ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-7-953-2016Deforestation in Amazonia impacts riverine carbon dynamicsLangerwischFannylangerwisch@pik-potsdam.deWalzArianeRammigAnjahttps://orcid.org/0000-0001-5425-8718TietjenBrittaThonickeKirstenhttps://orcid.org/0000-0001-5283-4937CramerWolfganghttps://orcid.org/0000-0002-9205-5812Earth System Analysis, Potsdam Institute for Climate Impact Research (PIK), P.O. Box 60 12 03, Telegraphenberg A62, 14412 Potsdam, GermanyBerlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, GermanyInstitute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, GermanyTUM School of Life Sciences Weihenstephan, Land Surface-Atmosphere Interactions, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, GermanyBiodiversity and Ecological Modelling, Institute of Biology, Freie Universität Berlin, Altensteinstr. 6, 14195 Berlin, GermanyInstitut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, Technopôle
Arbois-Méditerranée, Bât. Villemin – BP 80, 13545 Aix-en-Provence CEDEX 04, FranceFanny Langerwisch (langerwisch@pik-potsdam.de)9December20167495396830September201522October20153November201611November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://esd.copernicus.org/articles/7/953/2016/esd-7-953-2016.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/7/953/2016/esd-7-953-2016.pdf
Fluxes of organic and inorganic carbon within the Amazon basin are
considerably controlled by annual flooding, which triggers the export of
terrigenous organic material to the river and ultimately to the Atlantic
Ocean. The amount of carbon imported to the river and the further conversion,
transport and export of it depend on temperature, atmospheric CO2,
terrestrial productivity and carbon storage, as well as discharge. Both
terrestrial productivity and discharge are influenced by climate and land
use change. The coupled LPJmL and RivCM model system (Langerwisch et
al., 2016) has been applied to assess the combined impacts of climate and
land use change on the Amazon riverine carbon dynamics. Vegetation dynamics
(in LPJmL) as well as export and conversion of terrigenous carbon to and
within the river (RivCM) are included. The model system has been applied for
the years 1901 to 2099 under two deforestation scenarios and with climate
forcing of three SRES emission scenarios, each for five climate models. We
find that high deforestation (business-as-usual scenario) will strongly decrease (locally
by up to 90 %) riverine particulate and dissolved organic carbon amount
until the end of the current century. At the same time, increase in discharge
leaves net carbon transport during the first decades of the century roughly
unchanged only if a sufficient area is still forested. After 2050 the amount
of transported carbon will decrease drastically. In contrast to that,
increased temperature and atmospheric CO2 concentration determine the
amount of riverine inorganic carbon stored in the Amazon basin. Higher
atmospheric CO2 concentrations increase riverine inorganic carbon amount
by up to 20 % (SRES A2). The changes in riverine carbon fluxes have direct
effects on carbon export, either to the atmosphere via outgassing or to the
Atlantic Ocean via discharge. The outgassed carbon will increase slightly in
the Amazon basin, but can be regionally reduced by up to 60 % due to
deforestation. The discharge of organic carbon to the ocean will be reduced
by about 40 % under the most severe deforestation and climate change
scenario. These changes would have local and regional consequences on the
carbon balance and habitat characteristics in the Amazon basin itself as well as in the adjacent Atlantic Ocean.
Introduction
The Amazon basin, defined as the drainage area of the Amazon River, covers
approximately 6 million square kilometers, and more than 70 % of it is
still covered with intact rainforest (Nobre, 2014). The amount of
carbon in biomass in Amazonian rainforest is estimated to be
93 ± 23 × 1015 g C (Malhi et al., 2006). This biomass is
stored in a wide range of diverse habitats, including tropical rainforest
and savannahs, as well as numerous aquatic habitats, like lakes and wetlands
(Goulding et al., 2003; Eva et al., 2004; Keller et al.,
2009; Junk, 1997). The large diversity in habitats, partly already founded
in the geologic formation of Amazonia, leads to a high diversity of animal
and plant species (Hoorn et al., 2010), making the Amazon rainforest
one of Earth's greatest collections of biodiversity.
The Amazon River, which floods annually large parts of the forest, plays an
important role in supporting the diversity of Amazonian ecosystems. The
flooding is most decisive for the coupling of terrestrial and aquatic
processes by transporting organic material from the terrestrial ecosystems
to the river (Hedges et al., 2000). The input of terrigenous organic
material (Melack and Forsberg, 2001; Waterloo et al., 2006), acts,
for instance, as fertilizer and food source (Anderson et al.,
2011; Horn et al., 2011) and is a modifier of habitats and interacting
local carbon cycles (Hedges et al., 2000; Irmler, 1982;
Johnson et al., 2006; McClain and Elsenbeer, 2001). Across the Amazon basin,
the outgassing carbon from the river to the atmosphere and export of it to
the ocean are the two most important processes that have to be included
when assessing the effects on riverine carbon dynamics under climate and
land use change. Approximately 470 × 1012 g C yr-1 is
exported to the atmosphere as CO2 (Richey et al., 2002). In
comparison, about 32.7 × 1012 g C yr-1 of total
organic carbon (TOC) is exported to the Atlantic Ocean
(Moreira-Turcq et al., 2003). It is estimated that the large-scale
outgassing of carbon from the Amazon River plays an important role in
assessing the future carbon balance of the Amazon basin, integrating
riverine as well as terrestrial processes.
