Climate Change and Land Use Change in Amazonia A report for the Amazonia Security Agenda Project  

March 2013 amazoniasecurity.org

Jean P. Ometto, Gilvan Sampaio, Jose Marengo, Talita Assis, Graciela Tejada, Ana Paula Aguiar Earth System Science Center (CCST)

Brazilian Institute for Space Research (INPE)

Climate Change and Land Use Change in Amazonia was produced for this project by Jean P. Ometto, Gilvan Sampaio, Jose Marengo, Talita Assis, Graciela Tejada, and Ana Paula Aguiar of Earth System Science Center (CCST), National Institute for Space Research (INPE), Brazil. Suggested citation: OMETTO, J. P., SAMPAIO, G., MARENGO, J., ASSIS, T., TEJADA, G. & AGUIAR, A.P. (2013) Climate Change and Land Use Change in Amazonia. Report for Global Canopy Programme and International Center for Tropical Agriculture as part of the Amazonia Security Agenda project. This report was conducted by the International Center for Tropical Agriculture (CIAT) and the Global Canopy Programme (GCP) for the Amazonia Security Agenda. This report was supported with funds from the Climate and Development Knowledge Network (CDKN) and Fundación Futuro Latinoamericano (FFLA).

                     

Table of contents

1. Introduction .................................................................................................... 6

2. Land Use Change ...................................................................................... 10

2.1. Context ................................................................................................... 10

2.2. Land use and cover change (LUCC) ........................................................ 12

2.2.1. Global Scenarios .................................................................................... 15

2.2.2. Amazon Basin ........................................................................................ 18

2.2.3. Brazilian Amazon .................................................................................. 23

2.2.4. National Level ........................................................................................ 28

3. Climate change scenarios ............................................................................ 31

3.1 Climate change models .............................................................................. 31

3.2 Climate extreme events ............................................................................. 39

3.3 Climate change and land use change ....................................................... 40

4. Case studies - Climate extreme events in Amazonia: imminent threat to human security ................................................................................................ 42

5. Conclusions and Policy Options .................................................................. 47

6. References .................................................................................................... 51

Figure list

Figure 1: Study area .......................................................................................... 9

Figure 2. MEA (2005) . ................................................................................... 16

Figure 3. Behavior of the driving forces of Geo-Amazonia (2005) scenarios 20

Figure 4. Loss of forest cover overlapped with drought probability for 2050. .......................................................................................................................... 21

Figure 5. Model results for the extreme-case scenarios in the year 2050 .... 22

Figure 6. The future of the Brazilian Amazon scenarios by the year 2020 .. 24

Figure 7. Deforestation and carbon emissions in the Brazilian Amazon biome: average of two socioeconomic scenarios with five protected areas scenarios. .......................................................................................................... 25

Figure 8. Indirect land use changes caused by fulfilment of Brazil's biofuels production targets to 2020. .............................................................................. 28

Figure 9: Climate change projections for 2015-2034 of near surface temperature anomalies (C) for 15 CMIP3 Global Climate Models. .............. 33

Figure 10: Climate change projections for 2015-2034 of near surface temperature anomalies (C) for 9 CMIP5 Global Earth System Models . ..... 34

Figure 11 - Climate change projections for 2015-2034 of precipitation anomalies (mm/day) for 15 CMIP3 Global Climate Models .......................... 35

Figure 12 - Climate change projections for 2015-2034 of precipitation anomalies (mm/day) for 9 CMIP5 Global Earth System Models. ................. 36

Figure 13 - Changes in rainfall (a-c, %) and in air temperature (d-f, °C) in South America for December-January-February 2010-40 (column 1), 2041-70 (column 2) and 2071-2100 (column 3) relative to 1961-90. ....................... 38

Figure 14 - Projected climate change over Brazil and the Amazon, Sao Francisco and Parana river basins by 2011-40, 2041-70 and 2071-2100 relative to 1961-1990 associated with different levels of global warming and CO2 concentrations.. ........................................................................................ 39

Figure 15: Simplified potential mechanisms of Amazon ‘die-back’.. ............. 41

Figure 16 - Rainfall anomalies during December-February (peak of the rainy season in Amazonia), in mm/month, during dry years and wet years. ........ 46

Figure 17 - Time series of level anomalies (mm/month) of the Rio Negro at Manaus since 1903, for the peak season May-July. . ..................................... 47

Table list

Table 1. Land use and cover change data ....................................................... 11

Table 2. Scenarios of land use and cover change ........................................... 13

Table 3. Five Scenarios for 2050– Conditions for Agriculture and Land Use (Öborn, 2011) .................................................................................................... 17

Table 4. Estimates of land use in 2000 and additional land demand for 2030 .......................................................................................................................... 18

Table 5. Predicted rates (Laurence, 2001) ...................................................... 24

Table 6. Scenario exploration summary of the LUCC (adapted by Aguiar, 2006) ................................................................................................................. 26

1. Introduction

Global warming due to increased greenhouse gas emissions by human

activities and natural climate change presents a challenge for the world’s

natural ecosystems. The acceleration of human-driven climate change poses

serious questions and challenges for conservation strategies to cope with the

expected changes in the distribution, physiology and ecology of most species.

This is especially true for the tropical forests with its tremendous species

diversity. Several studies have discussed the future of the Amazon (Osborn

et al., 2011; Soares-Filho et al., 2010; Lapola et al. 2010; Gómez and

Nagatani et al., 2009; Malhi et al., 2008; Aguiar, 2006; Soares-Filho et al.,

2006; Laurance et al., 2001) in the wake of global concerns about

biodiversity loss, deforestation-driven CO2 emissions through the

intensification of droughts and vulnerability to forest fires and intense land

use and land cover changes.

The ecosystems of Amazonia are subjected to two different, but

interconnected, climatic driving forces: one is regional deforestation and

land use change such as biomass burning and forest fragmentation, which

affects local and regional climate, and the second is global climate change

(Salati et al., 2006, IPCC 2007, SREX 2012). Many studies indicate that

both of these changes in climate will contribute to regional increases in

temperature. However, uncertainties are still considerably high for

projections of regional changes of the hydrological cycle (e.g., Li et al., 2006,

IPCC 2007, Marengo et al 2009) and thus changes in precipitation patterns

are more difficult to determine. The Amazonia region holds the largest

contiguous tropical forest on the planet. The vegetation, including its deep

root system, is efficient in recycling water vapour, acting as an important

mechanism not only for the forest’s maintenance, but also for the water

flows in the region, possibly regulating regional climate [Spracklen et al.,

2012; Werth and Avissar, 2002].

In principle, deforestation and global warming acting synergistically could

lead to drastic biome changes in Amazonia. Oyama and Nobre (2003) have

shown that two stable vegetation-climate equilibrium states are possible in

Tropical South America. One equilibrium state corresponds to the current

vegetation distribution where tropical forest covers most of the Basin. The

other equilibrium state corresponds to a land cover in which most of the

eastern Amazonia is covered by scarce vegetation, with more open canopy

and more drought resistant species. It is not a trivial scientific question to

find out at which point the current stable state could switch (perhaps

abruptly) to the second state, given the combined forcing of land use and

cover change (e.g. deforestation, forest fragmentation, increased forest fire)

and global warming with a likely consequence of more intense droughts (

such as the severe drought which affected the region in 2005 and 2010;

Marengo et al 2008, 2011a, b, c, Zeng et al 2008, Tomasella et al., 2010,

2012).

