Land Use in LCA: Including Regionally Altered Precipitation to

Oct 7, 2016 - Enhancing the Water Accounting and Vulnerability Evaluation Model: ... Water scarcity footprint of dairy milk production in New Zealand ...
0 downloads 0 Views 1MB Size
Subscriber access provided by CORNELL UNIVERSITY LIBRARY

Article

Land use in LCA: including regionally altered precipitation to quantify ecosystem damage Michael Jacques Lathuillière, Cecile Bulle, and Mark S. Johnson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02311 • Publication Date (Web): 07 Oct 2016 Downloaded from http://pubs.acs.org on October 13, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 36

Environmental Science & Technology

1

Land use in LCA: including regionally altered

2

precipitation to quantify ecosystem damage

3

Michael J. Lathuillière*,†, Cécile Bulle♦, Mark S. Johnson†,§

4



5

Main Mall, Vancouver, BC V6T 1Z4, Canada

6



7

Gestion, Université du Québec à Montréal, CIRAIG, 315, rue Sainte-Catherine Est, Montréal,

8

QC H2X 3X2, Canada

9

§

Institute for Resources, Environment and Sustainability, University of British Columbia, 2202

Département de stratégie, responsabilité sociale et environnementale, École des Sciences de la

Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, 2207

10

Main Mall, Vancouver, BC V6T 1Z4, Canada

11

*Corresponding author, email: [email protected]

12

ABSTRACT

13

The incorporation of soil moisture regenerated by precipitation, or green water, into life cycle

14

assessment has been of growing interest given the global importance of this resource for

15

terrestrial ecosystems and food production. This paper proposes a new impact assessment model

16

to relate land and water use in seasonally dry, semi-arid, and arid regions where precipitation and

17

evapotranspiration are closely coupled. We introduce the Precipitation Reduction Potential mid-

ACS Paragon Plus Environment

1

Environmental Science & Technology

Page 2 of 36

18

point impact representing the change in downwind precipitation as a result of a land

19

transformation and occupation activity. Then, our end-point impact model quantifies terrestrial

20

ecosystem damage as a function of precipitation loss using a relationship between woody plant

21

species richness, water and energy regimes. We then apply the mid-point and end-point models

22

to the production of soybean in Southeastern Amazonia which has resulted from the expansion of

23

cropland into tropical forest, with noted effects on local precipitation. Our proposed cause-effect

24

chain represents a complementary approach to previous contributions which have focused on

25

water consumption impacts and/or have represented evapotranspiration as a loss to the water

26

cycle.

27 28

TOC Art

29 30 31

INTRODUCTION Global water resources are reaching a critical point with the convergence of population growth,

32

climate change and supply side approaches to water management, all contributing to water

33

scarcity to some extent.1,2 While proposed actions are often directed to domestic water use,

34

agriculture and industrial sectors also play an important role in reducing water consumption.3 In

35

addition to operations, production processes consume water indirectly through the supply chain,

36

oftentimes in distant and highly stressed watersheds.4 Water use in life cycle assessment (LCA)

ACS Paragon Plus Environment

2

Page 3 of 36

Environmental Science & Technology

37

has evolved in recent years to complement water resource management initiatives with an

38

impact-oriented assessment from water consumption and degradation within the LCA

39

framework.5,6,7 LCA is a scientific method which identifies impacts of a product or an activity

40

from “cradle to grave”, that is, by considering potential impacts over the entire life cycle, from

41

resource extraction to end-of-life.8 A LCA can focus specifically on water use (often described

42

as Water Footprinting),6 in which case the ISO 14046 standard applies.9 The ISO 14046 standard

43

outlines the principles, requirements and guidelines on how to determine the environmental

44

impact of water consumption and degradation considering the spatial and temporal implications

45

of the hydrological cycle, but by also recognizing that land management can affect water

46

availability.9 While several methods and challenges exist to assess the impacts of freshwater

47

consumption in LCA,6,10 formal methodological recommendations are only now beginning to

48

emerge in order to apply the standard.11,12

49

Water use impacts in LCA rely exclusively on the quantification of freshwater consumption

50

and degradation,6 with consumption defined by Bayart et al.7 as the amount of freshwater used,

51

in- or off-stream, that does not return to the watershed, either because of evaporation, product

52

integration, inter-basin transfers or direct release into the sea. Water resources have been

53

described in terms of green and blue water which are mainly differentiated by their consumptive

54

uses.13 Green water represents the soil moisture regenerated by precipitation which generally

55

returns to the atmosphere either through evaporation (e.g. soil, water intercepted by plants), or

56

transpiration when green water is consumed by plants during photosynthesis (together as

57

evapotranspiration, or ET); blue water is the liquid water in the water cycle (surface or

58

groundwater)13. This perspective encompasses ecohydrological processes and places an emphasis

ACS Paragon Plus Environment

3

Environmental Science & Technology

59

on water supplied to the atmosphere that plays a key role in regenerating precipitation through

60

ET in “atmospheric watersheds”,14 rather than viewing ET as a loss to the water cycle.15,16

61

This supply-side view of ET highlights the important role of forests as key providers of water

62

vapour via transpiration, in addition to evaporation processes from natural and human made

63

reservoirs and rainfall intercepted by vegetation.17 Green water resources are essential in

64

seasonally dry, semi-arid and arid regions where precipitation is tightly coupled to ET.15 Any

65

change in landscape ET can modify the water vapour supply to the atmosphere and therefore

66

affect precipitation downwind, in areas possibly located hundreds or thousands of kilometers

67

away.14 A reduction in precipitation, in turn, can potentially impact rain-fed agricultural

68

production as well as terrestrial ecosystems, both of which rely almost exclusively on green

69

water resources.15

70

The impact pathway linking a change in ET to a change in precipitation with potential

71

terrestrial ecosystem damage has not yet been modeled in LCA for several reasons: (1) the main

72

focus of water use in LCA has been on blue water resources due to the priority of developing

73

models which first address surface and groundwater within the context of global blue water

74

scarcity;10 (2) green water resources are intimately tied to land use and therefore changes to ET

75

can be seen as a consequence of a land transformation impact rather than a water consumptive

76

use impact;10 (3) the regeneration of precipitation through ET is not widely recognized17 due to

77

the focus of traditional water resources management on blue water.

78

Page 4 of 36

Many studies have already addressed some potential impacts of blue water consumptive use on

79

water scarcity.11,18,19,20,21,22 Other models have focused on ecosystem quality22,23,24 with

80

emphases on groundwater extraction,25 wetland ecosystems,26,27 or biodiversity more generally.28

ACS Paragon Plus Environment

4

Page 5 of 36

Environmental Science & Technology

81

Few studies have modeled impact pathways linked to ET (Table 1), and none so far have

82

described the damage incurred by terrestrial ecosystems resulting from changes in water vapour

83

flows to the atmosphere. Most methods focus exclusively on the changes in the fate of blue water

84

quantity as a result of changes in ET either quantified directly29,30 or indirectly through a soil

85

water balance estimate.21,31

86 87

Table 1. Summary of mid-point impacts which directly or indirectly include green water in LCA. Model

Inventory

Mid-point impact

Milà i Canals et al. (2009)21

Green water (soil moisture) and blue water (rivers, lakes, runoff and fossil groundwater)

Freshwater ecosystem impact; freshwater depletion

Ridoutt & Pfister (2010)32

Net green watera identifies changes in blue water flows

Contributions to blue water scarcity

Saad et al. (2013)31

Net green water identifies changes to groundwater recharge

Freshwater recharge potential

Núñez et al. (2013)30

Net green water flow

Contribution to green water scarcity

Net green water identifies effective green water flow based on the basin evaporation recycling ratio

Terrestrial green water flows; reductions in blue water production

33

Quinteiro et al. (2015)

88 89

a

Net green water is defined as the difference between current land use ET and ET from a potential natural vegetation landscape.30

90 91

This paper presents an extension to current life cycle impact assessment modeling (LCIA) of

92

land transformation and occupation to include the effects of a change in water vapour flows to

93

the atmosphere on precipitation and the resulting damage to terrestrial ecosystems. We propose

94

to follow current UNEP-SETAC guidelines of land transformation and occupation34 such that

