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© 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
© Natural Resources Institute Finland
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
© Natural Resources Institute Finland
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.
© Natural Resources Institute Finland 4 17.3.2017
© Natural Resources Institute Finland 5 17.3.2017
© Natural Resources Institute Finland
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
© Natural Resources Institute Finland 7 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
© Natural Resources Institute Finland 8 17.3.2017
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
© Natural Resources Institute Finland 10 17.3.2017
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)
© Natural Resources Institute Finland 11 17.3.2017
Models
NPP impacts
Data
Clay and C:N
ratio impacts
Imp
ac
t to
SO
C
Imp
ac
t to
SO
C
© Natural Resources Institute Finland
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
© Natural Resources Institute Finland 13 17.3.2017