Some model projections (Cox et al., 2004, Oyama and Nobre, 2004, Salazar

et al., 2007, Betts et al., 2008, Sitch et al., 2008, Salazar et al., 2010) show

over the next few decades this risk of abrupt and irreversible change in

vegetation structure in the region, with large-scale loss of biodiversity and

pressure on livelihoods. This process is referred to as the “die-back” of the

Amazon forest, which occurs after reaching a “tipping point” in regional

climate (e.g. air temperature) or in deforested area (e.g. beyond 40% of forest

cover loss, according to Sampaio et al., 2007). If the current pace of change

(land cover and climate) remains unaltered we may well only find out that

the “climate-vegetation” equilibrium has been reached after we have passed

the threshold for its establishment.

Temperature increases and disruption in the energy and water cycles have

the potential to seriously hamper the functioning of the Amazon as a forest

ecosystem, reducing its capacity to retain carbon, increasing its soil

temperature, and eventually affecting the regional hydrological cycle. In

simple terms, the increase in temperature induces larger

evapotranspiration in tropical regions which tends to reduce the amount of

soil water, even when rainfall does not reduce significantly. This can trigger

the replacement of the present-day vegetation by other vegetation types

more adapted to drier conditions. If severe droughts become more frequent

in the future, which is a common projection for a warmer planet, eastern

Amazonia would experience more dramatic changes in vegetation type

cover, since the models simulate a higher probability for that area to face

frequent and intense droughts (Hutyra et al., 2005).

Land cover and land use change are, per se, strong pressures over natural

systems. On the other hand, the Amazon in South America is home to more

than 40 million people, which, despite intense urbanization, still live and

depend on the region’s natural resources. The Amazon is a heterogeneous

and complex landscape, where multiple forces can potentially contribute to

changes in land use and cover (e.g. deforestation). Global markets pressure

for food and biofuels (Brasil, 2012; Foley et al., 2011; Lambin and Meyfroidt,

2011; Lapola et al., 2010), new transportation and energy infrastructure

projects (Brasil, 2011) and weak institutions (Vieira et al., 2008), can be

cited as some of key drivers in this process.

In this report we present a literature review of different Land Use and

Cover Change scenarios for the Amazon, with a focus on Bolivia, Brazil,

Colombia, Ecuador and Peru (Figure 1). A short summary discusses the

information available and highlights any research gaps related to climate

change and land use and cover change scenarios. We also review current

knowledge on climate variability and climate change in the region,

considering its possible effects and feedback with land use and land cover

change. The occurrence of extreme climate events, linked to extremes in

natural climate variability, is also discussed.

Figure 1: Study area

2. Land Use Change

 

2.1. Context

Land use data is the main input for land use and cover change scenarios. A

few land use and cover (LUCC) datasets are available for the whole Amazon

region. The Terra-i dataset is available with good spatial (250 m) and

temporal (annual from 2004-2011) resolution for the whole Amazon. Terra-i

detects land-cover changes resulting from human activities in near real-time

(updates every 16 days) (Terra-i, 2012). There is also a regional initiative

from the Amazon Geo-referenced Socio-environmental Information Network

(RAISG, www.raisg.socioambiental.org ), to obtain geo-referenced

information for all the countries within the Amazon Basin. Many

institutions that contribute to RAISG have worked on a deforestation map

using a standardized methodology for the whole Basin for the years 2000-

2005-2010 (RAISG, 2012).

In the Brazilian Amazon the PRODES project (INPE, 2012a) produces an

annual deforestation map and estimates annual deforestation rates. DETER

(INPE, 2012b) is an alert system which monitors deforestation monthly,

allowing the government to take rapid action to control and prevent

deforestation. Recently, Terraclass (INPE, 2012c) was released, classifying

the deforested areas in a Land Use Map for the Brazilian Amazon for the

year of 2008 (in 30x30 m2 spatial resolution). In addition, Brazil executes an

agricultural census every 10 years (the latest was released in 2006) (IBGE,

2006). Open and public access to satellite mapping datasets allows wide

monitoring and analysis of Brazilian Amazon land use change by different

stakeholders. The data is also important for drawing alternative land use

scenarios for the future. Generally, scenarios that cover the entire Amazon

Basin extrapolate data produced in Brazil, or combine these sources with

global datasets (Table 1) for the rest of the countries. In this sense, there is

a disproportionate amount of information and data available for the

Brazilian Amazon in comparison with the rest of the Amazon countries.

Outside of the Brazilian Amazon at national or sub national level there are

some efforts in generating land use and cover change data. In Bolivia, a land

use and cover change map for the lowlands generated by the Natural

History Museum Noel Kempff Mercado for the periods 1976-1990-2001-2004

and 2008 is available (Killen et al., 2007 and 2008), and recently Friends of

Nature Foundation, a RAISG member, presented its Deforestation map of

the Bolivian Lowlands and Yungas 2000-2005-2010 (FAN, 2012). In the

same context the institution “Instituto del bien Común” presented the

deforestation map of Peru for the same period (2000-2005-2010) (RAISG,

2012). Another interesting experience in Peru is the System to Monitor

Land Cover, Deforestation and Forest Degradation for the years 2000-2005

and 2009 (MINAM Peru, 2011). In the Ecuadorian Amazon some LUCC

data derived from satellite imagery interpretation also exists (i.e. Mena,

2008; Messina and Walsh, 2001). In Colombia there are publications that

address LUCC data (e.g. Etter et al., 2006; CONPES, 2011) however Cuervo

et al. (2012) mention that there is not a consistent wall-to-wall, multi-

temporal dataset for LUCC, and they generate a LUCC map from 2001-2010

in Colombia using MODIS (250 m) products coupled with high spatial

resolution imagery.

LUCC at the local level can be found also from REDD projects. A list of

certified REDD projects is available from the Climate, Community &

Biodiversity Alliance Standards (CBBA) (CBBA, 2012). Some of the

available datasets are summarized in Table 1.

Table 1: Land use and cover change data

Level LUCC data

Description Spatial/Temporal Resolution

Source

Global

GLC2000

Vegetation map of South America (Global Land Cover 2000)

1 km / 2000 GLC, 2003

GlobCover Global composites and land cover map

300 m/2005-2006; 2009

ESA, 2010

Amazon Basin Terra-i

Detects land-cover changes resulting from human activities in near

250m/2004 to 2011; updated every 16 days

Terra-I, 2012

real-time

RAISG Deforestation map of the Amazon Basin

30m/2000-2005 and 2010

RAISG, 2012

Brazilian Amazon

PRODES Yearly deforestation map 60 m/annual INPE,

2012a

DETER Monthly deforestation alerts 250m/monthly INPE,

2012b

IBGE Agricultural census data

Municipal level/decadal

IBGE, 2006

Terraclass Land use map 30m INPE, 2012c

2.2. Land use and cover change (LUCC)

The expansion of the agricultural frontier, climate change impacts,

ecosystem conservation, public policies and social well-being compose a

complex context in the Amazon. In this sense, various scenarios have been

proposed looking at several potential trajectories of land use and their

consequences for the landscape. These scenarios apply diverse

methodological approaches, use different scales and are built on top of a

diverse set of premises depending on the issues they address. However,

drivers related to climate change, ecosystem functioning/services and

biodiversity, are not included in ‘integrative modelling’ of land use change in

the Amazon region. Therefore, potential feedbacks concerning changes to

these drivers of the human alteration of land cover are not represented in

the current scenarios.

