Artigo Completo - Clique Aqui! - Universidade Federal de Viçosa

Transcription

Projections of climate change effects on
discharge and inundation in the Amazon
basin
Mino Viana Sorribas, Rodrigo
C. D. Paiva, John M. Melack, Juan
Martin Bravo, Charles Jones, Leila
Carvalho, Edward Beighley, et al.
Climatic Change
An Interdisciplinary, International
Journal Devoted to the Description,
Causes and Implications of Climatic
Change
ISSN 0165-0009
Climatic Change
DOI 10.1007/s10584-016-1640-2
1 23
Your article is protected by copyright and all
rights are held exclusively by Springer Science
+Business Media Dordrecht. This e-offprint
is for personal use only and shall not be selfarchived in electronic repositories. If you wish
to self-archive your article, please use the
accepted manuscript version for posting on
your own website. You may further deposit
the accepted manuscript version in any
repository, provided it is only made publicly
available 12 months after official publication
or later and provided acknowledgement is
given to the original source of publication
and a link is inserted to the published article
on Springer's website. The link must be
accompanied by the following text: "The final
publication is available at link.springer.com”.
1 23
Author's personal copy
Climatic Change
DOI 10.1007/s10584-016-1640-2
Projections of climate change effects on discharge
and inundation in the Amazon basin
Mino Viana Sorribas 1 & Rodrigo C. D. Paiva 1 &
John M. Melack 2 & Juan Martin Bravo 1 &
Charles Jones 3 & Leila Carvalho 3 & Edward Beighley 4 &
Bruce Forsberg 5 & Marcos Heil Costa 6
Received: 21 August 2015 / Accepted: 21 February 2016
# Springer Science+Business Media Dordrecht 2016
Abstract Climate change and its effects on the hydrologic regime of the Amazon basin can
impact biogeochemical processes, transportation, flood vulnerability, fisheries and hydropower
generation. We examined projections of climate change on discharge and inundation extent in
the Amazon basin using the regional hydrological model MGB-IPH with 1-dimensional river
hydraulic and water storage simulation in floodplains. Future projections (2070–2099) were
obtained from five GCMs from IPCC’s Fifth Assessment Report CMIP5. Climate projections
have uncertainty and results from different climate models did not agree in total Amazon
flooded area or discharge anomalies along the main stem river. Overall, model runs agree
better with wetter (drier) conditions over western (eastern) Amazon. Results indicate that
increased mean and maximum river discharge for large rivers draining the Andes in the
northwest contributes to increased mean and maximum discharge and inundation extent over
Peruvian floodplains and Solimões River (annual mean-max: +9 % - +18.3 %) in western
Amazonia. Decreased river discharges (mostly dry season) are projected for eastern basins, and
Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1640-2)
contains supplementary material, which is available to authorized users.
* Mino Viana Sorribas
mino_vs@hotmail.com
1
IPH/UFRGS - Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto
Alegre, Brazil
2
Bren School of Environmental Science and Management, University of California, Santa Barbara,
Santa Barbara, CA, USA
3
Geography Department, University of California, Santa Barbara, Santa Barbara, CA, USA
4
Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
5
Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
6
Universidade Federal de Viçosa, Viçosa, Brazil
Climatic Change
decreased inundation extent at low water (annual min) in the central (−15.9 %) and lower
Amazon (−4.4 %).
1 Introduction
The rivers and floodplains of the Amazon basin route large amounts of water, influence carbon
and nutrient biogeochemistry, emit carbon dioxide and methane to the atmosphere, and support
highly diverse ecosystems and productive fisheries. Rivers serve as major transportation
corridors. As most settlements lie along the rivers and floodplains, local people utilize these
environments for their subsistence. Energy demands rely on hydropower reservoirs, existing
and planned in the Amazon (EPE 2012). Climate warming and climate variability are
influencing the Amazon basin (Davidson et al. 2012). Large seasonal and inter-annual
variations in depth and extent of inundation are characteristic and, as water levels vary, the
proportion of aquatic habitats changes considerably. Results of simulations of flood height and
extent in the 20th century (Coe et al. 2002, 2007; Costa et al. 2009; Foley et al. 2002;
Decharme et al. 2008; Guimberteau et al. 2012, 2013; Rudorff et al. 2014; Yamazaki et al.
2011, 2012; Paiva et al. 2013) illustrate the potential scale of response of the system to climate
variability.
Exceptional hydrological events in the last decades, such as the floods in 2009, 2012 and
2014 and the droughts in 2005 and 2010 (Marengo and Espinoza 2015) impacted the region,
alerting scientists, governments and general public to climate variability impacts. Espinoza et
al. (2009a) found contrasting behavior in long-term trends in discharge based on historical data
across different regions of the basin showing a decrease(increase) in minimum (maximum)
annual discharge in the southern(northwestern) region More recently, Gloor et al. (2013)
demonstrated an increasing trend of precipitation and maximum annual discharge of the
Amazon River coincident with an upward trend in tropical Atlantic sea surface temperatures
for the 1990s. Characteristics of the complex dynamics of the climate system in the Amazon
basin are summarized in Nobre et al. (2009); Betts et al. (2009); Costa et al. (2009) and
Marengo et al. (2009, 2012).
Limnological and ecological conditions in floodplain lakes and wetlands are intimately
associated with flooding dynamics (Junk et al. 1989; Junk 1997; Melack et al. 2009). Amazon
rivers and flooded areas outgas large amounts of CO2 and methane that are significant in the
regional carbon cycle (Richey et al. 2002; Melack et al. 2004; Moreira-Turcq et al. 2004; Abril
et al. 2014; Melack 2015). Also, inundation dynamics influence vegetation structure (FerreiraFerreira et al. 2014; Junk et al. 2011), sediment transport (Bourgoin et al. 2007; Dunne et al.
