cm and gm reporting - forestforest.jrc.ec.europa.eu/media/cms_page_media/232/a...25./26. may 2015...

25./26. May 2015

Andreas Gensior et al.JRC technical workshop on LULUCF reporting

Slide 0

CM and GM Reporting

JRC technical workshop on LULUCF reporting

Arona, 26./27. May 2015

1 Thünen - Institute of Climate-Smart AgricultureBundesallee 50D-38116 [email protected]

Andreas Gensior1 , Wolfgang Stümer2, Andreas Laggner1 & Annette Freibauer1

- the German way meeting the challenge

2 Thünen - Institute of Forest EcosystemsAlfred-Möller-Straße 1D-16225 Eberswalde [email protected]

25./26. May 2015


Slide 1

Activities

Article 3.4 activities under Kyoto Protocol elected by Germany for thesecond commitment period:

Chosen

Hierarchy

(Article 3.3 ARD)

Optional (new):

Cropland management

Grazing-land management

Mandatory (old):

Forest management

on the basis ofUNFCCC reporting

Accounting: Commitment period

not elected: RV, WDR, Natural disturbances

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Slide 2

Consistent representation of land

Activity data

Reporting CM and GM: Data Availability

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Slide 3

Definitions

UNFCCC

Woody GrasslandGrassland

Cropland management

Grazing land management

KP

≙Grassland in a strict sense

Cropland

Grassland in a strict sense: Meadows, pastures, alpine pastures, rough pastures, heath areas(natural-condition grassland and swamp/reeds)

Cropland: Area for cultivation of field crops, vegetables, berries, fruit and flowers including specialised cultivation (e.g. orchards, vineyards, short rotation coppices, tree nurseries, christmas- tree plantations)

Hedges, field copses and shrubbery make up the sub-category “woody grassland”.They are not subject of GM

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Slide 4

Activity Data: Consistent Representation of Lands

Sample-based system (Approach 3)

Data Sources:

• National Forest Inventory (QL 1)

• Inventory Study (QL 1)

• ColorInfraRed Aerial Photography (QL 1)

• Basic Digital Landscape Model (QL 2)

• CORINE Land Cover (QL 3)

• GSE- Forest Monitoring (QL 4)

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Slide 5

Land-Use Matrix

Land-Use Matrix Germany 2013 [ha]

Initial\Final Forest Land Cropland Grassland

i.n.S. Woody

Grassland Terrestrial Wetlands

Waters Peat

Extraction Settlements

Other Land

Σ Losses (2)

(1)-(2)

Forest Land 10.981.066 36.771 57.980 16.690 3.783 7.345 0 97.620 0 220.190 174.190

Cropland 129.301 12.365.027 477.028 65.278 729 17.662 0 541.502 0 1.231.500 -105.382

Grassland 167.647 1.013.881 5.077.170 62.248 14.812 21.272 0 240.454 0 1.520.314 -811.539

Woody Grassland 26.315 13.099 32.886 417.725 770 1.758 0 14.615 0 89.442 90.113

Terrestrial Wetlands 6.953 2.157 2.657 539 44.503 501 0 7.547 0 20.355 857

Waters 10.545 5.151 27.944 2.019 278 526.88

1 0 7.013 0 52.951 15.877

Peat Extraction 0 0 0 0 0 0 19.857 0 0 0 0

Settlements 41.480 49.826 93.576 29.357 839 13.008 0 2.909.757 0 228.086 690.592

Other Land 12.139 5.234 16.703 3.424 0 7.282 0 9.925 20.102 74.809 -74.809

Σ Gains (1) 394.380 1.126.118 708.775 179.555 21.211 68.827 0 918.678 0

Σ Land-Use Category

11.375.446 13.491.146 5.785.945 597.280 65.715 595.709 19.857 3.828.434 20.102

Germany 35.779.633

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Slide 6

Land-Use Matrix

Land-Use Matrix Germany 2013 [ha]

Initial\Final Forest Land Cropland Grassland

i.n.S. Woody

Grassland Terrestrial Wetlands

Waters Peat

Extraction Settlements

Other Land

Σ Losses (2)

(1)-(2)

Forest Land 10.981.066 36.771 57.980 16.690 3.783 7.345 0 97.620 0 220.190 174.190