While aware of this limitation, a description of some of the well-known

scenarios of land use and cover change at different scales are presented in

Table 2.

Table 2: Scenarios of land use and cover change

Level or Scale

Scenarios

Quantitative/

Qualitative

Scenarios

(catastrophic-optimistic)

Source

Global

Ecosystems and Human Well-being: Scenarios

Quantitative

1. Global Orchestration 2. Order from Strength 3. Adapting Mosaic 4. TechnoGarden

MEA, 2005

Illustrate the future change of food production and land use

Qualitative

1. Forest cover for Business-as-usual scenario 2. Forest cover for Governance scenario

Osborn et al., 2011

Amazon Basin

GEO-AMAZONIA Qualitative

1. Emergent Amazonia 2. Inching along the precipice 3. Light and shadow 4. The once-green hell

Gómez and Nagatani et al., (2009)

Loss of forest cover overlap with drought probability for 2050

Quantitative

1. Business-as-usual scenario 2. Increased governance scenario

Malhi et al., 2008

Influence of conservation initiatives

Quantitative

1. Business-as-usual 2. Governance 3. Six intermediate scenarios

Soares-Filho et al., 2006

Brazilian Amazon

Impact of infrastructure projects

Quantitative

1. Pessimist 2. Optimist: zones near infrastructure projects were more localized and protected areas near developments are less likely to be degraded

Laurance et al., 2001

Contribution of protected areas for possible reductions in deforestation

Quantitative

1. Exclusion of all current protected areas 2. All protected areas created until 2002 3. Protected areas established by 2008, except for 13 areas established 2003-2008 through the Amazon Protected Area Program (ARPA)

Soares de Filho et al., 2010

4. Protected areas created until 2008 5. Protected areas created until 2002 plus expansion underway with support of the ARPA program.

Understanding the importance of different market accessibility determining factors in land use change

Quantitative

1. Alternative factor: Accessibility 2. Alternative factor: Local markets 3. Policy analysis: road paving and protected areas 4. Police analysis: Law enforcements 5. Market constraints

Aguiar, 2006

Impacts of increase in the production of biofuels in Brazil

Quantitative

1. With biofuel 2. Without biofuel

Lapola et al., 2010

2.2.1. Global Scenarios

This study presents global scale models that, in the context of land use

change, largely discuss the challenge of feeding a growing world while

charting environmentally sustainable paths.

i. The Millennium Ecosystem Assessment (MEA)

The Millennium Ecosystem Assessment (MEA) (2005) developed four

scenarios: “Global Orchestration”, “Order from Strength”, “Adapting Mosaic”

and “TechnoGarden” that focus on ecosystem change and the impacts on

human well-being. For land use and cover they represented only two

scenarios (Figure 2); “Order from Strength”, more pessimistic and

prioritizing national security, and “Techno Garden”, based on green

technologies and ecological economies. In Figure 2, the MEA land use

change scenarios showing the localised impacts of climate change on land

use change patterns are also represented. The main contribution of a global

effort such as this assessment is the recognition of interdependence between

climate change, energy, biodiversity, wetlands, desertification, food, health,

trade, and the economy, demonstrating the need for international

agreements (MEA, 2005).

For the Amazon Region, both MEA scenarios considered in this review

indicate higher impact in the South and South-Eastern part of the region,

known as the “Arc of Deforestation”.

Figure 2. MEA (2005). Two scenarios of land use change for 2050. The maps on the left indicate global cover in 2000, and 2050 under each of the two scenarios. The maps on the right indicate the cause of changes in land use between 2000 and 2050, including shifts in biome types as a result of climate change.

ii. Future Agriculture – livestock, crops and land use

This research program proposed by Öborn (2011) developed five global

scenarios to illustrate the future change of food production and land use.

The construction of these scenarios provides the tools to stimulate thoughts

and identify new challenges facing food security, gaps in knowledge and

research issues. None of the scenarios is a desirable vision of the future or a

target scenario, but all are examples of possible future worlds, which have a

direct impact on land use. The scenarios are: "An overexploited world", "A

world in balance", "Changed balance of power", "The world awakes" and "A

fragmented world" ( Table 3).

Table 3. Five Scenarios for 2050– Conditions for Agriculture and Land Use

(Öborn, 2011)

Scenario Description

An overexploited world

Population growth is high and poverty is prevalent. Unipolar world order (USA dominates) and the Western world shows relatively strong economic development. Political interest in the climate and environment is low. Climate change is large and there is considerable pressure on land resources.

A world in balance

Economic development is strong in large areas of the world and population increase is lower than the UN’s forecast. Strong intergovernmental actors are reaching global agreements on important issues. A global environmental policy has contributed to keeping global warming relatively low and pressure on land resources has been limited.

Changed balance of power

Population growth is relatively low. The balance of power has moved from the West to China and India, countries whose economies are developing fast. Economic development is weaker in Europe. Political ambitions regarding climate and the environment are low. A marked increase in global warming means that the main agricultural areas have moved towards the north and the equator where rainforest is being felled.

The world awakes

Population growth is as the UN forecast. People and their rulers have realized at last how serious the consequences of climate change and global environmental problems are, and are therefore taking more responsibility for achieving long-term, sustainable development. There are several centers of power in the world and agricultural policy is characterized by deregulation and free trade. Rural areas in Europe are flourishing and have well developed business enterprises.

A fragmented world

Population growth is high. There are no strong nations or supranational actors, which means that power relations are unclear. Thus, there are no global agreements on measures to regulate climate change or protect the environment. Private enterprise strongly influences development. Europe is forced to be largely self-sufficient in food. Pressure on land resources is very high.

iii. Lambin and Meyfroidt (2011)

These authors summarize various estimates for land demand in 2030 (Table

4) and show a global need for unused lands to be allocated to new croplands,

biofuel crops, grazing lands, industrial forestry, and urban expansion. The

low estimates represent a conservative view of both land reserve and

additional land demand whereas the high estimates represent a slightly

bolder view.

Table 4. Estimates of land use in 2000 and additional land demand for 2030

2.2.2. Amazon Basin

The scenarios of global land demand highlight risks and concerns related to

the preservation of tropical forests. These forests hold an enormous amount

of carbon (in vegetation and soil) and huge biodiversity, relating to concerns

about climate change (deforestation, hydrological cycle) and ecosystem

services. Several research groups and researchers are looking at this

pressure through both monitoring and mapping current changes in land

cover and use, and also developing scenarios aiming to glimpse the impacts

of different strategies at different scales (global, regional, local). Within the

Amazon region, most of these scenarios are constructed for the Brazilian

Amazon, which has the largest area within the biome and the greater

historical changes in land use and deforestation rates, especially until 2004.

Moreover, the Brazilian Amazon has a consistent and reliable land use and

cover monitoring system from which data is used as a reference for the

construction of most of the spatial models.

A current effort to produce land cover and land use change data for the

entire basin will lead to an increase of land use change scenarios at the

Amazon Basin scale (e.g. RAISG, 2012 and Terra-I, 2012). Gómez and

Nagatani et al. (2009) developed four scenarios for the Amazon Basin from

2006-2026, based on consultation with stakeholders and decision-makers.

The construction of these scenarios was founded on the identification and

analysis of driving forces from which three critical uncertainties were

selected; these were used to build the fundamental premises for each

scenario: "role of public policies regulating the use of natural resources",

"market behaviour" and "science, technology and innovation". Combining

these three critical uncertainties four scenarios were developed:

• "Emergent Amazonia": improvement in the role of public

policies; market forces provide incentives for sustainable production;

and a reduction in the available science, technology and innovation

necessary to optimize the sustainable use of its resources.

