Future changes in precipitation and impacts on extreme streamflow over Amazonian sub-basins

Abstract

Because of climate change, much attention is drawn to the Amazon River basin, whose hydrology has already been strongly affected by extreme events during the past 20 years. Hydrological annual extreme variations (i.e. low/high flows) associated with precipitation (and evapotranspiration) changes are investigated over the Amazon River sub-basins using the land surface model ORCHIDEE and a multimodel approach. Climate change scenarios from up to eight AR4 Global Climate Models based on three emission scenarios were used to build future hydrological projections in the region, for two periods of the 21st century. For the middle of the century under the SRESA1B scenario, no change is found in high flow on the main stem of the Amazon River (Obidos station), but a systematic discharge decrease is simulated during the recession period, leading to a 10% low-flow decrease. Contrasting discharge variations are pointed out depending on the location in the basin. In the western upper part of the basin, which undergoes an annual persistent increase in precipitation, high flow shows a 7% relative increase for the middle of the 21st century and the signal is enhanced for the end of the century (12%). By contrast, simulated precipitation decreases during the dry seasons over the southern, eastern and northern parts of the basin lead to significant low-flow decrease at several stations, especially in the Xingu River, where it reaches −50%, associated with a 9% reduction in the runoff coefficient. A 18% high-flow decrease is also found in this river. In the north, the low-flow decrease becomes higher toward the east: a 55% significant decrease in the eastern Branco River is associated with a 13% reduction in the runoff coefficient. The estimation of the streamflow elasticity to precipitation indicates that southern sub-basins (except for the mountainous Beni River), that have low runoff coefficients, will become more responsive to precipitation change (with a 5 to near 35% increase in elasticity) than the western sub-basins, experiencing high runoff coefficient and no change in streamflow elasticity to precipitation. These projections raise important issues for populations living near the rivers whose activity is regulated by the present annual cycle of waters. The question of their adaptability has already arisen.

Full text

IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 8(2013) 014035 (13pp) doi:10.1088/1748-9326/8/1/014035
Future changes in precipitation and
impacts on extreme streamflow over
Amazonian sub-basins
M Guimberteau1,2, J Ronchail1,2,3, J C Espinoza4,5, M Lengaigne1,2,
B Sultan1,2,6, J Polcher2,7,8, G Drapeau1,2,3,9, J-L Guyot10, A Ducharne8,11,12
and P Ciais8,13,14,15
1Laboratoire d’Oc´
eanographie et du Climat: exp´
erimentations et approches num´
eriques (LOCEAN),
UMR7159, Paris, France
2Institut Pierre Simon Laplace (IPSL), Paris, France
3Universit´
e Paris Diderot, Sorbonne Paris Cit´
e, Paris, France
4Instituto Geofisico del Per´
u, Lima, Peru
5Universidad Agraria La Molina, Lima, Peru
6Institut de Recherche pour le D´
eveloppement (IRD), Paris, France
7Laboratoire de M´
et´
eorologie Dynamique (LMD), Paris, France
8Centre National de la Recherche Scientifique (CNRS), Paris, France
9Pˆ
ole de Recherche pour l’Organisation et la Diffusion de l’Information G´
eographique (PRODIG),
Paris, France
10 Institut de Recherche pour le D´
eveloppement (IRD), Brasilia, Brazil
11 Unit´
e Mixte de Recherche (UMR) Sisyphe, Paris, France
12 Universit´
e Pierre et Marie Curie (UPMC), Paris, France
13 Laboratoire des Sciences du Climat et de l’Environment (LSCE), Gif-sur-Yvette, France
14 Joint Unit of Commissariat `
a l’´
energie atomique et aux ´
energies alternatives (CEA), Gif-sur-Yvette,
France
15 Universit´
e de Versailles Saint-Quentin (UVSQ), Versailles, France
E-mail: matthieu.guimberteau@upmc.fr
Received 21 December 2012
Accepted for publication 14 February 2013
Published 7 March 2013
Online at stacks.iop.org/ERL/8/014035
Abstract
Because of climate change, much attention is drawn to the Amazon River basin, whose
hydrology has already been strongly affected by extreme events during the past 20 years.
Hydrological annual extreme variations (i.e. low/high flows) associated with precipitation (and
evapotranspiration) changes are investigated over the Amazon River sub-basins using the land
surface model ORCHIDEE and a multimodel approach. Climate change scenarios from up to
eight AR4 Global Climate Models based on three emission scenarios were used to build future
hydrological projections in the region, for two periods of the 21st century. For the middle of
the century under the SRESA1B scenario, no change is found in high flow on the main stem of
the Amazon River ( ´
Obidos station), but a systematic discharge decrease is simulated during
the recession period, leading to a 10% low-flow decrease. Contrasting discharge variations are
pointed out depending on the location in the basin. In the western upper part of the basin,
which undergoes an annual persistent increase in precipitation, high flow shows a 7% relative
increase for the middle of the 21st century and the signal is enhanced for the end of the
century (12%). By contrast, simulated precipitation decreases during the dry seasons over the
southern, eastern and northern parts of the basin lead to significant low-flow decrease at
several stations, especially in the Xingu River, where it reaches −50%, associated with a 9%
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Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
reduction in the runoff coefficient. A 18% high-flow decrease is also found in this river. In the
north, the low-flow decrease becomes higher toward the east: a 55% significant decrease in the
eastern Branco River is associated with a 13% reduction in the runoff coefficient. The
estimation of the streamflow elasticity to precipitation indicates that southern sub-basins
(except for the mountainous Beni River), that have low runoff coefficients, will become more
responsive to precipitation change (with a 5 to near 35% increase in elasticity) than the
western sub-basins, experiencing high runoff coefficient and no change in streamflow
elasticity to precipitation. These projections raise important issues for populations living near
the rivers whose activity is regulated by the present annual cycle of waters. The question of
their adaptability has already arisen.