Cropland 129.301 12.365.027 477.028 65.278 729 17.662 0 541.502 0 1.231.500 -105.382

Grassland 167.647 1.013.881 5.077.170 62.248 14.812 21.272 0 240.454 0 1.520.314 -811.539

Woody Grassland 26.315 13.099 32.886 417.725 770 1.758 0 14.615 0 89.442 90.113

Terrestrial Wetlands 6.953 2.157 2.657 539 44.503 501 0 7.547 0 20.355 857

Waters 10.545 5.151 27.944 2.019 278 526.88

1 0 7.013 0 52.951 15.877

Peat Extraction 0 0 0 0 0 0 19.857 0 0 0 0

Settlements 41.480 49.826 93.576 29.357 839 13.008 0 2.909.757 0 228.086 690.592

Other Land 12.139 5.234 16.703 3.424 0 7.282 0 9.925 20.102 74.809 -74.809

Σ Gains (1) 394.380 1.126.118 708.775 179.555 21.211 68.827 0 918.678 0

Σ Land-Use Category

11.375.446 13.491.146 5.785.945 597.280 65.715 595.709 19.857 3.828.434 20.102

Germany 35.779.633

KP-CRF Table Article 3.3/3.4 Activities

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Slide 7

Reporting CM and GM: Data Availability

Activity data

Consistent representation of land

Emission factors

Management data Data for developing, feeding, fitting and validating models

• Cropland

• Grassland

Copland management

Grazing land management

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Slide 8

Remaining Categories: Data Gap

No management data no „remaining category“ reporting

Possibilities:

1. Adduce evidence: CM and GM are not a source

2. Reporting results of representative inventories

3. Searching for and working with proxies for management data

4. Combinations of 1., 2. and 3.

Gaining management data access

Implementation of representative national inventories

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Slide 9

CM, GM: Reported Pools and GHGs

Categories Pools R

emai

nin

g

Soils Biomass3)

organic1) mineral2) above-ground below-ground

CM X

GM X

Tran

siti

on

to CM4) X X X X

to GM4) X X X X

from CM5)

from GM)5

1) CO2, CH4, (N2O reported under agriculture)

2) CO2, N2O

3) CO2

4) not from Forest Land (otherwise Deforestation)

5) not to Forest Land (otherwise Afforestation or Reforestation)

(pools at equilibrium, not a source)

(accounted as zero)

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Slide 10

Pool: Organic Soils

Key source No. 1! High Accuracy

Shortcomings:

German definition of organic soils don´t match the IPCC-definition

no map of organic soils (according to IPCC-definition) with high resolution

creation of a map for organic soils in Germany

no representative country specific emission factors (EF) for CO2, N2O, CH4

depending on land use, water-table depth (WTD), soil type, climate, management

derivation of country specific emission factors

no management data

a proxy for management data

developing methods for the regionalisation of point results

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Slide 11

Structure

Modul 1: Activity data and regionalisationof modelling parameters

Modul 2: Emission factors

Modul 3: Model development

11 participating institutions

Design/Configuration

Sites: 14 Study Areas

Land use: Grassland, arable land, re-wetted and semi-natural

Mean annual WTD: -0,15 – -1,42 m

SOC in 0-20 cm: 5 – 57%

C:N ratio in 0-20 cm: 10 – 65

pH-value 0-20 cm: 3,3 – 7,4

Joint Research Project „Organic Soils“

Basemap (potential) organic soils: Geological map 1:200.000 (BGR)

Dümmer

(LBEG Hannover)

Peene Valley

(ZALF)

4 Paulinenaue

(ZALF)

Freisinger

Moos (HSWT)

Dummerstorf

(Uni Rostock)

9 Großes Moor (TI)

Spreewald(ZALF)

Ahlenmoor (Uni

Rostock, LBEG)

BERLIN

Bog peat

Fen peat

Other

Study area

Donaumoos

(HSWT, MPI)

Benediktbeuern

(HSWT)

Rhine Valley

(Uni Hohenheim)

Leegmoor

(LBEG Hannover)

Kendlmühlfilze (HSWT)

25./26. May 2015


Slide 12

HuMoor new map of organic soils of Germany

• map legacy data

• soil borehole databases

• detailed data on topography, hydrology and geology

• accompanied by additional ground verification

grid map

scale 1.25.000 – 1:50.000

detailed and almost complete dataset on organic soils

based on

New Map of Organic Soils

(ROßKOPF et al. 2015: Catena 133 (2015) 157–170)