• "Inching along the precipice": improvement in the role of

public policies; market forces provide incentives for the development

of non-sustainable production; and a reduction in the available

science, technology and innovation.

• "Light and shadow": improvement in the role of public

policies; market forces provides incentives for the development of

non-sustainable production; and an improvement in the available

science, technology and innovation.

• "The once-green hell": a reduction in the role of public

policies; market forces provide incentives for the development of

non-sustainable production; and an improvement in the available

science, technology and innovation.

This work also analyzed other drivers, both socioeconomic and

environmental aspects, which helped shape the scenarios (summarized in

Figure 3). Amazonia presents a complex heterogeneous system and

generalized scenarios face risks and uncertainties based on the diversity of

contexts and local social processes.

Figure 3. Behavior of the driving forces of Geo-Amazonia (2005) scenarios

Most of land use change scenarios described in the literature only

address human drivers of deforestation and do not consider the stress of

climate change in land use change patterns. The study of Malhi et al. (2008)

addresses the deforestation of the Amazon considering the impact of climate

change as a relevant driver in future land use cover and change. This study

compiled deforestation and climate change scenarios, overlapping and

crossing them to show the possible links and relationship. In Figure 4 two

scenarios to 2050 are shown.

Figure 4. Loss of forest cover overlapped with drought probability for 2050 (Malhi et al.,

2008). A) Business as usual scenario. B) Increased governance scenario.

Besides external demand and climatic variables, internal factors

significantly influence land use change in the region. Many authors discuss

these drivers, especially for Brazilian Amazon for which various scenarios

have been constructed using this approach. Soares-Filho et al. (2006)

analysed the influence of conservation initiatives, especially protected areas,

and also considered the impact of new paved roads as a key factor related to

changes in land use in the region. This study, which covers all, but only, the

Amazon Basin, developed eight scenarios for 2050, considering increases in

infrastructure through paved roads, the enforcement of environmental and

land tenure law and protected areas. The more pessimistic scenario

("Business-as-usual") assumed that the current deforestation trend would

continue, roads would be paved, legal reserves would not be complied with

and new protected areas would not be created. On the other hand, a more

optimistic scenario ("Governance") included the enforcement of forest

reserves, agro-ecological zoning of land use and the creation of new

protected areas. The remaining scenarios were intermediate. This study

shows a reduction from 5.3 million km2 to 3.2 million km2 of closed-canopy

forest in the Amazon for 2050 in the "Business-as-usual" scenario and to 4.5

million km2 in the "Governance" scenario. Intermediate scenarios show that

half of the reduction of deforestation is due to expanding protected areas

and enforcement. Figure 5 shows the spatial distribution of deforestation in

both extreme scenarios.

Figure 5. Model results for forest cover in the extreme-case scenarios in the year 2050 in the

Amazon Basin (Soares-Filho et al. 2006). a) Forest cover for Business-as-usual

scenario b) Forest cover for Governance scenario

2.2.3. Brazilian Amazon

The impact of interregional drivers on land use change, especially in the

Brazilian Amazon, is considered in several other works, and the historical

evolution of scenarios proposed hardly represents the actual situation of

deforestation reduction since 2004. This does not invalidate the proposed

models by different authors, or the potential future scenarios described in

each study, as policy regulation may lose its current presence and strength.

Scenarios proposed by Laurance et al. (2001) focus on the discussion on the

effects of "Avança Brasil" Program (Brasil, 1999), a National economic

development plan proposed by the Brazilian Government over the years

2000-2007, in which several infrastructure projects in the Amazon were

included. To calculate the impacts of new highways, railroads, gas pipelines,

hydroelectric projects, power lines and river-channelization projects

described in the development program, Laurence et al., (1999) developed

two scenarios for the future of Brazilian Amazon for the following twenty

years. They considered two scenarios, a “pessimistic”, which followed the

deforestation rate at that time, and an “optimistic”, where deforestation and

forest degradation were reduced by several protection strategies. In both

scenarios, the results show enormous changes in Amazon land cover,

especially in the “pessimistic”, where few areas of original forest remain

(Figure 6, Table 5).

Figure 6. The future of the Brazilian Amazon for two different scenarios by the year 2020

(Laurence et al., 2001). Scenarios a) optimistic and b) pessimistic. Areas in black

show deforested or heavily degraded regions, red shows moderately degraded,

yellow lightly degraded and green is pristine

 

Table 5. Predicted rates of deforestation and degradation (Laurence, 2001)

Optimistic

scenario

Pessimist scenario

Deforestation 2,690 Km2 per year 5,060 Km2 per year

Degraded (moderately or

heavily)

15,300 Km2 per year 23,700 Km2 per year

The contribution of protected areas to possible reductions in deforestation is

addressed in Soares-Filho et al. (2010). In this work five scenarios for 2050

that consider the importance of protected areas were developed for the

Brazilian Amazon: i) exclusion of all current protected areas, ii) all protected

areas created until 2002 iii) protected areas established by 2008, except for

13 areas established in the 2003-2008 ARPA (Amazon Protected Area

Program), iv) protected areas created until 2008, v) protected areas created

until 2002 plus expansion underway with the support of the ARPA program.

These land cover scenarios with different distributions of protected areas

were combined with two socioeconomic scenarios: high and moderate

agricultural growth. The first LUC scenario, with the drastic exclusion of all

protected areas, produced a higher risk map of deforestation in those areas,

and the other four scenarios depict the progressive contribution of protected

areas to a reduction in deforestation. Figure 7 shows the results of

deforestation and emissions for each of the protected areas scenarios.

Figure 7. Deforestation and carbon emissions in the Brazilian Amazon biome: average of two

socioeconomic scenarios with four protected areas scenarios (Soares-Filho et al.

2010).

The scenarios described in the studies by Laurence et al. (1999) and Soares-

Filho et al. (2010) [and references therein], highlight the importance of

infrastructure projects and protected areas for the landscape dynamics in

the Amazon. Nevertheless, other internal and external factors also modulate

and regulate land use change and must be considered. For example, a recent

study by the Brazilian Agriculture Ministry (Brasil, 2012), which projected

scenarios for Brazilian agro-business for 2022, shows an expressive increase

in agriculture production in the next years, with expected growth in internal

and external demand. This study projects an increase of 70,000km2 in crop

production area, mainly concentrated in beans (47,000km2) and sugarcane

(19,000km2). Considering these numbers, the pressure over forest areas

could be enhanced.

Aguiar (2006) advances beyond intraregional drivers by also considering

accessibility to markets and uses a dynamic spatial model to build different

exploration scenarios of LUCC until 2020. The scenarios proposed by Aguiar

(2006), detailed in Table 6, highlight the importance of differences in market

accessibility on determining drivers of land use change in the Brazilian

Amazon. The main conclusions drawn from these scenarios were: (a)

connection to national markets is the most important factor for capturing

the spatial patterns of the new Amazonian deforestation frontiers; (b)

intraregional dynamics are influenced by the interaction between

connectivity (e.g. to local and national markets) and other factors (e.g.

economic attractiveness, agrarian structure, environmental), where the

importance of determining factors vary across the Amazonia; (c) these

differences led to heterogeneous impact of policies (such as road paving,

creation of protected areas, law enforcement) across the region. Together,

the results of the five explorative scenarios presented in Table 6 are

complementary, helping to draw different aspects of the occupation process

in the Brazilian Amazon.

