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Lectures introduction to Environmental System Analysis
Introduction to Environmental Systems Analysis (Wageningen University & Research)
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Lectures introduc琀椀on to Environmental Systems Analysis
Lecture 1 Introduc琀椀on to course: 26-10-2020
Overarching theme: Climate Change
- Paris Agreements  we need to limit climate change to stay below the 2 degrees average
increase in temperature
- Climate change has devasta琀椀ng impacts  wicked problem
Environmental systems analysis
- Analyse complex problems that uses a mul琀椀-disciplinary approach
- Environmental system analysis involves the study of complex environmental problems by
iden琀椀fying and analysing
 Causes
 Processes
 Impacts
 Poten琀椀al solu琀椀ons
- The research is o昀琀en quan琀椀ta琀椀ve, measuring processes, components and interac琀椀ons at
di昀昀erent scales
- Analysing environmental problems generally requires a holis琀椀c, mul琀椀disciplinary approach.
ESA integrates:
 Disciplines (natural and social science)
 Compartments (atmosphere, terrestrial, ocean, etc.)
 Scales (local to global)
One way to approach is the 昀氀exible steps
Flexible steps
1. Problem formula琀椀on  formulate the climate change related problem
- Analyse boundary condi琀椀ons and scales (local, regional, na琀椀onal or interna琀椀onal)
- Stakeholder analysis  iden琀椀fy stakeholders  depends on the scale you use
- Detailed problem formula琀椀on
2. Analysis  analyse current and future impacts and formulate adapta琀椀on op琀椀on
- Impact analysis
- Capacity analysis
- Scenario analysis
- ???
3. Dissemina琀椀on  ??
- Synthesis
- Communica琀椀on  communicate what you found out
ESA tools
- Stakeholder analysis
- Ecosystem service analysis
- Scenario analysis
- Life cycle assessment
- Conceptual modelling
And many more
Tools can be applied in scien琀椀昀椀c analyses and assessments (to support policy making)  this course
only focuses on 3 (causal diagram, regression model, scenario analysis)
Causal diagram
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Aim: to link the di昀昀erent elements and processes of environmental change in order to relate
causes and e昀昀ects
System descrip琀椀on: a framework for analysis or modelling
Well -known example: DPSIR
Regression model
- A tool to quan琀椀fy rela琀椀onships, such as
 The impact of climate variables on waterborne pathogen concentra琀椀on in surface
water
- Y = B0 + B1x
- Could be the basis for scenario analysis
Scenario analysis
‘’A plausible descrip琀椀on of how the future may unfold based on ‘if-then’ proposi琀椀ons’’
The goal of scenario analysis:
- To an琀椀cipate future developments of society and the environment, and
- To evaluate strategies for responding to these developments
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Seminar 1: Introduc琀椀on to Climate Change
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Average temperature without GHGs would be -18 degrees Celsius
Greenhouse E昀昀ect: Solar radia琀椀on comes in to the earth’s surface  part is re昀氀ected back by
clouds and some is observed by the surface and atmosphere.  This heat is radiated back by
the surface to the atmosphere  this heat is captured by the GHGs and radiated back to the
surface of the earth
Natural varia琀椀on: Milankovitch cycles  3 main mechanisms
1. 100.000 years cycle  eccentricity  orbit around the sun  closer to the sun means
warmer temperature. Further away from the sun means colder  ice ages
2. 41.000 years cycle  obliquity  how slanted the earth is  seasons
3. 19.000 to 24.000 years cycle  Precession  where the north of the earth is poin琀椀ng
Natural varia琀椀on: Sunspots  cool and warm spots on the sun  white spots in the sun are
warmer  cycle dura琀椀on???
