This module aims to:

Train students in the concepts and techniques required to construct and use numerical forward models of Earth surface systems using high-level programming languages such as Matlab and Python;

Introduce students to the development and use of numerical forward models as an experimental tool that can be used to better understand and predict how Earth surface systems work;

Teach students important transferable skills in coding, general numeracy, and data and model visualisation;

Provide a broad overview of the range of numerical forward models in the environmental sciences, how they are applied, and why they are important.

(LO1) On successful completion of this module students should have knowledge and good understanding of the range and diversity of numerical forward model used in the environmental sciences, why they are important, and how they are useful to better understand and predict how environmental systems work.

(LO2) On successful completion of this module students should be able to understand provided examples, and construct and code their own examples, of basic algorithms using a high-level programming language, for example Matlab or Python.

(LO3) On successful completion of this module students should be able to translate simple conceptual ideas and data on how selected environmental systems work into the key elements of a simple representative numerical forward model written in a high-level programming language, for example Matlab or Python.

(S1) Science communication skills

(S2) Problem solving skills

(S3) Numeracy

Introduction to environmental forward models

Coding elements 1 - basic coding and numerical methods

Coding elements 2 - basic coding and data structures and model grids

Coding elements 3 - basic coding and graphics and visualization
Basic data structures and visualisation

Construction of simple numerical forward models, from a range of types and applications within environmental science, including stratigraphic and Earth-surface process models, geophysical models, for example isostatic models, oceanographic process models, for example simple models of themohaline convection, coastal evolution models, and ecological models, for example population dynamics models.

All learning resources will be available to students on Vital and the module will use Jupyter Notebooks (or similar) to develop and present the various practical exercises in the module.

Students will be expected to spend independent time practicing writing, testing and refining co de based on concepts and skills taught in lectures, seminars and supervised practical sessions.

Mix of formal tutor-led presentations to introduce and demonstrate topics with the bulk of module time spent on practical work developing numerical models. Use will be made of latest methods teaching coding with Jupyter Notebook systems, or an equivalent. Students will learn coding and model development skills primarily through self-guided application of the concepts and skills introduced and demonstrated in the lectures and seminars, with a strong focus on reflection and problem-solving during iterations of code development. Design of the lecture and seminar material will take into account points made at e.g. https://medium.com/the-mission/heres-why-learning-how-to-code-is-so-hard-and-what-to-do-about-it-3d6fda152409 on why students find learning coding difficult.

Students will be expected to engage to a higher level with discussion of what the various model outputs mean, and how they aid understanding of how environmental systems work, relative to students on the ENVS397 ver sion of this module.

Lecture, seminar and practical session attendance will be monitored as set out by the University Framework for Student Attendance.