water security in amazonia

Water security in Amazonia

A report for the Amazonia Security Agenda Project

February 2013

amazoniasecurity.org

Mark Mulligan

1

_{, Jorge Rubiano}

12

_{, Sophia Burke}

3

_{and Arnout van Soesbergen}

1 1

_{Department of Geography, King’s College London}

3

_{AmbioTEK Community Interest Company, UK}

Water Security in Amazonia was produced for this project by Mark Mulligan

1

_{, Jorge}

Rubiano

12

_{, Sophia Burke}

3

_{and Arnout van Soesbergen}

1

_{of the}

1

_{Department of}

Geography, King’s College London,

2

_{Department of Geography, Universidad del}

Valle, Colombia and

3

_{AmbioTEK Community Interest Company, UK.}

Suggested citation:

MULLIGAN, M., RUBIANO, J.R., BURKE, S. & VAN SOESBERGEN, A. (2013)

Water Security 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).

Contents

List of figures ... 5

List of tables ... 6

Purpose ... 7

The interactions between Amazonian ecosystem services and water security ... 7

Some definitions: Ecosystem services ... 7

Some definitions: Environmental vs. Ecosystem Services ... 7

Some definitions: Water security: ... 8

Relationships between ES and water security ... 9

The existing threats to water security ... 10

The baseline hydrology of the Amazon ... 10

Population across the Amazon ... 10

Poverty across the Amazon ... 11

Baseline water balance ... 12

Baseline water quality ... 15

The land cover and use threats ... 17

Current pressures vs. future threats ... 17

Protected areas ... 20

The impacts of key threats on water security ... 21

The mechanisms of land use change impacts on water security ... 21

Land-use and climate feedback mechanisms ... 22

Modelling studies on the impacts of land use change ... 23

Data-based studies on impacts of land use change ... 24

Agricultural Land management ... 26

Extractives - mining and oil and gas ... 26

Hydropower ... 27

Population ... 27

Water treatment, supply and sanitation ... 28

Modelling the impacts of land use change on the water security future of the Amazon . 29 The role of climate change in exacerbating these threats and generating new threats to water security ... 35

The climatic threats ... 35

The impact of climate variability: ... 35

Climate change scenarios for the Amazon ... 35

Gradual change vs. abrupt events ... 36

The impacts on water security ... 37

Water quantity ... 37

Water quality ... 40

Flow regulation and seasonality ... 42

Climate-induced wildcards ... 43

Migration ... 43

Agricultural change ... 43

Interactions with other securities ... 44

Health ... 44

Energy ... 44

Food ... 44

Climate change and Brazil’s global food basket ... 45

Case studies illustrating threats to water security in the Amazon ... 45

1: The status of dams in the Amazon and their downstream impact ... 45

2: The water security impacts of the Amazon droughts of 2005 and 2010 ... 47

3: Major changes in land use and their impacts ... 48

4: Mining and oil and gas development. ... 49

5: Infrastructural threats to water quality in the Amazon ... 53

The impacts of water security across different populations ... 54

Rural settlers ... 55

Institutional weakness ... Error! Bookmark not defined. The potential policy options to mitigate or exacerbate the impacts of security threats on water security ... 60

Water security hotspots ... 60

Climate change ... 60

Land use and cover change ... 61

Common myths and their corresponding realities ... 62

Key climate change impacts ... 62

Snow and ice ... 62

Implications for policy ... 64

Managing extreme events ... 64

Water supply and sanitation infrastructure planning ... 65

Agrarian Reform ... 65

Incentives for eco-efficient land management ... Error! Bookmark not defined. Ecosystem based adaptation and the role of protected areas policies ... Error! Bookmark not defined. References ... 69

List of figures

Figure 1 Population by major sub-basin ... 11

Figure 2 Percent of population considered poor (<$2 per day) by major sub-basin. Data source Elvidge et al. (2009) ... 12

Figure 3 Baseline water balance by major sub-basin ... 13

Figure 4 Baseline per-capita water balance by local administrative region (Mm3_{/person). . 14}

Figure 5 Baseline water stress index by major sub-basin ... 15

Figure 6 Baseline water quality (HF) index (%) by local administrative region. ... 16

Figure 7 Baseline number of people with poor quality drinking water by local administrative region ... 17

Figure 8 Current pressure index according to Co$ting Nature ... 18

Figure 9 Current pressure index according to Co$ting Nature, mean by country ... 18

Figure 10 Future threat index according to Co$ting Nature. ... 19

Figure 11 Future threat index according to Co$ting Nature, by country ... 19

Figure 12 Protected area coverage by major sub-basin ... 20

Figure 13 Protected area coverage by country ... 21

Figure 14 Tree cover (%) for top left : baseline (2000), top right: LUC (2050) and bottom left: ACCEL (2050) scenarios. ... 30

Figure 15 Tree cover (%) for top left: LUC (2050) and bottom right: ACCEL (2050) scenarios for Rondonia in relation to protected areas (WDPA, 2012) ... 31

Figure 16 Change in tree cover resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin ... 32

Figure 17 Change in tree cover resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin ... 32

Figure 18 Change in water stress index resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin ... 32

Figure 19 Change in water stress index resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin ... 33

Figure 20 Change in HF pollution index resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin ... 33

Figure 21 Change in HF pollution index resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin. ... 34

Figure 22 Change in persons per km2 _{with poor water quality resulting from continued} deforestation at current rates for a further 40 years (LUC), by major sub-basin ... 34

Figure 23 Change in persons with poor water quality resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin ... 34

Figure 24 Change in rainfall with AR4 A2 mean of all (20) models 2050s ... 38

Figure 25 Change in rainfall with AR4 A2 mean of all (20) models minus one sd 2050s ... 38

Figure 26 Change in rainfall with AR4 A2 mean of all (20) models 2080s ... 38

Figure 27 Change in water stress with AR4 A2 mean of all (20) models 2050s ... 39 Figure 28 Change in water stress with AR4 A2 mean of all minus one sd (20) models 2050s

... 40

Figure 29 Change in water stress with AR4 A2 mean of all (20) models 2080s ... 40

Figure 30 Change in human footprint on water with AR4 A2 mean of all (20) models 2050s ... 41

Figure 31 Change in human footprint on water with AR4 A2 mean of all minus one standard deviation (20) models 2050s ... 42

Figure 32 Change in human footprint on water with AR4 A2 mean of all (20) models 2080s ... 42

Figure 33 Estimate of flood affected populations summed by major sub-basin. ... 43

Figure 34 Current large dams by major sub-basin. ... 47

Figure 35 Current and planned large dams by major sub-basin. ... 47

Figure 36 Per-cent of major sub-basin converted during the period 2004-2012 according to terra-i ... 49

Figure 37 Number of current large mines by major sub-basin. ... 51

Figure 38 Fractional coverage of mining concessions by major sub-basin ... 51

Figure 39 Number of current oil wells by major sub-basin ... 52

Figure 40 Fractional coverage of oil and gas concessions by major sub-basin ... 52

Figure 41 Pixel-scale (left) and sub-basin scale (right) increase in HF pollution index resulting from operationalisation of 1% of oil and gas concessions. ... 52

Figure 42 Pixel-scale (left) and sub-basin scale (right) increase in HF pollution index resulting from operationalisation of 10% of mining concessions. ... 53

Figure 43 Planned roads by major sub-basin. ... 54

Figure 44 Sensitivity of water balance to rainfall change (mm/mm), at sub-basin scale ... 61

Figure 45 Sensitivity of water stress index to rainfall change (%/mm) at sub-basin scale .. 61

Figure 46 Sensitivity of water stress index to change in tree cover (%/%) associated with conversion to pasture ... 62