1998), fish distributions and fisheries yield (Junk et al. 2007; Lobón-Cervia et al. 2015). Future
changes in climate and hydrology are likely to alter floodplain inundation and related
ecological conditions in associated ecosystems (Melack and Coe 2013).
Future climate projections have uncertainties related to greenhouse gases emission scenarios and performance of General Circulation Models (GCMs) from Coupled Model
Intercomparison Project 3 (CMIP3) though a consensus suggests decreased annual precipitation in the eastern Amazon and increased precipitation in the western Amazon (Meehl et al.
2007; Alves and Marengo 2010). Past studies projected both positive (Nohara et al. 2006) or
negative anomalies (Milly et al. 2005) for the mean discharge of the basin. Guimberteau et al.
(2013) found a future decrease in low flow across the basin, especially in the southern Madeira
and Xingu rivers and northern Branco River. Melack and Coe (2013) evaluated inundation
Climatic Change
under altered climate and land uses for the Amazon using a basin-wide hydrological model
forced with observed climate data (1950–2000) and reported10% and 25 % decreases in
rainfall resulted in reductions in inundation similar to reductions in rainfall: -5 % to −20 % and
−12 % to −30 %, respectively. This result corroborates sensitivity analyses by Paiva et al.
(2013) which showed similar anomalies in total inundation, but also amplified anomalies in
discharge, in response to changes in precipitation. Others have considered actual or potential
climate change or land use impacts on hydrological conditions in the Amazon basin through
sensitivity analysis, land surface models and Special Report Emission Scenarios (SRES) from
past IPCC reports (Coe et al. 2009; Casimiro et al. 2011; Langerwisch et al. 2013; Lejeune et
al. 2015). Present studies do not assess surface hydrology impacts in the Amazon of climate
change projected from the new generation of General Circulation Modelsin IPCC’s Fifth
Assessment Report (AR5) CMIP5, which improved simulations of main precipitation features
of the rainy season and the South American Monsoon System (Alves and Marengo 2010;
Chou et al. 2012; Solman et al. 2013; Jones and Carvalho 2013; Joetzjer et al. 2013; Boisier et
al. 2015). There is now a better agreement among models regarding precipitation changes
projected for most of the South America. CMIP3 and CMIP5 models agree better in projections of drier conditions in eastern Amazonia during the dry season and wetter conditions in
western Amazonia (Malhi et al. 2008; Cook et al. 2012). Experiments with an ensemble of 25
models from the CMIP5 projected changes in annual precipitation ranging from −11 % to 1 %
and temperature ranging from 3.7 °C to 5.7 °C (25th to 75th percentiles) for the Amazon at
2100 RCP 8.5 (Christensen et al. 2013). Finally, the use of regional hydrological models with
better physical representation of hydrological processes in rivers and floodplains (Paiva et al.
2011, 2013; Yamazaki et al. 2012) are likely to improve realism of the predictions of land
surface hydrology as river discharge and inundation dynamics.
Two main scientific questions are investigated here: What are the potential impacts of
climate change on land surface hydrology of the Amazon basin? How do projections of
climate change impact discharge and inundation in different regions and seasons? This paper
presents analyses of potential climatic impacts on Amazon hydrology including discharge and
flood inundation dynamics based on new forcing data from IPCC AR5 CMIP5 and detailed
regional scale hydrologic modeling using the MGB-IPH model (Paiva et al. 2013).
2 Methods
2.1 Study area
The Amazon basin (Fig. 1) drains about 6 million km2 and discharges ~15 % of global
freshwater arriving to the oceans. It is formed by the Andes, the Guyanese and Brazilian
shields, and the Amazon plain. Extensive wetlands (~17 % of lowland Amazon, altitude
<500 m) are seasonally inundated, with total flooded extent ranging from 300 to 600 ×103 km2
(Melack and Hess 2010; Hess et al. 2015). The lowland Amazon has complex river hydraulics,
and low river slopes cause backwater effects (Meade 1991; Paiva et al. 2013). The Amazon
has high rainfall (average, 2200 mm/yr) and large spatial variability with especially high
rainfall (>3000 mm/yr) in the northeast, southeast, near the Amazon delta and in portions of
the Andes (Espinoza et al. 2009b; Espinoza et al. 2015). Rainfall decreases to the southeast and
at higher elevation in the Andes. Contrasting rainfall regimes are found in the northern and
southern parts of the basin, with the rainy season in June to August (December to February)
Climatic Change
Fig. 1 Amazon River basin: terrain relief (gray), stations in main rivers (black dots), wetland mask (Melack and
Hess 2010) and wetland regions
and in the north (south) with more (less) defined wet and dry seasons occurring in the southern
and eastern (northern and western) parts of the basin (Espinoza et al. 2009b).
2.2 General circulation models
CMIP5 GCMs (Taylor et al. 2012; Christensen et al. 2013) were used to provide future
projections of climate. This study focuses on changes in surface energy and water balances
by contrasting ‘historical’ (1970–1999) and one ‘high-emission’ scenario (2070–2099). In the
historical, 20th century scenario, climate models were forced by observed atmospheric
composition changes which include both anthropogenic and natural sources as well as timeevolving land cover. Simulations of climate projection are forced with specified concentrations
referred to as representative concentration pathways (RCPs) and provide an estimate of the
evolution of the radiative forcing until 2100, relative to preindustrial conditions (Moss et al.
2010; Taylor et al. 2012). To investigate future changes in inundation dynamics over the
Amazon basin, simulations from the ‘high-emission’ scenario labeled as RCP8.5 (i.e., radiative
forcing increases throughout the 21st century before reaching a level of about 8.5Wm−2 at
2100) was used. This scenario was used to estimate potentially large changes in the precipitation variability in the South American Monsoon and its consequences to hydrological
characteristic of the Amazon basin. Monthly averages of the following surface variables were
analyzed and used as input for the hydrologic model: precipitation, air temperature and relative
humidity at 2 m height, surface winds at 10 m height, surface pressure and incoming
shortwave solar radiation. Although additional models are available in CMIP5, the five models
Climatic Change
used here, CNRM-CM5, GFDL-ESM2M, HADGEM2-CC, MRI-CGCM3 and MIROC5,
were deemed to provide realistic simulations of the main climatological features of the
South American Monsoon (as explained further in S1 in Online Resource).
