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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

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Environmental Science & Technology

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Land use in LCA: including regionally altered

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precipitation to quantify ecosystem damage

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

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†

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Main Mall, Vancouver, BC V6T 1Z4, Canada

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♦

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

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QC H2X 3X2, Canada

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§

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

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Main Mall, Vancouver, BC V6T 1Z4, Canada

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*Corresponding author, email: [email protected]

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ABSTRACT

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

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

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

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

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

ACS Paragon Plus Environment

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

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

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

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

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

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

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

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

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cycle.

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TOC Art

29 30 31

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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emerge in order to apply the standard.11,12

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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water resources.15

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

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

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

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

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

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

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

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the focus of traditional water resources management on blue water.

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

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

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


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

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

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

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

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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

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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

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

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

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

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


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

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

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

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

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

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

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

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

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

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proposed LCIA methodology can improve transparency of environmental impacts of soybean

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

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Europe and China.48

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MATERIALS AND METHODS

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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point impacts such as Biodiversity Damage Potential and Ecosystem Services Damage

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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

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

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

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

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

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are described in turn.

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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).

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Land transformation and occupation impacts on precipitation potential. Following

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

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(1) and (2) , , , ,

(1)

, , ,

(2)

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

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

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


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

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

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

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

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

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

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

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

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

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

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ETLU – ETPNV. , ( − )

1 , , #$#, 2

(3)

(4)

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

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

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

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

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

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

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

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

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


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

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

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

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Terrestrial ecosystem damage from changes in precipitation. The reduction in

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

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

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

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

(5)

∆&, # , ,

(6)

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

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

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

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

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

(7)

# , ,

(8)

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

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


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

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water resources, as '( ' (#,

(9)

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

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

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annual precipitation in the region.53

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

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

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

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

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

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

(10)

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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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

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

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

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

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

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

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

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


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

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

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

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

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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


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256 257


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

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

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

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

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

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

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

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

267


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268 269 270 271 272

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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

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

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

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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


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

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

282

DISCUSSION

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Linking changes in evapotranspiration with environmental impacts. The above results

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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

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

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

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

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

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

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

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1996‒2008 period.69

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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,

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

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

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

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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


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

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precipitation with deforestation in the region.43,71

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

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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

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

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

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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

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production.74

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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-


18

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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


19


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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


20

Page 21 of 36


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


21


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


22

Page 23 of 36


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


23


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


24

Page 25 of 36


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

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Land Use in LCA - ACS Publications - American Chemical Society

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