Table 6. Scenario exploration summary of the LUCC (adapted by Aguiar,

2006)

Exploration Description Model Scenarios

Allocation Demand Law enforcement

Alternative factors: Accessibility

In this exploration, the focus is on connectivity factors.

Considering Roads

No Change Baseline No

Considering Roads

No Change Baseline No

Considering Roads

No Change Baseline No

Alternative factor: Local markets

The focus here is on accessibility to local markets.

Considering Roads

No Change Baseline No

Considering Urban centers

No Change Baseline No

Policy analysis: road paving and protected areas

Here the public policies that influence intraregional conditions for agricultural use, such as road paving

Considering Roads

Paving and protection

Baseline No

Considering Urban centers

Paving and protection

Baseline No

and the creation of protected areas, are considered

Policy analysis: Law enforcements

Law enforcement policies, such as deforestation limits inside private properties, are considered

Considering Roads

No Change Baseline Private reserves 50% local command and control

Considering Roads

No Change Baseline

Market constraints

Analyzes scenarios of increasing and decreasing demand for land in Amazonia, corresponding to higher or lower pressure for forest conversion determined by national and international agribusiness.

Considering Roads

Paving and protection

Decrease No

Considering Roads

Paving and protection

Increase No

Considering Urban centers

Paving and protection

Decrease No

Considering Urban centers

Paving and protection

Increase No

To investigate the impacts of biofuels production, in Southeast and Central

regions of Brazil, and its cascade effects over agricultural and cattle

ranching frontiers Lapola et al. (2010) focused on market pressure for land

use scenarios until 2020. The direct and indirect land use changes in

scenarios with and without biofuel expansion were then analysed. Lapola et

al. suggested a displacement of pastureland and cattle production towards

the Brazilian Amazon, showing an expansion of 121,970 km2 into the region.

Figure 8, extracted from this work, presents the difference between land use

maps with and without the expansion of biofuel plantations in 2020.

Figure 8. Indirect land use changes caused by the fulfillment of Brazil's biofuels production

targets to 2020 (adapted by Lapola et al. 2010).

2.2.4. National Level

In this section national and subnational land use change and cover

scenarios or deforestation progressions for Bolivia, Colombia, Ecuador and

Peru are described. We also aim to give a broad view of the availability of

data in these regions.

The information regarding land use change scenarios at country level is

dispersed, and generally, the efforts to generate land use multi-temporal

datasets are duplicated. This is mainly due to the lack of consistent and

available official land use data.

i. Bolivia

In Bolivia, Muller et al. (2011) identifies the three major proximate causes

of deforestation from 1992 to 2004; (i) the expansion of mechanized

agriculture, (ii) cattle ranching, and (iii) small-scale agriculture. The study

also analyses future deforestation trends (from 2004 to 2030) assuming that

the deforestation rate remains constant (using 1992-2004 rates) for each

proximate cause of deforestation. The results highlight the possible opening

of new deforestation frontiers due to mechanized agriculture, where the

drivers of deforestation are large-scale corporations from Bolivia or Brazil

(mostly soybean producers), highly mechanized, medium-scale national

landholders and Mennonite and Japanese foreign communities.

In addition, Andersen (2009) projected future deforestation until 2100

(methodology described in Andersen et al., 2009), highlighting that the total

deforestation in 2100 could be 370,000 km2, with only 60,000 km2 remaining

in flat areas and 70,000 km2 remaining in forest land with a slope of more

than 25 %, driven by mechanized and subsistence agriculture, mostly in the

lowlands, with high pressure on protected areas and indigenous territories.

Both studies, whilst not scenario approaches, use actual deforestation status

and general assumptions of deforestation in the future.

ii. Colombia

Studies in Colombia have highlighted the spatial patterns of forest

conversion for agricultural land uses by using different types of models to

generate a deforestation hotspots map (Etter et al., 2006a). The study shows

that modelling results should not be seen as spatially precise deforestation

forecasts, but rather as a planning tool for where the new deforestation

frontier is likely to occur (Etter et al., 2006a). On the other hand, Rodriguez

et al., (2012) refer to a quantification of LUCC that occurred from 1985-2008

in the Colombian Andes and generate a scenario until 2050 that shows 28–

30% of the forest cover could be lost.

iii. Ecuador

Messina and Walsh (2001) use a dynamic modelling approach to describe,

explain, and explore the consequences of land use and cover change (LUCC)

in the Ecuadorian Amazon. The study uses an integrated social, physical,

public policy and technology approach with two example scenarios, “Plan

Columbia Scenario” (drug control in the region) and “Beef Scenario”

(considering that cattle ranching increases due to global markets pressure),

which are not compared against each other. In both cases the model shows a

dramatic increase in the amount of urban areas and a significant decrease

in the amount of dense forest. In another study, Mena (2008) analyses the

spatial trajectories and probabilities of transitions in the LUCC of the

Northern Ecuadorian Amazon from 1974-2002, but does not generate future

LUCC scenarios.

iv. Peru

In Peru the book “Peruvian Amazon for 2021” (Dourojeanni, 2009) addresses

the future of the Peruvian Amazon until 2021, considering the high pressure

of road and dam construction and both legal and illegal natural resources

extraction. The study shows pessimistic and optimistic scenarios that

quantify deforestation for each of the pressures of infrastructure

construction (e.g. roads, dams) and natural resources extraction (e.g. oil,

mining and biofuels). The pessimistic scenarios consider that almost 70 % of

the Amazon forest could be lost by 2020 and 91 % by 2041 considering the

drivers mentioned above. This study does not use a modelling approach but

uses general assumptions to predict the deforestation of the Peruvian

Amazon. It aims to make all the information of the main drivers of change

available to inform the society about future deforestation risks. A specific

and published paper of land use and cover change scenarios for Peru was

not found, only publications addressing the environmental impacts of

infrastructure construction (MCT, Perú, 2012).

3. Climate change scenarios

3.1 Climate change models

The Amazon has a critical role in the global carbon balance with high net

primary productivity and as a huge carbon store, in both plant biomass and

soil. It also plays a crucial role in the climate regulation and moisture

recycling and transport in South America through its effect on the local and

regional water cycle.

Downscaling projections from Global Circulation Models for climate change

in the Amazon indicate an increase in temperature (ranging from 0.5 to 8oC

during the 21st century) and a reduction in precipitation (varying between

20% and 50%) depending on the IPCC emission scenario used (Marengo et

al., 2011c). More detailed studies using higher resolution climate change

scenarios, at 40 x 40 km, derived from the regional Eta Model run with the

boundary conditions of the HadCM3 global model (CMIP3 model) indicate

important changes in climate in the region up to 2100, including rainfall

reduction in Amazonia by about 30-40% and warming of about 4-5 o C (Chou

et al., 2011, Marengo et al., 2011c).

This report assesses future climate risks for South America using the new

projections from the models available at the CMIP5 (Coupled Model

Intercomparison Project phase 5). These models will be presented in the

next IPCC report (IPCC AR5) and are compared to the outputs of the

CMIP3 models (used in the previous IPCC report, IPCC AR4) in figures 9,

10, 11 and 12. Figures 9 and 11 show average temperature and precipitation

changes for 2015-2034 from 15 CMIP3 models, while Figures 10 and 12

show the mean temperature and rainfall anomalies from 9 models of

CMIP5. For CMIP3 models the A2 emissions scenario of high GHG

emissions (atmospheric CO2 concentration is 435 ppm; IPCC, 2007) is used

in the simulations and for CMIP5 models only one Representative

Concentration Pathway (RCP) is shown; 8.5 W/m2 (the atmospheric CO2

concentration in the period is 431 ppm).