Figure 47 The contribution of annual total snowmelt to water balance (% of water balance) at the pixel scale (left) and the sub-basin scale (right) for southern Peru. ... 63

Figure 48 The change in contribution of snowmelt to water balance between baseline and A2 mean of all GCMs 2040s (% of water balance) at the pixel scale (left) and the sub-basin scale (right) for southern Peru. ... 63

List of tables

Table 1 Links between services and water security ... 9

Table 2 Population by country in the greater Amazon. Source: GPW (2000) *UNEP and ACTO, 2009. ... 55

Table 3 Water demand for agriculture Sources: Goulding et al (2003), INRENA (2006),SUPELANO (2006) USACE (2001) ... 57

Purpose

This report will assess the scientific evidence-base for water security risks in the Amazon and the role of climate change as a threat multiplier. We will do this by examining the interactions between the land use and climate controls on supply of hydrological ecosystem services in the Amazon and the impact upon water security in the region. We will identify the existing water related conflicts between countries of the Amazon and examine which of these may be impacted by climate change. Further, we will examine the socio-economic opportunities arising out of water security in the region and will indicate the role of ecosystems and the hydrological services that they supply in ensuring that these opportunities can be sustainably realised.

The interactions between Amazonian ecosystem services

and water security

Some definitions: Ecosystem services

Ecosystem services are the supply of benefits to humans resulting from the operation of ecosystem processes. These benefits may accrue to all humans (for example climate mitigation services) or a subset of beneficiaries that receive a local service (for example those downstream of ecosystems providing hydrological services such as flow regulation). An ecosystem service (ES) is not a ‘service’ at all until it is utilised and thus any discussion or quantification of ES involves: (a) the quantification of the source of ecosystem attributes that will later become services, (b) understanding the distribution of beneficiaries (both local and distant) and (c) assessing the delivery of these attributes to the beneficiaries (Mulligan et al., 2010). For water as a service the first beneficiary may be the agricultural producer and their employees who gain livelihood from growing crops with that water but these crops continue to make profits along the supply chain from distributor/exporter through to the supermarket that sells them on to consumers. The profits made along the supply chain are often higher than those made by the original producer and the whole supply chain is sustained by the ES provided to the farmer. So, one of the first decisions we need to make in quantifying ES is who are the beneficiaries we are targeting and how far do they extend beyond the basin.

Some definitions: Environmental vs. Ecosystem Services

One should distinguish between those services that are provided by the environment (environmental services per se, EnS) and those that are a function of the ecosystem

(ecosystem services, ES), see Mulligan et al. (2010). In some cases services are provided by environments irrespective of the ecosystem, for example mountain zones have high rainfall and low evapo-transpiration as a function of orographic cloud and rainfall generation

processes and thus produce a lot of water. This environmental service is independent of the ecosystem that might occur on the mountain range in question (which has little effect on the mountain climate). The same mountain ranges might also supply specific ecosystem services such as the contribution to water resources of fog stripping by tropical montane cloud forests. This ecosystem service is a function of the cloud forest ecosystem and is significantly reduced when cloud forest is converted to pasture, whereas the aforementioned environmental service is unaffected. This cloud forest effect, like many hydrological ES, is highly scale-dependent and significant locally but becomes insignificant further downstream as the proportion of the catchment that is made up of cloud forest diminishes.

There tends to be a spatial autocorrelation between areas of high ecosystem service provision (especially for carbon and water) and areas of high biodiversity but this is likely because the same climatic and terrain drivers of ecosystem service provision also have a role to play in the generation and maintenance of biodiversity rather than because biodiversity itself controls these ecosystem services. It is thus important to recognise the real biophysical mechanisms of ES provision rather than make assumptions connecting ES provision to particular land use classes or areas of high biodiversity. Here we will use the MEA (2005) classification of ecosystem services into provisioning, regulating and

supporting services.

Some definitions: Water security

Narrowly defined, water security is the capacity of a population to ensure sustained access to water and clearly involves issues of water availability in the environment but also of socio-economic access to water. More broadly, the term water security also incorporates safeguarding against water related hazards such as floods and droughts. Water security has a fundamental basis in natural capital and the provision of ecosystem services so that we can define the following elements of water security alongside their basis in ecosystem services:

a. Mean (quantity and quality) of water supply vs. demand (provisioning services)

b. Risks to supply (such as unusual seasonal, inter-annual or long term variability or extreme events including floods, droughts and landslides) (atmospheric and terrestrial regulating services)

Socio-economic factors that affect the demand for services and the equality of access to them are also critical:

c. Risks to demand (progressive increases in demand associated with

result from environmental migration, conflict or rapid infrastructural and/or agricultural development)

d. Access to and sharing of water (infrastructure, socioeconomic conditions, poverty, rights)

Water security is trans-boundary in nature since water demand and contamination in upstream regions and countries can impact significantly on supply in downstream areas. Moreover, large-scale land use change downstream can destabilize regional climates with a resulting impact on rainfall (and thus water security) upstream. Water security

fundamentally underpins securities for health (through water quality), energy (through hydropower and biofuels) and food (through irrigation and crop ’green’ water use).

Relationships between ES and water security

At the scale of the Amazon basin, climate is considered an ecosystem service here since land use change at this scale has the potential to affect climate at the basin scale and thus land use-induced climate change is manageable by policies at the basin scale. It may not be so at the national scale, especially for the Andean countries whose climates may be affected by large scale land use change in downstream Brazil. Greenhouse induced climate change cannot be managed in the Amazon alone so it is considered an environmental service here. Environmental and ecosystem services underpin the elements of water security as shown in Table 1. There is no single service that is more important for water security than others: the services vary in importance from site to site.

Services Water security

Environmental services

Global climate regulation Underpins mean water provision and risks to supply

Ecosystem services (function of natural capital)

Regional climate regulation Underpins mean water provision and risks to supply

Hydrological provisioning services Underpins mean water provision Hydrological regulating Underpins risks to supply in the dry

season

Agricultural provisioning services Underpinned by hydrological ecosystem services

Biodiversity and cultural/aesthetic services Spatially correlated with climate and thus hydrological ecosystem services

The existing threats to water security

The Amazon River system is the single, largest source of freshwater on Earth and its flow regime is relatively un-impacted by human activity compared with many large basins. It is, however, subject to inter-annual and long-term variability in tropical precipitation and resulting large variations in downstream flow conditions. The Amazon drains an area of 6.2

× ₁₀6 _km2 _{and discharges an average of 6,300 km}3 _{of water to the Atlantic ocean annually -}

equivalent to 15-20% of global freshwater that flows into the oceans (Foley, 2002; Marengo, 2006; Tomasella et al., 2011). Interdependencies of water resources with other ecological and social systems exist at all spatial and temporal scales and are driven, for example, by proximal drivers such as land-use, water withdrawals, pollution, eutrophication and anthropogenic climate disruption. The underlying distal drivers of these proximal drivers include population growth, economic development and globalization as well as changes in consumption patterns, including diets (Hoff, 2009).

The baseline hydrology of the Amazon

In order to understand threats to water security we must first understand the baseline distribution of water and water security in the basin. The Amazon has a unique socio-cultural context being characterized by low population densities, few urban centres and relatively low levels of poverty. Despite the low population density, widespread human impacts on the Amazon have occurred and these even have global implications. Over the last few decades, 84 million ha of natural ecosystems have been lost in the basin (Malhi et al. 2008). This signifies an average direct impact (land clearing) of 1.8 ha per person in the Amazon (2.4 ha per rural inhabitant) (Porro et al., 2008). Since the hydrological cycle is fundamentally dependent upon land cover and land use, there may also be consequences for water security in the region.