Keywords: Amazon, ORCHIDEE, streamflow extreme, climate change, precipitation
SOnline supplementary data available from stacks.iop.org/ERL/8/014035/mmedia
1. Introduction
Many questions arise about the impact of climate change
on water resources, which are fundamental for ecosystems
and societies. Such questions are especially relevant in
the Amazon River basin where severe hydroclimatic
(precipitation, evapotranspiration (ET)) and anthropogenic
(deforestation, land-use change. . . ) changes are already
threatening the region. Runoff changes in the basin have
been characterized by more severe extreme values on the
main stream for about forty years (Callede et al 2004,
Espinoza et al 2009a) due to decadal modes of variability
at regional scale (Marengo et al 1998, Labat et al 2004,
Marengo 2004, Labat 2005, Espinoza et al 2006,2009b) and
to an interannual variability (Richey et al 1989, Marengo
1992, Guyot et al 1998, Uvo and Graham 1998, Ronchail
et al 2005a,2005b,2006, Zeng et al 2008, Espinoza et al
2009a) leading to severe droughts such as the 1998, 2005
and 2010 events (Marengo et al 2008, Zeng et al 2008, Yoon
and Zeng 2010, Espinoza et al 2011, Marengo et al 2011)
and floods as experienced in 1999, 2009 (Chen et al 2010,
Marengo et al 2010,2012) and 2012. These events alter
the livelihood of riverside populations (Drapeau et al 2011),
the ecosystems and the vegetation functioning (Phillips et al
2009). Drought has been suggested to increase tree mortality
and decrease photosynthetic activity in 2010 (Lewis et al
2011, Xu et al 2011). Furthermore the impact of such events
on water resources (Bates et al 2008, Cox et al 2008), on fire
and vegetation (Nepstad et al 2007, Cook et al 2012) might
increase in the future. Are present-time extreme discharges
precursors of future conditions?
Despite the uncertainties of future climate projections,
related to CO2emission scenarios and to General and
Regional Circulation Models (GCMs and RCMs respectively)
(Li et al 2006, Vera et al 2006, Boulanger et al 2007, Marengo
2009), some consensus is found regarding climate change in
the Amazon River basin. Indeed, a 4 ◦C warming is expected
in tropical South America (Boulanger et al 2006) and a
consensus is found about a precipitation decrease in austral
winter (Vera et al 2006), a longer dry season and more severe
droughts (Li et al 2006,2008). Moreover, annual precipitation
is expected to decrease in the eastern Amazon River basin and,
in contrast, to increase and be more intense over the western
Amazon (Meehl et al 2007, Alves and Marengo 2010).
Past studies on climate change and its impact on
hydrology at global scale do not show any robust agreement
about future runoff trends in the Amazon River basin. Some
authors simulate a discharge reduction on the main stem
of the Amazon River (Arora and Boer 2001) while others
find a 5% increase in discharge at the end of the 21th
century (Nohara et al 2006). Salati et al (2009), using
15 AR4 (4th Assessment Report) coupled models and the
RCM HadRM3P (Hadley Centre Regional Model version 3P),
project a 7–30% discharge decrease on the main stem of
the Amazon River depending on the scenario (SRESB2 and
SRESA2), the climate model and the period. At the regional
scale, literature on future runoff projections in the Amazon
River basin remains scarce because of hydrological modeling
issues (Coe et al 2007, Decharme et al 2008, Beighley et al
2009, Trigg et al 2009, Getirana et al 2010, Paiva et al 2011a,
Yamashima et al 2011, Guimberteau et al 2012, Paiva et al
2012). Milly et al (2005), using ensembles from 12 GCMs for
the middle of the 21st century under the SRESA1B scenario,
obtained a 5–10% runoff decrease in northeastern Amazon,
contrasted to a 10–40% increase in western Amazon. Along
the eastern edge of the Amazon River basin, Tomasella et al
(2009) found a 30% (60%) mean annual (low-flow) decrease
of the Rio Tocantins discharge at the end of the 21th century
under the SRESA1B scenario. In the upper Amazon, Lavado
Casimiro et al (2011) use two hydrological models, the
climatic data from 3 GCMs under 2 SRES scenarios, and
found both decreasing (4 basins) and increasing (3 basins)
discharge trends in the Peruvian Amazon Andes basins. As a
consequence of precipitation and runoff change, flooded areas
and flood duration are found to increase in about one third of
the basin, especially in Western Amazonia (Langerwisch et al
2012).
The above differences in future extreme flow estimations
(low and high flows) for the Amazon River are due to the
climatic forcing, model uncertainties but also to the lack of
simulations at sub-basin scale. Will annual extreme flows
be more severe during the 21st century than the present
ones? Will there be differences within sub-basins? To try
to answer these questions, our study focuses on sub-basin
2

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
scale annual extreme values in the context of a changing
climate. This was permitted by the availability of the Land
Surface Model (LSM) ORCHIDEE (ORganising Carbon and
Hydrology In Dynamic EcosystEms, section 2.1) which is
able to accurately simulate the present-time streamflow in
many stations over the basin (Guimberteau et al 2012).