ACS Paragon Plus Environment

5

Environmental Science & Technology

Page 6 of 36

95

environmental interventions with a similar structure as used in current life cycle inventory (LCI)

96

databases on land use — but with an improved level of resolution in terms of regionalization and

97

of culture type — can be used directly in our method. We apply the proposed method to

98

Southeastern Amazonia which is home to an agricultural frontier with vast expanses of soybean

99

production areas that were established within tropical forest and savanna landscapes.35,36,37,38,39,40

100

Crop production in the region is almost entirely rain-fed, suggesting a strong connection between

101

land use, green water and precipitation. Changes in land use and land cover have raised concerns

102

over threats to the region’s ecological integrity due to a reduction in biodiversity41,42 and changes

103

in precipitation patterns43,44 that could lead to increased drying of the Amazon biome.45,46 The

104

proposed LCIA methodology can improve transparency of environmental impacts of soybean

105

production and inform supply chain initiatives47 as a main concern to export centers such as

106

Europe and China.48

107

MATERIALS AND METHODS

108

Land transformation and occupation impacts to the water cycle. Green water is not

109

considered to have an environmental impact unless it is associated with a human intervention

110

such as land transformation and occupation.21 In this case, the LCI should reflect the area of land

111

transformed (m2) or area-time of land occupied (m2 y) following UNEP-SETAC guidelines.34

112

Briefly, a land transformation activity of potential natural vegetation (PNV) into a new land use

113

(LU) affects ecosystem quality over the land’s occupation period. As shown conceptually in

114

Figure 1, the land use change of area A at time t1 from PNV to LU may reduce ecosystem quality

115

from QPNV to QLU.34 Land occupation impacts are then quantified by the difference in ecosystem

116

quality over the time of occupation (t2 – t1). Similarly, ecosystem quality may increase from QPNV

117

to QLU in the case of an ecosystem quality benefit obtained from land use change (this special

ACS Paragon Plus Environment

6

Page 7 of 36

Environmental Science & Technology

118

case is not considered further here).34 At the end of occupation, natural ecological regeneration

119

processes return the landscape to QPNV over a regeneration period (t3 – t2) (Figure 1), which is

120

estimated at 159 years for tropical forest in the Southeastern Amazon region.49 Thus, the impacts

121

of a change from PNV to LU are represented by land occupation impact O and land

122

transformation impact T following the end of land occupation34 (Figure 1). By convention, land

123

transformation impacts are allocated to the first 20 years of land use immediately following the

124

land use change activity.34 This framework has been used as the starting point for modeling mid-

125

point impacts such as Biodiversity Damage Potential and Ecosystem Services Damage

126

Potential.31,34,50

127 128 129 130 131 132

Figure 1: Conceptual representation of land transformation (area T) and occupation (area O) impacts resulting from the replacement of potential natural vegetation (PNV) by a new land use (LU) represented by the ecosystem quality curve as a function of time and area (not shown) according to the UNEP-SETAC guidelines.34 Replacement of PNV by LU at time t1, reduces the land’s ecosystem quality from a PNV state (QPNV) to a new LU state (QLU).34

133

ACS Paragon Plus Environment

7

Environmental Science & Technology

134

Page 8 of 36

Land is seen as a buffer in the water cycle such that a change in land use can alter water

135

quantity21,22,33 and quality. We introduce a model resulting from this change to quantify

136

Precipitation Reduction Potential (mid-point impact) with a potential damage to terrestrial

137

ecosystem quality (end-point impact) due to a change in precipitation (Figure 2). Both models

138

are described in turn.

139 140 141 142 143

Figure 2: Proposed cause-effect chain of impacts following a change in landscape evapotranspiration (ET) after land use change of potential natural vegetation (PNV) into a new land use (LU).

144

Land transformation and occupation impacts on precipitation potential. Following

145

UNEP-SETAC guidelines,34 we calculate Precipitation Reduction Potential (PRP) in equations

146

(1) and (2) ,    , , ,

(1)

,    , ,

(2)

147

where PRPocc,j and PRPtrans,j (m3) are the mid-point impact scores representing the amount of

148

precipitation not returning to region j, respectively as a result of a land occupation and

149

transformation activity occurring on land i, CFocc-mid,i (m3/m2 y) and CFtrans-mid,i (m3/m2) are the

ACS Paragon Plus Environment

8

Page 9 of 36

Environmental Science & Technology

150

mid-point characterization factors associated with land occupation and transformation on land i,

151

Aocc,i and Atrans,i (m2) are the land areas of land i under consideration for occupation and

152

transformation respectively, and tocc,i (y) is the land occupation period on land i. Land i and

153

region j are the source and sink of precipitation from which the impact score is derived and

154

depends on the source of water vapour on land i and the amount that becomes precipitation in

155

region j. The product Aocc,i tocc,i (m2 y) in equation (1) and Atrans,i (m2) in equation (2) are the LCI

156

flows for land occupation and transformation on land i. Both CFocc-mid,i and CFtrans-mid,i are

157

expressed as a function of the difference in ET from the land use i between the PNV (ETPNVi,

158

m3/m2 y) and current land use (ETLUi, m3/m2 y) described in equations (3) and (4). Note that this

159

difference is of opposite sign to what has previously been called net green water30 and defined as

160

ETLU – ETPNV.   ,  ( −  )



1   ,    , #$#, 2

(3)

(4)

161

where erj (dimensionless) is the regional evaporation recycling ratio constrained to the

162

geographical unit of affected area j,51,52 and tregen,i (y) is the regeneration time of the PNV on land

163

i. In physical terms, erj represents the amount of water vapour returning to the land as

164

precipitation within region j, or the sink of the water vapour sourced from land i. The product

165

(ETPNV – ETLU)erj therefore represents the ET lost during land transformation and occupation

166

that would have returned to region j as precipitation and depends on the region j’s size and

167

shape.14,51,52 For the entire planet, erj = 1 with smaller values of erj generally occurring within

168

smaller areas and thinner shapes. The choice of the size of region j introduces a regionalization

169

effect that should be considered in the impact assessment, and is taken into account in the case

ACS Paragon Plus Environment

9

Environmental Science & Technology

Page 10 of 36

170

study described below. A convenient region j to consider is the continent,51 but previous research

171

has also considered river basins as a useful hydrological unit, in which case erj is equivalent to

172

what Berger et al.18 called the basin internal evaporation recycling coefficient.

173

Terrestrial ecosystem damage from changes in precipitation. The reduction in

174

precipitation expressed by PRP can potentially damage terrestrial ecosystems in seasonally dry,

175

semi-arid and arid regions that depend exclusively on green water. We quantify this ecosystem

176

damage ∆EQ (PDF m2 y, where PDF is the Potentially Disappeared Fraction of species)

177

following equation (5) and (6) ∆&,    # , , ,

(5)

∆&,    # , ,

(6)

178

where CFocc-end,j (PDF) and CFtrans-end,j (PDF y) are the respective end-point characterization

179

factors for land occupation and transformation. These factors are expressed as a product of a fate

180

factor (FFi) with an effect factor (EFj). The fate factor FFi describes the change in evaporation

181

supply from region i returning to region j as precipitation and already provided by CFocc-mid,i and

182

CFtrans-mid,i as shown in equations (3) and (4)   # ,    ,  

(7)

  # ,    ,  

(8)

183

The effect factor (EFj, PDF m2y/m3) expresses the change in woody plant species richness with

184

the change in average annual precipitation in region j as shown in equation (9), assuming that

ACS Paragon Plus Environment

10

Page 11 of 36

Environmental Science & Technology

185

water consumption by the ecosystem depends primarily on precipitation and resulting green

186

water resources, as '( '    ( #,

(9)

187

where CPSRmean,j (species/20,000 km2) is the mean climate potential species richness for the

188

region, and dCPSRj/dPj (species y/20,000 km2 mm) is the change in woody plant species with

189

annual precipitation in the region.53

190

Estimate and validation of climate potential species richness for Amazonia. The use of

191

meteorological data to infer woody plant species richness has been of interest in biogeography

192

and geographical ecology with several global products available,54,55 some of which use water-

193

energy dynamics to derive CPSR.56 One such relationship was derived globally by O’Brien53