Deforestation continues to be the largest threat to Amazonia. The
transformation of tropical rainforest to cropland and pasture impacts
ecosystem stability profoundly due to altered climate regulation and species
richness (Foley et al., 2007; Lawrence and Vandecar, 2014;
Malhi et al., 2008; Spracklen et al., 2012). By 2012
approximately 20 % of the original forest of the Brazilian part of the
Amazon basin had been deforested, corresponding to an area of about
750 000 km2 (Godar et al., 2014; INPE, 2013). This
deforestation was mainly driven by the land expansion for soybean and cattle
production and the expansion of the road network (Malhi et al.,
2008; Soares-Filho et al., 2006). Governmental and conservation efforts have
helped to decrease recent deforestation rates (Nepstad et al., 2014),
but economic instability might reverse this trend (Aguiar et al.,
2016; Fearnside, 2015). Deforestation also alters the soil stability and
increases erosion (Yang et al., 2003). Together with climate change
effects and forest burning, land-cover change is predicted to release carbon
at rates of 0.5–1.0 × 1015 g C yr-1 from this area
(Potter et al., 2009). Furthermore, the effects of deforestation on
terrestrial carbon storage and fluxes persist several decades after logging
because the forest needs about 25 years to recover approximately 70 % of
its original biomass, and at least another 50 years for the remaining 30 %
after abandonment of agriculture (Houghton et al., 2000; Poorter et al., 2016).
Deforestation immediately reduces the terrestrial organic carbon pools,
which fuel riverine respiration (Mayorga et al., 2005), while
increasing the velocity and amount of runoff, as well as the discharge
(Foley et al., 2002; Costa et al., 2003). Additionally, climate
change alters precipitation which then affects inundation patterns
(Langerwisch et al., 2013), such as temporal shifts in high and low
water months and changes of inundated area. The combined effects of
deforestation and climate change have the potential to tremendously alter
the exported terrigenous carbon fluxes, the amount of carbon emitted to the
atmosphere and exported the ocean. The local export of terrestrial organic
carbon to the river changes the nutrient supply and therefore alters the
habitat for riverine plants and animals (Hamilton, 2010).
The aim of our study is to elaborate on these combined effects of climate
change and deforestation on the riverine carbon fluxes, on the export of
organic material into the Atlantic Ocean and on the outgassing of riverine
carbon to the atmosphere. By considering the interactions between riverine
and terrestrial carbon processes a complete view on future changes in the
regional and basin-wide carbon balance can be achieved for the Amazon basin.
When referring to deforestation in this study, we mean the effects of
replacing tropical forest with soybean fields and pasture, as well as the
effects of newly established land use on carbon cycling.
To address these issues basin-wide data are needed, which not only describe
the current situation but also assess future changes, expanding our
knowledge obtained from on-site measurements. To partly overcome these
limitations we make use of the well-established dynamic global vegetation
model LPJmL together with the riverine carbon model RivCM. While LPJmL
(Bondeau et al., 2007; Gerten et al., 2004; Rost et al.,
2008; Sitch et al., 2003) provides plausible estimates for the carbon and
water pools and fluxes within the coupled soil–vegetation system, RivCM
(Langerwisch et al., 2016) focuses on the export, conversion and
transport of terrestrial fixed carbon in the river and to the atmosphere and
ocean. In Langerwisch et al. (2016) the sole effects of climate
change have been estimated. The results of the mentioned study show that
climate change causes a doubling of riverine organic carbon in the southern
and western basin while reducing it by 20 % in the eastern and northern
parts towards the end of this century. In contrast, the amount of riverine
inorganic carbon shows a 2- to 3-fold increase in the entire basin,
independent of the climate change scenario (SRES). The export of carbon to
the atmosphere increases on average by about 30 %. The amount of organic
carbon exported to the Atlantic Ocean depends on the SRES scenario and is
projected to either decrease by about 8.9 % (SRES A1B) or increase by
about 9.1 % (SRES A2). The current study, which is an extension of
Langerwisch et al. (2016), goes one step further and investigates the
combined effects of climate change and deforestation on the riverine carbon
dynamics. The coupled model LPJmL–RivCM was forced by several climate change
and deforestation scenarios that cover a wide range of uncertainties. We
estimated temporal and spatial changes in three riverine carbon pools as
well as changes in carbon emissions to the atmosphere and carbon export the ocean.
Methods
To assess the impacts of climate change and deforestation on riverine carbon
pools and fluxes in the Amazonian watershed, we applied the model system of
LPJmL and RivCM. RivCM is a grid-based model that assesses the transport and
export of carbon at monthly time steps and is driven climate data and
terrestrial carbon pools (Langerwisch et al., 2016). Climate inputs
are taken from different global climate model simulations driven by three
SRES scenarios (A1B, A2 and B1; Nakićenović et al., 2000).
Terrestrial carbon inputs are calculated by the process-based dynamic global
vegetation and hydrology model LPJmL (Bondeau et al.,
2007; Gerten et al., 2004; Rost et al., 2008; Sitch et al., 2003). To
estimate soil and vegetation carbon, LPJmL uses the above-mentioned climate
data and a set of deforestation scenarios from regional projections by
SimAmazonia (Soares-Filho et al., 2006). An overview of the
interconnection between the two models and the scenarios is given in Fig. 1.