We build plausible scenarios of hydrological conditions that
populations and ecosystems might have to cope with during
the 21st century, based on a multi-scenarios approach from the
IPCC’s (Intergovernmental Panel on Climate Change) AR4
(Solomon et al 2007). Our methodology is first described
(section 2). We detail the method based on anomalies
to build the future climate forcings (section 2.2) and the
hydrological signatures chosen to analyze the relationship
between precipitation and streamflow (section 2.3). The
hydrological signature results for present time are briefly
given in section 3. The results from future simulations under
SRESA1B at the middle of the 21st century are presented in
section 4and organized according to the different sub-basins.
Section 5summarizes the results obtained at the end of the
century and under other scenarios.
2. Methodology
2.1. The land surface model ORCHIDEE
Hydrological simulations under climate change are performed
using the hydrological module SECHIBA
(Sch´
ematisation des EChanges Hydriques `
a l’Interface
Biosph`
ere–Atmosph`
ere, Ducoudr´
eet al 1993) of the LSM
ORCHIDEE considering a 11-layer hydrology (De Rosnay
et al 2002, D’Orgeval et al 2008, Campoy et al 2013) where a
2 m-soil is vertically discretized to calculate unsaturated soil
water fluxes. The routing module (Polcher 2003, Guimberteau
et al 2012) is activated in the model to simulate the daily
transport of runoff and drainage to the ocean. Flooded areas
are taken into account in ORCHIDEE using the representation
of floodplains and swamps by D’Orgeval (2006) and a map of
their spatial distribution over the Amazon River basin built by
Guimberteau et al (2012). A detailed description of the LSM
ORCHIDEE is given in the latter study.
2.2. Construction of climate change forcings
GCMs are known to poorly simulate precipitation patterns,
even more at sub-basin scale (Solomon et al 2007). Moreover,
because of the importance of local precipitation for hydrology,
downscaling methods have been developed for hydrological
studies and lead to improved results at basin scale (Wilby et al
1999). We applied the delta downscaling method approach
to produce climate change forcings from GCMs results. This
method is based on the addition of the monthly anomalies
between two climatologies (simulated climate change and
current climate results from a given GCM) to a baseline
meteorological forcing. It appears to be a downscaling method
well adapted for hydrological studies (Fowler et al 2007). In
fact, if we assume that relationships between variables in the
present-time climate are likely to be maintained in the future,
the delta method incorporates from the present-time forcing
more realistic spatio-temporal patterns in precipitation which
are crucial for future streamflow simulation. However, this
method prevents the study of future interannual variability as
future monthly anomalies of climatologies are applied to the
present-time forcing whose variability is maintained.
Climate change anomalies are derived from up to 8 AR4
GCMs (see table s1 available at stacks.iop.org/ERL/8/014035/
mmedia), 2 different 20 yr running mean periods (2046–2065
and 2079–2098) and 3 emission scenarios (SRESB1, A1B
and A2). We mainly focus on the results coming from
SRESA1B, which projects a very rapid economic growth,
a low population growth, a rapid introduction of new and
more efficient technology, leading to a 720 ppm CO2
concentration by the year 2100. This choice was driven
by the better availability of the 8 atmospheric variables
required to run ORCHIDEE (see table s2 available at stacks.
iop.org/ERL/8/014035/mmedia) under SRESA1B scenario
compared to SRESA2. The global meteorological dataset
NCC (NCEP/NCAR Corrected by CRU data, Ngo-Duc et al
2005) is used as the baseline for this study. The 21 yr
period representing the current climate in NCC dataset is
the period 1980–2000, during which the precipitation dataset
was corrected by ORE (Environmental Research Observatory)
HYBAM (Geodynamical, hydrological and biogeochemical
control of erosion/alteration and material transport in the
Amazon River basin) (Cochonneau et al 2006) observations
that have improved the present-time streamflow simulation
(called ‘ORCH4’) by ORCHIDEE (Guimberteau et al 2012).
In each GCM grid-cell i, monthly anomalies
(ANO(XGCM,i,tGCM )) have been calculated from climatologies
of the atmospheric variables outputs derived from each
GCM (XGCM,i,tGCM ) (figure 1and equation (1)). Linear
spatial interpolation has been applied to the GCM outputs
whose resolution was coarser than NCC (1.0◦). The resulting
monthly anomalies of a given variable of a GCM were applied
to the corresponding NCC variable (XNCC,i,tNCC) at each NCC
time-step (tNCC =6 h) for the corresponding month (tGCM =
1 month) of each year of the 1980–2000 period (figure 1
and equation (2)). Thus, simulations are performed with
ORCHIDEE forced by these new climate change forcings
starting from the same state of equilibrium of the model (5 yr
spin-up over the 1975–1979 period).
ANO(XGCM,i,tGCM )
=(XSRESα−X20C3M)
X20C3MGCM,i,tGCM
(1)
XNCCfutur,i,tNCC
=XNCC,i,tNCC +(ANO(XGCM,i,tGCM )∗XNCC,i,tNCC ). (2)
2.3. Hydrological signatures
The variations of three hydrological signatures (runoff
coefficient, precipitation elasticity of streamflow and first/last
deciles of streamflow) are studied at sub-basin scale using
16 ORE HYBAM gauge stations (table 1) to characterize the
3

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
Figure 1. Flowchart of the future climate forcing construction.