194

who used a water + energy – energy2 empirical relationship described in equation (10) and

195

known as the Interim General Model (IGM)56 (  −150 + 0.3494 + 05.6294  − 0.0284  3 4

(10)

196

where PETmin (mm/month) is the minimum monthly potential ET in a given year for the

197

geographical unit j. Potential ET is different than ET in that it represents ET in a non-water

198

limiting case, and provides valuable information on the energy regime, potential transpiration

199

and ecosystem productivity.57 The value of PETmin provided the best fit for the IGM and is

200

therefore used in our estimate of CPSR.53 The above model was derived from data obtained for

201

South Africa (n=65) and continental Africa (n=980) before being validated in other parts of

202

world including South America (n=820).56 Both Field et al.56 and O’Brien53 used the

ACS Paragon Plus Environment

11

Environmental Science & Technology

Page 12 of 36

203

Thornthwaite model58 to derive potential ET from air temperature measurements (see Supporting

204

Information). We validate equation (10) using satellite information from the Tropical Rainfall

205

Measuring Mission (TRMM 3B43) for precipitation59 and PETmin from the MODerate resolution

206

Imaging Spectroradiometer (MODIS) MOD16 ET product60 to predict woody plant species

207

richness in 16 locations in the Brazilian Amazon.54 An average satellite derived CPSRmean of

208

1217 woody plant species per 20,000 km2 (sd = 258) was above the 969‒1093 woody plant

209

species per 20,000 km2 range (sd = 523 and 647 for minimum and maximum estimates

210

respectively) described by Ferry Slik et al.54 (see Supporting Information).

211

Case Study. We apply the above method to soybean production in Southeastern Amazonia

212

confined to the Brazilian state of Mato Grosso (Figure 3) by exclusively considering the impacts

213

of land transformation and occupation on PRP and damage to terrestrial ecosystems. Terrestrial

214

ecosystems in the region extend over a north to south ecotone from evergreen rainforest to

215

deciduous transitional forest and savanna.62 This distribution follows a precipitation gradient of

216

2200 mm/y in the north to 1200 mm/y in the southern part of the state of Mato Grosso,63 with

217

rain events concentrated in the September to April wet season. The May to August dry season

218

creates arid conditions which limit soil moisture supply to the atmosphere. While air masses

219

from the Atlantic Ocean carry about two thirds of precipitation to Amazonia, one third of

220

regional precipitation is recycled through ET processes.38 Close to 50% of the variance in dry

221

season ET across the vegetation gradient is explained by precipitation,62,64 indicating the extent

222

to which ET processes can be water-limited in the region. This is also demonstrated by several

223

months of arid conditions when the ratio of precipitation to reference ET (ET0, defined as ET

224

from a well-watered grass reference crop)65 is less than 0.75 (Figure S1, Supplemental Material).

ACS Paragon Plus Environment

12

Page 13 of 36

Environmental Science & Technology

225 226 227 228 229 230 231 232

Figure 3: The Amazon basin of South America containing the Brazilian state of Mato Grosso in Southeastern Amazonia showing forest and agricultural land covers as per the ESA GlobeCover 2009 Project (©ESA 2010 and UC Louvain).61 Regional Evaporation recycling ratios are shown in Table 2 for the Amazon and Xingu basins, as well as a 2.76 1010 m2 area described in reference 51.

We consider land transformation and occupation impacts of soybean produced in 2010

233

considering tropical forest to cropland transformation. We look at one tonne of soybean

234

harvested on 3251 m2 of land in 2010 in the municipalities of Mato Grosso located in the

235

Amazon biome.66 Since UNEP-SETAC guidelines recommend that land transformation impacts

236

be allocated over 20 years,34 the total transformation impact of soybean produced in 2010 is

237

allocated among 20 subsequent years. We apply the above methodology considering four

238

separate affected geographical units with their corresponding erj values (Table 2) as a sensitivity

239

analysis on this modeling assumption: the Amazon biome (7 x 106 km2), the Xingu Basin

ACS Paragon Plus Environment

13

Environmental Science & Technology

Page 14 of 36

240

(tributary to the Amazon River, 510,000 km2), and a sub-region of 27,600 km2 located in the

241

Amazon biome but not in the Xingu basin (Figure 3). Both crop modeling and remote sensing

242

were used to derive ET of soybean (ETLU) and tropical forest (ETNV). All model input

243

parameters are shown in Table 3 with a detailed description on crop modeling and remote

244

sensing steps for ETLU and ETNV available in the Supporting Information.

245 246

Table 2. Regional evaporation recycling ratios (erj) and area of affected region (aj) used in this study (also shown in Figure 3) Boundary

Regional evaporation recycling ratio (erj)

Area affected (aj) (m2)

Reference

Amazon biome

0.48

7.0 1012

van der Ent et al. (2010)52

Xingu Basin

0.22

5.1 1011

Berger et al. (2014)19

Sub-region

0.059

2.76 1010

van der Ent et al. (2011)51

247 248 249 250 251 252 253 254 255

ACS Paragon Plus Environment

14

Page 15 of 36

256 257

Environmental Science & Technology

Table 3. Input parameters used in this study to determine land transformation and occupation impacts of soybean from tropical forest in Southeastern Amazonia (Mato Grosso, Brazil). Input parameter

Symbol

Value

Unit

Reference

Precipitation

P

2096

mm/y

Rodrigues et al. (2014)62

Tropical forest evapotranspiration

ETNVi

1099

mm/y

Lathuillière et al. (2012)67

Tropical forest potential evapotranspiration

PETmin

126.9

mm/month

CPSRmean

1217

species per 20,000 km2

equation (10)

ETLUi

648

mm/y

This study

Climate potential species richness Soybean evapotranspiration

This study after Mu et al. (2011)60

Regional evaporation recycling ratio

erj

see Table 2

dimensionless

see Table 2

Regional area affected

aj

see Table 2

m2

see Table 2

Regeneration time for tropical forest

tregen,i

y

Curran et al. (2014)49

159

258 259

RESULTS

260

The production of one tonne of soybean occupying land in 2010 which was previously

261

tropical forest in Southeastern Amazonia resulted in an average loss of precipitation of 704 m3,

262

323 m3 and 86.5 m3 respectively in the Amazon biome, the Xingu Basin and the sub-region

263

(Table 4). Similarly, land transformation of tropical forest into soybean caused different impacts

264

in Amazonia, the Xingu Basin and the sub-region differently considering a 159-year regeneration

265

time for tropical forest with one tonne of soybean produced in 2010 reducing precipitation

266

potential by 2798 m3, 1282 m3, 344 m3 in the respective regions.

267

ACS Paragon Plus Environment

15

Environmental Science & Technology

268 269 270 271 272

Page 16 of 36

Table 4. Characterization factors (CF), mid-point (Precipitation Reduction Potential, PRP) and end-point impacts (∆EQ) of land occupation (occ) and transformation (trans) for one tonne of soybean produced in 2010 on tropical forest previously deforested in Southeastern Amazonia (Mato Grosso, Brazil). Amazonia

Xingu Basin

Sub-region

Characterization factors CFocc-mid

m3/m2 y

0.217

9.92 10-2

2.66 10-2

CFtrans-mid

m3/m2

17.2

7.89

2.12

CFocc-end

a

PDF

6.22 10-2

2.85 10-2

7.64 10-3

CFtrans-end

PDF y

4.94

2.26

0.61

Impact assessment (per tonne of soybean)

273

PRPocc

m3

704

323

86.5

PRPtrans

m3

2798

1282

344

∆EQocc

PDF m2 y

202

93

25

∆EQtrans

PDF m2 y

803

368

99

a

PDF: Potentially Disappeared Fraction of Species

274 275

The derivation of equation (10) with precipitation gave a value of dCPSR/dP equal to 0.3494

276

species y/20,000 km2 mm (or which, considering the average Amazonia species richness of 1217

277

species/20,000 km2 gave EFj = 0.2871 PDF m2 y/m3 of precipitation lost). As a result of reduced

278

precipitation in Amazonia, the Xingu Basin and the sub-region, damage to terrestrial ecosystems

279

was evaluated at 202 PDF m2 y, 93 PDF m2 y, and 25 PDF m2 y respectively when considering

