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Transcript of Karsten Grunewald - MeteoCosMap · PDF file Source: Grunewald et al. 2016, Ecol. Indic....

  • Implemenation of ecosystem service indicators in Germany

    Karsten Grunewald,

    Ralf-Uwe Syrbe, Benjamin Richter

    IOER Dresden, Germany

    MAES Conference in Sofia, 6./7. Febr. 2017

    Session: Mapping of ES and general

    assessment frameworks

  • 1. Background

    2. Framework

    3. Classification

    4. Template

    5. Example wood provision

    6. Conclusion/Experiences

    Implementation of Action 5 of the EU Biodiversity

    Strategy. Development and implementation of a

    methodology for capturing and assessing

    ecosystem services (ES) at the federal level in

    the context of the implementation of Target 2 and

    Action 5 of the EU Biodiversity Strategy for 2020

    „Project“ (BfN/BMUB)

    potenzielle Auenretention (2013)

    Verfügbarkeit der Flussauen (78. größten Ströme Deutschlands) für Hochwasserretention

    Datengrundlagen: Flussauen in Deutschland ©Bundesamt für Naturschutz (2009) ATKIS Basis-DLM ©GeoBasis-DE / Bundesamt für Kartographie und Geodäsie (2014) Gebietsstand: Rastergrundgeometrien (INSPIRE Grid 1 km); Karte: B.Richter, U.Walz, IÖR (2015)

    0 - 33

    >33 - 66

    >66 - 100

    Nationwide ES-statements of

    relevance in space and time

  • Framework – cascade, EPPS, IPBES…

    Evaluation schedule

    Mapping of ecosystems

    Assessment of ecosystem conditions

    Assessment of ecosystem services

    Integrated ecosystem assessment

    MAES (What to map?)

    MAES (2013) 6 dimensions of biodiversity

  • Framework for ES indicator selection

    Syrbe et al. 2017

  • Classification of Ecosystem services

    CICES V4.3 (2013): 48 ecosystem services classes

    44 ES relevant for Germany (Marzelli et al. 2014)

    Prioritization regarding

    Territorial/spatial importance

    Importance of the ES for people

    Position of the ES in the scheme

    Communicability of the ES-concept

    Data regularly available (for monitoring!)

     21 ES-classes to be processed in Germany

  • Principles of the description of indicandum and indicator

    Parameters/factors which determine the ES

    • Supply/capacity/potential (in the sense of the performance of nature)

    • Demand (in the sense of the intensity of demand)

    • Stock/flow (actual use – this is the ES in the original sense). This parameter can

    be roughly estimated by a superimposition of supply / demand


    • Name

    • Calculation and analysis steps

    • Results (values, maps) and interpretation

    • Relationship to other sustainability and

    biodiversity indicators

    Grunewald et al. 2016 (Ecol. Indic.)




    Template (includ.

    Target setting,

    Biodiversity etc.)

  • ES Class Main indicator Subindicators Structure

    1 Crop production Crops and crop products Soil fertility Farmland Counties

    2 Meat production Livestock Grassland share Livestock load Counties

    3 Water

    availability Freshwater provision Water use balance Nitrate content Counties

    4 Timber


    Vegetal and animal row

    products from forests Annual wood accrual (6 sub-ind.) Federal states

    5 Bioenergy Bioenergy plants Energy crop area Energetically used


    6 Grassland Vegetal and animal row

    products from agricult. Grassland grow Share of grassland

    Provisioning Ecosystem services

  • Main indicator:

    Annual wood accrual

    average 2002-2012 in m3 ha-1 a-1

    Example: Germany Raw wood provision

    Grunewald et al. 2016 (Ecol. Indic.)

     idea of the magnitude of sustainably extractable raw wood (9 to 13 m3 ha-1 a-1 )  in frame of the Biodiversity Strategy the developed indicator should not be communicated alone, because a causal relationship between the indicator and the naturalness of forests cannot be established

  • Supplement-indicators:

    S1 Forest area (in ha at the state, county, or community level)

    S2 Wood stock 2012 referred to the forest area (in m³ ha-1)

    S3 Development of the annual logging (in m³ timber)

    S4 Change in wood stock (2012-2002 in %)

    S5 Proportion of near-natural forest areas (in %)

    S6 Percentage of unfragmented forests > 50 km² to reference

    area (in %)

    Grunewald et al. 2016 (Ecol. Indic.)