Table 1. List of ORE HYBAM gauge stations over the Amazon River basin (see figure 2for their localization on a map). For each station,
mean annual streamflow (Qmean), runoff coefficients (Rcoeff) and streamflow elasticity to precipitation (εp) are computed from observations
(ORE HYBAM database) and present-time simulation ORCH4, on average over the 1980–2000 period (except for the observed streamflow
at the river mouth whose value is from Callede et al (2010), for the 1972–2003 period average). The colors indicate the localization of the
stations in the basin (black: mainstream; red: southeastern/southern region; green: western region; blue: northwestern/northern/
northeastern region).
hydrological response to future precipitation change. In the
stations where it accounts, the backwater effect (Meade et al
1991) that is not represented in ORCHIDEE, is eliminated
in the measurements (Espinoza et al 2009a). For each
signature, differences are performed between one simulated
year in climate change condition and the baseline ORCH4
climatology. Thus we estimate for each year the signature
variation between future simulation and average present-time
condition. The results are presented from an ensemble which
is an average of 168 differences (8 models ∗21 yearly
differences) between future and present conditions.
2.3.1. Annual runoff coefficient. The annual average runoff
coefficient (Rcoeff, equation (3)) varying from 0 to 1, is
the proportion of the long-term average precipitation (¯
Pin
kg m−2s−1) that runs off into streams (long-term average
discharge ¯
Qin kg m−2s−1), assuming no net change in
storage. A high (low) runoff coefficient points out that a large
amount of water leaves the basin as streamflow (as ET).
Rcoeff =
¯
Q
¯
P.(3)
Three runoff coefficients are computed for each sub-
basin:
•ROBS
coeff is computed from ORE HYBAM discharge data
(except for the river mouth discharge whose value is from
Callede et al (2010), for the 1972–2003 period average)
and from the precipitation dataset used to correct NCC
forcing (see section 2.2), on average over the 1980–2000
period.
4

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
•RORCH4
coeff is computed from simulated precipitation and
streamflow values of the present-time simulation, ORCH4.
•RENSEMBLE
coeff , is computed from the averaged simulated
precipitation and streamflow values of the different future
simulations, for each scenario and time horizon.
2.3.2. Precipitation elasticity of streamflow. In order to
quantify the response of discharge to the precipitation change,
we compute the coefficient of elasticity (εp, equation (4))
which is the relative change in mean annual streamflow
divided by the relative change in mean precipitation (Schaake
1990, Sankarasubramanian et al 2001). The streamflow
elasticity can vary usually between 1.0 and 4.0, i.e. a 10%
change in mean annual precipitation results in a 10–40%
change in mean annual runoff. The precipitation elasticity
of streamflow is strongly correlated to the runoff coefficient,
the mean annual precipitation and the streamflow according
to Chiew (2006). Thus, river basins with low (high) runoff
coefficient will be more (less) sensitive to precipitation
change.
εp=median "Qy−¯
Q
(Py−¯
P)·¯
P
¯
Q#(4)
Pyand Qy(both in kg m−2s−1) are the annual
precipitation and discharge respectively, averaged over the
year yof the time period.
The potential evaporation elasticity of streamflow is
computed in the same way according to equation (4).
2.3.3. First and last deciles of streamflow. The deciles are
used to distinguish the monthly low flow (i.e. first decile)
and monthly high flow (i.e. last decile) for a given sub-basin.
The first (last) decile of streamflow is the value not exceeded
by the lowest (highest) 10% of all streamflow values. They
are calculated for each sub-basin, each year of future climate
forcing and scenario, for present and future time horizons. For
each future simulation, the significance of the median change
of the first and last deciles is assessed using the Wilcoxon
signed-rank test at the 95% level.
3. Brief overview of simulated streamflow related to
precipitation in present time
The ability of ORCHIDEE to simulate the streamflow
variations over the end of the 20th century was thoroughly
tested over the Amazonian sub-basins by Guimberteau et al
(2012). The streamflow simulation has been improved using
the new ORE HYBAM precipitation forcing which better
takes into account the precipitation regimes. They are very
different according to the regions of the Amazon River basin
due to its huge size and its extension in both hemispheres. We
can distinguish different seasons of precipitation according
to the regions (Figueroa and Nobre 1990, Nobre et al 1991,
Ronchail et al 2002, Espinoza et al 2009a). From southeastern
to southwestern regions, the precipitation season occurs in
DJF (December, January and February) and the dry season
in JJA (June, July and August). The precipitation seasonality
in western regions is similar but less pronounced. The rainiest
and driest seasons in northwestern regions are slightly more
marked; they occur in MAM (March, April and May) and
DJF respectively. The precipitation regime slightly differs
in the northernmost and the northeastern regions where the
rainiest seasons occur in MJJ (May, June and July) and MAM,
respectively.
The hydrological signatures describing the link between
precipitation and runoff (runoff coefficient and precipitation
elasticity) in present time are briefly analyzed in sections 3.1
and 3.2, then used to assess future streamflow variations in
a climate change perspective (section 4). In the latter section
and section 5, we focus on the future change of the annual
extreme values of streamflow using the hydrological signature
of streamflow deciles.