ACS Paragon Plus Environment

16

Page 17 of 36

Environmental Science & Technology

280

land occupation impacts. Land transformation end-point impacts were 803 PDF m2 y,

281

368 PDF m2 y, and 99 PDF m2 y in the respective regions.

282

DISCUSSION

283

Linking changes in evapotranspiration with environmental impacts. The above results

284

can be put into the context of extensive research on land use change and impacts on Amazonia’s

285

water cycle. The Amazon biome plays an important role in the water cycle of South America and

286

processes of precipitation/evaporation recycling are one of many important ecohydrological

287

services on the continent.14 Since the 1998 El Niño event, concerns over increased drying of the

288

tropical forest have led researchers to question the potential beginning of a new phase in the

289

biome with a more important role played by anthropogenic climate and land use changes.38

290

Between 2000 and 2012, 69% of Amazonia’s tropical forest was affected by declines in

291

precipitation, with a 25% drop observed in in Eastern and Southeastern Amazonia.68 Analysis of

292

rain gauges across the biome also confirms a precipitation decline of 5.31 ± 0.68 mm/y for the

293

1996‒2008 period.69

294

Regional declines in precipitation result, in part, from a reduction in water vapour flows to the

295

atmosphere.70 In 2000–2009, total ET was reduced by 16.2 km3/y2 in Southeastern Amazonia,

296

mostly from reduced ET due to diminishing tropical forest cover resulting from expansion of

297

cropland and pasture in the region.39,40,67 Similar land use change during the 2000s in the Upper

298

Xingu Basin (part of the Xingu basin located in Mato Grosso, Figure 3) was responsible for a

299

35 km3 reduction in ET.36 Back trajectory analysis shows that simulated deforestation was

300

directly responsible for a decline in precipitation of up to 17‒20% (July‒September) based on

ACS Paragon Plus Environment

17

Environmental Science & Technology

301

rainfall conditions in the basin,44 which also agrees with 122 models linking reduced

302

precipitation with deforestation in the region.43,71

303

Page 18 of 36

Amazonia’s precipitation reduction can impact terrestrial ecosystems through a drying

304

process of “savannization”.45,46 Forest drying, in turn, can lead to an increased occurrence of tree

305

dieback and wildfires72 that cause additional land use change with subsequent environmental

306

impacts and atmospheric feedbacks. Two major droughts, in 2005 and 2010, affected 1.9 and

307

3.2 million km2 of Amazonia respectively, with important regional consequences on the carbon

308

balance.73 Some of these affected areas can be related to a reduction in soil moisture in drought

309

years. A precipitation exclusion experiment performed over six years led to a 60% decrease in

310

wood production and a strong correlation between soil moisture and above ground net primary

311

production.74

312

Including evapotranspiration recycling in LCA. Our mid- and end-point impacts are

313

complementary to land transformation and occupation impacts on Biodiversity and Ecosystems

314

Services Damage Potentials described by UNEP-SETAC guidelines.34 We suggest that the

315

regeneration of precipitation as defined by our mid-point impact Precipitation Reduction

316

Potential also be considered as an additional impact pathway toward the ecosystem quality area

317

of protection to highlight the importance of water vapour supply to the atmosphere with land use

318

and land cover.17

319

The mid- and end-point impact assessments using different boundaries for the affected areas

320

of Amazonia (7.0 1012 m2), Xingu Basin (5.11 1011 m2), and sub-region (2.76 1010 m2) highlight

321

differences with respect to values of erj as an illustrative example to demonstrate the importance

322

of the boundary to be selected for impact assessment. As the smallest area considered, the sub-

ACS Paragon Plus Environment

18

Page 19 of 36

Environmental Science & Technology

323

region showed the smallest mid- and end-point impact when compared to Amazonia and the

324

Xingu Basin. This result is intuitive: based on the value of erj, a similar difference in ET due to

325

land transformation and occupation will have a greater impact at the greater scale rather than a

326

smaller surface area where greater effects on local runoff are expected. In other words, a land

327

transformation activity will have a greater potential precipitation impact on a biome compared to

328

a river basin or farm property.

329

Given the importance in the spatial scale to be considered in the impact assessment model, we

330

recommend that the selection of the boundary for region j be done consistently. Previous

331

research has provided characterization factors specific to biomes31,34 in the case of land

332

transformation and occupation impacts, or river basins in the case of water use in LCA19,22

333

following specific guidelines.18 For instance, the use of a biome-wide boundary for impact

334

assessment (e.g. Amazon biome) avoids having to address basin to basin heterogeneity, but also

335

lacks relevance for water use in LCA (e.g. Amazon or Xingu basin). In a water-focused LCA, the

336

river basin boundary is more relevant for impact assessment, and therefore the basin internal

337

evaporation recycling ratio19 should be used as erj assuming that any evaporation exiting the

338

limits of the hydrological unit is considered to have been consumed. Such a consideration would

339

disregard the transfer of water vapour to neighbouring basins which could be considered as an

340

input and still needs to be addressed in LCA.10

341

Given these differences, we suggest that the river basin be used as the affected region j of

342

choice for two reasons: first, Precipitation Reduction Potential is an impact category that will

343

affect the water cycle as a whole and therefore should be defined within a hydrological unit;

344

secondly, the choice of the river basin as the boundary allows for the direct application of the

345

values of the basin internal evaporation recycling ratio already made available by Berger et al.19

ACS Paragon Plus Environment

19

Environmental Science & Technology

Page 20 of 36

346

for impact assessment with calculations for PRP possible to be made worldwide. End-point

347

impact assessments should be carried out considering additional data on the relationship between

348

woody plant species richness and precipitation based on local data availability. Our boundary

349

choice for region j is also consistent with ISO 14046 which makes notes about the consideration

350

of land use change and its effects on the water cycle, although this consideration is more specific

351

to blue water availability and scarcity.9 When focusing specifically on the water cycle and the

352

river basin boundary, potential impacts on the water cycle should also observe the temporal

353

aspects of the water cycle. The values of erj discussed so far (Table 2) have implicitly considered

354

annual recycling of water vapour when in fact such recycling ratios can change through the year

355

based on atmospheric conditions.44,75

356

While our case study has been focused on the Amazonia region, the method is transferable to

357

other regions provided the region is seasonally dry, semi-arid or arid with strong coupling

358

between precipitation and ET (described in detail by Núñez et al.).30 The IGM model results53,56

359

are available for other parts of the world but high resolution data for precipitation, ET, or PET

360

have only recently been made available through remote sensing and could be used in the future

361

to derive high resolution maps of CPSR similarly to what has been proposed in this paper,

362

provided that local information on species richness is available (see Supporting Information).

363

The proposed relationship between CPSR, P and PETmin was acceptable given possible

364

differences in the CPSR estimate when considering PETmin values obtained from temperature or

365

net radiation (see further discussion in the Supporting Information). The IGM model53 described

366

in equation (10) suggests a linear drop in CPSR with a reduction in precipitation such that a large

367

surface area transformed impacts more species from greater reductions in precipitation

368

considering an equal local value of PETmin. This linearity is in agreement with field observations

ACS Paragon Plus Environment

20

Page 21 of 36

Environmental Science & Technology

369

and modeling, but care must be taken to consider local ecosystem resilience, especially in

370

regions where terrestrial ecosystems have access to blue water reserves, in which case other,

371

complementary methods, may be more relevant to consider (see further discussion below).

372

Comparison with other models: mid-point impact. Our mid-point impact model is different

373

from other models that consider green water and ET in LCA. Following Milà i Canals et al.21 we

374

accounted for changes in land ET as the impact using the negative of the net green water

375

approach described by Núñez et al.30 multiplied by the regional evaporation recycling ratio erj.

376

Quinteiro et al.33 introduced two mid-point impacts resulting from a change in the local water

377

balance due to land use change: Terrestrial Green Water Flows (TGWI), or the change in water

378

vapour supply to the atmosphere, and Surface Blue Water Production (RBWP), or the change in

379

runoff associated with the land use change. Our PRP mid-point impact resembles TGWI but puts

380

more emphasis on the supply of water as precipitation rather than the difference in water vapour

381

supply to the atmosphere. Our approach therefore differs in principle with Quinteiro et al.33 and

382

Ridoutt and Pfister32 who interpret ET as a loss and modification to blue water resources.