  • Tab. Selected results to describe the ES wood provision in Germany

    Source: Grunewald et al. 2016, Ecol. Indic.

    Parameter Results

    Wood accrual in the

    forests as quantity

    of potentially

    usable raw wood

    (Indicator M1)

    Wood accrual is at a high level in Germany with 11.2 m3 ha-1 a-1 or 121.6 million m³ a-1

    (only describes the status quo; a certain wood accrual could be realized at different stock

    levels, e.g. by changing the tree species and age structures = “managed potential”). Of

    the widespread tree species, the spruce grows most quickly, followed by the beech.

    Douglas trees and firs have the greatest accrual, but they only account for 4% of the

    forest area together.

    Forest area

    (Indicator S1)

    Area used for forestry currently amounts to 11.4 million ha (about 31% of the land area of Germany) and is relatively constant. From 2002 to 2012 a slight increase by 0.4% (50,000 ha) was recorded (note: forest area increased in rural and peripheral areas, mostly at the expense of valuable, extensively used agricultural land, tended to decrease in conurbations).

    Wood stock

    (Indicator S2) Wood stock in German forests: 3.7 billion m³, or 336 m³ ha-1.

    Change in wood


    (Indicator S4)

    Wood stock increased by 7% from 2002 to 2012. The increase in stock (accrual

    minus use and mortality) is specified at currently 11.23 m³ per hectare and year.

    Use values

    The production value of raw wood production in the German forests amounted to about

    3.5 billion € in 2011, predominantly from softwood. More than 1.1 million people are

    employed in the cluster forest and wood in Germany. In practice, every inhabitant is an

    “end user” of raw wood production.

  •  Indicator-based approach

     Measures and sums up ES in their spatial expression and temporal

    change (trend) and compares them with objectives

     Data can directly be taken from governmental records (National Forest


     Result only provides information about a part of sustainability (Trees and

    timber are only a part of the forest ecosystem and wood provision is only

    one of the multiple services of woodland)

    Conclusion wood provision

  • Regulating Ecosystem services

    ES Class Main indicator

    1 Ground water


    Filtering, fixation, and accumulation by

    ecosystems Proportion of well-protected areas

    2 Purification of flowing

    waters Dilution by water High quality structure streams

    3 Prevention against

    soil erosion Stabillizing of soil Avoided soil loss

    4 Flood protection River flood protection Area for flood retention

    5 Climate regualtion Air exchange and transpiration Minimization of urban heat island

    6 Pollination Pollination and seeding Density of small structure

    landscape elements

    7 Genetic materials Protection of populations

    8 Pest control Pest control Density of small structure

    elements in arable land

    9 Water quality Freshwater quality Proportion of water quality areas

    10 Carbon sequestration Global climate protection Proportion of C-storing areas

    11 Climate regulation Micro, lokal and regional climate Access to urban green space

  • (Revised) Universal soil loss

    equation (R)USLE:

    R rain erosivity: summer

    precipitation (DWD 1981-2010)

    K soil resistance: BÜK1000Ob

    S slopeness: ATKIS-DHM25

    C land use: LBM-DE 2009 (2012)

    + DESTATIS agricultural data

    L slope length

    (field + small elements)

    No data: P plant cultivation factor

    Modelling Water Erosion

  • Main indicator

    Avoided water erosion

    Supplement indicators

    Actual water erosion

    Share of organic cultivation

    Effect of small


    Avoided wind


    Results: Indicators of the ES water erosion

  • ES Class Main indicator

    1 Recreation in

    landscapes Experiences of landscape, animals, plants

    Landscape potential for after-



    2 Recreation in cities Utilization of green infrastructure Accessibility of green spaces

    3 Esthetics Experiences of landscape, animals, plants Esthetic value of landscapes

    4 Potential for

    biodiversity Existence values Landscape diversity

    Cultural Ecosystem services Ecosystem Service Capacity

    “after work recreation”