3.1. Annual runoff coefficient
The runoff coefficient observed at the mouth (ROBS
coeff =0.49)
(table 1) indicates that about half of the precipitation reaching
the Amazon River basin runs off to the mouth. It is in
good agreement with Callede et al (2008) who found values
between 0.41 and 0.59 for the 1940–2003 period, with
Shuttleworth (1988) who evaluated that one half of the
incoming precipitation is returned to the atmosphere as ET
above the Amazonian forest, and with Marengo’s (2006)
compilation of results indicating that no more than 46% of
precipitation leaves the basin as runoff. The high ROBS
coeff values
in the stations at the outlet of the Andean basins show the
influence of the high precipitation values (hotspots) occurring
in these regions and the topography that induces a fast runoff
generation from soil layers toward the exit of the Andes. This
is observed at Rurrenabaque (0.68) and Tamshiyacu where
ROBS
coeff =0.81 is close to the value given by Espinoza (2009)
(0.79). On the contrary, southern runoff coefficients varying
from 0.28 (Guajara-Mirim) to 0.39 (Itaituba) are very low
in regions retaining large amount of water in floodplains.
Southern ROBS
coeff is in good agreement with Molinier (1992),
Molinier et al’s (1995) estimations which give 0.36, 0.29
and 0.39 at Fazenda Vista Alegre, Altamira and Itaituba,
respectively. Northern runoff coefficients generally exceed
0.5, with exception of the northernmost and tropical basins
(Caracarai and Sao Francisco basins). They are overestimated
compared to Molinier (1992) and Molinier et al (1995) (about
0.50 at Serrinha for the 1973–1989 period).
RORCH4
coeff (table 1) sums up the streamflow results obtained
in Guimberteau et al (2012), where the northern and western
streamflow simulation was improved by ORE HYBAM
precipitation forcing and new floodplains distribution in the
model. The main western basins at Tamshiyacu and Sao
Paulo de Olivenc¸a are subjected to a runoff coefficient
underestimation, probably due to the lack of precipitation
data in the northwestern Amazon (Azarderakhsh et al
2011). Sub-basins from southern regions present a large
overestimation in runoff coefficient maybe due to an ET
underestimation and an exaggerated extension of rainy spots
in the forcing.
5

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
3.2. Precipitation elasticity of streamflow
The streamflow elasticity simulated in this study for
present-time climate (εORCH4
p) varies between 0.8 (at ´
Obidos)
to 2.15 (Labrea) over the Amazon River basin and is generally
overestimated compared to the observations (εOBS
p) (table 1).
The streamflow elasticity computed over the Amazon River
sub-basins is of the same order of magnitude as other
estimations in the world: it ranges from 1.0 to 2.5 over the
US (Sankarasubramanian et al 2001), from 2.0 to 3.5 in 219
catchments across Australia (Chiew 2006), from 1.0 to 4.0 in
the Quarai River which is a tributary of the Uruguay River
(part of the La Plata basin) (Paiva et al 2011a), from 2.0 to 6.0
for different LSMs in the Colorado River (Vano et al 2012)
and from 1.0 to 3.0 at global scale according to Tang and
Lettenmaier (2012).
4. How do the different Amazonian sub-basins
respond to climate change (middle of the 21st
century under SRESA1B scenario)?
We assess that low flow (high flow) is strongly related to the
dry season (rainy season). This simple hypothesis is limited
by the complex relation between precipitation and streamflow
regimes within the Amazon River basin. Floodplains
extension can modify the relationship between precipitation
amount and extreme stream flows. However, for a small
sub-basin such as the Mamore at Guaraja-Mirim, the lag
between precipitation and runoff is not significant at monthly
timescale. In contrast, it can be significant (about two months)
over larger sub-basins including floodplains areas ( ´
Obidos,
Manacapuru, Fazenda Vista Alegre). After a brief overview of
the climate change projected over the basin for the 2046–2065
time horizon under the SRESA1B scenario (section 4.1), the
regional changes in extreme flows related to the seasonal
precipitation changes (see section 3) are presented for three
large coherent regions of the basin (see color code in
table 1) in section 4.2: southeastern/southern region (SE–S,
section 4.2.1), western region (W, section 4.2.2) and northern
region including northwestern/northern/northeastern regions
(NW–N–NE, section 4.2.3). A separate section is dedicated to
´
Obidos results (section 4.2.4), representing the hydrological
behavior of the Amazon River basin.
4.1. Overview of projected climate change
Annual ensemble mean temperature increases by 2.04 ◦C
(1.49 ◦C–2.63 ◦C according to the different simulations) on
average over the basin. Higher annual increase occurs in
southern sub-basins with the highest ensemble increase over
the Mamore River basin (2.15 ◦C). The temperature increase
leads to an enhanced ensemble mean ET by 7.0% on average
over the basin. A slight precipitation increase by 1.1% is
simulated over the basin. The precipitation change is spatially
contrasted. At the annual timescale, few GCMs (generally
3 out of 8) simulate a precipitation increase (i.e. most
GCMs project a precipitation decrease) in eastern (Xingu
and Tapajos Rivers), northernmost (Branco River) and, to
a lesser proportion, southern (Mamore and Beni Rivers)
regions (figure 2(a)). In contrast, wetter conditions are found
by a majority of GCMs (generally 7 out of 8) in western
(Amazonas River at Tamshiyacu outlet, Purus and Jurua
Rivers) and northwestern regions (Japura and Negro Rivers).