Population across the Amazon

According to Porro et al. (2008), the population in the Amazon was nearly 29 million in 2000 and has grown over 250% since 1960 with the greatest growth in Colombia, French Guiana1_{, and Venezuela (though these countries represent only 11% of the total population}

of the ACTO basin). In 2000, Brazil had over half of the Amazon population, followed by Peru at 25% and Ecuador at less than 10%. In the coming 30 years (based on 1960-2000 growth rates) we can expect population in the Amazon to reach 75 million with 52% in Brazil, 28% in Peru, 6% in Ecuador and nearly 9% in Colombia. By major sub-basin (Figure 1) current population is highly concentrated in the high altitude basins of Peru, Ecuador and Bolivia, around Manaus, Amazonas, Rondonia and the basins of Para, Maranhao and Tocantins in the east. Some 6 million (21%) Amazon inhabitants are considered urban

populations, 21 million (74%) are classed as rural colonisers and just over 1 million (4%) as indigenous. Growth rates are highest amongst the rural coloniser community at 170% and 30 years from now (based on 1960-2000 rates) urban population could reach 32 million (20%), rural colonisers 126 million (77%) and indigenous 5.4 million (3%).

Figure 1: Population by major sub-basin

Poverty across the Amazon

Measures of poverty such as per-capita income, unsatisfied basic needs, human

development index, and others are commonly used but these say little about the quality of life of people living in most of the Amazon. Using two poverty indicators - unsatisfied basic needs (expressed as a percentage of population lacking one basic need) and infant mortality (number per 1000 population) - Porro et al (2008) showed that Bolivia is the hotspot of poverty in the region with 56% of the population in the study region suffering from a lack of at least one basic need and infant mortality at 61 per 1000. Figure 2 indicates there is significant spatial variation in poverty (measured as income <$2 per day) with Peruvian, Bolivian and Ecuadorian sub-basins showing high levels of economic poverty, alongside the highly urbanised eastern Brazilian catchments.

Figure 2: Percent of population considered poor (<$2 per day) by major sub-basin. Data source Elvidge et al. (2009)

Baseline water balance

There is significant spatial variation in water balance (rainfall plus fog inputs minus transpiration) across the Amazon reflecting differences in precipitation inputs and evapo-traspiration losses. The WaterWorld model (www.policysupport.org/waterworld, Mulligan and Burke, 2005; Mulligan et al., 2010; Bruijnzeel, Mulligan and Scatena, 2011, Mulligan, 2012) is a sophisticated spatial hydrological model based on the best available global datasets for climatic inputs and models for inputs of fog and snowmelt and for outputs of actual evapo-transpiration (AET). WaterWorld (v2.5) has been applied here to understand the sub-basin level variability in water balance and indicates significant differences

between NW Amazonia and the S and E in terms of water availability. A negative water balance at the sub-basin scale indicates greater AET than is supported by local rainfall (i.e. AET depends on inputs from upstream or groundwater).

Figure 3: Baseline water balance by major sub-basin

Per-capita water balances from WaterWorld (Figure 4) reinforce the existing patterns since the greatest populations are in some of the drier areas of the Amazon and the least

populations in the wetter areas. It is important to note that these volumes represent

available (blue) water, only a fraction of which may be accessible for use through

appropriate water infrastructure. So called green water transpired in vegetative production is accounted for here in the evapotranspiration calculation which is based on the existing plant cover. Clearly regions such as Santa Cruz, Bolivia and much of eastern Brazil have much lower per-capita availability than other regions and, in some cases close to zero or even negative on a sub-basin basis.

Figure 4: Baseline per-capita water balance by local administrative region (Mm3_/person).

The WaterWorld water stress index calculates supply (as water balance) and demand (as population multiplied by a user-defined per-capita domestic and industrial demand, set here as 47 m3_{/yr) on a monthly basis. Agricultural demand is incorporated in the water}

balance since the water balance includes actual evapo-transpiration from the present land use (including the effects of irrigation). The water stress index is the average % of

industrial and domestic (blue water) demand not supplied in months at which

supply<demand across the year. This is thus a cumulative index of water stress for each month and assumes no (reservoir) storage of surpluses in one month that might offset the lack of supply in the next. It is thus a good measure of climatic and hydrological water stress, seasonally and annually, in the absence of infrastructural water management measures such as storage by dams and groundwater abstraction. Using this index (Figure 5), for much of the N and W Amazon almost all demand is met climatically every month whereas for some parts of the S Amazon some 20% of demand cannot be met climatically in some periods while in eastern Brazil >60% of demand in the dry months could not be met in the absence of using water stores.

Figure 5: Baseline water stress index by major sub-basin, % of industrial and domestic (blue water) demand not supplied in months at which supply<demand across the year

Baseline water quality

As well as water quantity, water quality is fundamental to water security since it

determines the quantity available for a particular purpose (such as irrigation, domestic use

etc.). Poor quality water may be unusable for some purposes and bring with it risks of health insecurity. Water quality is very difficult to measure at policy relevant scales since it cannot be remotely sensed per se. Thus, WaterWorld uses an index of the extent to which water is affected by human activities: the so-called human footprint on water quality index (HF, Mulligan, 2009). The HF essentially assumes that if rainfall falls on land that has low human impact then it will generate clean runoff, whereas if it falls on human land uses that may form point (mines, oil wells, roads, urban) or non-point (unprotected cropland and pasture) contaminant sources then the runoff generated is not clean (i.e. has a human footprint). The HF index (%) at a point is thus the proportion of water at that point that fell as rainfall on upstream human-impacted areas and is an indicator of the potential level of contamination of water. All land covers are assumed to have the same unit impact though users can change this if there is reason to believe that particular areas of cropland for example have a higher or lower pollutant release than others. The impacts on water quality are thus the magnitude and distributions of human land uses upstream in relation to where the rainfall falls.

Figure 6 shows the baseline HF index for the Amazon by local administrative region and indicates some significant impacts of current agriculture, mining and oil and gas in

Colombia, Ecuador, Peru and Bolivia as well as agricultural impacts in S and E Brazil. Rainfall occurring in protected areas is assumed to have no HF and thus the patterns shown also reflect the distribution of protected land. Since most of the rainfall in the N and W Amazon falls on un-impacted land the HF index is very low for these areas.

Figure 6: Baseline water quality (HF) index (%) by local administrative region.

WaterWorld assumes by default that, for withdrawals in all urban areas, 70% of the HF is routinely removed by water treatment (at a cost) and that 70% of the 100% HF of

populations living in urban areas is removed (at a cost) through sanitation. A HF index of >50% is considered to equate to poor quality water. Rural areas are assumed by default to have no effective water treatment and sanitation, such that effluent from rural populations is considered to return to the rivers and domestic water resources are considered to be available at the HF levels provided by the rivers. WaterWorld calculates the number of people affected by post-treatment and post-sanitation, poor quality water by combining its gridded population dataset with the resulting water quality map and the 50% threshold value. Figure 7 shows a number of areas in which significant rural populations are

currently likely to be subject to poor water quality, especially in Peru, Bolivia and parts of Brazil. Although urban areas will also be subject to this low water quality, investments in water treatment and sanitation are assumed to offset them but do so at a considerable economic cost.