Model descriptionsLPJmL – a dynamic global vegetation and hydrology model
The process-based global vegetation and hydrology model LPJmL
(Bondeau et al., 2007; Gerten et al., 2004; Rost et al.,
2008; Sitch et al., 2003) simulates the dynamics of potential natural
vegetation and thus carbon pools for vegetation, litter and soil and
corresponding water fluxes, in daily time steps and on a spatial resolution
of 0.5∘× 0.5∘ (lat, long). The main processes included are
photosynthesis (modeled according to Collatz et al., 1992;
Farquhar et al., 1980), auto- and heterotrophic respiration, establishment,
mortality, and phenology. For calculating these main processes LPJmL uses
climate data (temperature, precipitation, and cloud cover), atmospheric
CO2 concentration, and soil type as input. The simulated water fluxes
include evaporation, soil moisture, snowmelt, runoff, discharge,
interception, and transpiration, which are directly linked to abiotic and
biotic properties. In each grid cell LPJmL calculates the performance of
nine plant functional types, which represent an assortment of species
classified as being functionally similar. In the Amazon basin primarily
three of these types are present, namely tropical evergreen and deciduous
trees and C4 grasses. In addition to the potential natural vegetation LPJmL
can simulate the dynamics of 16 user-defined crops and pasture on area that
is not covered by natural vegetation. In analogy to natural vegetation,
LPJmL evaluates carbon storage in vegetation, litter and soil as well as
water fluxes for these areas. On areas that are converted to crops and
pasture the vegetation carbon stored in natural vegetation (carbon in
living above- and belowground biomass) is removed from the terrestrial
domain and added to the litter pool. Due to deforestation, a large amount of
carbon is removed from the living biomass – i.e., after some years, the pool
size of potential carbon that can be washed out to the river is decreasing
dramatically. On the deforested areas growth and harvest of soybean and
managed grasslands is simulated. We distinguished these two types of land
use, because soybean farming and pasture leave different amounts of litter
carbon on site. In LPJmL, during soy harvest a maximum of 30 % of the
aboveground soy biomass, representing the beans, is removed as harvest every
year. The remaining aboveground biomass as well as all belowground biomass
is left on site and enters the litter pool. Managed grasslands are harvested
regularly as well, but always 50 % of the aboveground biomass is removed.
The remaining aboveground biomass and the total belowground biomass enter
the litter pool. Once a stand is harvested the remaining above- and
belowground biomass is added to the litter pool. The soil pool remains
unchanged. Only after litter decomposition this carbon enters the soil
carbon pool. Therefore, after deforestation the amount of carbon washed out
from managed land to the river, entering the riverine carbon system, is
much less in size compared to litter exported to the river from undisturbed
forests. Changes of soil characteristics and soil carbon pools due to
erosion, which is a common consequence of deforestation (Yang et
al., 2003), are not included in the model. In summary, the terrestrial
ecosystem is losing carbon due to deforestation followed by harvest.
Therefore, the riverine ecosystem is receiving less carbon due to reduced
terrestrial carbon input after forest was converted to managed land.
LPJmL has been shown to reproduce current patterns of biomass production
(Cramer et al., 2001; Sitch et al., 2003), carbon emission
through fire (Thonicke et al., 2010), also including managed land
(Bondeau et al., 2007; Fader et al., 2010; Rost et al.,
2008) and water dynamics (Biemans et al., 2009;
Gerten et al., 2004, 2008; Gordon et al., 2004; Wagner et al., 2003). The
simulated patterns in water fluxes, like evapotranspiration, runoff and soil
moisture, are comparable to stand-alone global hydrological models
(Biemans et al., 2009; Gerten et al., 2004; Wagner et al., 2003).
Overview of the general transfer of data between scenarios and
models (a) and the detailed calculation of carbon fluxes within and between
LPJmL and RivCM. (b).
RivCM – a riverine carbon model
RivCM is a process-based model that calculates four major ecological
processes related to the carbon budget of the Amazon River (Fig. 1b).
These processes include (1) mobilization, (2) decomposition and
(3) respiration within the river, and (4) outgassing of CO2 to the
atmosphere (Langerwisch et al., 2016). During mobilization, parts of
terrigenous litter and soil carbon, as provided by LPJmL, are imported
to the river, depending on inundated area. The further processing of the
terrigenous carbon in the river happens during its decomposition, which
represents the manual breakup, and its respiration, representing the
biochemical breakup. Finally the CO2 that is produced during
respiration can outgas if the saturation concentration is exceeded
(Langerwisch et al., 2016). These four processes directly control
the most relevant riverine carbon pools, namely particulate organic carbon (POC),
dissolved organic carbon (DOC), and inorganic carbon (IC), as well as
outgassed atmospheric carbon (representing CO2), and exported riverine
carbon to the ocean (either as POC, DOC, or IC).
The model is coupled to LPJmL by using the calculated monthly litter and
soil carbon and water amounts as inputs. It operates at the spatial
resolution of 0.5∘× 0.5∘ (lat, long) and on monthly time
steps. The ability of the coupled model LPJmL–RivCM to reproduce current
conditions in riverine carbon concentration and export to either the
atmosphere or the ocean has been shown and discussed by Langerwisch et al. (2016).
A validation of the carbon pools and fluxes with observed data
shows that RivCM produces results that are within the range of observed
concentrations of both organic and inorganic carbon pools. Model results
strongly underestimate the amount of outgassed carbon, while the carbon
discharged to the ocean is overestimated. There are still large
uncertainties in the process understanding of riverine carbon processes that
translates into uncertainty in the parameter estimation. Therefore, a model like we have applied here can currently only reproduce
broad estimations of exported CO2 (outgassing) and exported organic
carbon (discharge). In general the model reaction to climate change alone
and in combination with deforestation and land use change is as expected
(e.g., reduction of organic carbon due to deforestation, increase of
inorganic carbon due to climate change). Therefore, we think it is
reasonable to use our model to estimate changes in process relations and
general trends. Further data–model comparison and improved parameterization
are still required to allow assessing the simulated absolute numbers.
Despite these shortcomings we make use of the coupled model system of LPJmL
and RivCM to assess the combined impacts of climate change and deforestation.
Model simulation
All transient LPJmL runs were preceded by a 1000-year spin-up during which
the pre-industrial CO2 level of 280 ppm and the climate of the
years 1901–1930 were repeated to obtain equilibria for vegetation, carbon,
and water pools. All transient runs of the coupled model LPJmL–RivCM have
been preceded by a 90-year spin-up during which the climate and CO2
levels of 1901–1930 were repeated to obtain equilibria for riverine carbon pools.
Fraction of deforested area per cell (%) in 2050. Data are
based on Soares-Filho et al. (2006). (a) refers to the BAU
deforestation scenario, whereas (b) refers to the GOV scenario. The
three subregions discussed in the main text are highlighted in the map. The
timelines (right panels) show the development until 2050 for each subregion
(deforestation kept constant after 2050).