4.2. Regional response of streamflow to precipitation change
4.2.1. Southeastern/southern region. The annual precip-
itation decrease predicted by most models, associated to
an ET increase (not shown), leads to a runoff coefficient
decrease by more than 5% at the six stations of the
region (figure 3). The persistent south-easterly precipitation
decrease from March to November predicted by most
GCMs (figures 2(c)–(e)) particularly affects streamflow at
Altamira where a near 10% decrease in runoff coefficient
occurs (figure 3). A south-easterly decrease in dry-season
precipitation (figure 2(d)) leads to more pronounced low flows
at all the stations of the region (figure 4(b)). The signal is
rather robust in the region because at each station, 75% of
the ensemble low-flow differences between future and present
are negative. The effect is significant over the Madeira (Porto
Velho and Fazenda Vista Alegre stations) and the Xingu
(Altamira station) rivers where JJA precipitation decreases
by 9% and 22% respectively and is associated with a 5%
ET increase due to temperature increase (about 2.5 ◦C) over
these basins (not shown). Up to 7 out of 8 simulations give
a significant average decrease in the median low flow by
about 30% (50%) at Porto Velho (Altamira). No change
in high flow is simulated at southerly stations except at
Altamira where 6 future simulations out of 8 give a significant
decrease by 18% in average (figure 4(a)). Southern sub-basins
tend to become more responsive to precipitation. Indeed,
precipitation elasticity of streamflow increases by more than
15% at Guajara-Mirim, Altamira and Itaituba (figure 5).
Potential evaporation elasticity of streamflow also increases in
these stations and in Porto Velho (between 5 and 19%), except
at Altamira where it decreases by 66% (not shown) suggesting
that the Xingu River basin becomes more (less) responsive to
precipitation (evaporation) change.
4.2.2. Western region. Most GCMs project an annual
precipitation increase over all the western part of the Amazon
River basin (figure 2(a)). However, annual runoff coefficients
are little affected at Tamshiyacu and Sao Paulo de Olivenc¸a
(less than 2% decrease), whereas they decrease by more
than 5% at Gaviao and Labrea (figure 3), where the lower
proportion in swamps and floodplains does not compensate
for the change in precipitation contribution to runoff. The
wetter projected conditions persist seasonally during the wet
season (DJF), in particular in the northwesternmost part of
the basin and along the eastern slopes of the Andes, where
all the models simulate a precipitation increase (figure 2(b)).
High-flow variation is not significant in the four westerly
stations. At Tamshiyacu (upper Solim˜
oes), all the simulations
show a median high-flow increase ranging from 5 to 25% (not
shown) but only 3 simulations are significant (figure 4(a)). No
significant low-flow decrease is simulated over the western
6

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
Figure 2. Number of GCMs out of eight that project a precipitation increase over the basin, for the 2046–2065 time horizon under
SRESA1B scenario: (a) annual (b) DJF (c) MAM (d) JJA and (e) SON. Contour indicates the precipitation anomaly (mm d−1) between
ensemble and ORCH4 (solid line when the anomaly is positive and dashed line when negative). The localization of the ORE HYBAM
gauge stations are indicated on each map with a color point (see table 1for their coordinates and the color code).
part of the basin (figure 4(b)). Little change in precipitation
elasticity of streamflow (less than 5% except at Labrea,
figure 5) occurs at the westerly sub-basins, which include
flooded regions that regulate the surplus of precipitation.
These basins become more responsive to evaporation change
where potential evaporation elasticity of streamflow increases
up to 56% at Tamshiyacu (not shown).
4.2.3. Northern region. Most GCMs simulate an annual
precipitation decrease in the north of the river mouth region
and in the northernmost part of the basin (figure 2(a)). The
tri-monthly decreases are projected with good confidence in
the northeastern region except in MAM (figures 2(b)–(e)).
Northerly streamflow decrease is going to be more severe
from west (Acanaui) to east (Caracarai and Sao Francisco).
7

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
Figure 3. Mean annual runoff coefficients at the different stations across the Amazon River basin for ORCH4 simulation (in gray),
ensemble simulation (in black, for the 2046–2065 time horizon under SRESA1B scenario) and mean annual relative difference (%) between
both (points in orange). Blue lines on the ensemble histograms indicate the standard deviation. The colors on top indicate the localization of
the station in the Amazon River basin (see table 1for the coordinates of the stations and the color code).
Runoff coefficient highly decreases at Sao Francisco and
Caracarai (10.5 and 13% decrease respectively) while low
change (less than 2%) occurs at the most westerly stations
(Acanaui and Serrinha) (figure 3). The same kind of severity
is found regarding northerly low flows. They significantly
decrease in at least six simulations out of eight at each
station (figure 4(b)). Ensemble median low flows significantly
decrease by 20% in the Japura (Acanaui station) and the
Negro (Serrinha station) rivers, and it is much higher (55%) in
the Branco River (Caracarai station) (figure 4(b)). The signal
is rather robust for Serrinha and Caracarai, as 75% of the
ensemble low-flow differences between future and present are
negative. The discharge decreases at Caracarai during the high
flow and recession periods. Overall, the seasonal variations
of the ensemble are less contrasted than in the ORCH4
simulation (figure 6(a)). No significant change in ensemble
high flow is simulated at Acanaui and Serrinha stations
(figure 4(a)). Generally, the northern sub-basins at Acanaui
and Serrinha are not responsive to future precipitation change
(figure 5) but rather to evaporation; streamflow elasticity
to potential evaporation increases by 22% in the Japura
River basin (Acanaui station) (not shown). In contrast, the
streamflow elasticity to precipitation (potential evaporation)
increases (decreases) by 12% (22%) at Caracarai, suggesting
that the Branco River basin becomes more (less) responsive to
precipitation (evaporation) change.