383

Of all the current land transformation and occupation impacts to ecosystems services,31 the

384

Freshwater Recharge Potential (FWRP) is the mid-point impact category that most resembles

385

PRP, with the difference that FWRP focusses exclusively on blue water through groundwater

386

recharge (GWR). Similar to equations (1) and (2), a characterization factor of FWRP is

387

calculated as the difference in GWR from PNV and the current land use (GWRPNVi – GWRLUi)

388

with recharge estimated through the water balance (P – ET)/R where R (dimensionless) is the

389

runoff coefficient which depends on slope and depth of the water table.31 Just like ET, the value

390

of R can be affected by the land occupation activity and the sealing factor (kseal) of the new land

391

use such that GWRLU = GWRPNV(1 – kseal).31 The characterization factors for both PRP and

ACS Paragon Plus Environment

21

Environmental Science & Technology

Page 22 of 36

392

FWRP are therefore closely connected through the water balance equation, the value of erj in the

393

atmospheric water balance for PRP and R for the terrestrial water balance of FWRP.

394

Comparison with other methods: end-point impact. Our end-point impact model is

395

complementary to two other models which present an end-point impact from blue water

396

consumption on terrestrial ecosystems. By assuming that terrestrial ecosystems are mainly blue

397

water dependent, Pfister et al.22 estimate ecosystem damage using the relationship between net

398

primary production (NPP) and water consumption. This method therefore relies on the water use

399

efficiency of the terrestrial ecosystem under consideration, and assesses an ecosystem damage

400

based on the change in blue water availability for the ecosystem. Given the strong correlation

401

between NPP and the number of vascular plant species diversity, Pfister et al.22 equate gC/m2 y

402

with PDF m2 y. For example, the median value of water use efficiency for tropical forest in

403

Amazonia76 is 736 mgC/m2 mm and may act as a characterization factor in the Pfister et al.22

404

damage assessment method. This assessment however remains indirect since there is no link

405

established between blue water consumption and loss of species.

406

Our model more closely resembles that of van Zelm et al.25 who map the cause-effect chain of

407

groundwater extraction impacts on ecosystem quality in the Netherlands. Their model relies on

408

the time required to replenish groundwater following extraction (fate factor) and the impact of

409

this drawdown on species (effect factor).25 Similarly, our end-point characterization factor is

410

expressed as the product of a fate factor (amount of lost precipitation as a result of land

411

transformation) and an effect factor (loss of species per change in amount of precipitation), and

412

also offers a direct link between precipitation and species loss in the effect factor. The use of

413

climate potential species richness and the IGM model of O’Brien et al.53 at higher resolution

ACS Paragon Plus Environment

22

Page 23 of 36

Environmental Science & Technology

414

could provide interbiome and interbasin species richness maps which could be used with our

415

proposed model to provide spatial impact assessments based on available data.

416

Unlike the above described end-point impact assessment models, our model focusses

417

specifically on the relationship between terrestrial ecosystems and ET. When considering the

418

loss of biodiversity from land occupation and transformation as proposed by de Bann et al.,50 we

419

find that one tonne of soybean would lead to 1756 PDF m2 y and 14.9 104 PDF m2 y for

420

occupation and transformation, respectively. Our values therefore represent 1‒11% of

421

biodiversity loss, meaning that ecosystem damage may be underestimated by up to 11% in the

422

biome. Similarly, we modify our values of CFocc-end,i and CFtrans-end,i obtained for the Xingu basin

423

to compare to values reported by Chaudhary et al.77 Characterization factors were 1.73 10-9 plant

424

species/m2 and 1.38 10-7 plant species y/m2 respectively and represent 12‒24% of values

425

reported by Chaudhary et al.77 for the Xingu-Tocantins-Araguaia moist forest of 1.55 10-8 plant

426

species/m2 and 1.16 10-6 plant species y/m2.77 In Amazonia, blue water and particularly

427

groundwater consumption provides a buffer to terrestrial ecosystems, especially in the dry

428

season,78 so we still expect some impact from reduced blue water availability on ecosystem

429

damage. This cause-effect chain is better represented by Pfister et al.22 and van Zelm et al.25

430

which both complement the method described in this paper. Similar to Pfister et al.22 and van

431

Zelm et al.,25 our assessment of ecosystem quality relies on the sensitivity of woody plants rather

432

than other forms of ecosystem quality such as animals or insects which could adapt better to

433

changes in water availability. This sensitivity is apparent not only in recent field studies linking

434

reduced precipitation to observed changes in Amazonia,38,45,46 but also a precipitation exclusion

435

experiment confirming impact on local aboveground biomass.74 However, our model does not

436

consider other non-natural terrestrial ecosystems such as agro-ecosystems which rely on

ACS Paragon Plus Environment

23

Environmental Science & Technology

Page 24 of 36

437

precipitation and are also of great interest for Amazonia’s future agricultural production,79 nor

438

does it consider the exclusive damage to regional biodiversity as assessed by Chaudhary et al..77

439

Such losses could be complement potential losses to ecosystems services and biodiversity as

440

already proposed by UNEP-SETAC guidelines.34

441

In this paper, we have proposed a new impact pathway to reflect how changes in ET from land

442

transformation and occupation may affect precipitation with a potential damage to terrestrial

443

ecosystems. Such a change is especially relevant in seasonally dry, semi-arid and arid regions

444

where ET and precipitation are strongly coupled as in Southeastern Amazonia. Amazonia is

445

prone to further degradation this century that could lead to savannization of the Amazon forest

446

through atmospheric feedbacks caused by deforestation and agricultural expansion. The use of

447

the models presented in this paper can help in providing a more complete environmental impact

448

assessment of agricultural products by linking land and water uses in LCA, which is highly

449

relevant for soybean production which has been replacing Amazonia’s tropical forest in recent

450

years, but also pasture which could benefit from similar models for the beef production system.

451

Other regions of the world whose agricultural production is strongly linked to land

452

transformation could also make use of the method proposed here to further understand the

453

impacts of land and water use in product supply chains.

454

Author Contributions

455

The manuscript was written through contributions of all authors. All authors have given approval

456

to the final version of the manuscript.

457 458

ACS Paragon Plus Environment

24

Page 25 of 36

Environmental Science & Technology

459

ACKNOWLEDGMENTS

460

This research was supported by the Vanier Graduate Scholarship through the Natural Sciences

461

and Engineering Research Council (NSERC) to MJL (#201411DVC-347484-257696) and

462

constitutes a contribution to the project “Integrating land use planning and water governance in

463

Amazonia: Towards improving freshwater security in the agricultural frontier of Mato Grosso”

464

supported by the Belmont Forum and the G8 Research Councils Freshwater Security Grant

465

G8PJ-437376-2012 through NSERC to MSJ. We kindly thank Richard Field and Robert

466

Whittaker for their input on species richness models, and Ruud van der Ent for valuable feedback

467

on regional evaporation recycling ratios. We thank Higo José Dalmagro for help with

468

meteorological data, as well as three anonymous reviewers for their valuable input.

469 470

SUPPORTING INFORMATION

471

Validation of the Climate Potential Species Richness model with remote sensing input data

472

Monthly

473

evapotranspiration of natural vegetation

474

Crop modeling and remote sensing approaches for determining evapotranspiration

precipitation,

reference

evapotranspiration,

evapotranspiration

and

potential

475 476

REFERENCES

477

(1)

Falkenmark, M. Adapting to climate change: towards societal water security in dry-climate

478

countries. International Journal of Water Resources Development 2013, 29 (2), 123‒136;

479

DOI 10.1080/07900627.2012.721714.

ACS Paragon Plus Environment

25

Environmental Science & Technology

480

(2)

481 482

Page 26 of 36

Gleick, P.H. Global freshwater resources: Soft-path solutions for the 21st century. Science 2003, 302 (5650), 1524‒1528; DOI 10.1126/science.1089967.

(3)

Hoekstra, A.Y. The water footprint of industry. In Assessing and measuring environmental

483

impact and sustainability; Klemes, J.J., Ed.; Butterworth-Heinemann (Elsevier): Oxford,

484

UK 2015; pp 221‒254.