Non-natural varia琀椀on:
 Natural varia琀椀on cannot account for the record increase in temperatures in recent
years
 Overall trend is clear and increasing
Main explana琀椀on of increase in temperature is the CO2 concentra琀椀on in the atmosphere
 Concentra琀椀on is expected to increase more
Causes of the oscilla琀椀ons in the graph??  Vegeta琀椀on takes up CO2 from the atmosphere in
the growing season, there is more vegeta琀椀on in the northern hemisphere so it absorbs more
CO2  seasonal changes in CO2 concentra琀椀on  overall per year the concentra琀椀ons are
increasing
4 main factors of growing emissions:
1. Popula琀椀on
2. GDP/economic growth
3. Energy e昀케ciency
4. Emission intensity energy
CO2 = popula琀椀on * GDP/capita * Energy/GDP * CO2/energy
- Very di昀케cult to reduce the world popula琀椀on
- Di昀케cult to not allow other people/countries to become more wealthier
- Only addressing one of the issues to reach a very low emission rate is almost not possible
 You have to make a combina琀椀on of the di昀昀erent factors
- Emissions intensity or energy e昀케ciency are the ones that are easier to address
- Energy intensity economy  amount of energy used by the popula琀椀on per euro/dollar
Extreme temperatures, theory
- There are more periods of extreme heat, but there can s琀椀ll be periods of extreme cold
- The increase in temperature is not the same everywhere
Global Warming Poten琀椀al (GWP)
Amount of heat trapped by 1 kg of a GHG over a certain 琀椀me period rela琀椀ve to the amount of heat
trapped by 1 kg of CO2 over the same 琀椀me period
- Methane (CH4) and Nitrous Oxide (N2O) much stronger GHG
- The a value (radia琀椀ve e昀케ciency) in the formula is much higher, but there is more to it
 GWP CH4 (20 years): 84 without climate feedbacks
 GWP N2O (20 years): 264 without climate feedbacks
 GWP CH4 (100 years): 28 without climate feedbacks
 GWP N2O (100 years): 265 without climate feedbacks
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Concentra琀椀on of GHG in the atmosphere is also important
E昀昀ect of atmospheric life琀椀me
- Time horizon in which the GHG is ac琀椀ve in the atmosphere
For a 1000 year 琀椀me horizon are the GWP for CH4 and N2O higher or lower than a 100 year 琀椀me
horizon?
 Lower for both  most of the N2O and all of the CH4 is already gone a昀琀er a li琀琀le
over 100 years, while a good propor琀椀on of the CO2 remains in the atmosphere. 
CO2 will have a higher GWP over a 琀椀me horizon of 1000 years
 Time horizon to measure the GWP is very important
 Most of the 琀椀mes we take a 100 year 琀椀me horizon
 The short term e昀昀ect of our reduc琀椀ons in GHG are di昀昀erent for GHG  Some
e昀昀ects of reduc琀椀ons are visible much quicker.
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Lecture 28-10-2020 Introduc琀椀on to Problem Formula琀椀on
Ditch coverage with duckweed in NL
Problem formula琀椀on in Flex-Step method
- Analyse boundary condi琀椀ons
- Iden琀椀fy stakeholders
- Detailed problem de昀椀ni琀椀on
Which problems arise with rising temperatures?
 Water gets warmer
 Duckweed grows quicker
 How are people a昀昀ected?
 How is biodiversity in the lakes a昀昀ected?
Find literature for:
- Causes
- Processes (incl. feedbacks)
- Impacts (climate change related impacts)
Answer the ques琀椀ons
- What is the problem?
- Who is the problem owner?  if you are talking about allergies for instance
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Seminar 2: Climate Change Impacts on Biodiversity
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Only one observa琀椀on of an extreme temperature is not a good example of climate change 
however, if you look at the amount weather records each year, we can clearly see an
increasing trend  this is a clear indica琀椀on of climate change
- Corona Crisis caused the biggest drop in CO2 emissions in the Netherlands ever
Today only focus on Climate Change impacts  adapta琀椀on other lecture
IPCC formula琀椀on
- Evidence (type, amount, quality, consistency, etc.) classi昀椀ed on 3 scales  limited, medium,
high
- Agreement (is there agreement among the evidence?)  again 3 scales  low, medium,
high
- Con昀椀dence (combina琀椀on of evidence and agreement)  very low, low, medium, high, very
high
- Likelihood  Di昀昀erent grada琀椀ons in likelihood  likelihood that something might occur.