4.2.4. Main stem of the Amazon River. The precipitation
change is low on average over the basin, even though it
is strongly spatially contrasted. At ´
Obidos station, the last
gauged station before the mouth of the Amazon River, the
runoff coefficient slightly decreases (about 4%) (figure 3)
and no change in precipitation elasticity of streamflow
is found (figure 5). The streamflow seasonal cycle only
changes during the recession period when all the future
simulations project more pronounced low flow than ORCH4
(figure 6(b)). Ensemble simulations provide a 10% median
low-flow decrease (figure 4b) but only five future streamflow
simulations out of eight give a significant decrease. No change
in high flows is simulated at ´
Obidos station (figure 4(a)).
5. Hydrological extreme variations for the end of the
21st century and under other scenarios
The annual increase in mean ensemble temperature over
the Amazon River basin does not depend much of the
SRES scenario at the middle of the 21st century. But
differences between scenarios are pointed out at the end of
the century (+2.0, +3.0 and +3.8 ◦C for SRESB1, SRESA1B
and SRESA2 respectively). The spatial contrast between
an eastern decrease and a western increase in ensemble
precipitation occurring during the middle of the century
under SRESA1B is maintained for the end of the century.
However, rainfall increase is accentuated in the west while the
precipitation decline is confined in the north-east. In contrast,
the eastern rainfall decrease is enhanced under SRESB1 at the
end of the century. Regarding SRESA2, the eastern decrease
is much weaker than under SRESA1B at the middle of the
century and increased rainfall dominates at the end of the
century, being much stronger in the west and in the north.
Considering the A1B scenario, the mean ensemble
discharge simulations for the end of the century present a
low-flow attenuation in western rivers (Madeira, Purus and
Jurua) and on the main stem, when compared to the middle
of the century. The most striking are observed at Gaviao and
Labrea, on the Purus and Jurua Rivers, where the deficit falls
to −5%, when compared to the present, instead of −30% at
the middle of the century. In contrast, northwestern high-flow
increase (Amazonas and Negro rivers) is enhanced (+12% at
Tamshiyacu instead of +7% at the middle of the century) in
agreement with the western rainfall increase at the end of the
century.
No change in low flow under SRESB1 is found when
compared to SRESA1B results for both time horizons. Eastern
high flow decreases are slightly attenuated under SRESB1
when compared to SRESA1B at the middle and the end of the
8

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
Figure 4. Relative change (%) of the (a) last deciles (i.e. high flow)
and (b) first deciles (i.e. low flow) of streamflow between ensemble
and ORCH4, for each station, for the 2046–2065 time horizon under
SRESA1B scenario. For each year of future-time simulation,
differences were performed between the result of the future-time
simulation and the climatology of the present-time simulation. The
boxes correspond to the interquartile range (IQR, the distance
between the 25th and the 75th percentiles), the bold horizontal line
in each box is the median and the whiskers extend from the
minimum value to the maximum value unless the distance from the
minimum (maximum) value to the first (third) quartile is more than
1.5 times the IQR. Circles indicate the outliers that are 1.5∗IQR
below (above) the 25th percentile (75th percentile). The colors of
the boxes indicate the localization of the station in the Amazon
River basin (see table 1for the coordinates of the stations and the
color code). The numbers of future simulations out of 8 that give a
significant change of the median (Wilcoxon signed-rank test at the
95% level) are indicated below each box.
century. Western high-flow increase under SRESB1 is lower
than under SRESA1B at both time horizons. No change in
high flow is observed at ´
Obidos.
The comparison of the results obtained under SRESA2
with those under other scenarios is biased by the fact that only
5 climatic simulations are available instead of 8. Nonetheless,
the available data show that extreme discharge anomalies
do not differ much from SRESA1B results in the middle
of the century. By contrast, at the end of the century, and
contrarily to the results obtained under B1 and A1B scenarios,
the low flow increases consistently in a large western part
of the basin (from +10 to +30%) and consequently on
the main stem (+10% at Manacapuru, +5% at ´
Obidos).
Elsewhere, the low-flow decrease is of the same order than
for SRESA1B. Western and northern high flow increases
substantially, in agreement with rainfall variation, leading to
a 10% increase in Manacapuru and ´
Obidos. Interestingly for
impact applications, intensification in extreme values (low
and high flow) predominantly occurs in the Negro River at
Serrinha (−20% and +20% respectively).
Figure 5. Scatter plot of relative difference (%) in streamflow
elasticity to precipitation changes (between ensemble (2046–2065
time horizon under SRESA1B scenario) and ORCH4) and annual
ORCH4 runoff coefficient. The colors of the squares indicate the
localization of the station in the Amazon River basin (see table 1for
the coordinates of the stations and the color code).
In conclusion, river discharge changes and differences
between scenarios are less pronounced at the middle of the
century and more accentuated at its end, especially between
SRESA2 and the others; indeed, all year round, a large runoff
increase is observed in a large part of the western Amazon
River basin.
