Folie 1 - Working group: „Ecosystem Dynamics“

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Dec 8, 2013 (4 years and 7 months ago)

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Ecosystem modelling

31.5.2006

Bettina Ohse

1

Ecosystem modelling

Reasons!

Ecosystem modelling

Types &
Methods…

Problems?

Ecosystem modelling

31.5.2006

Bettina Ohse

2

ALFRESCO

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

3

Reasons

develop understanding of processes in environment
(>understanding driven)

evaluate human impacts like landuse as well as climate
change, disturbances etc. (>application driven)

Ability to reconstruct historical states (postdiction)
or to extrapolate into the future (prediction)

Complexity increases with higher number of components

Models as possibility to reduce complexity by breaking

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

4

Term

In general: model = abstraction of reality

Best model: achieves greatest realism with the least
parameter complexity and the least model complexity

„parsimony“ (simplest way that is adequate for purpose
of model)

! Often easier to model complexity than provide data to
parameterize, calibrate, validate it

if parsimony

principle fascilitates validation > it also
fascilitates utility of the model

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

5

Objectives

Tool for understanding

As an aid for research

Tool for simulation and prediction

As a virtual laboratory

Integrator within & between disciplines

http://www.wiz.uni
-
kassel.de/ecobas.html

Ecosystem modelling

Ecosystem modelling

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Bettina Ohse

6

1. Types of Modelling

Maps & drawings

abstraction of
form

of
nature

Models

abstraction of
processes

in nature

Ecosystem modelling

Ecosystem modelling

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Bettina Ohse

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2. Types of Modelling

Ecosystem modelling

Physical / hardware
models

means:

Scaled down version of
reality

Mathematical models

are:

Simple equations up to
complex software
codes

Ecosystem modelling

31.5.2006

Bettina Ohse

8

Classification of Models

1.
Conceptual type

2.
Mathematical type

3.
Spatial type

4.
Temporal type

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

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1. Conceptual types

a)
Empirically based: describes observed
behaviour of the single variables, with
high predictive power

b)
Conceptual models: describe observed
relationship between variables

c)
Physically based: processes are
calibrated from physical principles

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

10

2. Mathematical types

a)
Deterministic equations: single set of
input > one output (spatially and
temporally exactly defined)

b)
Stochastic approach: single set of input >
different outputs

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

11

3. Spatial types

a)
Lumped models: simulates spatially
homogenious environment (no
subdivision of the area)

b)
Semi
-
distributed m.: multiple lumps
representing defined units

c)
Distributed models: rasters or triangulare
networks (TINs)

Ecosystem modelling

Ecosystem modelling

31.5.2006

Bettina Ohse

12

4. Temporal types

a)
Static models: excluding time aspect

b)
Dynamic models: including time aspect

Ecosystem modelling

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Bettina Ohse

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5 steps to a mathematical model

1.
Problem identification > wordmodel

2.
Arrangement & parametrisation >
boundaries etc.

3.
Model building > development of
feedback control system

4.
Equations for basic processes >
programming

5.
Simulation / pay
-
off

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Bettina Ohse

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Ecosystem modelling

Ecosystem modelling

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Bettina Ohse

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Ecosystem modelling

Ecosystem modelling

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Bettina Ohse

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Problems

Choice of model depends on:

Spatial/temporal extent of the area

Differentiation level you wish

Available data

Ecosystem modelling

you really have to know, for what you need
your model and seriously check and compare
it to your observations and sampled data

Ecosystem modelling

31.5.2006

Bettina Ohse

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Sources

He H.S., Hao Z.H., Mladenoff D.J., Shao G., Hu Y., Chang Y. (2005):
Simulating forest ecosystem response to climate warming incorporating
spatial effects in north
-
eastern China.
Journal of Biogeography

32
: 2043
-
2056

and a landscape model to study forest species response to climate
warming.
Ecological modelling

114
: 213
-
233

Klopatek J.M., Gardner R.H (1999): Landscape Ecological Analysis:
Issues and Applications. Springer, New York, NY