485

(4)

486 487

Ridoutt, B.G.; Pfister, S. Reducing humanity's water footprint. Environ. Sci. Technol. 2010, 44 (16), 6019‒6021; DOI 10.1021/es101907z.

(5)

Boulay, A.; Hoekstra, A.Y.; Vionnet, S. Complementarities of Water-Focused Life Cycle

488

Assessment and Water Footprint Assessment. Environ. Sci. Technol. 2013, 47 (21), 11926‒

489

11927; DOI 10.1021/es403928f.

490

(6)

491 492

Berger, M.; Finkbeiner, M. Water Footprinting: How to address water use in life cycle assessment? Sustainability 2010, 2, 919‒944; DOI 10.3390/su2040919.

(7)

Bayart, J.; Bulle, C.; Deschenes, L.; Margni, M.; Pfister, S.; Vince, F.; Koehler, A. A

493

framework for assessing off-stream freshwater use in LCA. Int. J. Life Cycle Assess. 2010,

494

15 (5), 439‒453; DOI 10.1007/s11367-010-0172-7.

495

(8)

496 497

Hellweg, S.; Milà i Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 2014, 344 (6188), 1109‒1113; DOI 10.1126/science.1248361.

(9)

International Standard ISO 14046:2014: Environmental Management – Water Footprint –

498

Principles, Requirements and Guidelines; International Organization for Standardization:

499

Geneva, Switzerland.

500

(10) Berger, M.; Finkbeiner, M. Methodological Challenges in Volumetric and Impact-Oriented

501

Water Footprints. J. Ind. Ecol. 2013, 17 (1), 79‒89; DOI 10.1111/j.1530-

502

9290.2012.00495.x.

ACS Paragon Plus Environment

26

Page 27 of 36

Environmental Science & Technology

503

(11) Boulay, A.M. The WULCA consensus for water scarcity footprints: Assessing impacts of

504

water consumption based on human and ecosystem demand; SETAC Europe: Nantes,

505

France, 25 May 2016.

506

(12) Boulay, A.M.; et al. Consensus building on the development of a stress-based indicator for

507

LCA-based impact assessment of water consumption: outcome of the expert workshops.

508

Int. J. Life Cycle Assess. 2015 20(5), 577‒583; DOI 10.1007/s11367-015-0869-8.

509 510

(13) Falkenmark, M.; Rockström, J. Balancing water for humans and nature: the new approach in ecohydrology; Earthscan: London, UK, 2004.

511

(14) Keys, P.W.; van der Ent, R.J.; Gordon, L.J.; Hoff, H.; Nikoli, R.; Savenije, H.H.G.

512

Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions.

513

Biogeosciences 2012, 9 (2), 733‒746; DOI 10.5194/bg-9-733-2012.

514

(15) Falkenmark, M.; Rockström, J. The new blue and green water paradigm: Breaking new

515

ground for water resources planning and management. Journal of Water Resources

516

Planning and Management-Asce 2006, 132 (3), 129‒132; DOI 10.1061/(ASCE)0733-

517

9496(2006)132:3(129).

518 519

(16) Oki, T.; Kanae, S. Global hydrological cycles and world water resources. Science 2006, 313 (5790), 1068‒1072; DOI 10.1126/science.1128845.

520

(17) Ellison, D.; Futter, M.N.; Bishop, K. On the forest cover-water yield debate: from demand-

521

to supply-side thinking. Global Change Biology 2012, 18 (3), 806‒820; DOI

522

10.1111/j.1365-2486.2011.02589.x.

523

(18) Bayart, J.; Worbe, S.; Grimaud, J.; Aoustin, E. The Water Impact Index: a simplified

524

single-indicator approach for water footprinting. Int. J. Life Cycle Assess. 2014, 19 (6),

525

1336‒1344; DOI 10.1007/s11367-014-0732-3.

ACS Paragon Plus Environment

27

Environmental Science & Technology

Page 28 of 36

526

(19) Berger, M.; van der Ent, R.; Eisner, S.; Bach, V.; Finkbeiner, M. Water Accounting and

527

Vulnerability Evaluation (WAVE): Considering Atmospheric Evaporation Recycling and

528

the Risk of Freshwater Depletion in Water Footprinting. Environ. Sci. Technol. 2014, 48

529

(8), 4521‒4528; DOI 10.1021/es404994t.

530

(20) Boulay, A.; Bulle, C.; Bayart, J.; Deschenes, L.; Margni, M. Regional Characterization of

531

Freshwater Use in LCA: Modeling Direct Impacts on Human Health. Environ. Sci.

532

Technol. 2011, 45 (20), 8948‒8957; DOI 10.1021/es1030883.

533

(21) Milà i Canals, L.; Chenoweth, J.; Chapagain, A.; Orr, S.; Anton, A.; Clift, R. Assessing

534

freshwater use impacts in LCA: Part I-inventory modelling and characterisation factors for

535

the main impact pathways. Int. J. Life Cycle Assess. 2009, 14 (1), 28‒42; DOI

536

10.1007/s11367-008-0030-z.

537

(22) Pfister, S.; Koehler, A.; Hellweg, S. Assessing the Environmental Impacts of Freshwater

538

Consumption in LCA. Environ. Sci. Technol. 2009, 43 (11), 4098‒4104; DOI

539

10.1021/es802423e.

540

(23) Tendall, D.M.; Hellweg, S.; Pfister, S.; Huijbregts, M.A.J.; Gaillard, G. Impacts of River

541

Water Consumption on Aquatic Biodiversity in Life Cycle Assessment-A Proposed

542

Method, and a Case Study for Europe. Environ. Sci. Technol. 2014, 48 (6), 3236‒3244;

543

DOI 10.1021/es4048686.

544

(24) Hanafiah, M.M.; Xenopoulos, M.A.; Pfister, S.; Leuven, R.S.E.W.; Huijbregts, M.A.J.

545

Characterization Factors for Water Consumption and Greenhouse Gas Emissions Based on

546

Freshwater Fish Species Extinction. Environ. Sci. Technol. 2011, 45 (12), 5272‒5278; DOI

547

10.1021/es1039634.

ACS Paragon Plus Environment

28

Page 29 of 36

548

Environmental Science & Technology

(25) van Zelm, R.; Schipper, A.M.; Rombouts, M.; Snepvangers, J.; Huijbregts, M.A.J.

549

Implementing Groundwater Extraction in Life Cycle Impact Assessment: Characterization

550

Factors Based on Plant Species Richness for the Netherlands. Environ. Sci. Technol. 2011,

551

45 (2), 629‒635; DOI 10.1021/es102383v.

552

(26) Verones, F.; Pfister, S.; Hellweg, S. Quantifying Area Changes of Internationally

553

Important Wetlands Due to Water Consumption in LCA. Environ. Sci. Technol. 2013, 47

554

(17), 9799‒9807; DOI 10.1021/es400266v.

555

(27) Verones, F.; Saner, D.; Pfister, S.; Baisero, D.; Rondinini, C.; Hellweg, S. Effects of

556

Consumptive Water Use on Biodiversity in Wetlands of International Importance. Environ.

557

Sci. Technol. 2013, 47 (21), 12248‒12257; DOI 10.1021/es403635j.

558

(28) Verones, F.; Huijbregts, M.A.J.; Chaudhary, A.; de Baan, L.; Koellner, T.; Hellweg, S.

559

Harmonizing the Assessment of Biodiversity Effects from Land and Water Use within

560

LCA. Environ. Sci. Technol. 2015, 49 (6), 3584‒3592; DOI 10.1021/es504995r.

561

(29) Núñez, M.; Pfister, S.; Anton, A.; Munoz, P.; Hellweg, S.; Koehler, A.; Rieradevall, J.

562

Assessing the Environmental Impact of Water Consumption by Energy Crops Grown in

563

Spain. J. Ind. Ecol. 2013, 17 (1), 90‒102; DOI 10.1111/j.1530-9290.2011.00449.x.

564

(30) Núñez, M.; Pfister, S.; Roux, P.; Anton, A. Estimating Water Consumption of Potential

565

Natural Vegetation on Global Dry Lands: Building an LCA Framework for Green Water

566

Flows. Environ. Sci. Technol. 2013, 47 (21), 12258‒12265; DOI 10.1021/es403159t.