2.3 Hydrologic-hydrodynamic model
Several hydrologic models have been developed for the Amazon basin (Coe et al. 2007; Paiva
et al. 2013; Beighley et al. 2009; Yamazaki et al. 2012). The MGB-IPH model in its
implementation for the Amazon basin (Paiva et al. 2013), was selected due (i) its capability
to represent physical processes, such as water balance components, river hydrodynamics and
large-scale inundation in the Amazon, and (ii) its performance demonstrated by previous
validation against observations (S2 in Online Resources). This model used as the reference
run for the climate change projections presented in this paper. Inundation results, including for
the was reference run and climate change projections, were post-processed to improve the
representation when compared to estimates from synthetic aperture radar (SAR) images from
the JERS-1 satellite (S4 in Online Resources).
2.4 Bias removal
Climate models often have biases in precipitation and temperature such as under or overestimation and incorrect seasonal variations due conceptual errors, discretization and spatial
averaging within grid cells (Christensen et al. 2008; Teutschbein and Seibert 2012). Bias
correction methods can be applied to reduce errors from biased GCM outputs and several
methods exist as described by Teutschbein and Seibert (2012). We used both distribution
mapping (‘quantile-quantile’) and the delta-change methods to derive future scenarios (S3
Online Resource).
2.5 Assessment of climate change effects on discharge and inundation extent
The models runs using different projections had considerable uncertainty (see results, Fig. 3),
thus changes in discharge and inundation extent should be compared to the GCM model
uncertainties. Also, it is reasonable to compare the magnitude of climate change induced
differences to the interannual variability of discharge. To consider statistical significance, a ttest (Wilks 2006) was used to assess if changes in projected discharges from the five GCM
climate forcing simulations are different from zero (n = 5). In a second step, a two sample t-test
was used to assess the difference between projected future discharges from the 5 GCMs
(n1 = 5) and annual values from the reference period (n2 = 12). All tests used a 5 %
significance level and were applied to Qmean, Qmin and Qmax. The first test assesses changes
in Q considering only GCM uncertainty and the second test accounts for the natural interannual variability. Changes in Q were considered significant only when not rejected by both tests.
The same approach was used to evaluate the potential changes in inundation extent.
3 Results
Climate change effects on the Amazon basin hydrology are presented using projected changes
in water balance, discharges and inundation. We consider terms ‘change’ and ‘anomalies’
Climatic Change
interchangeable as a measure of relative change (%) in variables simulated for the future
projection model runs when compared to the reference scenario (1998–2009). The water
balance section describes local fluxes in precipitation, evapotranspiration and local runoff
and we describe discharge separately, as it integrate all upstream hydrological processes.
3.1 Water balance
Assessment of climate change in river basins hydrology can be evaluated by comparing
anomalies in water balance components. Average anomalies in annual precipitation, evapotranspiration and local runoff have different patterns for the western and eastern Amazon (Fig. 2): (i)
mean change of water balance components among the five model runs (Fig. 2, top), and (ii)
variability between models runs, shown for runoff as it integrate changes in surface water
availability (Fig. 2, bottom). Simulation results indicate increased (decreased) precipitation
towards northwestern and western (northeastern and eastern) parts of the basin. Positive changes
in evapotranspiration are observed mostly over the southeastern and central Amazon and are
driven by positive changes in potential evapotranspiration and/or precipitation (fig. S2 and S3 in
Online Resource). Projections indicate increased local runoff in the western Amazon related to
increased precipitation, while increased ET and reduced P leaded to drier conditions in the
eastern portion of the basin.
Figure 2 also illustrates the uncertainty in runoff anomalies computed from different GCM
climate projections. For instance, the model run forced with CNRM-CM5 indicates increased
(decreased) runoff in west and south (central, east and north) and results using the GFDLESM2M have increased (decreased) runoff in the north and west (central, east and southeast).
While runs with HADGEM2-CC projections result in decreased water availability in most of
the basin, models agree with drier conditions only in the eastern Amazon. Simulations with
MIROC-5 and MRI-CGCM3 projections produce less uniform patterns than others, but agree
Fig. 2 Mean projected anomalies among GCM model runs in annual precipitation, evapotranspiration and local
runoff (top) and anomalies in local runoff for each GCM model run (bottom) Local runoff integrates vertical
water balance, thus summarizes changes in surface water availability. Blank values represent small changes (i.e.
interval from −5 % to 5 %)
Climatic Change
with wetter conditions in the western Amazon. Furthermore, projected effects on water
availability in the central and southern Amazon are not clearly defined.
3.2 Discharge
Projected changes in discharge integrate upstream hydrological processes by routing local
runoff in the river network. Mean daily discharges over the annual cycle for the reference
conditions and future predictions based on five model simulations for the main Amazonian
rivers are shown in Fig. 3. Average anomalies in annual mean (Qmean), minimum (Qmin) and
maximum (Qmax) discharge, based on the five models runs, have noteable spatial variability
throughout the Amazon (Fig. 3).
Fig. 3 Seasonal variation of discharges for the reference (black line) and future scenarios from CNRM-CM5
(orange), GFDL-ESM2M (pink), HadGEM2-CC(ciano), MIROC-5(red) and MRI-CGCM3 (green) GCM
models
Climatic Change
Models runs generally indicate wetter (drier) conditions in western (eastern) Amazon, and
climate change effects on high or low waters are different throughout the basin. In the upper
Solimões River discharge increases are mostly expected during high water. Annual mean and
minimum discharges are expected to decrease in most of the basin. In the central and lower
Amazon discharge predictions from different models are over and below the reference
simulation, and in the Negro River the dispersion of the results is large. In general, models
have better agreement for negative anomalies in discharge during low water in lower Amazon,
Tapajos, Xingu and Negro rivers.