6. Conclusion
Future climate change should substantially modify the
Amazon River basin hydrology and impact the water
resources availability. In this study, we use the LSM
ORCHIDEE to provide discharge projections, according to
future climate forcings built from several GCM projections
under the emission scenarios B1, A1B and A2. Various
hydrological responses are found in the different sub-basins of
the Amazon under SRESA1B scenario. Some of them are not
significant since climate projections from the different GCMs
diverge, as already assessed in previous studies (Bates et al
2008, Kay et al 2009, Bl¨
oschl and Montanari 2010, Nobrega
et al 2011, Paiva et al 2011a), but in some sub-basins and for
specific periods of the year, the projections are more reliable.
In particular, low flows are projected to decrease severely in
most stations, especially in the southern Madeira and Xingu
Rivers and in the northern Branco River, and to a lesser extent
in the northern Negro and Japura Rivers. By the middle of
the 21st century, the decrease is projected to reach −50%
(−55%) in the Xingu (Branco) Rivers. A low-flow decrease is
projected in ´
Obidos, on the main stem of the Amazon River,
which has already been experiencing an increasing number of
droughts over the last twenty years. In contrast, the western
and upper part of the Amazon are projected to undergo
an annual precipitation increase so that high-flow discharge
is expected to increase by 7% in the middle of the 21st
century and even more by the end of the century. Therefore,
9

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
Figure 6. Mean seasonal streamflow (m3s−1) simulated with eight simulations of future (gray lines) and ensemble (bold black line), for the
2046–2065 time horizon under SRESA1B scenario, at (a) station Caracarai (CARA) and (b) station ´
Obidos (OBI). The envelope (in gray)
defines for each month the minimal and maximal values between the 8 simulations of future. ORCH4 (in red) is the present-time simulation
for the present-time period 1980–2000.
more flooding events are expected in this region which is
already affected by increasing extreme discharge. In the main
stem of the Amazon River, however, there is no consistent
signal of more flooding, neither of varying mean discharge,
although previous studies projected a positive change (Nohara
et al 2006) or a negative change (Milly et al 2005) of the
Amazon runoff. By computing elasticity coefficients, we also
show that the southern basins with low runoff coefficient are
generally more responsive to precipitation change than the
western basins with a high runoff coefficient. This result is in
agreement with the findings by Chiew (2006) and Paiva et al
(2011a).
Some uncertainties relative to the hydrological parame-
terizations of the LSM can be addressed. The present-time
ET underestimation (Guimberteau et al 2012) and the
infiltration parametrization are sources of uncertainties in
future land water budget. Moreover, the routing module
of ORCHIDEE does not represent the river dams and the
backwater effect likely to affect the extreme stream flows in
some tributaries of the Amazon (Meade et al 1991, Tomasella
et al 2011). An advanced large-scale river routine module
which represents backwater effect would represent more
realistically the simulated water level in the rivers (Paiva
et al 2011b, Yamazaki et al 2011,2012). The modeling of
vegetation growth with its interactive environment such as
an interannual LAI variation and ET response to elevated
CO2, would lead to better realism using the STOMATE
(Saclay-Toulouse-Orsay Model for the Analysis of Terrestrial
Ecosystems, Viovy 1996) and LPJ (Lund-Postdam-Jena, Sitch
et al 2003) modules. Other errors can result from the
downscaling method. First, we assume that climate change
varies only over large areas (i.e. as large as a GCM cell
area) what leads to underestimate landscape heterogeneity
effect due to the coarse resolution of the GCM. Thus, further
simulations with better resolution than one degree would test
if there is a dependence of simulated Amazonian hydrological
processes on the spatial resolution of the forcing, as suggested
by Verant et al (2004). Secondly, precipitation interannual
variations in response to climate change could not be taken
into account due to the simple downscaling method adopted
in this study. Some bias-correction methods which conserve
the changes of mean and standard deviation of the model
simulation data (Watanabe et al 2012) could be adopted to
preserve interannual variations. Uncertainties remain also in
the emission scenario or the magnitude of rise in mean global
temperature and the choice of the GCM. Indeed, a comparison
between more AR4 GCMs and a new generation of GCMs
(AR5) corroborates high consensus on drying during the dry
season but highlights a lower confidence in wettening over
the western part of the basin (Joetzjer et al 2013). Finally,
considering land surface changes and deforestation in our
LSM would probably affect the simulated mean and extreme
flows over the Amazon River basin, since several studies
reveal that land-use change accounts for at least 50% of the
reconstructed global runoff trend over the last century (Piao
et al 2007, Sterling et al 2012). The important deforestation
in the Amazon River basin may in turn strongly alter the
climate and eventually amplify the climate change (Malhi
et al 2008). This topic, as well as improvement in the model
parametrization, will be addressed in future work.
Acknowledgments
This work was financially supported by the GIS (Groupement
d’Int´
erˆ
et Scientifique) REGYNA (REGioNAlisation des
pr´
ecipitations et impacts hYdrologiques et agronomiques) and
the EU-FP7 AMAZALERT (Raising the alert about critical
feedbacks between climate and long-term land-use change
in the Amazon) projects. Simulations with ORCHIDEE
were performed using computational facilities of the Institut
du D´
eveloppement et des Ressources en Informatique
Scientifique (IDRIS, CNRS, France).
10

Environ. Res. Lett. 8(2013) 014035 M Guimberteau et al
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