567

(31) Saad, R.; Koellner, T.; Margni, M. Land use impacts on freshwater regulation, erosion

568

regulation, and water purification: a spatial approach for a global scale level. Int. J. Life

569

Cycle Assess. 2013, 18 (6), 1253‒1264; DOI 10.1007/s11367-013-0577-1.

ACS Paragon Plus Environment

29

Environmental Science & Technology

570

(32) Ridoutt, B.G.; Pfister, S. A revised approach to water footprinting to make transparent the

571

impacts of consumption and production on global freshwater scarcity. Global

572

Environmental Change-Human and Policy Dimensions 2010, 20 (1), 113‒120; DOI

573

10.1016/j.gloenvcha.2009.08.003.

574

Page 30 of 36

(33) Quinteiro, P., Dias, A.C., Silva, M., Ridoutt, B.G., Arroja, L. A contribution to the

575

environmental impact assessment of green water flows. J. Cleaner Prod. 2015, 93, 318‒

576

329; DOI 10.1016/j.jclepro.2015.01.022.

577

(34) Koellner, T.; de Baan, L.; Beck, T.; Brandão, M.; Civit, B.; Margni, M.; Milà i Canals, L.;

578

Saad, R.; de Souza, D.M.; Mueller-Wenk, R. UNEP-SETAC guideline on global land use

579

impact assessment on biodiversity and ecosystem services in LCA. Int. J. Life Cycle

580

Assess. 2013, 18 (6), 1188‒1202; DOI 10.1007/s11367-013-0579-z.

581

(35) Gibbs, H.K.; Rausch, L.; Munger, J.; Schelly, I.; Morton, D.C.; Noojipady, P.; Soares-

582

Filho, B.; Barreto, P.; Micol, L.; Walker, N.F. Brazil's Soy Moratorium. Science 2015, 347

583

(6220), 377‒378; DOI 10.1126/science.aaa0181.

584

(36) Silvério, D.; Brando, P.M.; Macedo, M.N.; Beck, P.S.A.; Bustamante, M.; Coe, M.T.

585

Agricultural expansion dominates climate changes in southeastern Amazonia: the

586

overlooked non-GHG forcing. Environ. Res. Lett. 2015, 10 (10), 104015; DOI

587

10.1088/1748-9326/10/10/104015.

588

(37) Lapola, D.M.; Martinelli, L.A.; Peres, C.A.; Ometto, J.P.H.B.; Ferreira, M.E.; Nobre, C.A.;

589

Aguiar, A.P.D.; Bustamante, M.M.C.; Cardoso, M.F.; Costa, M.H.; Joly, C.A.; Leite, C.C.;

590

Moutinho, P.; Sampaio, G.; Strassburg, B.B.N.; Vieira, I.C.G. Pervasive transition of the

591

Brazilian land-use system. Nat. Clim. Change 2014, 4 (1), 27‒35; DOI

592

10.1038/NCLIMATE2056.

ACS Paragon Plus Environment

30

Page 31 of 36

593

Environmental Science & Technology

(38) Davidson, E.A.; de Araujo, A.C.; Artaxo, P.; Balch, J.K.; Brown, I.F.; Bustamante,

594

M.M.C.; Coe, M.T.; DeFries, R.S.; Keller, M.; Longo, M.; Munger, J.W.; Schroeder, W.;

595

Soares-Filho, B.S.; Souza, C.M., Jr.; Wofsy, S.C. The Amazon basin in transition. Nature

596

2012, 481 (7381), 321‒328; DOI 10.1038/nature10717.

597

(39) Macedo, M.N.; DeFries, R.S.; Morton, D.C.; Stickler, C.M.; Galford, G.L.; Shimabukuro,

598

Y.E. Decoupling of deforestation and soy production in the southern Amazon during the

599

late 2000s. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (4); DOI 1341‒1346;

600

10.1073/pnas.1111374109.

601

(40) Barona, E.; Ramankutty, N.; Hyman, G.; Coomes, O.T. The role of pasture and soybean in

602

deforestation of the Brazilian Amazon. Environ. Res. Lett. 2010, 5 (2), 024002; DOI

603

10.1088/1748-9326/5/2/024002.

604

(41) Chaplin-Kramer, R.; Sharp, R.P.; Mandle, L.; Sim, S.; Johnson, J.; Butnar, I.; Milà i

605

Canals, L.; Eichelberger, B.A.; Ramler, I.; Mueller, C.; McLachlan, N.; Yousefi, A.; King,

606

H.; Kareiva, P.M. Spatial patterns of agricultural expansion determine impacts on

607

biodiversity and carbon storage. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (24), 7402‒7407;

608

DOI 10.1073/pnas.1406485112.

609 610 611

(42) Klink, C.A.; Machado, R.B. Conservation of the Brazilian Cerrado. Conservation Biology 2005, 19 (3), 707‒713; DOI 10.1111/j.1523-1739.2005.00702.x. (43) Spracklen, D.V.; Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon

612

basin rainfall. Geophys. Res. Lett. 2015, 42 (21), 9546‒9552; DOI

613

10.1002/2015GL066063.

ACS Paragon Plus Environment

31

Environmental Science & Technology

614

Page 32 of 36

(44) Bagley, J.E.; Desai, A.R.; Harding, K.J.; Snyder, P.K.; Foley, J.A. Drought and

615

Deforestation: Has Land Cover Change Influenced Recent Precipitation Extremes in the

616

Amazon? J. Clim. 2014, 27 (1), 345‒361; DOI 10.1175/JCLI-D-12-00369.1.

617

(45) Silvério, D.V.; Brando, P.M.; Balch, J.K.; Putz, F.E.; Nepstad, D.C.; Oliveira-Santos, C.;

618

Bustamante, M.M.C. Testing the Amazon savannization hypothesis: fire effects on

619

invasion of a neotropical forest by native cerrado and exotic pasture grasses. Philos. Trans.

620

R. Soc., B 2013, 368 (1619), 20120427; DOI 10.1098/rstb.2012.0427.

621

(46) Nepstad, D.C.; Stickler, C.M.; Soares-Filho, B.; Merry, F. Interactions among Amazon

622

land use, forests and climate: prospects for a near-term forest tipping point. Philos. Trans.

623

R. Soc., B 2008, 363 (1498), 1737‒1746; DOI 10.1098/rstb.2007.0036.

624

(47) Nepstad, D.; McGrath, D.; Stickler, C.; Alencar, A.; Azevedo, A.; Swette, B.; Bezerra, T.;

625

DiGiano, M.; Shimada, J.; da Motta, R.S.; Armijo, E.; Castello, L.; Brando, P.; Hansen,

626

M.C.; McGrath-Horn, M.; Carvalho, O.; Hess, L. Slowing Amazon deforestation through

627

public policy and interventions in beef and soy supply chains. Science 2014, 344 (6188),

628

1118‒1123; DOI 10.1126/science.1248525.

629

(48) Lathuillière, M.J.; Johnson, M.S.; Galford, G.L.; Couto, E.G. Environmental footprints

630

show China and Europe's evolving resource appropriation for soybean production in Mato

631

Grosso, Brazil. Environ. Res. Lett. 2014, 9 (7), 074001; DOI 10.1088/1748-

632

9326/9/7/074001.

633 634

(49) Curran, M.; Hellweg, S.; Beck, J. Is there any empirical support for biodiversity offset in policy? Ecological Applications 2014, 24 (4), 617‒632; DOI 10.1890/13-0243.1.

ACS Paragon Plus Environment

32

Page 33 of 36

635

Environmental Science & Technology

(50) de Baan, L.; Alkemade, R.; Koellner, T. Land use impacts on biodiversity in LCA: a global

636

approach. Int. J. Life Cycle Assess. 2013, 18 (6), 1216‒1230; DOI 10.1007/s11367-012-

637

0412-0.

638 639 640

(51) van der Ent, R.J.; Savenije, H.H.G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 2011, 11 (5), 1853‒1863; DOI 10.5194/acp-11-1853-2011. (52) van der Ent, R.J.; Savenije, H.H.G.; Schaefli, B.; Steele-Dunne, S.C. Origin and fate of

641

atmospheric moisture over continents. Water Resour. Res. 2010, 46, W09525; DOI

642

10.1029/2010WR009127.