LPJmL–RivCM was run on a 0.5∘× 0.5∘
(lat, long) spatial resolution for the years 1901 to 2099. For the estimation
of the impact of projected climate change (CC) and deforestation (Defor),
simulations have been conducted that were driven by five general circulation models (GCMs),
each calculated for three SRES emission scenarios, and three land use change scenarios.
Climate change and deforestation data sets
To assess the effect of future climate change, projections of five GCMs
(see also Jupp et al., 2010; Randall et al., 2007),
using three SRES scenarios (A1B, A2, B1) (Nakićenović et
al., 2000), have been applied (Fig. 1a). The GCMs – namely, MIUB-ECHO-G,
MPI-ECHAM5, MRI-CGCM2.3.2a, NCAR-CCSM3.0, and UKMO-HadCM3 – cover a wide range in terms of temperature and
precipitation and have therefore been chosen to account for uncertainty in
climate projections. The emission scenario SRES A1B describes a development
of very rapid economic growth with convergence among regions, and a balanced
future energy source between fossils and non-fossils. SRES A2 describes a
development of a very heterogeneous world with slow economic growth. And
SRES B1 describes a development of converging world similar to A1B but with
more emphasis on service and information economy.
Location and characteristics of the three subregions.
Regions are depicted in Fig. 2. * Changes in inundation
compared to the average of 1961–1990, as estimated and discussed in
Langerwisch et al. (2013).
To estimate the additional effects of deforestation on riverine carbon pools
and fluxes three land use scenarios were applied: two scenarios directly
relate to different intensity of deforestation, and one represents a
reference scenario with complete coverage by natural vegetation (NatVeg
scenario, hereafter). The two deforestation scenarios are based on the
SimAmazonia projections (Soares-Filho et al., 2006, see also
Fig. 2). The authors estimate the development of
deforestation in the Amazon basin until 2050 based on historical trends and
projected developments. In the business-as-usual (BAU) scenario they assume
that recent deforestation trends continue, the number of paved highways
increases, and new protected areas are not established. In contrast,
deforestation is more efficiently controlled in the governance scenario (GOV).
For this scenario the authors assume that the Brazilian environmental
legislation is implemented across the Amazon basin and the size of the area
under the Protected Areas Program increases. The SimAmazonia
scenarios cover the years from 2001 to 2050. After 2050 the fraction of
deforested area is kept constant. From 2051 until the end of the century the
only driver of change is the continuing climate change. This approach
enables us to estimate the consequences of combined dynamics of
deforestation and climate change until 2050 and the effects of intensified
climate change after 2050, when deforestation is halted at its maximum.
Deforestation rates preceding the scenarios (before 2001) were derived from
extrapolating the data into the past. LPJmL requires historic land-cover
information to correctly capture transient carbon dynamics. The model starts
to simulate vegetation dynamics from bare ground and cannot be initialized
with a land-cover map of a particulate year. It was therefore necessary to
develop an approach which produced consistent land-cover information for the
(undisturbed) past and the deforestation scenarios. For that, the mean
annual rate of deforestation was calculated for the reference period of 2001
to 2005 (Eq. 1), and this rate was applied to calculate the fraction of
deforested area Ft for the years 1901 to 2000 for each cell (Eq. 2).
r=∑t=20012005FtFt+1×12006-2001Ft=F2001×r2001-t
To evaluated spatial differences in the basin we defined three subregions
(see Table 1). The regions were selected for further detailed analysis and
differ in projected changes in inundation patterns and in deforestation
intensity. R1 is located in the western basin with projected increase in
inundation length and inundated area (Langerwisch et al., 2013)
combined with low land use intensity. R2 is a region covering the Amazon
main stem with intermediate changes in inundation (Langerwisch et
al., 2013) and intermediate land use intensity. And R3 is a region with
projected decrease in duration of inundation and inundated area
(Langerwisch et al., 2013) combined with high land use intensity. In
the deforestation scenarios we assume that on 15 % of the deforested area
soybean is grown and 85 % of the area is used as pasture for beef
production (Costa et al., 2007).
Analysis of simulation results
The separate effect of deforestation (EDefor) is estimated by
calculating the differences between future carbon amounts (2070–2099)
produced in the deforestation scenarios (GOV or BAU) and future carbon
amounts produced in the potential natural vegetation scenario (NatVeg),
where no deforestation is assumed. The combined effect of climate change and
deforestation (ECCDefor) is estimated by calculating the
differences between future carbon amounts produced in the deforestation
scenarios and reference carbon amounts (1971–2000) produced in the NatVeg
scenario. We analyzed all four riverine carbon pools (riverine particulate
organic carbon (POC), dissolved organic carbon (DOC), riverine inorganic
carbon (IC) and outgassed carbon). The relative changes in POC and DOC show
similar patterns (see Fig. S1 in the Supplement); therefore, exemplary POC is
shown and discussed in detail.
Evaluation of potential future changes
Spatial effects of the two deforestation scenarios (GOV and BAU) on the
different riverine carbon pools and fluxes have been estimated by
calculating the common logarithm (log10) of the ratio of mean future
(2070–2099) carbon amounts of the deforestation scenarios and mean future
carbon amounts of the NatVeg scenario (EDefor, Eq. 3) for each simulation run.
EDefor=log10∑t=20702099CDefort∑t=20702099CNatVegt
To estimate changes caused by the combination of climate change and
deforestation, ECCDefor compares future carbon pools in the
deforestation scenarios to carbon pools during the reference period (1971–2000)
in the NatVeg scenario (Eq. 4).
ECCDefor=log10∑t1=20702099CDefort1∑t2=19712000CNatVegt2
Each simulation run combines deforestation and emission scenarios and
aggregates the outputs for all five climate model inputs used. To identify
areas where the differences between values in the reference period and
future values are significant (p value < 0.05), the Wilcoxon rank-sum test for non-normally distributed data sets (Bauer, 1972) has been
applied for each cell.