- Risk  func琀椀on of the hazard, vulnerability and exposure
 Hazard: a physical threat caused by climate change
 Vulnerability: propensity/likelihood that the hazard occurs  also adaptability
 Exposure: The number of people/ecosystems that are exposed to the hazard
Climate Change impacts on
1. Hydrology and water resources
Temperature a昀昀ect the hydrological cycle across the world  but the impacts di昀昀er across regions
- Ho琀琀er and we琀琀er in some regions, ho琀琀er and dryer in others
Around the world, some regions will get we琀琀er and others dryer  most dry regions get dryer 
other regions (mostly in the north) get dryer and in the south get we琀琀er
- Another way to express this is the amount of water available to people  water availability
and stress  amount of freshwater available per person
- Rain is not distributed equally  leads to di昀昀erences in availability of water and water stress
2. Coastal systems
Glaciers
- Glaciers are shrinking  due to climate change but also due to a change in precipita琀椀on
(rainfall)  glaciers loose snow because of sunshine, but with less rainfall the snow is not
replaced anymore
- The mel琀椀ng of glaciers is contribu琀椀ng to the rise in sea level
Sea level rise
- Sea level has already risen by about 26 cm in the past decade
- The rise in sea level depends on precipita琀椀on and the entry of mel琀椀ng water from glaciers
- The rise of sea level is mainly caused by the expansion of water. Frozen water is smaller than
liquid water. When frozen water melts it expands  thermal expansion
- Also the Greenland ice sheet, Antarc琀椀c ice sheet are mel琀椀ng
- Sea level is expected to rise up to 80 cm in a very pessimis琀椀c scenario
What are the consequences of sea level rise
- Floods
- Damage to build infrastructure
- Salt water intrusion  sea level that intrudes soil has e昀昀ects on agriculture
- Agricultural impacts
- Ecosystem damages
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3. Ecosystems
- The oceans are acidifying fast with the CO2 emissions  has an in昀氀uence in the PH level 
we see a drop in the oceans PH
 This is bad for coral reefs. They take up calcium, and the more acidic the oceans
are, the more di昀케cult it becomes for them to take in calcium
 Thermal threshold  rising temperatures cause reef polyps to lose their symbio琀椀c
algae (‘’bleaching’’)
 Both are very bad for the coral reef  if we see a further increase in atmospheric
co2 and rising sea temperatures, coral reefs die
With the changing temperatures and changing ecosystems we see animal migrate
- If animals can’t migrate their survival chances decrease
- Plants can migrate less easy
- Some species groups can migrate very fast  other species are more vulnerable to climate
change (trees for example)
Di昀昀erences in Europe
- Some species are expected to increase. They have low vulnerability  they are resilient for
change and they have broad habitats
- Strong vulnerability  alpine regions with many specialized species in small habitats
4. Food security and food produc琀椀on systems
What factors in昀氀uences these systems?
- Changes in rainfall
 Increased rainfall can poten琀椀ally increase produc琀椀on
 Lower rainfall reduces produc琀椀on
 Higher variability in rainfall more threat to subsistence farming  in commercial
farming they can solve a bit for variability in rainfall
 Timing of rainfall is important
- Higher temperatures
 Increases growth rate and reducing growing season  in some areas where it is
more cold it can increase the growing season
 More heat stress (grain 昀椀lling stops)
 Pests and diseases develop faster
- Higher CO2 concentra琀椀ons
 Increases produc琀椀on – if enough nutrients are available
 Improves water use e昀케ciency
 Plants need CO2 to grow
 In higher CO2 concentra琀椀ons plants loose less water from due to their ….. ?
 Agricultural produc琀椀on is predicted to grow in the cold areas (mainly in the Norths
and Souths) and closer to the equator the yield will decrease
Overall there are more areas where the agricultural yield will decrease
- Some crops are much more vulnerable to climate change (co昀昀ee for instance can only grow
with certain temperature and certain rainfall)
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5. Human Health
6. Livelihoods and Poverty
7. Con昀氀ict
Take these three impacts together because they are all related to humans
- Di昀昀erent impacts from climate change to human health
 Mental health
 Water and food supply
 Forced migra琀椀on
 Injuries, fatali琀椀es
 Allergies
 Etc.
Mostly in the hot areas, number of people per 100.000 that die because of heat increases. In cold
areas you see that people that die from cold related death decreases as temperatures overall
increases
Climate Change and Malaria (vector borne disease)
- Expect them to increase and move to the north as their living area increases with the
warming temperatures
Livelihood impacted by climate change
- Amount of 昀椀sh that can be harvested from the sea decreases (mostly around the equator)
- Overall vulnerability of humans  poorest are hit hard
- The people that emit the most emissions are hit less hard than the poor people
Con昀氀icts
- Because of scarcity of water or food, etc.