The projected temperature warming derived from the CMIP3 global models

for Amazonia range from 0.5 to 3°C for 2015-2034, but all models show the

same tendency, i.e. warming (Figures 9 and 10). The analysis is much more

complicated for rainfall changes (Figures 11 and 12). Different climate

models show rather distinct patterns, even with almost opposite projections.

In sum, current GCMs do not produce projections of changes in the

hydrological cycle at regional scales with confidence. That is a great limiting

factor to the practical use of such projections for active adaptation or

mitigation policies.

The CMIP5 models project an even larger expansion of the South American

Monsoon over southern Amazonia (Kitoh et al., 2011). In this study, eight

CMIP3 and CMIP5 models were compared to identify improvements in the

reliability of projections, and while no significant differences are observed

between both datasets, some improvements were found in the new

generation models. For example, in summer CMIP5 inter-model variability

of temperature was lower over north-eastern Argentina, Paraguay and

northern Brazil in the last decades of the 21st century. Although no major

differences were observed in both precipitation datasets, CMIP5 inter-model

variability was lower over northern and eastern Brazil in summer by 2100

(Blazquez and Nunez, 2012). On El Nino simulations and projections there

are indications that ENSO may become more frequent in a warmer climate,

however, the confidence is low because of large natural modulations of El

Niño patterns, and there is no consistent indication of discernible changes in

projected ENSO amplitude or frequency in the 21st century in CMIP5

models. Furthermore, the study has found that there is robust evidence that

the simulation of the ENSO has improved from CMIP3 to CMIP5, with

several models now realistically simulating the ENSO frequency spectrum

and amplitude in sea surface temperature. Both CMIP3 and CMIP5 models

tend to do somewhat better (Coelho and Goddard, 2009) at precipitation

reductions associated with El Niño over equatorial South America.

Figure 9: Climate change projections for 2015-2034 of near surface temperature anomalies

(C) for 15 CMIP3 Global Climate Models (with respect to each model’s average

temperature for the base period 1961-1990) for emissions scenario A2. (Source:

IPCC-AR4, 2007).

Figure 10: Climate change projections for 2015-2034 of near surface temperature anomalies

(C) for 9 CMIP5 Global Earth System Models (with respect to each model’s average

temperature for the base period 1961-1990) for RCP 8.5. (Source: CMIP5, 2012 and

Sampaio et al., 2013 – to be submitted).

Figure 11 - Climate change projections for 2015-2034 of precipitation anomalies (mm/day)

for 15 CMIP3 Global Climate Models (with respect to each model’s average

precipitation for the base period 1961-1990) for emissions scenario A2. (Source:

IPCC-AR4, 2007).

Figure 12 - Climate change projections for 2015-2034 of precipitation anomalies (mm/day)

for 9 CMIP5 Global Earth System Models (with respect to each model’s average

precipitation for the base period 1961-1990) for RCP 8.5. (Source: CMIP5, 2012

and Sampaio et al. – not published).

To model the complex climate system a climate model requires a very large

amount of computer resources, which places a limit on the number of

calculations that can be made and hence the size of the grid. Grid boxes

within a global climate model are currently fairly coarse - to the order of

100-300 km square. Even at this resolution they give a valuable picture of

how large-scale changes may be manifest. But to see how country-level

changes may occur, and how different levels of concentrations of greenhouse

gases may affect any changes, there is a need for finer-scale information.

One way this can be achieved is through increasing the spatial resolution of

the climate model in the region of interest, such as South America, which is

computationally feasible because of the limited size of the region. The finer

spatial resolution allows a more realistic representation of features such as

the coastline and mountains, and of smaller-scale atmospheric processes.

Thus, a regional climate model should provide a better representation of a

particular country’s climate than a global model.

This is why we used the Eta regional model from INPE run into the

HadCM3 global model, for the present (1961-1990) and future (2010-2100),

for various realizations of the A1B emission scenario. Changes in rainfall

and temperature in the South America region projected from the Eta-

CPTEC high-resolution climate model over the 21st century are shown in

Figure 13. As we move through the century, the projected changes become

larger. Over the South America domain, there are areas predicted to become

wetter in the future and other regions that are predicted to become drier

(Figure 13a-c). On a finer scale, the Eta model also projects large percentage

decreases in rainfall and increases in air temperatures over the Amazon,

with the changes becoming more pronounced after 2040. For temperature

(Figure 13 d-f) the projected warming in the tropical regions varies from 0.5

- 3 °C from 2010-40 to 6-8 °C by 2071-2100, with increases being largest in

the Amazon region. In addition to changes in temperature, information

about possible future changes in rainfall with its implications for water

resources is critically important in climate change management decisions.

The direct output from this particular model (Figure 14) indicates

substantial percentage decreases in summer (December-February) rainfall

by the end of the 21st century. However, decreases in rainfall are projected

throughout the year, not just in summer. It is always important to put the

results in the context of other model projections, and it should be noted that

the HadCM3 driving model simulates strong drying over Amazonia over the

21st century, while other GCMs do not. HadCM3 lies on the extreme drying

end of the multi-model group of projections (Marengo et al 2011 a). We can

say that in general, CMIP models still show uncertainties in rainfall

projections for Amazonia, but most of the models agree in rainfall reductions

in eastern Amazonia. Eastern Amazonia is the region that shows more

impacts due to the extremes of climate variability and climate change, and

perhaps could be considered as a climatic “hotspot”.

Figure 13 - Changes in rainfall (a-c, %) and in air temperature (d-f, °C) in South America

for December-January-February 2010-40 (column 1), 2041-70 (column 2) and

2071-2100 (column 3) relative to 1961-90 derived from the downscaling of

HadCM3 using the Eta-CPTEC 40 km regional model. Maps represent the mean of

4 of the 17 scaled regional projections of change. Source: Marengo et al. 2011c.

Figure 14 - Projected climate change over Brazil and the Amazon, Sao Francisco and

Parana river basins by 2011-40, 2041-70 and 2071-2100 relative to 1961-1990

associated with different levels of global warming and CO2 concentrations.

Direction of the changes in rainfall (%) is indicated by arrows, and the regional

warming is also shown in the figure. Source: Marengo et al. 2011c.

3.2 Climate extreme events

Considering the extreme drought in 2005, and using a version of UK Hadley

Centre global climate model, Cox et al. (2008) estimated how the probability

of a ‘2005-like’ drought year in Amazonia changes over time. It suggests

that under present conditions, 2005 was an approximately a 1-in-20-year

event (one drought like 2005 would be expected in a 20-year period), but

may become a 1-in-2-year event by 2025 and a 9-in-10-year event by 2060.

In other words it may become the rule rather than the extreme. If severe

droughts like that of 2005 do become more frequent in the future this

demands adaptation measures to avoid impacts on the population,

particularly those living on the river’s bank. The impacts felt during this

drought of 2005, and again in the extreme drought in 2010 (Marengo et al.,

2011b), show how local populations are vulnerable to climate extremes: local

farmers are affected by drought due to high temperatures and dry

conditions; and river levels are extremely low making transportation along

the main channels impossible, which in many cases is the only way for

populations to move around and remain connected. Two record extreme

droughts in less than five years is something that has highlighted the

negative impacts of extremes of climate variability and climate change in

the region. There is positive evidence that effective measures directed

towards climate change mitigation are needed. Examples would include the

reduction of deforestation and also in the emissions of GHG, reducing

warming and thus impacts. Effective measures sought by decision-makers

should also include adaptation plans to cope with the possibility of extreme

droughts and floods becoming more frequent and intense in Amazonia in the

near term.

