Use case advising a forest land purchase
Starting point
A forestry consultant has been contracted by a wealthy, forest-owning family who wants to purchase more forest land in southern Germany, Austria, Slovakia or the Czech Republic in order to expand their beech tree portfolio. With a long-term perspective they want the new forests to be located in climatically favourable conditions and in a region where the forest area is expected to increase, i.e. forest is not under development pressure vis-à-vis other land-uses like settlements or agriculture.
Basic Approach
The consultant breaks down his task into three main steps:
- Identify areas where climatic conditions for beech are improving in the future
- Identify areas where (according to a land-use model) forest areal is increasing in the future
- Combine the results of steps 1 and 2 to identify the overall best suited areas
Basic Indicators
- Annual length of thermal growing season (current and future projection)
- Maximum number of consecutive dry days (current and future projection)
- Forest land-use (current and future projection)
Extended approach
Instead of using the simple 'thermal growing season' indicator (number of days with Tmean >5°C) the user
- a) calculates (with the CLIPC processing tool) a tailor-made temperature indicator that focuses on optimum growth conditions for beech trees with a lower (4°C), upper (9.5°C) and optimum (8°C) annual mean temperature
- b) combines this with a tailor-made precipitation indicator with a lower (500 mm), upper (2000 mm) and optimum (800 mm) annual precipitation
Extended indicators
- Annual mean temperature (Tmean) (current and future projection)
- Annual precipitation (Ptot) (current and future projection)
CLIP-C tools featured
Basic version: Map viewer, ScenarioScenario
A plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions. See also Climate scenario, Emission scenario, Representative Concentration Pathways and SRES scenarios. viewer, Compare tool
Extended version: Processing tool (in addition to the above)
Contact person
Johannes Lückenkötter (TUDO)
johannes.lueckenkoetter(at)tu-dortmund.de Tel. +49-231-755-2127
Tjark Bornemann (TUDO)
tjark.bornemann(at)tu-dortmund.de Tel. +49-231-755-2215
Analytical steps in detail
Basic approach
- Navigate to the Map Viewer
Click on the arrow next to the CLIPC logo and select "Map Viewer".
- Identify the best suited climate indicator among available CLIPC indicators
Load the indicators 'consecutive dry days' and 'thermal growing season' the RCP 8.5 dataset for the period 2040-2060 for both indicators into the Compare Tool.
Interpret both maps regarding their values in southern Germany, Austria, Slovakia and Czech Republic (the area the client is interested in). A greater variation is observable in the indicator 'thermal growing season', therefore this indicator is given priority for further analysis.
- Calculate a climate changeclimate change
Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use. Note that the United Nations Framework Convention on Climate Change (UNFCCC), in its Article 1, defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric composition, and climate variability attributa indicator
Load 'thermal growing season' indicator twice into the Combine Tool: on the left side select the RCP 8.5 scenario and the 2040-2060 time period and on the right side the same scenario but then the 2020-2040 time period.
Subtract the values of the 2020-2040 dataset from the 2040-2060 dataset.
Save the result as new indicator ‘thermal growing season change’.
- Calculate a forest area change indicator
Load 'forest land use' indicator twice into the Combine Tool: on the left side select the 2040-2060 time period and on the right side the 2020-2040 time period.
Subtract the values of the 2020-2040 dataset from the 2040-2060 dataset.
Save the result as new indicator 'forest change'.
- Combine the growing season change and forest change indicators
Load the two new indicators into the Combine Tool. Because they have different units (days vs. km²), both indicators need to first be normalised (select the min-max option) so that they both have values ranging from 0 to 1.
Combine the two indicators by using the 'add’ function with a weight of 0.5 for each indicator.
Interpret the map of the new combined indicator and show on the map which areas are best suited for the purchase of forest land because they are at the same time benefitting from better future climate conditions and are least expected to have decreasing forest areas vis-à-vis other land uses.
Save the result as new indicator 'forest purchase suitability'.
Extended approach
- Review of existing literature on climatic growth conditions for beech trees
User consulted literature on beech trees in central Europe, in particular Felbermeier 1994, Czaijkowski 2006, Leitgeb/Englisch 2006 in order to define upper, lower and optimum temperature and precipitation values.
- Calculate tailor-made temperature change indicator
In the Processing Tool load the 'annual mean temperature' indicator, select the RCP 8.5 scenario and the 2000-2020 time period and normalize using the ‘upper, lower, centre’ normalization function with defining lower = 4°C, upper = 9.5°C and centre = 8°C.
Save the newly created indicator.
Repeat the procedure but choose 2040-2060 time period instead.
- Calculate tailor-made precipitation indicators
In the Processing Tool load the 'annual precipitation' indicator, select the RCP 8.5 scenario and the 2000-2020 time period and normalize using the 'upper, lower, centre' normalization function with defining lower = 500 mm, upper = 2000 mm and centre = 800 mm.
Save the newly created indicator.
Repeat the procedure but choose 2040-2060 time period instead.
- Calculate combined climate indicators
In the Combine Tool load the new temperature and precipitation indicators for 2000-2020, select normalisation none, weight 0.5 for each indicator. Executive. Save the resulting new indicator as 'ClimateForBeech_2000-2020'.
Repeat the same procedure for the two 2040-2060 indicators and save result as 'ClimateForBeech_2040-2060'.
- Calculate a climate change indicator
In the Combine Tool load the two newly created ClimateForBeech indicators and subtract the values of the 2000-2020 indicator from the 2040-2060 indicator.
Save the result as new indicator 'ClimateChangeForBeech_2000-2060'.
- Calculate a forest area change indicator
In the Combine tool load the 'forest land use' indicator for 2000-2020 and 2040-2060, select the min-max normalisation function for both indicators then subtract the values of the 2000-2020 dataset from the 2040-2060 dataset. Save the result as new indicator 'ForestChange_2000-2060'.
- Combine the climate change and forest change indicators
Load the two ClimateChangeForBeech and ForestChange indicators into the Combine Tool. Select normalisation none (they already are the result of normalised indicators), then combine by using the 'add' function with a weight of 0.5 for each indicator.
Interpret the map of the new combined indicator and show on the map which areas are best suited for the purchase of forest land because they are at the same time benefitting from better future climate conditions and are least expected to have decreasing forest areas vis-à-vis other land uses.
Save the result as new indicator 'forest purchase suitability'.