Although projected changes in annual mean discharge of larger rivers are expected throughout the whole basin, they are not significant everywhere (Fig. 4a). There is consistency among
models with local water balance and expected significant changes in Qmean, as highlighted for
western and eastern Amazon contrasting patterns. The numerical simulations indicate a decrease in annual minimum discharge in most of the basin (Fig. 4b). In Peru, results for minimum
discharge changes were positive (negative) in main stem of Marañon (Upper Ucayali) catchments, although not significant. Low-flow discharge changes were mostly significant in the
Xingu River basin and lower Amazon, including its tributaries (ΔQmin < −20 %). Maximum
annual discharges and Qmean have significant increase for the upper reaches of Solimões and
decrease in the Xingu. Increased runoff and floods from the left bank tributaries contribute to
higher flows in the main stem, further increasing maximum flow downstream (Fig. 4c).
3.3 Inundation extent
Average anomalies among 5 GCMs in annual mean (IEmean), minimum (IEmin) and maximum
(IEmax) inundation extent are spatially heterogeneous in Amazon (Fig. 4). As the inundation
dynamics are closely related to the flood-pulse in most of the basin, projected changes of
annual inundation extent followed runoff and discharge patterns. Figure 5 illustrates changes in
seasonal inundation as monthly inundation extent (unbiased, see S3 in Online Resources) for
the reference and five future scenarios for wetland regions (see Fig. 1). Assessment of the
significance of projected changes was performed using the same methods as for discharge
(GCM uncertainty x interannual variability, see Section 3.2).
Table 1 summarizes average projected anomalies, standard deviation and interannual
coefficient of variation (CV) for IEmean, IEmax, IEmin for five regions: Amazon basin, central
Amazon, Peruvian Amazon, Bolivian Amazon and lower Amazon. Anomalies were found
significant and positive for annual mean (+9 %) and maximum (+18 %) inundation extent in
Peruvian Amazon wetlands. Anomalies for average mean inundation extent are negative in the
central Amazon wetlands, but not significant (Fig. 4d). While positive anomalies occur for the
Solimões River (in Brazil), negative anomalies occur for the Negro, Juruá, Purus and lower
Madeira rivers. Likewise, maximum inundation extent in the central Amazon (Fig. 4f)
haspositive anomalies for the main stem Amazon and Negro rivers, but negative for right
bank tributaries. The projected anomalies in IEmin (Fig. 4e) are negative and significant for
wetlands in the central (−16 %) and lower Amazon (−4.4 %).
4 Discussion
Climate projections for the Amazon basin resulted in spatially heterogeneous effects on the
basin’s hydrological regime. The surface hydrology response to climate forcing modeled by
Climatic Change
Fig. 4 Average projected anomalies among five models runs in for mean (a), minimum (b) and maximum (c)
annual discharge and mean (d) minimum (e) and maximum (f) annual inundation extent. Significant anomalies
are presented as pink in bottom right figures
MGB-IPH resulted in wetter (drier) conditions over northwestern (southeastern and eastern)
regions in the Amazon basin, based on multi-model ensemble anomalies against interannual
Climatic Change
Fig. 5 Seasonal variability of inundation extent (unbiased) for the reference (black line) and future scenarios
simulated with CNRM-CM5 (orange), GFDL-ESM2M (pink), HADGEM2-CC(ciano), MIROC-5(red) and
MRI-CGCM3 (green) GCM models. Wetland regions are defined in Fig. 1
variability. These results were derived from numerical experiments, thus interpretation must
consider that GCM forcings are not always consistent with the present climate (see Fig. S1 of
Online Resources). Hence, we used the delta change method to prevent model errors
representing current variables to be propagated into the analysis of projected variables.
Modeled future scenarios indicate an increase in mean and maximum discharge in large
rivers draining the eastern Andes in northwestern Amazon, which also contributes to increase
in maximum inundation extent over Peruvian wetlands and along the Solimões River.
Projected changes in maximum inundation extend further downstream due to increased
precipitation and discharges from northwestern tributaries. Langerwisch et al. (2013) reported
an increase in inundation in the western Amazon using 24 IPCC GCM forcing in the Dynamic
Global Vegetation and Hydrology Model LPJml. Similarly, Guimberteau et al. (2013)
simulations with the land surface model ORCHIDEE forced with 8 AR4 GCMs showed an
increase of 12 % in high flows in northwestern Amazonia (i.e., Tamshiyacu station) for the
end-of-century SRESA1B scenario. Also, Zulkafli et al. (2016) simulations using the
JULES land surface hydrological modeling forced with 18 models from the RCP 4.5
and 8.5 scenarios showed an increase in severity of the wet season flood pulses in Western
Amazonia. In this region, our study indicates that significant anomalies in mean and
maximum discharges (Fig. 4a, c) are mostly between +5 to +20 %, while changes in mean
(maximum) inundation extent for Peruvian Amazon was found +9 % (+18 %) (Table 1).
Trend analyses for annual discharges (1974–2004) from Andean draining basins reported
by Espinoza et al. (2009a) indicated (i) a decreasing trend in Tapajós River, upstream
Madeira River, Peruvian Amazonas rivers, and Amazonas River, and (ii) increasing runoff
in northwestern Putumayo and Napo rivers.
13.7
8.6
10.1
−6.5
−6.6
−2.9
Central
Lower
Amazon
Significant values are marked as bold
5.2
20.6
+9.0
−6.1
Peru
Bolivia
8.9
7.5
7.8
5.2
17.6
−0.7
−9.9
−15.9
−4.4
−12.8
8.6
11.2
2.1
10.3
6.3
Standard Dev.
(%)
Relative Change
(%)
Interannual CV
(%)
Relative change
(%)
Standard Dev.