Five reasons for concern  IPCC iden琀椀昀椀es di昀昀erent reasons for concern
- Di昀昀erent scenarios for emissions
- Depending on the increase in emissions we will see di昀昀erent scenarios
- For higher levels of emissions, there will be a higher level of risk  from low up to very high
- Already there are some regions that are labelled very high risk, meaning the ecosystems in
these regions are exposed to dangers
- It makes a very big di昀昀erence for the impacts of climate change if we keep the temperature
increase between 1.5 and 2 degrees, or if we let it increase to 3 or 4 degrees
There is also some cause for op琀椀mism/hope
- Small coali琀椀ons can induce breakthrough
- Ge琀�ng the ball rolling  at the beginning it is di昀케cult, but once something becomes
mainstream and cheaper other innova琀椀ons will follow
- Another example is the emission intensity of electricity  The emissions intensity across the
globe is decreasing  share of green energy is increasing and the very pollu琀椀ng emissions
are decreasing
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Tutorial Causal Diagram
ESA tool: Causal Diagram  Tool to be used in the Flex step method
Flex stem:
1. Problem formula琀椀on
2. Analysis
3. Dissemina琀椀on
Causal diagram in our case will be used in the problem formula琀椀on
- Causal diagram is used to determine system components and conceptualises them and
rela琀椀ons between them
- Indicate input and output
- De昀椀ne system boundaries in space and 琀椀me
- Only include elements and rela琀椀ons that are needed to analyse the causes and e昀昀ects of the
problem
- Include rela琀椀ons that are needed to explain
cause and e昀昀ect  simpli昀椀ca琀椀on of a causal
diagram
There can be posi琀椀ve and nega琀椀ve feedbacks between
the di昀昀erent causes and impacts (processes involved in
climate change have impacts on each other and have feedback impact)
Determine the Impacts  E-coli bacteria
- Temperature increased inac琀椀va琀椀on  decreased concentra琀椀on
- ……..?
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Tutorial 3 – Regression Models
11-10-2020
How to do regression? Part 1:
1. What type of data do we have?  discrete or con琀椀nuous
2. What is your climate variable and why is this variable relevant? Are there scale issues? Select
the relevant 琀椀me-series (day, week, month, year, etc.)
3. What assump琀椀ons do your data need to ful昀椀l before they can be used for regression
analysis?
4. Are your data immediately suitable for use, or do you need to pre-process them? Do, for
instance, the dates of the observa琀椀ons correspond to the dates of the climate?/hydrological
data?
5. Plot you climate variable and you 琀椀me variable using excel. Do you see any outliers> would
you trust the quality of the data?
6. Plot the trend line. Can you see a trend over 琀椀me? Is the trend relevant?
7. Are the assump琀椀ons that can be tested before doing the regression analysis valid> are the
data suitable for the regression analysis?
The regression analysis itself – part 2:
1. What is your hypothesis for the rela琀椀on between your climate variable and the dependent
variable?
2. Make a sca琀琀erplot of your climate variable and your dependent variable and inspect whether
a linear rela琀椀onship between the two variables seems reasonable
Is there a reasonable linear rela琀椀on?
3. Then you can perform the regression analysis
4. Is the rela琀椀onships between the two variables as expected?
5. Explain what your model coe昀케cients mean in your case. Re昀氀ects on their sta琀椀s琀椀cal
signi昀椀cance
6. Plot the residuals against the 昀椀琀琀ed values to verify the linear rela琀椀onship. Check for
heteroscedas琀椀city by plo琀�ng the residual against the x values and make a QQ-plot with the
standardized residuals and the theore琀椀cal values of the quan琀椀les to check the normality of
the errors
7. What is the R^2 of your model and what does this mean?
How can we do be琀琀er?
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What could a future above 2 degrees Celsius look like?
When thinking about climate change, it is important to think about the di昀昀erent consequences we
face. It is a complex issue/wicked problem
- How does climate change interact with the socio-economic environment around us?
