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 Brazils 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 km
2
, with only 60,000 km
2
remaining in flat areas and 70,000 km
2
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 8
o
C during the 21
st
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 CO
2
concentration is 435 ppm; IPCC, 2007 is used in the simulations and for CMIP5 models only one Representative