(%)
Annual Min (IEmin)
Annual Mean (IEmean)
8.3
11.1
1.9
9.7
9.1
Interannual CV
(%)
+3.5
+3.6
−5.0
−0.2
+18.3
Relative Change
(%)
13.7
16.7
14.4
23.5
7.6
Standard Dev.
(%)
Annual Max (IEmax)
9.8
8.4
7.6
20.3
11.8
Interannual CV
(%)
Table 1 Average projected anomalies and standard deviation (from five GCMs climate forcing model runs) and interannual coefficient of variation in annual unbiased inundation
extent (mean, maximum, minimum) for regions defined in Fig. 1
Climatic Change
Climatic Change
Projections of precipitation increase over the western Amazon and Andean region are
currently under debate. This region is the least affected by historical climate variability, land
use changes and has high biodiversity (Malhi et al. 2008). Although the precipitation patterns
for the GCM historical period in this region are not in agreement with TRMM estimations (see
Fig. S1 Online Resources), the accuracy of future projections were improved using bias
removal methods. Neukom et al. (2015) reports that in mountain areas, climate models have
limited capabilities simulate precipitation variability. Thus, while increasing rainfall for the
Andean region is projected in our work and others (Seth et al. (2010); Thibeault et al. 2010),
other recent papers suggest rainfall diminution by the end of the 21st century (Minvielle and
Garreaud 2011; Urrutia and Vuille 2009; Neukom et al. 2015). These contrasting projections
for rainfall results from the use of different methods. While our approach considers changes in
rainfall directly from GCMs, the latter obtained projections for rainfall based on a proxy with
projected winds at 200 hPa, based on significant correlations between these variables over the
Andean region (Garreaud et al. 2003). This indicates that investigation of atmospheric
transport mechanisms and climate proxies are needed to improve consistency of hydrological
projections and evaluation of future changes in surface waters.
Recent studies which couple climate and ecology suggest that south-southeastern Amazon
is particularly vulnerable. Main impacts are related to climate feedbacks due deforestation,
cropland and pasture expansion with reduction of flood-pulse magnitude and enhanced dry
season (Costa et al. 2003, 2009; Sampaio et al. 2007; Coe et al. 2013). Recent simulations
indicate a decrease in low-flows in the northern Branco river, southern Madeira and Xingu
rivers (−50 %), and in lower Amazon at Óbidos (−4 %) (Guimberteau et al. 2013). Our
simulations also indicated decreased river discharge, mainly during the dry season for south
(not significant) and eastern basins, as well as decreased inundation extent during low water in
the central and lower Amazon.
We do not consider explicit changes in vegetation or land cover and their climate
feedbacks. Some Amazonia regions have suffered large changes in land cover throughout
the last decades. More than 60 % of the land in the Tocantins basin is under agricultural
use today. Costa and Foley (1997) used the Land Surface Scheme coupled to a large-scale
hydrological model and estimated that changes in land cover (natural vegetation to
pasture) could decrease annual mean ET (3.4 mm/day to 2.7 mm/day). This reduction
contributed to increased runoff routing from 0 (where natural vegetation is considered to
be grasslands) to a maximum of 47 % (in regions with forest vegetation cover). A
comparison of two periods where precipitation over the basin is not statistically different,
one with relatively small changes in land cover (1949–1968) and the second with larger
changes in land cover (1979–1998) indicated that annual mean discharge was 24 % greater
in the latter case, with high-flow season discharge greater by 28 % (Costa et al. 2003). Dias
et al. (2015) assessed the influence of land cover changes in small catchments in the upper
Xingu River (southeastern Amazon) and reported that observed and simulated mean
annual streamflows in agricultural ecosystems (pasture and soybean croplands) were more
than 100 % higher than in natural ecosystems (tropical rainforest and cerrado). In a recent
comparative analysis, Lejeune et al. (2015) emphasized (i) the need for model improvements with large-scale feedbacks induced by land-use change on the climate system and
that (ii) historical development of climate models led to a reduction of uncertainty, but did
not modify median estimate of Amazonian climate sensitivity to deforestation.
Other modeling studies in the Amazon basin have investigated discharge and inundation
sensitivity to changes in precipitation, and basin responses to future climate and deforestation
Climatic Change
scenarios (e.g., Costa and Foley 1997; Paiva et al. 2013; Melack and Coe 2013; Coe et al.
2009; Guimberteau et al. 2013). It is important to understand that modeling studies are in
some degree dependent on both experiment design and model structure (Coe et al. 2009).
When compared to other large scale climate-hydrology simulations for Amazonia the
MGB-IPH provides improved in-stream and floodplain inundation and hydraulic response
to changes in hydrological forcing. as it uses a full 1D hydraulic model with floodplain
storage solution. Furthermore, as the reference scenario was subject to extensive validation
using in situ and remote sensing data, it represents a reliable baseline for comparison with
future climate projections. Another important aspect was the use of robust bias removal
techniques on GCM climate historical and future data to build the future scenario model
runs.
While discharges integrate the whole upstream hydrological changes on water availability,
the inundation extent is particularly important for floodplain ecosystems (Junk 1997; Junk et
al. 2011; Melack et al. 2009). Hence, our findings provide useful information for aquatic
ecosystem management strategies, other human related issues (i.e. flood vulnerability, transportation, fisheries and planned hydropower generation) and for investigation of the future of
inland water carbon and biogeochemical cycles.
5 Conclusion
This study focused on projections of potential climate change impacts on the hydrology of the
Amazon basin for the end of the 21st century, using the regional model MGB-IPH forced by 5
AR5 GCMs from RCP8.5 scenario.
