How Temperature and GDD Trends Are Transforming the Growing Season in Chaudières-Appalaches?

How is climate change affecting temperature, Growing Degree Days (GDD), and the vegetation period in Chaudières-Appalaches? This analysis explores 20 years of historical climate data, uncovering trends, anomalies, and shifts in temperature patterns. By examining GDD calculations and vegetation period variations, we highlight the impacts on agriculture, crop cycles, and ecosystem resilience. Using R for data analysis and visualization, this study provides key insights into how climate trends are reshaping growing conditions in the region.

Aminated Visualisation for Centre-du-Québec’s Precipitation

Building upon previous analyses and predictive modeling, I details the process of creating this visualization, including data preparation, disaggregation to daily levels, and kriging for enhanced spatial resolution. The post culminates in an animated map that illustrates precipitation trends and anomalies over time, providing valuable insights for climate analysis, agriculture, and water resource management.

From Trends to Predictions: Machine Learning Forecasts for Centre-du-Québec’s Precipitation

In this phase of the analysis, we aim to model precipitation patterns in Centre-du-Québec using machine learning techniques, leveraging historical climate and environmental data. We will train an XGBoost models and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.

St. Lawrence Lowlands Precipitation Data: 30-Year Trends Prediction

In this phase of the analysis, we aim to model precipitation patterns in the St. Lawrence Lowlands using machine learning techniques, leveraging historical climate and environmental data. We will compare Random Forest, XGBoost, and Mars models to assess their ability to capture complex relationships and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.

St. Lawrence Lowlands Precipitation Data: 30-Year Trends & Anomalies

Understanding long-term precipitation patterns is essential for climate research, agriculture, and water resource management. In this post, we analyze 30 years of precipitation data from the AgERA5 dataset for St. Lawrence Lowlands, using exploratory data analysis (EDA) techniques to uncover trends, seasonal variations, and anomalies.