Models are everywhere in science. Even though many scientists do not think of themselves as ‘modellers’, models are embedded in their data (e.g. calibrated instrument outputs, interpolated data products) and in almost any generalization from those data (e.g. data-driven models from simple least-squares regressions to more sophisticated statistical analyses). The term model encompasses almost any generalization and most scientists strive to generalize their findings into universal laws, predictive equations, or wider principles. This course introduces the nature and scope of modelling and some of the different approaches used. Building blocks of model-building are illustrated through 'toy models' coded in Matlab, and a modelling workflow is presented that extends to the critical evaluation of model performance and limitations and to effective communication of model-based science. Case study applications cover a range of environments including surface and groundwater hydrology, coastal waters, and climate dynamics.

The course aims are as follows:

- to outline the nature and scope of modelling within the environmental sciences
- to introduce a variety of different approaches to environmental system modelling with particular reference to climate change impacts
- to present a range of modelling applications from hydrology, coastal science, and climate science
- to encourage a critical approach to the evaluation and application of model-based environmental and climate change science, including the challenge of model with stakeholders

The Models in Environmental Science compulsory course commences with an introduction to the various types of model and the role of modelling in the environmental sciences in general and climate change science in particular. Lectures cover the basic principles of empirical modelling (with application examples drawn mainly from the field of past climate change and palaeoenvironmental reconstruction) and mechanistic modelling (with examples drawn from ecology, hydrology, geomorphology and climate). Students are introduced to scientific computing basic aspects of programming, data manipulation and visualisation using Matlab.
A range of case study examples are presented in more detail, including the use of graphical system modelling building software (STELLA) to simulate hydrological processes, application of analytical models (MSExcel) to simulate soil moisture and groundwater recharge, coastal wave climate (including use of the SWAN numerical wave model), and climate dynamics. The course also covers key issues related to the validation of model outputs against observational data and the challenge of modelling with stakeholders.

Pre-requisite knowledge: there are no formal prerequisites since this is an introductory core module for the MSc Climate Change and MSc Environmental Modelling programmes.

Primary career skills developed: the module will contribute to the development of skills relating to numerical modelling and a critical appreciation of the role of models in science; coding and problem solving; team working; communication and presentation; and critical thinking.