Analyses indicated contrasting patterns for future hydrological conditions for the western
and eastern Amazon. Results based on different climate forcings demonstrate a large variability among models, such that projections of hydrological change are highly dependent on the
GCMs. Predictions did not agree on changes for total Amazon inundation extent or average
discharge along the main stem of the Amazon River. Surface water projections agreed better
with wetter (drier) conditions over western (eastern) regions of the basin. Results indicate
increased mean and maximum river discharge for large rivers draining the eastern Andes in
northwestern Amazon. Projections of increased in precipitation resulted in increased mean and
maximum discharge and inundation extent over Peruvian floodplains and Solimões River in
western and central Amazonia. Decreased river discharges (mainly in the dry season) are
projected for eastern basins, and decreased inundation extent at low water period in the central
and lower Amazon.
Despite of the limitations regarding the adopted approach, our findings provide a reasonable overview of potential effects on Amazon basin discharge and inundation due climate
change prescribed in RCP8.5 scenarios. While future climate and hydrological conditions are
not certain, they are relevant to water and environmental conservation and management
strategies, since hydrological changes have important implications for ecological and biogeochemical dynamics.
Acknowledgments The synthetic work for this paper was supported by the Science for Nature and People
(SNAP) sponsored by the National Center for Ecological Analysis and Synthesis (NCEAS), Wildlife Conservation Society (WCS) and the Nature Conservancy (TNC). SNAP funding was provided by the David and Lucile
Packard Foundation (Grant # 2013-38757 & #2014-39828), Ward Woods (Grant # 309519), WCS and TNC.
Also we thank the editor and reviewers for comments that improved this paper andWalter Collischonn for advice.
Climatic Change
References
Abril G, Martinez J-M, Artigas F, et al. (2014) Amazon River carbon dioxide outgassing fuelled by wetlands.
Nature. doi:10.1038/nature12797
Alves LM, Marengo J (2010) Assessment of regional seasonal predictability using the PRECIS regional climate
modeling system over South America. Theor Appl Climatol. doi:10.1007/s00704-009-0165-2
Beighley RE, Eggert K, Dunne T, et al. (2009) Simulating hydrologic and hydraulic processes throughout the
Amazon River basin. Hydrol Process. doi:10.1002/hyp.7252
Betts AK, Fisch G, Von Randow C, et al. (2009) The Amazonian boundary layer and mesoscale circulations. In:
Keller M et al. (eds) Amazonia and global change. Geophysical monograph, vol 186. AGU:Washington, D.
C.. doi:10.1029/2008GM000720
Boisier JP, Ciais P, Ducharne A, Guimberteau M (2015) Projected strengthening of Amazonian dry season by
constrained climate model simulations. Nat Clim Chang. doi:10.1038/NCLIMATE2658
Bourgoin LM, Bonnet M-P, Martinez J-M, et al. (2007) Temporal dynamics of water and sediment exchanges
between the Curuaí floodplain and the Amazon River, Brazil. J Hydrol. doi:10.1016/j.jhydrol.2006.11.023
Casimiro WSL, Labat D, Guyot JL, Ardoin-Bardin S (2011) Assessment of climate change impacts on the
hydrology of the Peruvian Amazon–Andes basin. Hydrol Process. doi:10.1002/hyp.8097
Chou SC, Marengo JA, Lyra AA, et al. (2012) Downscaling of South America present climate driven by 4member HadCM3 runs. Clim Dyn. doi:10.1007/s00382-011-1002-8
Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional
climate change projections of temperature and precipitation. Geophys Res Lett. doi:10.1029/2008GL035694
Christensen JH, Krishna Kumar K, Aldrian E, et al. (2013) Climate phenomena and their relevance for future
regional climate change. In: Stocker TF et al. (eds) Climate change 2013: the physical science basis.
Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate
change. Cambridge University Press, Cambridge
Coe M, Costa M, Botta A, Birkett C (2002) Long-term simulations of discharge and floods in the Amazon Basin.
J Geophys Res-Atmos. doi:10.1029/2001JD000740
Coe MT, Costa MH, Howard EA (2007) Simulating the surface waters of the Amazon River basin: impacts of
new river geomorphic and flow parameterizations. Hydrol Process. doi:10.1002/hyp.6850
Coe MT, Costa MH, Soares-Filho BS (2009) The influence of historical and potential future deforestation on the
stream flow of the Amazon River – Land surface processes and atmospheric feedbacks. J Hydrol. doi:10.
1016/j.jhydrol.2009.02.043
Coe MT, Marthews TR, Costa MH, et al. (2013) Deforestation and climate feedbacks threaten the ecological
integrity of south-southeastern Amazonia. Philos Trans R Soc Lond Ser B Biol Sci. doi:10.1098/rstb.2012.
0155
Cook B, Zeng N, Yoon J-H (2012) Will Amazonia dry out? Magnitude and causes of change from IPCC climate
model projections. Earth Interact. doi:10.1175/2011EI398.1
Costa MH, Foley JA (1997) Water balance of the Amazon Basin: dependence on vegetation cover and canopy
conductance. J Geophys Res 102(D20):23973–23989
Costa MH, Botta A, Cardille JA (2003) Effects of large-scale changes in landcover on the discharge of the
Tocantins River, Southeastern Amazonia. J Hydrol. doi:10.1016/S0022-1694(03)00
Costa MH, Coe MT, Guyot J-L (2009) Effects of climatic variability and deforestation on surface water regimes.
