Soil carbon models for carbon stock estimation – where do we fail?

© Natural Resources Institute Finland

Soil carbon models for carbon stock estimation –

where do we fail?

1 17.3.2017

Soil carbon models for carbon

stock estimation –

where do we fail?

Aleksi Lehtonen, LUKE

Boris Tupek, LUKE

Shoji Hashimoto, FFFPRI


Backround

• Greenhouse gas inventories use litter

driven soil carbon models – We need models ! [only few countries have repeated

soil C inventory, we also need future predictions]

• We can quantify uncertainty of those (Lehtonen & Heikkinen 2015)

• Earth System models fail with soil

carbon estimation due to uncertainty

of initial soi carbon stocks (Todd-Brown et

al. 2013)

• Are those models biased ?

2 17.3.2017


Objective • Can our soil carbon models estimate

soil carbon stock based on litter and

weather data, without calibration ?

3

Based on: Tupek et al. 2016. Underestimation of boreal soil carbon stocks by

mathematical soil carbon models linked to soil nutrient status.

Biogeosciences, 13.

Lehtonen et al. 2016. Forest soil carbon stock estimates in a

nationwide inventory: evaluating performance of the ROMULv and

Yasso07 models in Finland. Geosci. Model Dev. 9.

Hashimoto et al. 2017. Data-mining analysis of factors affecting

the global distribution of soil carbon in observational databases

and Earth system models. Geosci. Model Dev.


Methods – Tupek et al. 2016 & Lehtonen et al.

2016

Tupek et al. 2016

Grouped Swedish soil carbon stock data vs. Yasso07, Q &

Century (soil module) models

Lehtonen et al. 2016

Finnish soil carbon stocks according to latitute vs. Yasso07

& ROMULv models

Both studies:

In grid, trees from National Forest Inventories + biomass modelling + litter turnover → input to soil C models

+ Weather data → soil carbon stocks

Soil carbon data to compare model estimates

6 17.3.2017


Soils with: high N deposition, water logging, high fertility, high CEC, ….

Swedish soil carbon stocks vs. Yasso07,

ROMUL & Century – soil chemistry

Soils with clay, Century ok, others fail


So

il c

arb

on

sto

ck

← South [Finland] North

Model

Measurements

No

un

de

rsto

rey

R

OM

UL

v

Wit

h u

nd

ers

tore

y

© Natural Resources Institute Finland 9 17.3.2017 9 17.3.2017

So

il c

arb

on

sto

ck

← South [Finland] North

So

il c

arb

on

sto

ck

Role of undersotrey vegetation crucial

In south lack of soil moisture limits decay

ROMULv

with soil

water

Yasso07

parametrised

with too small

understorey

litter


Methods – Hashimoto et al. 2017

Hashimoto et al. 2017

Analyzing both global observational soil carbon databases

and Earth System Model outputs for soil carbon with data

mining methods.

Boosted regression trees (BRT)


Models

NPP impacts

Data

Clay and C:N

ratio impacts

Imp

ac

t to

SO

C

Imp

ac

t to

SO

C


Conclusions

• We need to improve soil C models

– Models were successful on medium fertility soils

– Models failed: nutrient rich, water logging and on

drought prone soils

– Carbon stock on fertile soils have additional factors

driving carbon efflux, e.g. microbial carbon use

efficiency and soil carbon-minerals association

– Models would be improved by including these longer-

term effects on carbon stock (nutrients and soil

texture)

• If we want to estimate soil C emission due to land-use

change (e.g. REDD+) we need to have models that are

able to predict both stocks and stock changes right

12 17.3.2017


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Soil carbon models for carbon stock estimation – where do we fail?

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