Figure 7: Baseline number of people with poor quality drinking water by local administrative region

The land cover and use threats

Current pressures vs. future threats

Having understood a little of the hydrological baseline for the Amazon in terms of water supply, demand and quality we will now consider the current pressures and future threats to water security in the region. The Co$ting Nature system

(www.policysupport.org/costingnature, Mulligan et al., 2010) can be used to understand the spatial distribution of a range of ecosystem services and the current pressures and future threats upon them using the best available global datasets. Current active pressures on the Amazon are summarised using Co$ting Nature in the following variables: population growth, wildfire frequency, grazing intensity, agricultural intensity, dam density and infrastructural state (roads, dams, mines, oil and gas, urban) density. All of which are mapped. Note that protected areas are not considered to reduce pressure in this metric since that depends upon their effective management for which there is no data. Figure 8 shows the Co$ting Nature pressure index for the Amazon indicating areas in which the ecosystems that provide hydrological services are currently pressured by human impact. Pressure is clearly concentrated in E Brazil (largely outside the Amazon watershed), in Rondonia and Mato Grosso, in the Andes and in Beni, Bolivia. These values are indexed at the pixel level to a maximum of 1.0 globally so it is clear that with a maximum of 0.25 in the Amazon, there are much more highly pressured areas in the world. Figure 9 presents statistical means by country to provide an indication of geopolitically important differences in pressure on ES within Latin America and beyond.

Figure 8: Current pressure index according to Co$ting Nature

Figure 9: Current pressure index according to Co$ting Nature, mean by country

In addition to these current pressures, the Amazon is subject to a number of future threats (changes in current pressures and/or emergence of new pressures). These can be mapped in Co$ting Nature (CN) using the following indices: accessibility from populated centres, proximity to recent deforestation, projected change in population and GDP, projected climate change and current distribution of night-time lights (as an indicator of otherwise unmapped threats). Note that protected areas are – again - not considered to reduce threat in this metric because this would depend on their effective management. Figure 10 shows

the CN threat index for the Amazon, indicating some areas of very high threat on a global scale (0.6 out of 1.0). Much of the Amazon is under considerable threat associated with proximity to current roads and urban areas, population growth and climate change. Only the Colombia/Brazil border and the countries NE of Brazil have lower threat which results largely from the lower threat of climate change in those areas.

Figure 10: Future threat index according to Co$ting Nature

Globally, as national averages, the threat index is quite different to the pressure index and indicates a threatened shift in pressure compared with the present, towards the rapidly developing countries that are currently under little pressure. In South America we see all countries except Argentina, Chile, Uruguay, Guyana, French Guiana and Suriname with very high pressure on a global scale.

Protected areas

However, the Amazon as defined here includes 35.2% coverage of current nationally and internationally protected areas according to WDPA (2012) and these protected areas occur in many of the areas with emerging threat. Deforestation between January 2004 and May 2012 according to terra-I (Reymondin et al, 2012) was lower (0.7% of land) in protected areas of the Amazon compared with 2.4% outside them. Some whole sub-basins are protected (Figure 12), including large basins in Venezuela and Brazil. If effective, these protected areas could help manage some of the threats to hydrological ecosystem services identified here. Lower coverage of protected areas is seen in densely populated eastern Brazil, Guyana, parts of Peru and Bolivia. Overall, the Amazon countries have a high level of coverage of protected areas on a global scale (Figure 13), though there are few indicators of the effectiveness of these except the deforestation rates given above and the considerable overlap between protected areas and planned roads and oil and mining concessions.

Figure 13: Protected area coverage by country

The impacts of key threats on water security

The mechanisms of land use change impacts on water security

The recycling of local evaporation and precipitation by forest is thought to account for a sizable portion of the regional water budget in the Amazon and thus there is some concern about how large-scale land surface changes may affect the water cycle in the tropics

(Marengo, 2006;Sampaio et al., 2007). Changes in land use may produce changes in albedo, aerodynamic resistance and trace gas emissions affecting the surface net radiation balance and thus the partitioning of sensible and latent heat and ultimately convection, cloud generation and precipitation. Evapo-transpiration adds moisture to the atmosphere which, if recycled within the basin, directly increases rainfall. Consequently it is suspected that large-scale deforestation would lead to decreases in rainfall in the basin. Though there are a number of modelling studies that support this contention (Henderson-Sellers and Gornitz, 1984; Lean and Warrilow, 1989; Werth, and Avissar, 2002; Ramos da Silva et al., 2008, Hasler et al., 2009; Nobre et al, 2009) for simulations of very large scale deforestation, there is little observational evidence in areas of recent deforestation (Porro et al., 2008). Indeed the observational evidence suggests the opposite - an increase in rainfall with deforestation (Butt et al., 2011). However, since less precipitation can lead to further decreases in vegetation cover, positive feedback cycles are possible with less forest cover resulting in less evapo-transpiration, less precipitation, reduced water availability and dieback of the remaining forest. Hence further intensification of land use in Amazonia could – if the models are correct - affect water, food and environmental security, both within and outside the basin.

Land-use practices can also disrupt the supply of fresh water through more localized changes in the surface water balance and the partitioning of precipitation into

evapotranspiration, runoff and groundwater flow (Marengo, 2006). For example, according to Coe et al. (2011), 55% of the Araguaia River in east-central Brazil was deforested and this has significantly altered the hydrological and morphological characteristics of this 82,632 km2 _{watershed. Observed discharge increased by 25% from the 1970s to the 1990s}

and computer simulations suggest that about 66% of the increase is from deforestation with the remaining 33% from climate variability over the period. Changes of this type are likely occurring throughout the 2,000,000 km2 _{savannah region of central Brazil. Indeed,}

large-scale deforestation appears to have significantly affected the Tocantins and Araguaia Rivers, where discharge has increased 25% between the 1970s and the 1990s (a period of significant deforestation in the region) with little change in precipitation over the period (Coe et al., 2009).

Land-use and climate feedback mechanisms

Under gravity, land inevitably loses water to the ocean. This water is replenished by atmospheric ocean-to-land moisture transport. Makarieva and Gorshkov (2007) used data for five terrestrial transects of the International Geosphere Biosphere Program to show that the mean distance over which air fluxes can transport moisture over non-forested areas, is less than several hundred kilometres and that precipitation decreases

exponentially with distance from the ocean. In contrast, precipitation over extensive

natural forests does not depend on the distance from the ocean. This points to the existence of an active ‘biotic pump’ transporting atmospheric moisture inland from the ocean. A novel physical principle is formulated according to which the low-level air moves from areas with weak evaporation to areas with more intensive evaporation. Due to the high leaf area index, natural forests maintain high evaporation fluxes, which support an ascending air motion over the forest and ‘draw in’ moist air from the ocean, which is the essence of the biotic pump. Though compelling, this mechanism has been widely critiqued by for example Meesters, Dolman and Bruijnzeel (2009). However, Spracklen et al. (2012) used TRMM rainfall and MODIS vegetation data combined with simulated atmospheric transport patterns, to assess the pan-tropical effect of forests on tropical rainfall, finding that air that has passed over extensive vegetation in the preceding few days produces at least twice as much rain as air that has passed over little vegetation. Using current trends of Amazon deforestation they estimate reductions of between 12 and 21 per cent in wet-season and dry-season precipitation respectively across the Amazon basin by 2050, due to less-efficient moisture recycling. This is, however, not strictly an observational study since it uses modelling at various stages in the analysis and the debate is thus very much open.

Mulligan (2008) and Montenegro et al., (2009) examined cloud cover (MODIS) and rainfall (TRMM) for neighbouring pixels of MODIS VCF tree cover data (Hansen et al., 2006) and found no significant relationship between change in forest cover and change in rainfall or cloud frequency.