In: Keller M et al. (eds) Amazonia and global change, Geophysical Monograph, vol 186. AGU, Washington,
D. C., pp. 543–553. doi:10.1029/2008GM000721
Davidson E, de Araújo AC, Artaxo P, et al. (2012) The Amazon basin in transition. Nature. doi:10.1038/
nature10717
Decharme B, Douville H, Prigent C, et al. (2008) A new river flooding scheme for global climate applications:
off-line evaluation over South America. J Geophys Res-Atmos. doi:10.1029/2007JD009376
Dias LPC, Macedo MN, Costa MH, et al. (2015) Effects of landcover change on evapotranspiration and
streamflow of small catchments in the upper Xingu River basin, Central Brazil. Journal of Hydrology:
Regional Studies. doi:10.1016/j.ejrh.2015.05.010
Dunne T, Meade RH, Richey JE, Forsberg BR (1998) Exchanges of sediment between the floodplain and
channel of the Amazon River in Brazil. GSA Bull 110(4):450–467
EPE (2012) Balanço energético nacional 2012: ano base 2011. Empresa de Pesquisa Energética (Brasil), Rio de
Janeiro, p. 282
Espinoza JC, Guyot JL, Ronchail J, et al. (2009a) Contrasting regional discharge evolutions in the Amazon basin
(1974–2004). J Hydrol. doi:10.1016/j.jhydrol.2009.03.004
Espinoza JC, Ronchail J, Guyot JL, et al. (2009b) Spatio-temporal rainfall variability in the Amazon basin
countries (Brazil, Peru, Bolivia, Colombia, and Ecuador). Int J Climatol. doi:10.1002/joc
Climatic Change
Espinoza JC, Chavez S, Ronchail J, et al. (2015) Rainfall hotspots over the southern tropical Andes: spatial
distribution, rainfall intensity, and relations with large-scale atmospheric circulation. Water Resour Res. doi:
10.1002/2014WR016273
Ferreira-Ferreira J, Silva TSF, Streher AS, et al. (2014) Combining ALOS/PALSAR derived vegetation structure
and inundation patterns to characterize major vegetation types in the Mamirauá Sustainable Development
Reserve, Central Amazon floodplain, Brazil. Wetl Ecol Manag. doi:10.1007/s11273-014-9359-1
Foley JA, Botta A, Coe MT, Costa MH (2002) El Niño-Southern oscillation and the climate, ecosystems and
rivers of Amazonia. Glob Biogeochem Cycles. doi:10.1029/2002GB001872
Garreaud R, Vuille M, Clement A (2003) The climate of the Altiplano: observed current conditions and
mechanism of past changes. Palaeogeogr Palaeoclimatol Palaeoecol. doi:10.1016/S0031-0182(03)00269-4
Gloor M, Brienen RJW, Galbraith D, et al. (2013) Intensification of the Amazon hydrological cycle over the last
two decades. Geophys Res Lett. doi:10.1002/grl.50377
Guimberteau M, Drapeau G, Ronchail J, et al. (2012) Discharge simulation in the sub-basins of the Amazon
using ORCHIDEE forced by new datasets. Hydrol Earth Syst Sci. doi:10.5194/hess-16-911-201w
Guimberteau M, Ronchail J, Espinoza JC, et al. (2013) Future changes in precipitation and impacts on extreme
streamflow over Amazonian sub-basins. Environ Res Lett. doi:10.1088/1748-9326/8/1/014035
Hess LL, Affonso AG, Barbosa C, et al. (2015) Amazonian wetlands: extent, vegetative cover, and dual season
inundation area. Wetlands. doi:10.1007/s13157-015-0666-y
Joetzjer E, Douville H, Delire C, Ciais P (2013) Present-day and future Amazonian precipitation in global climate
models: CMIP5 versus CMIP3. Clim Dyn. doi:10.1007/s00382-012-1644-1
Jones C, Carvalho LMV (2013) Climate change in the South American monsoon system: present climate and
CMIP5 projections. J Clim. doi:10.1175/JCLI-D-12-00412.1
Junk W (1997) General aspects of floodplain ecology with special reference to Amazonian floodplains. In: Junk
W (ed) The central Amazon floodplain, ecological studies, vol Volume 126. Springer, Berlin. doi:10.1007/
978-3-662-03416-3_1
Junk W, Bayley PB, Sparks RE (1989) The floodpulse concept in River-Floodplain Systems. In: proceeding of
the international large River symposium. Can Spec Publ Fish Aquat Sci 106:110–127
Junk W, Soares MG, Bayley PB (2007) Freshwater fishes of the Amazon River basin: their biodiversity, fisheries,
and habitats. Aquat Ecosyst Health Manag. doi:10.1080/14634980701351023
Junk W, Piedade MTF, Wittmann F, et al. (2011) Amazonian floodplain forests: ecophysiology, biodiversity and
sustainable management 210. Springer Netherlands, London. doi:10.1007/978-90-481-8725-6
Langerwisch F, Rost S, Gerten D, et al. (2013) Potential effects of climate change on inundation patterns in the
Amazon Basin. Hydrol Earth Syst Sci. doi:10.5194/hess-17-2247-2013
Lejeune Q, Davin EL, Guillod BP, Seneviratne SI (2015) Influence of Amazonian deforestation on the future
evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim Dyn. doi:10.
1007/s00382-014-2203-8
Lobón-Cervia J, Hess LL, Melack JM, et al. (2015) The association between forest cover and fish abundance on
the Amazon floodplain. Hydrobiologia 250:245–255
Malhi Y, Roberts JT, Betts RA, et al. (2008) Climate change, Deforestation and the fate of the Amazon. Science.
doi:10.1126/science.1146961
Marengo JA, Espinoza JC (2015) Review: extreme seasonal droughts and floods in Amazonia: causes, trends and
impacts. Int J Climatol. doi:10.1002/joc.4420
Marengo J, Nobre CA, Betts RA, Cox PM, Sampaio G, and Salazar L (2009) Global warming and climate change
in Amazonia: climate-vegetation feedback and impacts on water resources In: Keller M, Bustamante M, Gash
J, Silva Dias P (eds) Amazonia and global change. American Geophysical Union, Washington, D. C. doi:10.
