold (POT) methods for EVA.
• Overview of the Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD).

R Packages for EVA:

• Introduction to relevant R packages (e.g., extRemes, evd, ismev).

Learning Outcomes:

Performing Extreme Value Analysis in R

Task:

Apply EVA to a precipitation dataset and estimate the return period for an extreme rainfall event using R.

Day 2 : Module Details

Module 1 : Visualization and Interpretation of Hydroclimatic Extremes

Concepts Covered:

Advanced Data Visualization Techniques:

• Visualizing spatial and temporal trends in hydroclimatic extremes.
• Mapping extreme events using R (ggplot2, spatial data).
• Time series analysis and visualization for extreme events.

Interpreting Results from Extreme Value Models:

How to interpret return levels, confidence intervals, and model diagnostics.

Learning Outcomes:

Visualization of Extreme Events

Task: Visualize a time series of extreme rainfall and map the spatial distribution of a flood event in a selected region.

Module 2 : Predicting Hydroclimatic Extremes Using Statistical Models

Concepts Covered:

Statistical Models for Predicting Hydroclimatic Extremes:

• Introduction to predictive modeling using regression and machine learning approaches.
• Applying generalized linear models (GLMs) and generalized additive models (GAMs) to extreme event prediction.

Evaluating Model Performance:

Model evaluation metrics (RMSE, AIC, BIC, etc.).

Learning Outcomes:

Building Predictive Models in R

Case Studies :

Predicting future extremes based on historical data and climate projections.