In addition to the spatial assessment, time series were deduced based on
mean values over the entire basin and each of the three exemplary
regions R1, R2 and R3. These means of the carbon pools were calculated for every
year during the simulation period. Changes have been expressed as the
5-year running mean of the quotient of annual future carbon amounts in
the deforestation and in the NatVeg scenarios. These analyses have been
conducted both for the whole Amazon basin and for three selected subregions.
Estimating the dominant driver for changes
We estimated which factor is causing the observed changes the most. To
estimate the contribution of either climate change (DCC, Eq. 5)
or deforestation (DDefor, Eq. 6), reference carbon amounts of the NatVeg
scenario have been compared to future amounts of the NatVeg scenario (DCC),
and future carbon amounts of the NatVeg scenario have been compared to future
amounts of the deforestation scenarios (DDefor).
DCC=log10∑t1=20702099CNatVegt1∑t2=19712000CNatVegt2DDefor=EDefor
We define a cell as dominated by climate change effects if
DCC>DDefor and dominated by
deforestation effects if DCC<DDefor.
The impact values DCC and DDefor
(medianPOC= 0.9695, medianIC= 1.0106, and
medianoutgassedC= 0.9982) have been rounded to the
second decimal place. If both values are equal, the two effects balance each other.
Change in carbon caused by deforestation. Climate model mean (EDefor)
of the change of particulate organic carbon POC (a, b), outgassed
carbon (c, d) and inorganic carbon IC (e, f). Results of the
SRES emission scenario A1B are averaged over five climate models. Areas in
yellow and red indicate a gain, and areas in green and blue indicate a loss
in carbon caused by deforestation (GOV and BAU). White areas within the
Amazon basin represent cells where changes are not significant (p value > 0.05).
Averaged annual amounts and change in the basin carbon budget due
to climate change and deforestation. Dark boxes indicate the amount of
carbon during the reference period (1971–2000), intermediate boxes during
the future period (2070–2099) under climate change only (Langerwisch
et al., 2016), light boxes during the future period under the forcing of
climate change and deforestation (BAU) together (average over all SRES
scenarios and GCMs). Amount is given for future period with relative change
compared to reference. Arrows indicate the direction of carbon transfer.
Change in carbon caused by deforestation and climate change.
Climate model mean (ECCDefor) of the change of particulate
organic carbon POC (a, b), outgassed carbon (c, d) and inorganic carbon
IC (e, f). The inset maps show blue areas where changes are predominantly
caused by climate change (DCC) and red areas where changes are
predominantly caused by deforestation (DDefor). For further details see
Fig. 3. White areas within the Amazon basin represent cells where changes are
not significant (p value > 0.05).
Temporal change in riverine organic carbon due to land use change
only. Change of annual sum of carbon in the deforestation scenario (GOV or
BAU) compared to the NatVeg scenario for the whole basin (a–c) and the three
subregions (R1–R3; d–l) as 5-year mean for GOV (green) and BAU (blue),
representing EDefor. The shaded areas indicate the full range of values
of all five climate models. Bold lines represent the 5-year mean of the five
climate models.
ResultsChanges caused by deforestation
Deforestation decreases riverine particulate and dissolved organic carbon
(POC and DOC). When continuing high deforestation rates as projected under
the BAU deforestation scenario, the decrease in POC is more intense than
under GOV deforestation rates (Fig. 3a and b; for DOC see Figs. S1A and S1B). In
some highly deforested sites in the south-east of the basin the amount of
POC is only 10 % of the amount under no deforestation (indicated by
EDefor). This pattern is robust between the model realizations
with a high agreement of the results amongst the five climate models. In the
deforestation scenarios the changes in future POC are drastic, even though
the differences between the three emission scenarios A1B, A2, and B1 are very
small. However, in some regions within the Amazon basin POC increases (up to
3-fold), especially in mountain regions (e.g., Andes and Guiana Shield).
Although POC and DOC respond similarly in relative terms (see Fig. S1), the
absolute amounts are approximately twice as high for DOC compared to POC
(Table 2). The mean basin-wide loss in POC ranges between 0.13 × 1012 g yr-1
(A2) and 0.24 × 1012 g yr-1 (A1B) in
the GOV scenario, and between 0.37 × 1012 g yr-1 (A2) and
0.48 × 1012 g yr-1 (A1B) in the BAU scenario. The SRES A2
scenario causes the largest changes in POC, further increasing the loss
caused by deforestation.
Changes in outgassed riverine carbon caused by deforestation (Fig. 3c and d)
show a similar pattern as the changes in POC, with an even clearer effect of
deforestation on a larger area. In both scenarios deforestation decreases
outgassed carbon to up to 1/10 compared to the amount produced under
the NatVeg scenario. The agreement between the five climate models is even
larger than in POC. In contrast to the overall pattern, some areas in the
Andes and the Guiana Shield show an increase in outgassed carbon of up to a
factor of 30, but these areas are an exception. Like in POC the differences
between the SRES scenarios are only minor. For the absolute values see Table 2.
For riverine inorganic carbon (IC) deforestation caused significant changes
(EDefor, p value < 0.05) only in small areas (Fig. 3e and f).
In these regions, in the very south of the basin and in single spots in the
north, i.e., in the headwaters of the watershed, IC increases by a factor of
up to 1.2. Besides these areas of increase, a slight decrease of about 5 %
is simulated for the region along the main stem of the Amazon River,
downstream of Manaus and along the Rio Madeira and the Rio Tapajós. In
contrast to POC, the spatial pattern of change in IC does not obviously
follow the deforestation patterns. Therefore, the differences between the
two deforestation scenarios GOV and BAU scenarios are minor. Whereas POC,
DOC, and outgassed carbon show a clear decrease due to deforestation, IC
shows a nearly neutral response with maximal mean basin-wide gains (for
absolute values see Table 2).