1029/2008GM000744
Marengo JA, Liebmann B, Grimm AM, et al. (2012) Recent developments on the South American monsoon
system. Int J Climatol. doi:10.1002/joc.2254
Meade RH (1991) Backwater effects in the Amazon River Basin of Brazil. Environ Geol Water Sci 18(2):105–114
Meehl GA, Stocker TF, Collins WD, et al. (2007) Global climate projections in climate change 2007: the physical
science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel
on climate change. In: Solomon S et al. (eds) . Cambridge University Press, Cambridge
Melack JM (2015) Aquatic ecosystems. In L. Nagy B. Forsberg, Artaxo P (eds) The large-scale biosphere
atmosphere programme in Amazonia. Ecological Studies, Springer
Melack JM, Coe MT (2013) Climate change and the Floodplain Lakes of the Amazon Basin. In: Goldman CR,
Kumagai M, Robarts RD (eds) Climatic change and global warming of inland waters: impacts and
mitigation for ecosystems and societies. Wiley, Chichester. doi:10.1002/9781118470596.ch17
Melack JM, Hess LL (2010) Remote sensing of the distribution and extent of wetlands in the Amazon Basin. In:
Junk WJ et al. (eds) Amazonian floodplain forests: ecophysiology, biodiversity and sustainable management,
210. Springer Netherlands, Dordrecht, pp. 43–59
Climatic Change
Melack JM, Hess LL, Gastil M, et al. (2004) Regionalization of methane emissions in the Amazon Basin with
microwave remote sensing. Glob Chang Biol. doi:10.1111/j.1529-8817.2003.00763.x
Melack JM, Novo EMLM, Forsberg BR, et al. (2009) Floodplain ecosystem processes. In: Keller M et al. (eds)
Amazonia and global change. AGU, Washington, D. C. doi:10.1029/2008GM000744
Milly P, Dunne K, Vecchia A (2005) Global pattern of trends in streamflow and water availability in a changing
climate. Nature. doi:10.1038/nature04312
Minvielle M, Garreaud RD (2011) Projecting rainfall changes over the South American altiplano. J Clim. doi:10.
1175/JCLI-D-11-00051.1
Moreira-Turcq P, Jouanneau JM, Turcq B, et al. (2004) Carbon sedimentation at Lago Grande de Curuai, a
floodplain lake in the low Amazon region: insights into sedimentation rates. Palaeogeogr Palaeoclimatol
Palaeoecol 214:27–40
Moss RH, Edmonds JA, Hibbard KA, et al. (2010) The next generation of scenarios for climate change research
and assessment. Nature. doi:10.1038/nature08823
Neukom R, Rohrer M, Calanca P, et al. (2015) Facing unprecedented drying of the Central Andes? precipitation
variability over the period AD 1000–2100. Environ Res Lett. doi:10.1088/1748-9326/10/8/084017
Nobre CA, Obregón GO, Marengo JA, et al. (2009) Characteristics of Amazonian climate: main features. In:
Keller M et al. (eds) Amazonia and global change. AGU, Washington, D. C. doi:10.1029/2009GM000903
Nohara D, Kitoh A, Hosaka M, Oki T (2006) Impact of climate change on river discharge projected by
multimodel ensemble. J Hydrometeorol. doi:10.1175/JHM531.1
Paiva RCD, Collischonn W, Tucci CEM (2011) Large scale hydrologic and hydrodynamic modeling using
limited data and a GIS based approach. J Hydrol. doi:10.1016/j.jhydrol.2011.06.007
Paiva RCD, Buarque DC, Collischonn W, et al. (2013) Large-scale hydrologic and hydrodynamic modeling of
the Amazon River basin. Water Resour Res. doi:10.1002/wrcr.20067
Richey JE, Melack JM, Aufdenkampe AK, et al. (2002) Outgassing from Amazonian rivers and wetlands as a
large tropical source of atmospheric CO2. Nature. doi:10.1038/416617a
Rudorff CM, Melack JM, Bates PD (2014) Flooding dynamics on the lower Amazon floodplain: 2. Seasonal and
interannual hydrological variability. Water Resour Res. doi:10.1002/2013WR014714
Sampaio G, Nobre C, Costa MH, et al. (2007) Regional climate change over eastern Amazonia caused by pasture
and soybean cropland expansion. Geophys Res Lett. doi:10.1029/2007GL030612
Seth A, Thibeault J, Garcia M, Valdivia C (2010) Making sense of twenty-first-century climate change in the
Altiplano: observed trends and CMIP3 projections. Ann Assoc Am Geogr 100:835–847
Solman SA, Sanchez E, Samuelsson P, et al. (2013) Evaluation of an ensemble of regional climate model
simulations over South America driven by the ERA-interim reanalysis: model performance and uncertainties. Clim Dyn. doi:10.1007/s00382-013-1667-2
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the Experiment Design. Bull Am
Meteorol Soc. doi:10.1175/BAMS-D-11-00094.1
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climatechange impact studies: review and evaluation of different methods. J Hydrol 12–29. doi:10.1016/j.jhydrol.
2012.05.052
Thibeault JM, Seth A, Garcia M (2010) Changing climate in the Bolivian altiplano: CMIP3 projections for
temperature and precipitation extremes. J Geophys Res. doi:10.1029/2009JD012718
Urrutia R, Vuille M (2009) Climate change projections for the tropical Andes using a regional climate model:
temperature and precipitation simulations for the end of the 21st century. J Geophys Res. doi:10.1029/
2008JD011021
Wilks DS (2006) Statistical methods in the atmospheric sciences. In: International geophysics series, Vol. 91, 2d
edn. Academic Press, Amsterdam, p. 627
Yamazaki D, Kanae S, Kim H, Oki T (2011) A physically based description of floodplain inundation dynamics in
a global river routing model. Water Resour Res. doi:10.1029/2010WR009726
Yamazaki D, Lee H, Alsdorf D, et al. (2012) Analysis of the water level dynamics simulated by a global river
model: A case study in the Amazon River. Water Resour Res. doi:10.1029/2012WR011869
Zulkafli Z, Buytaert W, Manz B, et al. (2016) Projected increases in the annual flood pulse of the Western
Amazon. Environ Res Lett. doi:10.1088/1748-9326/11/1/014013

Artigo Completo - Clique Aqui! - Universidade Federal de Viçosa

Transcription

Similar documents

Chaiwat Ekkawatpanit, Weerayuth Pratoomchai Naota Hanasaki So