Modelling at HL: Modelling works exactly the same for SL and HL — none of it is level-specific. The only difference is how far you stretch it: HL students reuse these same modelling skills on a few larger systems that SL doesn't cover — the full nutrient cycles and human population.
So nothing new to fear — get the modelling basics solid, and they carry straight into the HL topics.
Free preview
This is the free notes preview
You're reading the free notes. Aimnova Pro unlocks the full study experience — and you can try it with your first topic free to keep:
- FlashcardsLock in vocabulary and key terms with spaced repetition.
- Practice questionsAnswer exam-style questions and get instant AI marking.
- Mock exams & past-paper vaultSit full mocks and see exactly how examiners award marks.
- Personalised study planA daily plan built around your exam date and weak areas.
What is a model?
A model is a simplified version of reality.
Models help us understand how a system works and what might happen if conditions change.
They are used because real systems are usually too complex to study in full.
A model is a simplified representation of reality used to understand, explain, or predict a system.
Why do models simplify reality?
Models do not include every detail of the real world.
When creating a model, scientists choose:
- what information is important
- what details can be left out
This makes models easier to understand and use, but less accurate than reality.
All models involve a trade-off: simpler models are easier to use but less precise.
Examples of models
The water cycle
Interactive diagram
Explore the labelled diagram, charts and maps for this topic in full study mode.
Shows evaporation, condensation and precipitation.
A food chain
Interactive diagram
Explore the labelled diagram, charts and maps for this topic in full study mode.
Shows feeding relationships between organisms.
A population graph
Interactive diagram
Explore the labelled diagram, charts and maps for this topic in full study mode.
Shows how population size changes over time.
A climate model
Interactive diagram
Explore the labelled diagram, charts and maps for this topic in full study mode.
Uses data and equations to predict future climate conditions.
None of these models show every detail of the real system.
Memorize terms 3x faster
Smart flashcards show you cards right before you forget them. Perfect for definitions and key concepts.
Types of models
Model types & worked walkthroughs
Unlock the full breakdown with worked examples in study mode.
ESS examples
- Diagram — A food web showing feeding relationships in a coral reef ecosystem.
- Mathematical — An equation predicting population growth: N = N₀eʳᵗ.
- Physical — A scale model of a river catchment to study flooding.
- Computer — A climate simulation predicting temperature rise under different CO₂ scenarios.
- Written — A text description explaining how deforestation leads to soil erosion and biodiversity loss.
Uses of models
Models help us to:
- understand complex systems
- identify key components
- make predictions
- test different scenarios
- communicate ideas
- recognise patterns
Example: Climate models help predict future temperature rise under different emission scenarios.
Limitations of models
Because models are simplified, they have limitations.
Limitations:
- They are based on assumptions
- Important information may be missing
- Predictions may be inaccurate
- Results depend on data quality
If assumptions are wrong, conclusions can also be wrong.
Models and values
Models are influenced by:
What scientists think is important, Current knowledge, Human values and priorities
As new knowledge is gained, models must be updated.
Key idea: Models should never be treated as perfect or final.
Models of sustainability
Different models have been used to show the relationship between environment, society, and economy.
This highlights that environmental protection is essential for long-term sustainability.
Interactive diagram
Explore the labelled diagram, charts and maps for this topic in full study mode.
Exam tips
- Always mention simplification when defining models
- State at least one strength and one limitation
- Link models to prediction and decision-making
- Remember: models change as knowledge and values change
Models simplify reality to help us understand and predict systems, but this simplification always causes loss of accuracy.