Changes caused by a combination of deforestation and climate change
Climate change and deforestation together will lead to large overall changes
in the amount of riverine and exported carbon. Riverine POC and DOC amounts
will decrease by about 19.8 and 22.2 %, respectively, and exported
organic carbon will decrease by about 38.1 %
(Fig. 5). In contrast riverine IC will increase
by about 100 %, combined with a slight increase of outgassed carbon by
about 2.7 % (Fig. 5). In detail, the basin-wide
changes in the amount of POC (Fig. 5a and b and
Fig. S2) caused by deforestation and climate change range between a
2.5-fold increase and a decrease to 1/10. The increase is mainly caused
by climate change (indicated as blue area in the inset in
Fig. 5), whereas the decrease is mainly caused by
deforestation (red area in inset). The differences mainly induced by
deforestation are larger in the BAU compared to the GOV scenario. In
contrast, the differences caused by climate change show no large differences
between the two deforestation scenarios. The differences between the
emission scenarios are minor (see also Table 2). In some areas the dominance
of forcing shifts from climate change dominance (DCC) for the
GOV scenario (blue area in the inset of Fig. 5) to deforestation dominance (DDefor)
for the BAU scenario (red area in inset) due to the
higher land use intensity as a result of deforestation (see also Table 3).
While in the GOV scenario 20 % of all cells are dominated by deforestation
impacts, this value increases for the BAU scenario to 30 %. During the
first decades (2000–2030) basin-wide POC is partly larger in the
deforestation scenarios than in the NatVeg scenario by up to 2 % in 2000
and about 1 % in 2020 (Fig. 6a). All climate
models show reduced POC amounts in the deforestation scenarios compared to
the NatVeg scenario after 2040. The POC amount in the GOV deforestation
scenario decreases gradually until the decrease levels off in the late 2060s,
i.e., 10 years after the constant deforestation area is kept
constant. In the BAU scenario, POC decreases strongly in the 2040 to 2060s
leading to a loss of about 25 % compared to 10 % in the GOV scenario. In
addition to Fig. 6, which shows the temporal development under
deforestation only, we provide Fig. S2, which shows the developments
taking the combination of deforestation and climate change into account.
The three subregions R1 to R3 show different patterns
(Fig. 6a). While in region R1 the difference in
the POC amounts between the GOV and the BAU scenario is only small,
reflecting the low deforestation in this region, the differences between the
two deforestation scenarios are more explicit in regions R2 and especially
in R3 (with the largest area deforested), where in addition model
uncertainty is low. Starting in the 2050s, the variation between different
emission scenarios and climate models increases. Alike the results of the
impact of deforestation alone, POC and DOC show a similar pattern (see also Table 2).
The changes in outgassed carbon (Fig. 5c and d and
Fig. 6b) are in the same range as changes in POC.
Climate change increases outgassed carbon by about 20 %, especially in the
north-western basin (Fig. 5c and d). The
deforestation induces a decrease on outgassed carbon to 1/10 in areas
with a high fraction of deforested area, i.e., in the eastern and south-eastern
basin. Again, the differences in effects are much larger between the two
deforestation scenarios (GOV vs. BAU) than between the different emission
scenarios (see also Table 2). After 2050 the rate of deforestation
determines the differences in the amount of outgassed carbon
(Fig. 6b) as well. The outgassed carbon directly
depends on the available POC; therefore, the time series of both POC and IC
widely match. Under the GOV scenario the basin-wide loss of outgassed carbon
is about 16 % towards the end of the century. The results of the BAU
scenario show an average loss of outgassed carbon of 28 %.
Changes in inorganic carbon (IC) are mainly driven by climate change (under
all emission scenarios) and less by the magnitude of deforestation
(Fig. 5e and f and Fig. 6c, Tables 2 and 3). In about half of the Amazon basin the IC amount
significantly changes due to climate change (insignificant changes in the
other 50 %), but in no cell due to deforestation. The magnitude of change
varies between emission scenarios: the increase in IC is up to 4-fold in the
A2 scenario and up to 2.5-fold in the B1 scenario (see Table 2). For both
deforestation scenarios the gain of IC is dominant until 2050, while the
basin-wide trend becomes unclear afterwards. However, subregions like R1
and R3 show a slight increase during the whole century (Fig. 6f, j, m).
Discussion
Deforestation is, besides climate change, the largest threat to Amazonia. It
leads directly to a decrease in terrestrial biomass and an increase in
CO2 emissions (Potter et al., 2009) and has indirect effects on
aquatic biomass, diversity of species and their habitats, and the climate
(Asner and Alencar, 2010; Bernardes et al., 2004; Costa et
al., 2003). Our results show that deforestation is also likely to change the
amount of riverine organic carbon as well as exported carbon.