Rupp T.S., Starfield A.M., Chapin F.S., Duffy P. (2002): Modeling the
Impact of Black Spruce on the Fire Regime of Alaska Boreal Forest.
Climatic Change

55
: 213
-
233

Steinhardt U., Blumenstein O., Barsch H. (2005): Lehrbuch der
Heidelberg

Turner M.G., Gardner R.H. (1991): Quantitative Methods in Landscape
Ecology. Springer, New York, NY

Wainwright J., Mulligan M. (2004): Environmental Modelling: Finding
Simplicity in Complexity. Wiley, Hoboken, NJ

Ecosystem modelling

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Thank you for your patience with
the systematics!

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Bettina Ohse

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Ecosystem modelling

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Land
-
use change

This is an example of a spatial model implemented in Simile. It shows that Simile is capable of handling quite complex spatia
l b
ehaviour

in this case, landuse change in which the neighbours of a patch influence the likelihood of transition from one landuse state

to

another

even though Simile has no built
-
in constructs for spatial modelling.

The model illustrates three types of submodel:

1.The PATCH submodel is a fixed
-
membership, multiple
-
instance submodel, and is used to represent the fact that there are many la
nduse
patches. Each patch has row and column attributes, and each one is engineered to have a unique combination of values between
1 a
nd 20,
thereby defining each one’s position on a grid. (But to drive home the point: Simile understands nothing from the labels “row
” a
nd
“column”: it is up to us, the modellers, to ensure that these are given values which can be used as grid co
-
ordinates.)

2.
The FOREST and CROP submodels are contained inside the PATCH submodel. Therefore, each patch can (potentially) contain both a

forest and a crop submodel. However, note that each one is a conditional submodel: see the question
-
mark “condition” symbol in t
he top
-
left of each one, and note the little rows of dots leading from the bottom
-
right edges. This indicates that the submodel may exi
st in only
some of the patches, not all of them. In fact, the conditions are engineered so that each patch contains only forest or crop,

no
t both (but it
would be quite possible for us to have some agroforestry patches containing both, if that’s what we wanted.)

3.
Finally, the NEXT TO submodel is an association submodel, defining an association (relationship) between some patches and oth
ers
. We
can see that this is an association between patches by the presence of the two broad grey arrows (role1 and role2), pointing
fro
m the PATCH
submodel to the NEXT TO submodel. As the name suggests, this association defines which patches are next to which other patche
s.
Again,
it has a condition symbol, which is used to indicate under which conditions the association holds. We note that this has infl
uen
ces coming
from the row and column variables in the PATCH submodel: the condition is true when the row and/or column value for one patch

is

one
away from the row and/or column value of another patch.

The model works as follows. Each patch contains a state variable (the compartment “state”) which defines the state it starts
off

in. This is 1
for forest and 2 for crop. If a crop patch has been a crop patch for a certain length of time, then it is abandoned and rever
ts
to forest (as
mediated by the variable “change to forest”). If the volume of the trees exceeds a certain amount and the patch has crop neig
hbo
urs (this is
why we need to know which patches are next to which others), then the forest is cleared and it changes to crop as the landuse

(a
s mediated by
the variable “change to crop”).

The following figure show how the model behaves, using Simile’s grid map display to show the patches on a spatial basis.
Important note:
Spatial Grid

display.

The light (yellow) squares represent the patches under crop; the dark (green) squares
represent forest. When users request this display, they are required to specify a variable indicating the column number for e
ach

patch, and the
actual variable to be displayed: Simile then has sufficient information to lay the patches out in the correct grid
-
based manner.

This display
shows how Simile can handle spatially
-
referenced information, even though it has no built
-
in concept of spatial modelling.

Initially, most of the area is forest, with a band of cropping land on the left. After 40 years, patches of forest on the for
est

margin have been
cleared, leading to a ragged edge to the forest boundary. In this particular run, the model was set up so that there was no r
eve
rsion of
cropped land back to forest, but as indicated above it has been designed to allow for this to happen.