The Impression approach
- Focuses on sustainability and indicators of human well being
- Developing prac琀椀cal op琀椀ons to dealing with them
- Developed scenarios of the e昀昀ects of climate change, what their implica琀椀ons and op琀椀ons are
- Scenario’s developed with stakeholders from the na琀椀onal and sub na琀椀onal level
- What are the e昀昀ects on the environment in the di昀昀erent scenario’s and what are the
implica琀椀on for human welfare and socio-economic problems
- Scenarios  economy-society-environment
- We can never be certain of what the future might hold, but we can make predic琀椀ons of what
we think might happen
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Seminar Week 4 – Scenario analysis
In a scenario you are looking at the future  possible future, non-implausible future, most likely
future, desirable future, extreme future  they all have di昀昀erent purposes  what type of future is
this?
How well can we know the future?
Look at two dimensions  causality (know or unknown) and uncertainty (low or high)
- Causality = factual
- Predic琀椀on could be the weather  most likely
- Further into the future the uncertainty becomes
higher
De昀椀ni琀椀on of a scenario
A plausible descrip琀椀on of how the future may unfold
based on ‘if-then’ proposi琀椀ons
What are they not: …..?
Objec琀椀ve of a scenario analysis
- To an琀椀cipate future developments of society and
the environment
- To evaluate strategies for responding to these developments
- Raise the awareness of managers and decision makers about the uncertainty of the future
A key idea is to explore alterna琀椀ve future developments  to try and change our ac琀椀ons to avoid the
scenario’s we don’t desire and come closer to the scenarios we do desire
Climate scenarios: di昀昀erent approaches
2 ways have been used to make these scenario
1. Sequen琀椀al approach
- If this, then that. Star琀椀ng with a vision and going further in the sequence to see what
happens in the end
2. Parallel approach
- What if we have…..  what will happen to the climate, to the environment, etc.  link the
di昀昀erent consequences together to show what the impacts will be, how to adapt, etc.
Parallel approach
1. Representa琀椀ve concentra琀椀on pathways: scenarios of di昀昀erent concentra琀椀ons of greenhouse
gasses. RCP8.5 is the worst case scenario, RCP2.6 is the most ambi琀椀ous scenario
2. Shared Socio-Economic Pathways (SSP): Di昀昀erent RCP scenarios lead to di昀昀erent SSP
scenario’s  How will people live, what will their lifestyles look like? SSP1 ……SSP5
- SSP1 – taking the green road  focus on sustainability, reduced inequality, low material
growth and lower resource and energy intensive
- SSP2 – middle of the road
- SSP3 – regional rivalry
- SSP4 – Inequality
- SSP5 – Fossil-fuel development  taking the highway: integrated global markets, strong
investment in health, educa琀椀on and ins琀椀tu琀椀ons, energy and emission intensive lifestyles
around the world
 Di昀昀erent scenarios lead to di昀昀erent challenges for society
 You need to be rich in order to get rich  this increases the inequality scenario
 The rich countries are not so far ahead as they used to be
Di昀昀erent policy ac琀椀ons to reach di昀昀erent SSP’s and RCP’s
 We will use a combina琀椀on of RCP and SSP during the scenario analysis
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Tutorial 18-11-2020 Scenario Analysis
Perform a scenario analysis for your environmental problem, using two SSR-RCP scenarios
implemented in at least two di昀昀erent climate models
- Select two of the four SSP-RCP combina琀椀ons and explain why you have chosen to analyse
these two op琀椀ons
- Find the la琀椀tude and longitude for your loca琀椀on, so that you can select the data for the
correct grid
- As a baseline period we will use the period 1991 and 2010 (20 years). In a scenario analysis
we usually take at least 10 years of data into account
- What is the spa琀椀al resolu琀椀on of the data. Data is not for a speci昀椀c point but for a certain grid
size.
- Can data from this grid cell be used directly or does it need some modi昀椀ca琀椀on? Is it
representa琀椀ve for every loca琀椀on in that grid cell. Data can be very di昀昀erent in the grid cell if
there are lakes, mountains, etc. in that you grid cell
 Some things can make the average temperature for example milder than average
 This can be a reason for downscaling
- Now that you have the right predicted data (a昀琀er downscaling or upscaling) you can run a
regression model
- Run the regression with the predicted temperatures for the di昀昀erent scenarios.
-
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