1. GEO 130 Earth’s Physical Environments

2. Introduction to Physical Geography
Define physical geography
Explain how we study physical geography
List several types of models
Describe and define models
List the factors that complicate the study of pattern and process and provide examples
Give examples of how scale influence the perception of pattern and process
Explain how contingent historic events complicate the interpretation of pattern and process
Explain the difference between non-linear and linear change
Explain why models are limited in describing patterns in the environment
Define and give examples of positive and negative feedback
Define/explain: steady state, dynamic equilibrium, phase shifts, and transient states

3. What is Physical Geography?
Integrated study of three subdisciplines
Climatology
Biogeography
Geomorphology
Holistic and reductionist understanding

4. What is Physical Geography?
Includes the role of humans and their influence on natural processes
Anthropocene
Mudflow is the result of natural and human processes which we will talk about later.

5. Why study physical geography?
The natural world is as complex as the human world
Consider these facets of the Earth’s surface
Its extent (size) and resolution (detail)
History and time since origin (4.5 billion years)

6. Why study physical geography?
To understand human-environment interaction, you need to understand how the non-human world works.
Its important to know the politics of oil, but also how the natural world might respond to climate change.

7. How do we do physical geography?
We document pattern and process

9. Turf bank terraces

10. Erosion at base of terrace landform creates protected mineral soil needed by germinating conifers.

11. We use models to study pattern and process
Models are a simplified, often idealized representation of reality.
Intent of a model is to recreate patterns by capturing the underlying explanatory processes
Your role is to learn models, recognize they are idealized, and try to identify why they are incomplete.

12. What are the types of models?
Graphical conceptual models

13. Types of models
Maps

14. Types of models Data visualizations and simulations
Mathematical in essence, but enhanced through computational power of the computer.

15. Types of models Dynamical models
Use field-derived conditions to model the behavior of a phenomena
Can be used to predict or forecast the future

17. Types of models
Dynamical models can also be used to hindcast the past and study events that have already happened

18. Types of models Statistical models
Use the record of the past to predict the future

19. What complicates the study of pattern-process?

20. 1. Multiple driving variables
Driving variables are most directly responsible for the observed patterns.
They have to be identified, but are not always readily apparent or easily separated from each other

21. Proximate and ultimate causation
A proximate cause is an event which is closest, or immediately responsible, for causing some observed result.
This exists in contrast to a higher-level ultimate cause which is usually thought of as the "real" reason something occurred.
Registered for this class versus my mom and dad meeting
Requirements for graduation versus evolution of humans in Africa

22. Why did the ship hit the rock?
Proximate cause: Because the ship failed to change course to avoid it.
Ultimate cause: Because the ship was under autopilot and the autopilot received bad data from the GPS.
Separating proximate from ultimate causations frequently leads to better understandings of multiple driving variables
So, not useful to always say we have the same history – we evolved out of the primordial soup. Nor is it useful to think of only the most recent cause. We have to disregard some patterns and choose others to look at.

23. 2. Contingent events
What contingencies complicate the prediction of wildfires?

24. 3. Feedbacks
Feedbacks make prediction of the outcome of interactions difficult
Positive feedback: externally generated change is reinforced
Negative feedback: externally generated change is minimized

25. Example of positive and negative feedbacks associated with global warming

26. Thermokarst lakes have positive and negative feedbacks associated with global warming.

27. 4. Spatial and temporal scale
Scale determines how we understand pattern and process
Example: controls on temperature: what makes it warm or cold?

28. Answer depends upon temporal and spatial scale
Cloud cover and humidity (minutes to hours)
Diurnal (day-night) cycles
Seasonal cycles (1 year)
Cyclical fluctuations due to sunspots (10-50 years)
Anthropogenic contribution of greenhouse gases ( years)
Milankovitch orbital cycles (10,000 yrs)

30. 5. Thresholds and time lags
Melting of ice sheets and glaciers
A photo taken in 1906 shows the calving terminus of Carroll Glacier at the head of Queen Inlet. No vegetation is visible. Photo courtesy of U.S. Geological Survey A 2004 photograph shows that the terminus of Carroll Glacier has changed to a stagnant, debris-covered glacier. U.S. Geological Survey photo by Bruce Molnia

32. 6. Teleconnections

33. Global monthly temperatures illustrating the effect of the June 1991 volcanic eruption of Mount Pinatubo in the Philippines. The temperature effect apparently lasted to the end of 1992, as indicated by the shaded area.

34. 7. The earth is constantly changing
Earth is an open system
Change is the norm
Change itself, however, has different types
Steady state equilibrium
Dynamic equilibrium
Phase shift once tipping point (threshold) is reached
Transitional states

35. Steady state equilibrium
Inputs = outputs, balance achieved. Same value maintained.

36. Dynamic equilibrium
Constantly changing

38. Phase shifts
Systems may shift to another state once a threshold is reached
Can be natural or caused by humans
Like a treadmill, when suddenly a cliff appears…..

40. No analog states
Novel assemblages of species, climates, and human influences give rise to states that have not been seen before and their relative degree of stability is uncertain.

41. Transitory states
Systems do not settle down to any dynamical equilibrium.
Like a treadmill on random, with no pattern