Modelling studies on the impacts of land use change

Numerous modelling studies have been developed to improve our understanding of the impacts of deforestation on the hydrology of the Amazon. These studies have varied from macro-scale (>105 km2_{) to meso-scale (102-105 km}2_{) as well as one dimensional studies}

using single column models (SCMs). Macro-scale models (Atmospheric GCMs) have been applied at the Amazon scale and the comparison between scenarios of baseline (current) forest cover and (usually rather extreme) deforestation scenarios are made in order to understand the role of land cover in regulating the regional and global climate. Findings with these models indicate an overall decrease of water resources in the Amazon resulting from deforestation, due to reduced evapotranspiration and thus lower cloud generation and rainfall. The response seems to be nonlinear with thresholds of deforestation leading to a significant fall in rainfall and runoff because of the significant rainfall recirculation that occurs in the basin (Salati and Nobre 1991; Leopoldo et al., 1995; Laurance and Williamson 2001; D’Almeida et al 2007). In addition, large- scale atmospheric circulations and thus water and energy cycles as well as sensible and latent heat flux could also be affected by deforestation and forest fragmentation due to a decline in surface roughness length and an increase in albedo leading to reductions in surface net radiation (Nobre et al. 1991; Eltahir and Bras,1996; Roy and Avissar 2002; Berbet and Costa 2003; Durieux et al. 2003; Voldoire and Royer, 2004; D’Almeida 2007).

Meso-scale (regional) models have also been used to simulate deforestation impacts upon atmospheric circulations at finer scales (Roy and Avissar 2002; D’Almeida 2007). In the Amazon these models project change of intensity and spatial distribution of precipitation as well as an increase in the seasonality of clouds in areas of high deforestation (Avissar and Liu, 1996; NASA 2004; D’Almeida 2007). These effects are attributed to disruption of

atmospheric circulations due to impacts of land use on the convective boundary layer depth, soil moisture, surface temperatures and sensible and latent heat fluxes (Chen and Avissar, 1994; Roy and Avissar 2002; Durieux et al. 2003). In addition, it has been suggested that significant seasonal and diurnal changes in cloud cover occur in areas of significant forest conversion to pasture (Roy and Avissar 2002; Durieux et al 2003). Over deforested areas, lower level clouds are simulated in early afternoons and less convection occurs at night and in the early morning in the dry season, while convective cloudiness increases at night in the wet season (Roy and Avissar 2002; Durieux et al. 2003). However, these effects are

mediated in areas or periods of wind activity (Pielke et al., 1991; D’Almeida et al. 2007). Model simulations for the Amazon have shown that projected agricultural expansion in the SRES A2A scenario would cause significant additional warming (compared to greenhouse forcing alone) over the Amazon as well as cooling of the nearby oceans resulting in La Nina-like conditions (Feddema et al., 2010). Sampaio et al. (2007) used an AGCM to simulate the impacts of replacing rainforest by pasture or soybean cropland on regional climate for the eastern Amazon and found an accelerated decrease of rainfall for both land use conversions,

particularly in the dry season. Results of these model simulations depend on the model and assumptions but indicate that significant deforestation-driven climate change can occur when between 30-60% of the basin is deforested (Costa et al., 2007; Sampaio et al., 2007). These simulations though, rarely include feedback mechanisms such as forest fires, atmospheric aerosols and dynamic vegetation which can cause a negative feedback with regional climate.

The effects of deforestation are likely to vary throughout the basin according to varying climatic and topographic contexts though the mechanisms and patterns are highly uncertain. While Eltahir and Bras (1994) report a weaker impact of deforestation on the water cycle in west-central Amazonia, Chou et al. (2002) reports stronger effects.

Simulations carried out with an RCM by Ramos da Silva et al (2008) show a gradual decrease in rainfall with increase in deforestation although more spatial variability and stronger effects under El Nino conditions are observed. Land use change and climate change are strongly connected through the effect of land use change on land surface albedo and thus rainfall and cloud generation but also through the effect of land use on carbon cycle dynamics influencing global atmospheric sources and sinks for carbon dioxide. Land use and cover are themselves fundamentally dependent upon climate and can thus be expected to respond to changes in climate with obvious feedback cycles between the two.

Results from Single Column Models (SCM) simulate greater precipitation over forested areas in Amazonia due to greater evapotranspiration (Rocha, et al. 1996; D’Almeida et al.

2007). Their results sometimes differ from field observations due to the lack of

consideration of horizontal discontinuities such as thermal instabilities. In the Amazon, the use of SCM to model the Continuous Boundary Layer (CBL) (in Rondonia) differ from in situ observations of the same properties (D’Almeida et al. 2007).

As well as direct energy balance related impacts on the climate, deforestation could

potentially affect many other processes relevant to the provision of ecosystem services and human securities, including: (a) mitigation of global climate change, (b) disruption to the natural flow regimes of the Amazon river and its tributaries, which are fundamental to the wellbeing of riparian communities for transport (navigation), (c) water, agricultural and fisheries security and thus health (Vorösmarty et al., 1989; Salati and Nobre, 1991; Victoria

et al. 1991; Eltahir and Bras, 1994; Bruijnzeel 2004; D’Almeida et al 2007).

Data-based studies on impacts of land use change

Not many studies report the analysis of change in river flows as an effect of deforestation in the ACTO area. The scarcity of long-term, high resolution rainfall and river discharge records coinciding with reliable land cover change analysis over the same periods, at the country scale, has limited the ability to achieve consensus on the impacts of deforestation on river flows. While Sternberg (1987) report no discernible trends towards higher flood

peaks at Manaus during the period 1903 – 1955, Gentry and Lopez-Parodi (1980) and Costa

et al. (2003) propose an increase in the duration of floods with deforestation over the period. Gentry and Lopez-Parodi (1980), also attributed higher flood crests of the Amazon river at Iquitos during the 1970s, compared to the 1960s, to greatly enhanced deforestation in the upper parts of the river basin in Peru and Ecuador. Nordin and Meade (1982) attributed the same changes to normal climate variability. Nordin and Meade also indicate that river stage changed due to alterations of the streambed at Iquitos coupled with higher rainfall and inter-annual storage. Sternberg (1987) points out the limitations of analyses based only upon river stage records, because of the confounding arising from inter-annual and longer term climate variability. Nonetheless, he indicated a potential upward trend of river stage of the Amazon river at Manaus due to deforestation over the period 1903 –1985. Chu et al. (1994) highlights the significant uncertainty in the estimate of absolute amounts of rainfall over contributing watersheds. Harden (2006) considered the impacts of reduced storage capacity of Andean landscapes of Ecuador, mainly due to paramo removal, concluding that whilst local scale effects are apparent, extrapolation over regional contexts is fraught with difficulty.

Comparison of runoff ratios at the Amazon basin scale

Perhaps the most comprehensive study looking at the impacts of deforestation on river flows in the Amazon basin at the large scale is that of Costa et al (2003), which used runoff ratios (runoff per unit rainfall) to avoid confounding by changes in rainfall volume over time (though any changes in intensity and seasonal distribution remain confounding). They considered a 50 year time series (1949 - 1998) of river flows at Porto National station of the Tocantins river (drainage area of 175 360 km2 _{in south-east Amazon), rainfall data from the}

New et al (2000) dataset and land cover change from two periods with great contrasts in land use (1949 to 1968 with 30% deforested and 1979 to 1998 with 50% deforested). Costa et al (2003) suggest a statistically significant (but small) increase in the runoff ratio, i.e.

more runoff per unit rainfall (from 0.237 to 0.285 respectively), with negligible rainfall changes between the two periods. This means that historic land cover changes could have affected the long-term and seasonal maximum and mean flows of the Tocantins river. However, the rainfall dataset (New et al., 2000) still remains a source of uncertainty due to its coarse nature and thus important rainfall contributing areas might not be well factored in the analysis (D’Almeida et al. 2007).