25./26. May 2015


Slide 13

Organic Soils: New Method

Results of the project „Organic Soils“ relating to GHG emissions:

no or neglegible differences between fens and bogs

differences between cropland and grassland

differences in dependence of intensity of use on cropland andgrassland

1. Calculating response functions between annual mean water level and GHG emissions (CO2 and CH4; 104 locations, 208 measurement years)

Water-table depth (m)

GrasslandCroplandOthers

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Slide 14


Map of water-table depth in organic soils ofGermany

wet

dry

Water-table depth (m)2. Developing a map of mean annual waterlevel for all German organic soils

Model building• Boosted regression trees

Predictor variables:• Land cover• Drainage network• Peatland characteristics• Climatic boundary conditions• Relative altitude• Topographic wetness index• Protection status

(Bechtold et al. 2014: Hydrol. Earth Syst. Sci., 18, 3319–3339)

Hydrologic data• 1094 dip wells (> 7000 measurement years)• 53 German peatlands (well distributed)• covering the major types of organic soils

Basic data

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Slide 15


3. Calculating GHG-Emissions and derivating representative country specific GHGemission factors for organic soils in Germany

Grassland

Cropland

Are

a (h

a)



GrasslandCroplandOthers

Are

a (h

a)

GrasslandCropland

CO2

EFCropland = 28,6 Mg ha-1 a-1 (15,0-33,7)

EFGrazingland = 26,0 Mg ha-1 a-1 (11,0-33,7)

CH4

EFCropland = 0,29 Mg CO2-Eq. ha-1 a-1 (-0,07-1,83)

EFGrazingland = 0,26 Mg CO2-Eq. ha-1 a-1 (0,09-1,73)

N2O (mean value, Leppelt et al. 2014)

EFCropland = 3,17 Mg CO2-Eq. ha-1 a-1 (0,46-12,26)

EFGrazingland = 0,81 Mg CO2-Eq. ha-1 a-1 (0,01-2,63)

DOC; Ditch-CH4 according to IPCC, 2014(TI-AK/Freibauer et al. 2014)

The combination of national GHG measurement data and the classification of sites in national water level distributions

enables high representativeness

25./26. May 2015


Slide 16

Organic Soils: Effects on the Inventory

Area Backdrop GHG - Emissions

NIR

20

15

Soil map BÜK1000 (BGR 2007, Hannover)

Map of Organic Soils (Roßkopf et al., submitted)

NIR

20

14

Σ = 1725 kha

Σ = 1825 kha

Σ = 46400 Gg

Σ = 46800 Gg

ForestLand

GH

G (

Gg

CO

2-E

q.)

GH

G (

Gg

CO

2-E

q.)

Forest Land

Wetlands

Grassland

Cropland

Peat extractionOthers

Forest Land

Wetlands

Grassland

Cropland

Peat extractionOthers

(TI-AK/Freibauer et al. 2014)

2015)

The new data base has led to significant changes in land use specific emissions

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Slide 17

Remaining Categories: Mineral Soils

Present assumption:

mineral soils of the remaining categories under CM and GM are not a source

No management data no „remaining category“ reporting

Evidence (Tier 1 approach):

1. Results of long term soil monitoring sites

2. Rough calculation of carbon input in mineral soils under cropland based on the data for crop yields, crop residues and manure input of UNFCCC reporting

3. Using simple modells for carbon balance calculation on agricultural soils (firstdata of the ongoing German agricultural soil inventory)

25./26. May 2015


Slide 18

Mineral soils: Not a source

1.Results of long term soil monitoring sites in Germany

0

10

20

30

40

50

60

70

80

90

no change increase decrease

Fre

qu

en

cy [

%]

Cropland (n = 48)

Grassland (n = 21)

Frequency [%] of significant carbon stock changes (1997 – 2010) on long

term monitoring sites for agricultural soils in Lower Saxony, Germany

Sites:

Cropland: 344

Grassland: 146

Current access:

Cropland: 141

Grassland: 48

25./26. May 2015


Slide 19


2. Rough calculation of carbon input in mineral soils under cropland based on

the data for crop yields, crop residues and manure input of UNFCCC reporting

C-input [Mg C a-1]

crop residues animal manure

1990 54.027.217 10.634.496

2012 65.151.329 9.249.269

2012 - 1990 11.124.112 -1.385.227

25./26. May 2015


Slide 20


3. Using models for carbon balance calculation on agricultural soils

(Dreysse 2015)

Calculating the humus balance with

two models

180 sites

first data of the ongoing German

agricultural soil inventory

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Slide 21

Model VDLUFA - Humusbilanzierung CANDY Carbon Balance

Authors KÖRSCHENS, et al., 2004; Team of authors, 2014 FRANKO, et al., 2011

Type of model balance approach process based

Model basics Reproduktionswirksame organische Substanz - ROS (ASMUS, et al., 1977)

Humuseinheitenschlüssel (RAUHE, et al., 1966)

Carbon and Nitrogen Dynamics - CANDY (FRANKO, et al., 1995)

Carbon turnover in Pore Space - CIPS (KUKA, et al., 2007)

Biologic Active Time - BAT (FRANKO, et al., 1995)

Validated yes yes

Site adaption no yes

Time period year, crop rotation year

Input data (mandatory) species of fruit

amount of organic fertilisers

yield

species of fruit

amount of organic and mineral fertilisers

yield

management options

climatic parameters

chemical and physical soil properties

field size

Output / results humus balance humus and nitrogene balance

carbon stocks

production

Assessment of results VDLUFA – supply classes non specific


(Dreysse 2015)


Comparison of the used models

25./26. May 2015


Slide 22


0

5

10

15

20

25

30

35

40

VDLUFA

CCB

n

kg SOM C ha-1 a-1

(Dreysse 2015)


frequency distribution of calculated balances

25./26. May 2015


Slide 23


n Mean Standarderror Min 25% Median 75% Max

[kg SOM C ha-1 a-1]

VDLUFA 180 205,83 21,81 -426,00 72,00 195,00 319,50 2641,00

CCB 180 75,82 41,72 -1857,60 -181,21 188,35 456,71 1261,96

∆𝑪𝒐𝒓𝒈 [% 10a-1] 180 0,0192 0,0096 -0,406 -0,0454 0,0374 0,0979 0,298

(Dreysse 2015)


descriptive statistics of the model calculations

25./26. May 2015


Slide 24

Planned Improvements: Mineral soils

Main objectives

development of a consistent, nation-wide data set on current carbon stocks of agricultural soils

utilization of an adequate population for geo-statistical interpolation

using advanced modelling techniques under consideration of the theory of errors

provide the data basis to separate the influence of site and climate factors from land use history and land management

improvement of the national reporting on emissions under the UNFCCC, KP and EU 529/2013

Design

Random based 8 x 8 km grid, > 3.100 sites

Questionaire

Information about land use history and management of the sampling plots (at least for the last 10 years)

German agricultural soil inventory

Developing a model-based assessment of C stock

changes based on

• upcoming data from the German Inventory of

Agricultural Soils by 2018

• additional data from long-term monitoring

and long-term experiments

25./26. May 2015


Slide 25

Remaining Categories: Biomass

German approach

(In addition to annual biomass), woody biomass is assumed to be inequilibrium in the “remaining categories”

Annual and perennial (woody) biomass are reported as „NO“

Reason/Motivation

We determined representative country specific “dynamic equilibrium carbon stocks” for Germany's woody biomass outside of forests

Loss of orchard area was compensated by higher tree density and small shiftings in age class structure

25./26. May 2015


Slide 26


Method

Research project: “Development of methods for determining the biomass of plants, outside of

forests, with multiple years of wood growth” (Orchards, vineyards, tree nurserys,

christmas-tree plantations and hedges)

(Destructive) analysis of

• 100 fruit trees

• 76 grapevines

• 50 hedges

Survey on a large number of plants and plantation structures (literature)

Calculation of regression functions (e.g. agetree parameter; tree parameter carbon stocks)

summation of calculated carbon stocks of above- and below-ground biomass over all age classes,

plant types and plantation structures and combinations

deriving of representative type-specific mean carbon stocks for woody plants and plantation

structures outside of forests in consideration of the (short) rotation periods (about 10 – 12 years)