We identified a basin-wide reduction in riverine particulate and dissolved
organic carbon pools by about 10 to 25 % by the end of this century
(Figs. 3 and 6). This reduction is particularly pronounced in areas of high deforestation
intensity along the “Arc of Deforestation”, at the Rio Madeira and
the last 500 km stretch of the Amazon River, where large deforestation rates
reduce terrestrial carbon storage. In the first decades of the 21st century
the differences in carbon amounts between the two deforestation
scenarios are only small (Fig. 6). During these
decades the deforestation-induced increase in discharge is able to partly
offset the decreasing amount of terrigenous organic matter, which is the
source of riverine organic matter. In the model, the increase of discharge
after deforestation is caused by a less intense use of the available (soil)
water by the crops, as compared to natural vegetation, which leaves more
water for discharge (as also reported by Costa et al., 2003). After
the 2050s, the differences in the organic carbon pools caused by
deforestation become more obvious (Fig. 6), with
larger carbon decrease under the more severe BAU scenario. The same patterns
occur in the two regions with the pronounced deforestation (R1 and R2). Here
the reduction of terrestrial carbon directly reduces the amount of riverine
carbon. The variation in future riverine carbon fluxes within each
deforestation scenario can be attributed to the differences climate
projections and emission scenarios, especially after 2060 when deforested
area remains constant and the lagged deforestation effects vanish. In
regions with low deforestation intensity (i.e., R1) the effects of land use
change are much smaller and the climate change effects dominate the change
in riverine organic carbon and outgassed carbon. Under the GOV scenario
litter is constantly provided by the natural vegetation and small-scale
deforestation, therefore filling up the litter and soil carbon pools,
which are responsible for the POC and the outgassed carbon. There is a much
clearer drop in the BAU scenario, where a larger fraction of the cell is
subject to deforestation; in some areas, 100 % of the cell area is deforested in
this scenario. In areas where the drop already starts before 2050
(e.g., Fig. 6k and l, showing the results for R3) the deforestation in parts of
the area already reached 100 % before 2050 (also compare with timelines in
Fig. 2b). In these cells there is a drastically reduced influx of carbon
to the litter pool (only from crops), and therefore we already see the drop
earlier than in other areas (e.g., R1).
The reduction in the riverine organic carbon pools will have consequences
for the floodplain and the river itself. Floodplains as well as riverine
biotopes depend on the annually recurring input of organic material, either
as food supply or fertilizer (Junk and Wantzen, 2003). The
productivity of the floodplain forests is mainly driven by the input of
nutrients, which are basically sediments and organic material (Worbes,
1997). While the sediment input (also adding nutrients) might increase due
to increased discharge, the input of organic material from upstream areas
will decrease, leading to a reduced terrestrial and riverine productivity.
This reduced productivity will certainly impact many animal species that
rely on the food supplied by trees, such as fruits or leaves. The reduced
supply of fertilizer and food will therefore likely affect plant and animal
species compositions on local and regional scales (Junk and Wantzen, 2003; Worbes, 1997).
Additionally, deforestation will have secondary effects, including a
reduction in evasion of CO2 from the water (outgassed carbon). Lower
terrestrial productivity after deforestation decreases the organic carbon
material in the river and thus also the respiration to CO2. This is
opposed by the higher respiration rate as a result of increased temperatures
due to climate change. These indirect effects of deforestation on riverine
carbon dynamics have to be included in future carbon balance estimates of
the sink/source behavior of the Amazon basin, since it directly couples the
change in land use to the atmospheric, marine and therefore global carbon fluxes.
In contrast to the amount of riverine organic carbon and outgassed carbon
the amount of riverine inorganic carbon does not show a significant effect
of deforestation. The inorganic carbon in the water is only marginally
affected by deforestation because the amount of IC that remains in the water
depends on the saturation of the water with of IC, which is calculated
depending on the water temperature and the atmospheric CO2
concentration. Climate-change-induced higher water temperature causes a
reduction in solubility of CO2, and higher atmospheric CO2
concentrations lead to an increase in dissolved CO2. The combination of
both effects leads to a slight increase in dissolved inorganic carbon in the
beginning and a neutral signal towards the end of the century independently
of the deforestation. Any changes in the amount of IC can be
attributed either to climate change (increasing temperatures and atmospheric
CO2 concentration) or – to a much smaller extent – to changes in the water
amount in the cell. The latter can be an effect of deforestation as it is
known that deforestation alters the discharge (Costa et al., 2003).
Basin-wide (B) and region-wide (R1–R3) amount of carbon in POC and
DOC, outgassed carbon and IC (1012 g month-1) averaged over 30 years
and five climate models.
“ref” refers to mean amounts during reference period 1971–2000.
“fut” refers to mean amounts during future period 2070–2099. Values given are
the mean ± standard deviation of the five climate models.
Proportion (%) of area dominated by climate or land use change impacts.
Significantly changed Climate change Land use change fraction dominated*dominated*Balanced*A1BA2B1A1BA2B1A1BA2B1A1BA2B1POC GOV50.8550.9150.8658.858.754.940.940.744.60.30.60.5BAU50.8050.8550.8542.343.740.157.556.259.80.20.10.1IC GOV50.8050.8050.80100.0100.0100.00.00.00.00.00.00.0BAU50.8050.8050.80100.0100.0100.00.00.00.00.00.00.0Outgassed carbon GOV97.697.6097.6170.577.768.429.322.331.10.20.00.4BAU97.5597.6597.6052.456.950.247.643.049.70.10.10.1
If both impacts compensate each other the cell is balanced. * The
proportions refer to the significantly changed overall fraction (first columns).
The deforestation of tropical forests will affect not only processes within
the rainforest but also processes in the adjacent Atlantic Ocean.
Currently, the annual export of about 6300 km3 of freshwater is
accompanied by 40 × 1012 g of organic carbon to the Atlantic
Ocean (Gaillardet et al., 1997; Moreira-Turcq et al., 2003). The
present study shows that deforestation leads to a reduction in the exported
organic carbon to the ocean by approximately 40 %. In the NatVeg scenario
the proportion of exported organic carbon to the ocean makes up about
0.8–0.9 % of the net primary production (NPP), whereas in the heavily
deforested BAU scenario this proportion is reduced to about 0.5–0.6 %. The
reduction in the ratio of exported carbon to NPP by deforestation indicates
a less pronounced future sink, since the organic carbon is directly
extracted from the forest and additionally indirectly from the ocean. The
Amazon basin is considered a carbon sink (Lewis et al., 2011). In
central Amazonia net primary production sums up to about 1 × 109 g C km-2 yr-1 (Malhi et al., 2009). Earlier
results showed that climate change alone will increase the amount of
outgassed carbon from the Amazon basin by about 40 %, while the export to
the Atlantic Ocean remains nearly unchanged (Langerwisch et al.,
2016). Our results show that additional deforestation will offset the trend
in outgassed carbon (only +3 %), but will have larger effects on the
export to the ocean (-38 %). Therefore, future assessments of
climate-change- and deforestation-induced changes on the carbon balance of
the Amazon basin have to include the amount of carbon exported to the ocean
and outgassed from the river basin to the atmosphere.