Comparison of runoff ratios at the sub-basin scale

At small catchment scales (circa 1ha.) studies have reported changes in runoff ratios due to land use changes in the Amazon basin. Chaves et al (2007) in a paired catchment study in Rancho Grande, Rondonia, studying the impacts on flow regimes of the conversion from forest to pasture, reports an increase in surface stream flow from 0.8% to 17% of rainfall from a forest to a pasture site, which was attributed to changes in soil hydraulic

2006). These results coincide with similar studies reported for the Amazon states of Para (Moraes et al. 2006) and Manaus (Trancoso et al 2007). These studies highlight the

potential impacts of forest conversion to pasture at larger scales in the Amazon, though the extent to which such changes persist at those scales is – as yet – unknown and

extrapolation by modelling suggests that these effects reduce with scale. No studies referring to the change in peak flows, mean and minimum base flows are reported for flow stations in the Amazon tributaries in the countries of Colombia, Ecuador, Venezuela and Bolivia. Work by Buytaert et al (2006) and Buytaert et al (2011) looked specifically at the impacts of land use and climate change on high altitude paramo, though these cover very small areas at the Amazon scale.

Agricultural land management

In addition to changing land cover and use, land management practices can significantly affect the delivery of hydrological ecosystem services (and thus water security) downstream. Examples of such impacts are pesticide and fertilizer use in agriculture. Soybean and non-traditional crops in horticultural production in the Brazilian Amazon are some of the main pesticide consumers in the country and can severely contaminate soil and water in the region (Laabs et al., 2002; Römbke et al., 2008). Furthermore, fertilizer use has been increasing in the last decades leading to increased water pollution (Börner and Wunder, 2012). Intensification of agriculture through the use of mechanization has also been shown to increase deforestation as a result of increased capacity to deforest (Börner et al., 2007, Börner and Wunder, 2012) in contrast to the Borlaug hypothesis. Accidental forest fires, particularly in dry years due to slash-and-burn agriculture can also be a cause of

deforestation (Nepstad, 2001) and can also lead to increased overland flow generation, erosion and soil leaching (Williams et al., 1997).

Extractives - mining and oil and gas

Mining activities and oil and gas production are economically important for the Amazon basin but can also have significant impacts on water security both through their use of water and their contamination of downstream water resources. Small-scale artisanal gold mining is a significant source of mercury pollution to surface waters in the Tapajos river basin (De Sousa, 2010; Berzas Nevado et al., 2010) while oil extraction in the Ecuadorian Amazon is responsible for many oil spills and the release of untreated wastes into the environment (San Sebastian et al., 2002). These extractive activities are also partly

responsible for extensive forest degradation through the development of infrastructure such as access roads. The wave of development and deforestation that follows mining and oil can have a great impact on water quality and quantity. With nearly fifty per cent of the

Peruvian Amazon receiving oil and gas concessions in recent years (Finer and Orta

Martinez, 2010) this is potentially a huge threat to the environment and water security (see case study).

Hydropower

Hydropower potential is plentiful in the Amazon but little of it has yet been realised (see case study). For instance, Brazil has nearly two times the potential than currently installed with 63% of this potential located in the Amazon basin (Da Silva Soito and Freitas, 2011). This source of energy is economically important for the region but also has significant associated risks for water security downstream with potential impacts on water quality (Fearnside, 2001; Viana, 2002; Finer and Jenkins, 2012) and quantity. For instance, as a result of releases associated with HEP operations at the Samuel dam on the Jamari river, downstream dry season flows have increased and the occurrence of peak flows is now occurring 1-2 months earlier (Viana, 2002). As well as direct impacts on water, the

development of hydropower dams requires significant changes to the environment such as forest clearing for transmission lines and access roads and the flooding of large areas of forest for the creation of reservoirs. In some cases huge landscape destruction has taken place for only small potential hydropower generation, for example the Balbina dam where 2360km2 _{was flooded for a potential generation of only 250MW (De Sousa, 2010).}

Population

Water security is as much about the demand for water as the supply of it. Population growth can significantly affect demand for water (both directly for domestic supply but also indirectly for industrial and agricultural water uses). If demand approaches supply on an average basis or periodically (e.g. seasonally) then the impact on water security can be profound.

Growth and migration

Population in the Amazon is highly concentrated in urban areas (Porro et al., 2008) and this is where population growth is also high and water security risks are most intense. There has been some international emigration from the Amazonian countries, largely within the Americas but also to Europe and elsewhere (Pellegrino, 2000). Internal migration within and between Amazon countries have been also important. In Colombia and Peru, due to population displacement from the Andean region either by armed conflict or lack of job opportunities many people moved to the Amazon area attracted also by oil, mining and coca “bonanzas” (Gomez, 1999; Dominguez, 1999; Puyana, 1999; INEI, 2009). Attracted by the potential of agriculture in the Bolivian floodplains, there have been significant movements from people of Brazil and Argentina, most being self-employed as farmers (Vargas, 2009).

However significant impacts of climate change or other hazards on food or water security could exacerbate current patterns of rural to urban migration and lead to further economic migration between countries. This would place further pressure on some of the largest

cities such as Rio and Sao Paolo, Brazil or some of the Amazon border cities. The current population in the Amazon is found mostly in urban areas such as Iquitos, Leticia, Manaus, Rio Branco, Porto Velho, Boa Vista and Macapa with the remainder along the river and its main tributaries. However, immigration and settlement into the Amazon has been

accelerated in recent decades with population growth rates of between 5 and 7% which is above the national averages for the individual Amazon countries (Braga et al., 2011). The growing urban population is a driver for deforestation as a result of more infrastructure development (Weinhold and Reis, 2001) and is also a cause of water pollution due to increased domestic sewage that generally leaves the cities in an untreated state (Braga et al., 2011).

Per-capita demand

With economic development per-capita demand for water, especially that water embedded in food commodities (i.e. consumed as transpiration during crop production), increases significantly. Per-capita demand for food-water is increasing both regionally and globally. Whether the demand is within the Andes/Amazon or globally, the increasing trade of embedded water and thus the agriculturalisation of the Amazon could significantly threaten water security for some downstream communities.

Water treatment, supply and sanitation

Water treatment for urban areas in the Amazon countries is well developed. Drinking water sources are considered improved for the following proportions of the urban population at 2010: Brazil (100%), Colombia (99%), Ecuador (96%), Peru (91%), Bolivia (96%),

Venezuela (94% year 2000), Suriname (97%), Guyana (98%), French Guiana (88%). Rural improved drinking water coverage is less at: Brazil (85%), Colombia (72%), Ecuador (89%), Peru (65%), Bolivia (71%), Venezuela (74%, year 2000), Suriname (81%), Guyana (93%), French Guiana (71%) (UNICEF, 2012). The proportion of the population served by improved sanitation in urban areas has also much improved in recent decades in the Amazon countries: for 2010 Brazil (85%), Colombia (82%), Ecuador (96%), Peru (81%), Bolivia (35%), Venezuela (93%, year 2000), Guyana (88%), Suriname (90%) and French Guiana (85%, year 2000) (UNICEF, 2012). In rural areas the improved sanitation figures for 2010 are much lower: Brazil (44%), Colombia (63%), Ecuador (84%), Peru (37%), Bolivia (10%), Venezuela (54%, year 2000), Guyana (82%), Suriname (66%), French Guiana (57%, year 2000). This is good for water security and associated health security. However, in these countries sanitation systems are poorly developed even in urban areas and cannot usually cope with storm flow, so much of this effluent produced may find its way untreated into the river systems, creating hazards for downstream (especially rural, poor and

marginal) communities without access to water treatment. Using national statistics disaggregated for the Amazonian provinces and municipalities, improved water in urban areas is much lower than the national average: Colombia (47% year 2005), Peru (52% year 2009), Bolivia (48% year 2001) (SIGOT, 2012; INE, 2012; MINAM, 2012).