643

(53) O'Brien, E. Water-energy dynamics, climate, and prediction of woody plant species

644

richness: an interim general model. Journal of Biogeography 1998, 25 (2), 379‒398; DOI

645

10.1046/j.1365-2699.1998.252166.x.

646 647 648 649 650

(54) Ferry Slik, J.W.; et al. An estimate of the number of tropical tree species. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (24), 7472‒7477; DOI 10.1073/pnas.1423147112. (55) Kreft, H.; Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (14), 5925‒5930; DOI 10.1073/pnas.0608361104. (56) Field, R.; O'Brien, E.; Whittaker, R. Global models for predicting woody plant richness

651

from climate: Development and evaluation. Ecology 2005, 86 (9), 2263‒2277; DOI

652

10.1890/04-1910.

653

(57) Fisher, J.B.; Whittaker, R.J.; Malhi, Y. ET come home: potential evapotranspiration in

654

geographical ecology. Global Ecology and Biogeography 2011, 20 (1), 1‒18; DOI

655

10.1111/j.1466-8238.2010.00578.x.

656 657

(58) Thornthwaite, C.W. An approach toward a rational classification of climate. Geographical Reviews 1948, 38, 55‒94.

ACS Paragon Plus Environment

33

Environmental Science & Technology

Page 34 of 36

658

(59) Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Gu, G.; Nelkin, E.J.; Bowman, K.P.; Hong, Y.;

659

Stocker, E.F.; Wolff, D.B. The TRMM multisatellite precipitation analysis (TMPA):

660

Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of

661

Hydrometeorology 2007, 8 (1), 38‒55; DOI 10.1175/JHM560.1.

662

(60) Mu, Q.; Zhao, M.; Running, S.W. Improvements to a MODIS global terrestrial

663

evapotranspiration algorithm. Remote Sensing of Environment 2011, 115, 1781‒1800; DOI

664

10.1016/j.rse.2011.02.019.

665

(61) ESA GlobeCover Project Website; http://due.esrin.esa.int/page_globcover.php

666

(62) Rodrigues, T.R.; Vourlitis, G.L.; Lobo, F.d.A.; de Oliveira, R.G.; Nogueira, J.d.S. Seasonal

667

variation in energy balance and canopy conductance for a tropical savanna ecosystem of

668

south central Mato Grosso, Brazil. J. Geophys. Res.: Biogeosci. 2014, 119 (1), 1‒13; DOI

669

10.1002/2013JG002472.

670

(63) INMET Website; http://www.inmet.gov.br/sonabra/maps/automaticas.php.

671

(64) Biudes, M.S.; Vourlitis, G.L.; Machado, N.G.; Zanella de Arruda, P.H.; Rodrigues Neves,

672

G.A.; Lobo, F.d.A.; Usher Neale, C.M.; Nogueira, J.d.S. Patterns of energy exchange for

673

tropical ecosystems across a climate gradient in Mato Grosso, Brazil. Agricultural and

674

Forest Meteorology 2015, 202, 112‒124; DOI 10.1016/j.agrformet.2014.12.008.

675

(65) Allen, R.; Pereira, L.; Raes D, S.M. Crop evapotranspiration: guidelines for computing

676

crop water requirements; Food and Agriculture Organization of the United Nations: Rome,

677

1998.

678

(66) IBGE Website; http://www.sidra.ibge.gov.br/.

679

(67) Lathuillière, M.J.; Johnson, M.S.; Donner, S.D. Water use by terrestrial ecosystems:

680

temporal variability in rainforest and agricultural contributions to evapotranspiration in

ACS Paragon Plus Environment

34

Page 35 of 36

Environmental Science & Technology

681

Mato Grosso, Brazil. Environ. Res. Lett. 2012, 7 (2), 024024; DOI 10.1088/1748-

682

9326/7/2/024024.

683

(68) Hilker, T.; Lyapustin, A.I.; Tucker, C.J.; Hall, F.G.; Myneni, R.B.; Wang, Y.; Bi, J.; de

684

Moura, Y.M.; Sellers, P.J. Vegetation dynamics and rainfall sensitivity of the Amazon.

685

Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (45), 16041‒16046; DOI

686

0.1073/pnas.1404870111.

687

(69) Brando, P.M.; Goetz, S.J.; Baccini, A.; Nepstad, D.C.; Beck, P.S.A.; Christman, M.C.

688

Seasonal and interannual variability of climate and vegetation indices across the Amazon.

689

Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (33), 14685‒14690; DOI

690

10.1073/pnas.0908741107.

691

(70) Coe, M.T.; Marthews, T.R.; Costa, M.H.; Galbraith, D.R.; Greenglass, N.L.; Imbuzeiro,

692

H.M.A.; Levine, N.M.; Malhi, Y.; Moorcroft, P.R.; Muza, M.N.; Powell, T.L.; Saleska,

693

S.R.; Solorzano, L.A.; Wang, J. Deforestation and climate feedbacks threaten the

694

ecological integrity of south-southeastern Amazonia. Philos. Trans. R. Soc., B 2013, 368

695

(1619), 20120155; DOI 10.1098/rstb.2012.0155.

696

(71) Marengo, J.A. On the hydrological cycle of the Amazon basin; a historical review and

697

current state‐of‐the‐art. Revista Brasileira de Meteorologia 2006, 21(1), 1‒19.

698

(72) Morton, D.C.; Le Page, Y.; DeFries, R.; Collatz, G.J.; Hurtt, G.C. Understorey fire

699

frequency and the fate of burned forests in southern Amazonia. Philos. Trans. R. Soc., B

700

2013, 368 (1619), 20120163; DOI 10.1098/rstb.2012.0163.

701 702

(73) Lewis, S.L.; Brando, P.M.; Phillips, O.L.; van der Heijden, G.M.F.; Nepstad, D. The 2010 Amazon Drought. Science 2011, 331 (6017), 554‒554; DOI 10.1126/science.1200807.

ACS Paragon Plus Environment

35

Environmental Science & Technology

703

(74) Brando, P.M.; Nepstad, D.C.; Davidson, E.A.; Trumbore, S.E.; Ray, D.; Camargo, P.

704

Drought effects on litterfall, wood production and belowground carbon cycling in an

705

Amazon forest: results of a throughfall reduction experiment. Philos. Trans. R. Soc., B

706

2008, 363 (1498), 1839‒1848; DOI 10.1098/rstb.2007.0031.

707

Page 36 of 36

(75) Zemp, D.C.; Schleussner, C.-F.; Barbosa, H.M.J.; van der Ent, R.J.; Donges, J.F.; Heinke,

708

J.; Sampaio, G.; Rammig, A. On the importance of cascading soil moisture recycling in

709

South America. Atmos. Chem. Phys. 2014, 14, 1337‒13359; DOI 10.5194/acp-14-13337-

710

2014.

711

(76) Xia, L.; Wang, F.; Mu, X.; Jin, K.; Sun, W.; Gao, P., Zhao, G. The water use efficiency of

712

net primary production in global terrestrial ecosystems. J. Earth Syst. Sci. 2015, 124(5),

713

921‒931; DOI 10.1007/s12040-015-0587-4.

714

(77) Chaudhary, A., Verones, F., de Baan, L.; Hellweg, S. Quantifying land use impacts on

715

biodiversity: combining species-area models and vulnerability indicators. Environ. Sci.

716

Technol. 2015, 49, 9987‒9995; DOI 10.1021/acs.est.5b02507

717

(78) Pokhrel, Y.N.; Fan, Y.; Miguez-Macho, G. Potential hydrologic changes in the Amazon by

718

the end of the 21st century and the groundwater buffer. Environ. Res. Lett. 2014, 9 (8),

719

084004; DOI 10.1088/1748-9326/9/8/084004.

720

(79) Oliveira, L.J.C.; Costa, M.H.; Soares-Filho, B.S.; Coe, M.T. Large-scale expansion of

721

agriculture in Amazonia may be a no-win scenario. Environ. Res. Lett. 2013, 8 (2),

722

024021; DOI 10.1088/1748-9326/8/2/024021.

ACS Paragon Plus Environment

36