| The course introduces students to important principles and concepts regarding eco-hydrometeorological modelling. Specific topics may deal with modelling of biogeochemical cycling in ecosystems, eco-hydrometeorological system response and feedback, automatic differentiation, machine learning (e.g., random forests, extreme gradient boosting), water flow and flooding dynamics, model calibration with gradient and non-gradient methods, including Particle Swarm Optimization and Genetic Algorithms, and climate change response, tree species distribution. The course will be taught while developing and/or using models and analytical tools in R (e.g., Structural Equation Modelling, Convergent Cross Mapping, Mann-Kendall trend detection, algorithmic differentiation, etc.) and other software, such as SAGA GIS, Global Mapper, and possibly others. |