The import of organic material to the ocean positively impacts the
respiration and production of the Atlantic Ocean off the coast of South
America (Körtzinger, 2003; Cooley and Yager, 2006;
Cooley et al., 2007; Subramaniam et al., 2008). A reduction of the import
might therefore reduce the productivity in the ocean off the coast since these
coastal zones depend on the imported organic matter (Cooley
and Yager, 2006; Körtzinger, 2003; Subramaniam et al., 2008) and might
have further impacts along the trophic cascade including herbivorous and
piscivorous fish. Besides the reduced organic carbon, higher amounts of
nutrients may be imported to the ocean, because the nutrients are only
marginally taken up within the river and by the former intact adjacent
forests. The imports of both less organic carbon and more nutrients might
induce changes in oceanic heterotrophy and primary production.
Shortcomings of the approach
The strong decrease of organic carbon may be overestimated because of our
model assumptions, which include a complete removal of the natural
vegetation carbon during deforestation (see, e.g.,
Fig. 6). In reality, the complete conversion of
the floodplain forests to cropland or pasture is not very likely. In the
more severe deforestation scenario (BAU) about 6 % of the area is
deforested (Soares-Filho et al., 2006). In our scenarios this also
includes areas which are temporarily flooded. Since temporarily inundated
areas cannot be easily converted to agricultural area or settlements, this
might lead to an overestimation of deforested area. But, for example in
Manaus, floodplains within a radius of about 500 km around the city were
extensively logged for construction purposes between 1960 and 1980
(Goulding et al., 2003).
In our study deforestation is simulated by partial or complete removal of
vegetation carbon. This also reduces the litter and soil carbon through
respiration over time because these carbon pools are reduced in size;
after harvest, less dead organic material, generated by the crops and managed land, remains
on site. Therefore, our estimates represent more drastic
changes in riverine carbon dynamics. The sharp decrease in POC and outgassed
carbon after 2050, as it is one result of our study, is caused by the
implementation of carbon removal in the model. During inundation the cells
are partly or completely covered with water, which leads to the export of
organic material. After the gradual decrease of forest cover (and therewith
input of organic material) before 2050, there is a depletion of the
remaining organic material in the following years. By a more gradual
implementation of inundation in the model this harsh decrease would be softened.
In this study the mobilization of terrigenous organic material is
exclusively controlled by inundation. A model that also considers the impact
of precipitation, vegetation cover and slope on erosion would likely lead to
an increase in erosion and thus to the import of organic matter to the river
(McClain and Elsenbeer, 2001) in the first years after deforestation.
However, this additional influx of carbon would only be temporal, since the
soil and litter carbon pools would be eroded after some years (McClain
and Elsenbeer, 2001). Thus, we assume that for the investigation of the
long-term dynamics of carbon pools and fluxes, such erosion effects are only
of minor importance.
Conclusion
Deforestation decreases terrestrial biomass and contributes to a further
increase in CO2 emissions, which reduces the terrestrial carbon
sequestration potential (Houghton et al., 2000; Potter et al.,
2009). Moreover, our results show that deforestation will lead to a
significant decrease of exported terrigenous organic carbon, leading to a
reduction of the amount of riverine organic carbon. The climate change
effects additionally increase in the amount of riverine inorganic carbon.
Deforestation further decreases the amount of riverine organic carbon
leading to a combined decrease by about 20 % compared to 10 % under
climate change alone (Langerwisch et al., 2016). While climate change alone
leaves the export to the Atlantic Ocean with +1 % nearly unchanged
(Langerwisch et al., 2016), considering deforestation will now decrease the
export of organic carbon to the ocean by about 40 %. In contrast climate
change will strongly increase the outgassed carbon by about 40 %
(Langerwisch et al., 2016), but including deforestation will reduce this
increase to only +3 %.
These changes in the hydrological regimes and the fluvial carbon pools might
add to the pressures that are being imposed on the Amazon ecosystems
(Asner et al., 2006; Asner and Alencar, 2010), with strong
consequences for ecosystem stability (Brown and Lugo, 1990;
Foley et al., 2002; von Randow et al., 2004). For instance, fish play a key
role in seed dispersal along the Amazon. If floodplains turn into less
productive grounds for juvenile fish, these changes might have considerable
effects on local vegetation recruitment dynamics and regional plant
biodiversity (Horn et al., 2011). We therefore strongly advocate the
combined terrestrial and fluvial perspective of our approach, and its
ability to address both climate and land use change.
Data availability
The data are available upon request from the corresponding author.
The Supplement related to this article is available online at doi:10.5194/esd-7-953-2016-supplement.
Model development: F. Langerwisch, B. Tietjen, W. Cramer.
Data analysis: F. Langerwisch, A. Rammig, K. Thonicke. Drafting the article:
F. Langerwisch, A. Walz, B. Tietjen, A. Rammig, K. Thonicke.
Acknowledgements
We thank the “Pakt für Forschung der Leibniz-Gemeinschaft” for funding the
TRACES project for F. Langerwisch. A. Rammig was funded by FP7 AMAZALERT
(Project ID 282664) and Helmholtz Alliance “Remote Sensing and Earth System
Dynamics”. We also thank Susanne Rolinski and Dieter Gerten for discussing the
hydrological aspects. We thank Alice Boit for fruitful comments on the manuscript.
Additionally we thank our LPJmL and ECOSTAB colleagues at PIK for helpful
comments on the design of the study and the manuscript. We also thank the
anonymous reviewers and the handling editor whose comments and suggestions
greatly improved the manuscript.
Edited by: C. Reick
Reviewed by: two anonymous referees
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