Dramatic improvements in sanitation have been made in the last 30 years in all Amazon countries except Bolivia. Open defecation is currently estimated at 11-25% in Brazil, Colombia, Ecuador and Suriname, less than 10% in Guyana but 25-50% in Peru and still >50% in Bolivia (UNICEF,2012), though this is predominantly a rural practice. Water pollution as a result of urban sewage in the Amazon has been studied by Coucero et al., (2007) who found high levels of nutrients in 65 urban streams around the city of Manaus.

Water treatment in rural areas is generally absent in the Amazon with most people obtaining water from rivers, springs and wells and consuming it untreated. Sanitation in rural areas is equally poorly developed with pit latrines being commonplace and many communities having even more poorly defined sanitation conditions (UNICEF, 2012).

We have examined the multitude of threats to water security in the Amazon, some of which are coupled with climate security and some of which are fundamentally linked to the drive to increase food and energy security in the region. We will now look at how realistic

scenarios of land use and then climate change may resolve spatially in the region to impact on water.

Modelling the impacts of land use change on the water security future of the Amazon

Here we use WaterWorld to examine the impacts of two scenarios for land use change on elements of water security. In each case the differences between the baseline water security conditions highlighted in the previous section and the conditions created by the scenario are compared to indicate areas in which water security might increase or decrease as a result. Figure 14 shows the land use change baseline (top left) and scenarios (top right and bottom left). In WaterWorld, land use is represented as a cover of trees, herbaceous vegetation and bare ground in each 1km pixel alongside an indication of land use (cropland, pasture, mining, urban, protected etc). The land use change scenarios used were generated by the

WaterWorld land use change model which works on the basis of projecting recent rates of deforestation as a mean per regional administrative boundary (for the last 12 years) into the future, deforesting areas with greatest current accessibility and closest to the existing deforestation fronts first. Two scenarios were run. In the first (called LUCC) we projected recent rates forward by 40 years to 2050 with protected areas ineffective at halting

deforestation in the areas nominally protected. For the second scenario (called ACCEL) we quadrupled the recent rate of observed deforestation (to levels seen in the late 20th _Century)

but with no deforestation within protected areas because they are considered effectively protected. Current forest cover was converted to pasture with a tree, herb and bare cover of 5%, 90% and 5% respectively and an intensity of use of 1.0 (the standard for the land use, indicating eco-efficient methods not used). Figure 14 represents tree cover for the baseline (top-left), LUCC scenario (top right) and ACCEL scenario (bottom-left). Clearly in both scenarios deforestation continues in the areas in which it is currently taking place but is

much more extensive in the ACCEL scenario, except in protected areas. Even the ACCEL scenario shows much less deforestation than that proposed by the Soares-Filho (2006) business as usual scenario, though the ACCEL scenario is based on recent rates (last ten years) and is more consistent with the overall rates of change experienced over the last 40 years (Figure 15). The Soares-Filho BAU scenario appears to show a much greater deforestation for the next 40 years than was experienced in the last 40 years. The Soares-Filho scenario could not be used because it does not generate the tree, herb and bare cover required by WaterWorld and also it does not cover the entire ACTO area.

Figure 14: Tree cover (%) for top left : baseline (2000), top right: LUC (2050) and bottom left: ACCEL (2050) scenarios, bottom-right Soares et al. (2006) BAU scenario.

Figure 15: Tree cover (%) for top left: LUC (2050) and bottom right: ACCEL (2050) scenarios for Rondonia in relation to protected areas (WDPA, 2012)

Figure 16 and Figure 17 show the change in tree cover by major sub-basin resulting from the LUCC scenario and the ACCEL scenario respectively. Clearly the decreases in the tree cover are significant at the sub-basin scale in both scenarios but much more extensive in the ACCEL scenario. We now examine the hydrological impacts of these scenarios according to the difference between baseline and scenario outputs for key variables

influencing water security: the water stress index, the HF pollution index and the number of persons with poor quality drinking water. In terms of water stress, deforestation leads to decreases in fog water inputs (through reduced cloud water interception by trees, sensu

Bruijnzeel et al 2011) thereby increasing stress in a few coastal and cloud forest areas subject to frequent fog. In all other deforested sub-basins deforestation leads to an increase in available water because the low herbaceous cover that replaces forest lead to lower evapo-transpiration. The resulting decreases in the water stress index are, however minimal at the sub-basin scale, of the order of a few per cent only (Figure 18 and Figure 19). These impacts on water quantity are minor because there is relatively little difference in AET between the cover types and the changes in runoff that result tend not to affect the stress index significantly since AET is much lower than precipitation for much of the basin. Whether these impacts result in changes in hydropower potential depend upon the design flows for the dams in question relative to the changes identified here.

Figure 16: Change in tree cover resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin, %

Figure 17: Change in tree cover resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin,%

Figure 18: Change in water stress index resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin, % of industrial and domestic (blue water) demand not supplied in months at which supply<demand across the year

Figure 19: Change in water stress index resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin, % of industrial and domestic (blue water) demand not supplied in months at which supply<demand across the year

The changes in HF pollution index from the scenarios are much more significant since the differences between non-agricultural and agricultural land in terms of their contaminant inputs to downstream are significant. For the LUCC scenario many of the S and E

Brazilian sub-basins show high levels of HF index. For the ACCEL scenario the extent and intensity of impact on water quality is greater and it pervades further downstream.

Figure 20: Change in HF pollution index resulting from continued deforestation at current rates for a further 40 years (LUC), by major sub-basin,%

Figure 21: Change in HF pollution index resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin, %.

In terms of impact on water security both of these scenarios see increases in the number of people with poor quality (HF>50%) water, particularly in Caqueta, Colombia and some basins in Rondonia, Brazil and Beni, Bolivia (Figure 22 and Figure 23).

Figure 22: Change in persons per km2 _{with poor water quality resulting from continued deforestation}

at current rates for a further 40 years (LUC), by major sub-basin, persons per km2

Figure 23: Change in number of persons with poor water quality resulting from continued deforestation at quadrupled current rates for a further 40 years (ACCEL), by major sub-basin

Overall, land use change projections that might be expected in the next 50 years lead to only very small effects on water quantity, echoing the impacts of historic deforestation indicated by the literature review. The resulting impacts on water stress are minor though the impacts on water quality are much more significant.

The role of climate change in exacerbating these threats and

generating new threats to water security

The Amazon basin straddles the equator and the basin stretches from 5 degrees N to 20 degrees S and 79 degrees W to 49 degrees W. A complex mosaic of climates exists in this area. Rainfall varies from more than 3000 mm/yr to less than 1500 mm/yr in a NW to SE gradient across the basin and there are also strong elevational gradients in the western Amazon.

The climatic threats

The impact of climate variability

A review of the relevant literature on observed changes in flow resulting from land use change for the Amazon indicates that significant variability in flow exists as a result of climate variation and this makes the assessment of land use impacts difficult, also because flow records are often short and interrupted and few controlled (paired catchment) studies exist. This variability includes decadal and inter-annual climate variability that has affected the discharge of the Amazon river in different parts of the basin. ENSO phenomena have produced severe drought and strong flow and rainfall deficits in the western Amazon with consequent widespread fires (Carneiro, 1957; Sternberg 1987;

Williams et al 2005). Mulligan et al. (2011a) showed an inter-annual variability in rainfall for the nearby Andes of 9% and for the Sao Francisco of 2.4%.