3.3 Climate change and land use change

The combination of climate change, on a long-term and large scale, and

deforestation, through changing local climate patterns, might result in a

warmer and possibly drier climate in the Amazon region. The positive

feedback of these processes, with possible changes in the Amazon vegetation

structure (“savannization”) and forest die back, is illustrated in Figure 15.

In general, changes in humidity (e.g. precipitation amount, frequency) and

increases in temperature can cause forest decline. A key element is the

ecological adaptation to the intensity and frequency of drought spells. As

observed by Choat et al (2012) the xylem embolism could represent a serious

risk for forests in adapting to changes in climate. Species more resilient to

longer periods with water deficit in the soil and higher atmospheric water

demand, which forces evapotranspiration, are the ones that will last longer

in drier climate conditions. This might cause change in the forest

biodiversity and ecological functioning, at least until a new equilibrium is

reached.

Figure 15: Simplified potential mechanisms of Amazon ‘die-back’. CO2 is not the only

greenhouse gas emitted, but is highlighted here because of its importance in

climate change, its role in the earth’s carbon budget, and effects on plant

physiology relevant to the Amazon rainforest. Through feedbacks on the global

and regional climates, loss of the Amazon forest may also have implications for

the climate, ecosystems and populations lying outside the Amazon basin

(Marengo et al 2011d).

Forest fire is another key process acting on land cover changes, the

vegetation structure, the energy balance and emissions of greenhouse gases

(Figure 15). In drought conditions, fires set for forest, or even pasture,

clearance burn larger areas. Anthropogenic forest fires, logging and drought

act in a positive loop on increasing forest vulnerability and susceptibility to

subsequent burning while deforestation and smoke can inhibit rainfall,

exacerbating the potential for a dry climate. Smoke and reduced rainfall has

a direct impact on human health and living, disrupting transport at local

and regional level, and compromising access to medicines and food (as

experienced during the Amazon drought of 2005 and 2010). Climate change

acting on a region already fragmented by deforestation could have larger

effects than on a continuous forest.

4. Case study - Climate extreme events in Amazonia:

imminent threats to human security

The last seven years have featured severe droughts and floods in Amazonia,

with some of these events characterized at the time as “once in a century”

seasonal extremes. These relatively recent extreme climatic events in the

Amazon demonstrate the potential threat of such events to water security

for humans and for ecosystems. Droughts were experienced in 2005 and

2010 while severe floods occurred in 2009, 2011 and 2012 in various sectors

of the Amazon.

Various studies have shown that inter-annual variability of rainfall and

consequently of rivers in the Amazon region is in part attributed to

variations in sea surface temperature (SST) in the tropical Pacific,

manifested as the extremes of El Niño-Southern Oscillation (ENSO), and in

the meridional SST gradient in the tropical Atlantic, or to a combination of

both (See reviews in Ronchail et al 2002, Zeng et al 2008, Yoon and Zeng

2010 and Marengo et al 2008, 2011c, d, 2012 a, b, Espinoza et al 2009, 2011,

2012, Tomasella et al 2010, 2012, Aragao et al 2007, and Coelho et al.,

2012).

Figure 16 shows rainfall anomalies as derived from the Global Precipitation

Climatology Centre (Marengo et al 2008) data sets for three dry and three

wet years in Amazonia for the summer time peak rainfall season December-

February. The main difference among dry years is the regional distribution

of negative rainfall anomalies across the region. In 1997-1998, negative

rainfall anomalies covered almost all Amazonia, while in 2005 and 2010 the

anomalies were restricted to Southern and Northern Amazonia,

respectively. In wet years, most of the regions with rainfall above normal

were detected in central Amazonia. This rainfall distribution pattern has

consequences for the river discharge anomalies depending on the rainfall

patterns in the basins of the main Amazon rivers. However, changes in river

levels are not proportional to the magnitude of the rainfall anomalies, and

in one or more sections of the Amazonian rivers, short or long-term changes

in stream flow cannot be explained in terms of rainfall variability alone

(Sternberg 1987, Marengo et al 2008, and Tomasella et al 2010).

Most of these extreme events were classified as such using river data

statistics rather than on rainfall anomalies, considering that flood and

drought hazards represent the integrated impacts due to changes in rainfall

across the basin. River data is perhaps the best indicator of impacts due to

excessive or deficient rainfall in the basin. At the Amazon main channel, or

on the tributaries in the northern (Rio Negro) and southern basins

(Solimões and Madeira Rivers), levels could vary in the same sense, or not,

because rainfall anomalies may exhibit different spatial coverage.

Figure 17 shows a time series of the mean water levels of the Rio Negro at

Manaus, for the peak season May-July. Since the levels of the Rio Negro at

Manaus show the impacts of rainfall over the Rio Negro basin in Northern-

Central Amazonia and from the Solimões river basin in Southern Amazonia,

rainfall anomalies on those basins can vary from extreme to extreme, and

show different impacts on the river levels. For instance, in 2005 and 2010

the drought was characterized by low rainfall in southwestern/northern

Amazonia and very low levels of the Madeiras and Solimões river (Marengo

et al 2011 c, d, Tomasella et al 2012), but not as low levels at Manaus. In

contrast, in 1925, 1964, 1980 and 1983 the levels of the Rio Negro were

below normal. The flooding in 1953, 1989, 1999, 2009 and 2012 also appears

on the figure well above normal. The river levels at Manaus allow for the

detection of large periods with low river levels indicative of drought. Low

river levels were detected in the past in the 1910’s and 1920’s (Marengo et al

2012a, b), but perhaps the best case study of a previous extreme drought in

Amazonia was that one of 1925-26 (Meggers et al 1994, Williams et al 2005),

when drought and fires killed many people.

The 2005 drought caused a simultaneous recession of the major tributaries

of the Amazon river which led to a sharp fall in Amazon river runoff

(Marengo et al 2008, Zeng et al 2008, Tomasella et al., 2010). Similar

behaviour has been observed after the 2010 drought (Marengo et al., 2011c,

d, 2012a, b). The impact of the 2005 and 1997-98 drought on floodplain

communities was studied by Tomasella et al (2012) who found that since all

economic activities of these communities depend on the hydrological regime

of the main stem they were heavily impacted by the droughts. Their results

revealed that the effects of the 2005 drought were exacerbated because

rainfall was lower and evaporation rates were higher at the peak of the dry

season compared to the 1997 drought. This induced a more acute depletion

of water levels in floodplain lakes and was most likely associated with

higher fish mortality rates (Pinho et al, 2012). Based on the fact that the

stem growth of many floodplain species is related to the length of the non-

flooded period, it is hypothesized that the 1997 drought had more positive

effects on floodplain forest growth than the 2005 drought. The fishing

community of Silves in central Amazonia considered both droughts to have

been equally severe.