25./26. May 2015


Slide 27


(Short rotation coppices: Results of “BEST - bioenergy for climate mitigation” (Walter et al 2014) and literature)

Plant type /plantation structure

Bioabove Biobelow Biototal

Mg C ha-1

Apple 5,31 2,27 7,58

Pear 4,91 2,04 6,95

Sweet cherry 8,44 1,57 10,01

Morello cherry 17,31 3,13 20,45

Plum/ damson plum 9,60 1,90 11,51

Mirabelle / greengage 8,25 1,51 9,76

Vine 1,12 0,54 1,66

Christmas-tree plantations 8,10 3,15 11,25

Tree nurseries 9,06 2,91 11,97

Short rotation coppices 40,75 12,96 53,71

Hedges/shrubs 32,69 10,47 43,16

the values represent a

type-specific state of

dynamic equilibrium

Type-specific mean carbon stocks [Mg C ha-1] of woody plants and plantation structures in Germany

25./26. May 2015


Slide 28

Emissions: Cropland Management

Cropland Management (CM) Germany 2013

Subcategories

C-Stock Change Biomass

C-Stock Change Mineral Soils

CO2 from Organic Soils

CH4 from Organic Soils

Direct/indirect N2O associated with loss of soil organic matter

∑ 2013

[Gg C] [Gg C] [Gg C] [Gg CH4] [Gg N2O] [Gg CO2-Eq.]

CM remaining CM 0 0 -2.179,06 6,99 0 8.164,76

LUC from GM to CM 63,79 -826,17 -521,72 1,67 1,25 5.123,91

LUC from Woody Grassland to CM -24,99 -8,42 -2,38 0,01 0,01 135,38

LUC from Terrestrial Wetlands to CM 0 -1,51 0 0 0 6,08

LUC from Flooded Land to CM 0 0 0 0 0 0

LUC from Settlements to CM -8,4 2,27 -131,62 0,42 0 515,63

LUC from Other Land to CM 0 1,16 0 0 0 -4,25

∑ LUC to CM 30,4 -832,67 -655,73 2,1 1,27 5.776,76

∑ LUC from CM 0 0 0 0 0 0

∑ 2013 30,4 -832,67 -2.834,79 9,1 1,27 13.941,51

∑ 1990 15.341,19

ccounting -1 99

25./26. May 2015


Slide 29

Emissions: Grazingland Management

Grazingland Management (GM) Germany 2013

Subcategories

C-Stock Change Biomass

C-Stock Change Mineral Soils

CO2 from Organic Soils

CH4 from Organic Soils

Direct/indirect N2O associated with loss of soil organic matter

∑ 2013

[Gg C] [Gg C] [Gg C] [Gg CH4] [Gg N2O] [Gg CO2-Eq.]

GM remaining GM 0 0 -5.561,55 17,29 0 20.824,50

LUC from CM to GM -17,62 338,88 -600,18 1,87 0 1.069,33

LUC from Woody Grassland to GM -18,26 4,94 -66,22 0,21 0 296,78

LUC from Terrestrial Wetlands to GM -2,43 0,3 -6,33 0,02 0 31,52

LUC from Flooded Land to GM 4 0 -74,6 0,23 0 264,65

LUC from Settlements to GM -9,8 70,68 -124,8 0,39 0 244,07

LUC from Other Land to GM 0 18,23 0 0 0 -66,84

∑ LUC to GM -44,1 433,02 -872,12 2,71 0 1.839,50

∑ LUC from GM 0 0 0 0 0 0

∑ 2013 -44,1 433,02 -6.433,68 20 0 22.664,00

∑ 1990 21.057,27

ccounting 1 0

25./26. May 2015


Slide 30

Summary

Under the current circumstances and possibilities Germany

could report CM and GM according to the requirements of

the regulatory framework

With a little brain power and creative thinking it seemed to

be easy (easier than previously thought)

Problems (not fully satisfying solutions) have been

identified; solutions for them are under progress

Remaining problem: Reporting on Grazing land management

25./26. May 2015


Slide 31

The End!?

What do you think about the German way meeting the

challenge „CM/GM reporting“?

Thank you very much for your attention and patience!

cm and gm reporting - forestforest.jrc.ec.europa.eu/media/cms_page_media/232/a...25./26. may 2015...

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