Climate change scenarios for the Amazon

Climate change scenarios for the Amazon can be separated into those that result from global greenhouse-forced climate change (of which the Amazon is only one of many contributors) and those that are a function of regional change in land surface conditions including land use. Generally, climate model projections for the Amazon based on

greenhouse forcing alone point to a reduction of precipitation and increased temperature. Decreases in precipitation can mostly be attributed to projected longer and more intense dry seasons as a result of a northward shift of the Inter-Tropical Convergence Zone (ITCZ) due to increased tropical Atlantic north-south Sea Surface Temperature (SST) gradient mostly affecting southern and eastern Amazonia (Malhi, 2009). Simulations of greenhouse warming with regional climate models have shown that this effect can lead to reductions in rainfall of up to 40% (Marengo et al., 2012), significantly reducing river runoff in the

eastern Amazon and the Sao Francisco basin. For the western Amazon though, more precipitation extremes are projected (Marengo, 2009). Simulations for the Peruvian-Andes basin part (~10%) of the Amazon by Casi et al., (2011) using several GCMs have shown that impacts of climate change on hydrology vary with watershed and scenario with some basins projected to have an increase in runoff and others a decrease. As part of an analysis of 11 large basins globally, Mulligan et al. (2011a) examined climate change in Brazil’s Sao Francisco basin and the Andes using an ensemble mean for 17 GCMs from AR4 A2 to the 2050s. Temperature was projected to increase across the Andes by a mean of 2.7 degrees Celsius with a standard deviation of 0.9, whilst rainfall would increase by 137 mm/yr but with a standard deviation between GCMs of 882 mm/yr. The Sao Francisco shows similar warming and highly uncertain wetting. This level of uncertainty in rainfall projection makes scenario generation challenging.

Gradual change vs. abrupt events

As well as projected continuous and gradual climate change, it is likely that there are tipping points in the Amazon system (Malhi, 2009). An example of such an abrupt possible change is the notion of Amazon rainforest 'die back' which is the replacement of forests by savannah and semi-arid vegetation as a result of climate change. Such a die back of parts of this vast rainforest would mean the Amazon shifts from net absorbing carbon from the atmosphere to net emitting it. Early model simulations suggest this could happen as early as 2050 (Cox et al., 2000) although more recent studies indicate that this may happen later in the future (Huntingford et al., 2004) or not at all (Schaphoff et al., 2006). However, two severe and unusual recent droughts in 2005 and 2010 have demonstrated the mechanism of a shift from the Amazon buffering the anthropogenic increase in atmospheric CO2 to

accelerating it (Lewis et al., 2011). These droughts were caused by a combination of

intensified ENSO events as a result of increased Pacific SST and high Atlantic SST causing a northwest displacement of the inter-tropical convergence zone (Zeng et al., 2008; Lewis et al., 2011). Droughts increase the forests' vulnerability to fire occurrence which acts as a positive feedback that can lead to accelerated forest degradation.

Climate uncertainties

Projections of precipitation in climate models are highly uncertain. This is particularly the case for the deep tropics in South America (Rowell, 2011). Most IPCC AR4 GCM

underestimate rainfall for the Amazon (Malhi et al., 2009) which is mostly due to highly uncertain modelling of sea-surface temperature changes in the oceans. Jupp (2010)

compared the ability to capture the mean and variability of observed rainfall for 24 GCMs in 5 regions around the Amazon and found that not one model performs well in all cases. Precipitation projection uncertainty also stems from the coarse grid scale of GCMs that do not take into account finer scale processes that are known to intensify precipitation in the region (Malhi et al, 2009; Ramos da Silva and Avissar, 2006). For example, GCMs simulate

that wet seasons penetrate further south than they actually do, due to cool westerly air over the Andes that dissipate the convective energy of moist Amazonian air masses in reality which are not well represented in the GCMs. This process limits the southern extent of rains and increases actual rainfall over the Amazon (as a latitudinal concentration mechanism). In a study comparing 19 GCM, Malhi et al., (2009) found that 18 of them underestimate current rainfall compared to Tropical Rainfall Monitoring Mission (TRMM) and CRU observed datasets in western Amazonia. A recent study by Torres and Marengo (2012) comparing 24 GCMs from the IPCC AR4 concluded that there is a more than 90% probability that temperature increase in the Amazon will exceed 2 deg. C by 2100 but that projected changes in precipitation have a similar magnitude as the uncertainty range and are comparable to natural variability. Precipitation is fundamental to water security but also essentially unpredictable even on short time scales.

The impacts on water security Water quantity

The projected drying in southern and eastern Amazonia will likely cause a reduction in river flows in those areas and thus a decrease in water supply. A number of studies have assessed the potential impacts of climate change on water supply in the Amazon with projected changes in Amazon river runoff reflecting the uncertainty in climate change projections, i.e. some studies project increase in runoff (Nijssen et al., 1999) while others project decreases in runoff (Aurora and Boer, 2001; Palmer et al., 2008). Generally though, a reduction in river runoff for eastern and southern Amazonia is expected. Furthermore, greater variability in precipitation is projected which could lead to higher spatial and temporal variability in river discharge (Coe et al., 2002). Studies into changing water demand in the basin are scarce but simulations carried out with the global WaterGap model for the 2050s indicate that despite projected reductions in available water,

withdrawals will remain low enough to prevent water stress on an annual basis (Palmer et al., 2008). Seasonal water stress was not analysed by Palmer et al. but may be important as may local excesses of demand over supply (see below).

In order to examine the likely impacts of projected climate change on the water security of the Amazon, we used WaterWorld with a series of climate change projections. We chose the A2 scenario representing high growth and a 3.5ºC warming (relative to 1900) by 2100. Rather than running the model with the projections of a single GCM we ran with an ensemble mean scenario for the 2050s of all 20 available GCMs, followed by an ensemble mean minus 1 ensemble standard deviation. These scenarios represent the central tendency of the projections and the dry end of the projections respectively. We also ran the mean scenario for the 2080s to understand longer term water security and the consistency of projected trends into the future. Figure 24 to Figure 27 show the change in rainfall for the different scenarios, to the same scale. All scenarios showed an increase in temperature

across the basin but the much more hydrologically significant change is the change in rainfall. There is clearly significant uncertainty in the projections with the mean of all GCMs indicating a significant wetting relative to the present day baseline in most of the basins to the W, central and S Amazon, whereas the mean minus 1 SD shows drying throughout the basin. The wetting for the mean of the ensemble appears to increase further towards the 2080s, especially in the W of the basin.

Figure 24: Change in rainfall with AR4 A2 mean of all (20) models 2050s

Figure 25: Change in rainfall with AR4 A2 mean of all (20) models minus one sd 2050s

WaterWorld uses the monthly temperature and precipitation grids from the scenarios (downscaled to 1km spatial resolution) to simulate the hydrology. Processes of snowfall and snowmelt, fog and fog water interception, rainfall and evapo-transpiration respond to these climatic forcings. The outcome in terms of change in the water stress index (mean monthly % of water demand that is not supplied) is shown in Figure 27 to Figure 29 to the same scale. Clearly the mean scenario leads to increases in precipitation but also in evapo-transpiration such that the mean overall water stress increases slightly in some basins and decreases slightly in others. Note that we did not combine the climate scenario with an increased population and agricultural land use which would likely further increase the water stress. If we look at the mean minus 1 sd then the picture changes completely with most of the outer basins of the Amazon to the S and W showing a significant increase in water stress whilst the populous eastern Brazil basins show a significant decrease in stress (though this decrease may be lessened by continued population growth in that area). The results reflect the differences in annual total and especially seasonal precipitation changes between the regions. Whether these impacts result in changes in hydropower potential depend upon the design flows for the dams in question relative to the changes identified here. Both increases and decreases in flows may have impacts on the operation of dams which have been designed for the baseline climate. Areas which dry may have multiplier effects for other threats such as fire events and drought. Areas that wet may have

multiplier effects on flooding. The reality is that such extreme events are impossible to quantify into the future so that their management is difficult.

Figure 27: Change in water stress with AR4 A2 mean of all (20) models 2050s, % of industrial and domestic (blue water) demand not supplied in months at which supply<demand across the year

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