The 2009 flood event caused mudslides and drove nearly 200,000 people

from their homes (Marengo et al., 2011b) and resulted in record discharge

being observed for the Amazon river. The record flooding in the Amazon in

2012 surpassed the previous record extreme in 2009, and river levels during

the drought 2005 and 2010 were among the lowest during the last 40 years

(Marengo et al 2012 c). In contrast, Brazilian newspapers and various

government monitoring agencies reported that in 2012 the Amazon region

experienced one of the worst flooding episodes in history with most of the

State of Amazonas under a state of emergency as rivers overflowed as an

emergency was declared in 52 of the 62 districts of this State. The rising

levels of the Solimões River and the Rio Negro, the two main branches of the

Amazonia River, led to floods in both rural areas along the riverbanks and

in neighborhoods of the city of Manaus. Similar situations were observed in

the rivers in the Peruvian, Colombian and Bolivian Amazonia, for both

drought and flood extremes.

Studies into these extreme events conclude that changes in the timing of

positive and negative rainfall anomalies puts river discharges from the

northern and southern tributaries of the Amazon river 'in phase' resulting

in extreme (positive and negative) discharges whereas in 'normal' years, the

timing is different attenuating the main-stem flood waves (Tomasella, 2010;

Marengo et al., 2011b, d, 2012a, b). Such unexpected and high magnitude

changes in water availability are likely to have a great impact on water

security in the region, for transportation, agriculture and hydroelectric

generation. Hydropower potential is directly associated with discharge and

therefore generally increases when forests are replaced with crops and

pastures because forests tend to release more vapor to the atmosphere

through evapotranspiration, leaving less water for river discharge

(Bruijnzeel 1991). Ecological impacts of extremes may affect the ecological

functioning of trees; and large potential impacts on regional biogeochemical

and carbon cycles can be related to increase forest fires and biomass

burning, as those observed during the droughts of 2005 and 2010 in

Amazonia. Lewis et al (2012) showed that while in most years the forests

are a carbon sink, drought (such as in 2005 and 2010) reverses this sink to

behave as a source.

There are limited quantitative results about the effects of changes in

climate for human activities in the Amazon. The uncertainties in the

modelling of downscaling projections are still high, and further research on

the effect of current extremes events is needed.

Figure 16 - Rainfall anomalies during December-February (peak of the rainy season in

Amazonia), in mm/month, during dry years: 1997-98, 2004-2005 and 2009-10,

and wet years: 1998-99, 2008-2009 and 2011-12. Source of data is the Global

Precipitation Climatology Centre (See details in Marengo et al 2008).

Figure 17 - Time series of level anomalies (mm/month) of the Rio Negro at Manaus since

1903, for the peak season May-July. Anomalies are in relation to the 1902-2012

mean. Dry and wet years are shown in red and blue colors, respectively.

5. Conclusions and Policy Options

The synergistic combinations of local to regional climate impacts, due to

deforestation, and global climate change, result in warmer and possibly

drier conditions in the Amazon basin. The forests recovery after an extreme

dry event might take much longer than previous thought (Saatchi et al,

2013; Choat et al, 2012), leading to an increased vulnerability if the

frequency of these events increases in the future.

The loss of the Amazon forest, either by deforestation or in the long term

through climate change, could have widespread regional impacts. A positive

feedback from the loss of forest could be expressed by further change in

regional and global climate, which would further impact the forest.

Although there are no explicit results from integrative modeling of the

effects of direct deforestation combined with climate change, there is

evidence that these two drivers of change in forest cover are unlikely to act

independently of one another.

The Amazon is a mosaic of different environmental, political and

socioeconomic interactions, compounding a complex and heterogeneous

region. This complexity requires wide analyzes which consider interactions

between various factors involved in the processes. In this report, we have

explored several studies aiming to evaluate the impacts of alternative

pathways for land use in the region and consequently the importance of

some drivers in land use change dynamics.

Among these drivers it is important to consider the interaction between

intraregional factors, such as infrastructure projects, protected areas, law

enforcement, and external forces such as increases in demand for food and

biofuels. The suit of drivers, within complex and local particular

arrangements, define different levels of uncertainties, related to land use

change in each country and, consequently, in the region as a whole. It is

important that mechanisms to value the forest, its biodiversity and

ecosystem services, are used by local and national financial and political

stakeholders. Mechanisms such as: Payment for Ecosystem Services (PES);

Investment in Natural Capital (PINC-GCP); Reducing Emissions from

Deforestation and Forest Degradation, including the role of conservation,

sustainable management of forests and enhancement of forest carbon

stocks, (REDD+) are important to be included and promoted in the policy

agenda for the entire region. However these, and other mechanisms, need to

work in line with local communities and stakeholders - from indigenous

people to caboclos, from small, family oriented, to medium scale agriculture;

from large scale agriculture and beef production to built-up infrastructure -

investments need to be coupled with socio-ecological sustainable principles,

otherwise they are likely to fail.

These aspects affect local policy options, institutional arrangements and

social opportunities in each country in the Amazon basin. We summarize

the main contrasting trends:

In Bolivia, Peru, Colombia and Ecuador the current published knowledge on

LUCC patterns and dynamics does not have enough historical trend

analysis to allow further analysis on LUCC scenarios. Recent efforts of land

use and cover change data generation (for instance produced by RAISG,

2012: Terra-I, 2012) will improve the quantity and quality of information

allowing the production of better LUCC scenarios. What is clear is that the

deforestation rates in Bolivia, Peru and Ecuador are increasing significantly

in the past recent years (less so in the case of Colombia). Despite the low or

recent production of land cover data, the studies cited in this report suggest

that deforestation and land use change shall increase in the future.

Therefore, enhancing the economic value of local products and the

promotion of sustainable land use mechanisms are key actions, and options

that could work to reduce poverty and maintain ecosystem services. Land

tenure in these regions is also an issue to be urgently solved. The legal

support for landowners would help make land use and environment

conservation policies more effective. Currently there is no view, in the short-

term, that climate change perspectives drive land use actions under such a

social-political framework, however, considering current climate variation

(as reviewed in this report), this should be strongly considered.

In Brazil, pressures for new productive areas in order to meet the demand

for food and biofuel have progressively increased. This fact, associated with

the land market and timber industry, has caused a fast increase in

deforestation rates in the Amazon in the past 30 years. In response to this

various measures were taken in order to reduce deforestation, resulting in a

substantial decrease since 2004 so that in November 2012 the INPE (2012a)

annual estimate of deforestation rate was estimated at 4656 km2. This rate

is very close to what Brazil has set as target, for greenhouse gases emission

reduction until 2020, in the COP15 (Copenhagen, 2009). However, the

pressure over the forest is not dwindling, and different patterns of

deforestation and forest degradation are being observed in the region

(DEGRAD-INPE, http://www.obt.inpe.br/degrad/).

Thus, continuous efforts to secure compliance with environmental laws, but

also on proposing innovative and economic sustainable production activities

for local communities are crucial. As in other Amazonian regions, the land

tenure situation needs to be solved, as well as strategic definition of new

conservation areas (as being suggested by some authors), and an integrative

plan to develop economic activities in the region, to reduce poverty and

create new opportunities (for instance valuating environmental services and

biodiversity preservation).

Climate models outcomes need to be considered in policy and investment

planning in the region. Public and private investment in infrastructure, as

hydropower reservoirs, paved roads, storage facilities for grains, saw-mils

for timber production, slaughterhouses, and so on, need to consider the risks

of changing precipitation patterns and extreme events (as mentioned in the

climate component of this report). The example of Brazil highlights the need

for improvements in production arrangements and law enforcement in order

to feed a growing demand through sustainable production in the whole

Amazon.

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