Editing IPCC:AR6/SRCCL/Chapter-4 (section)

== 4.2 Land degradation in the context of climate change ==

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Land degradation results from a complex chain of causes making the clear distinction between direct and indirect drivers difficult. In the context of climate change, an additional complex aspect is brought by the reciprocal effects that both processes have on each other (i.e. climate change influencing land degradation and vice versa). In this chapter, we use the terms ‘processes’ and ‘drivers’ with the following meanings:

'''Processes of land degradation''' are those direct mechanisms by which land is degraded and are similar to the notion of ‘direct drivers’ in the Millennium Ecosystem Assessment framework (Millennium Ecosystem Assessment, 2005 <sup>[[#fn:r135|135]]</sup> ). A comprehensive list of land degradation processes is presented in Table 4.1.

'''Drivers of land degradation''' are those indirect conditions which may drive processes of land degradation and are similar to the notion of ‘indirect drivers’ in the Millennium Ecosystem Assessment framework. Examples of indirect drivers of land degradation are changes in land tenure or cash crop prices, which can trigger land-use or management shifts that affect land degradation.

An exact demarcation between processes and drivers is not possible. Drought and fires are described as drivers of land degradation in the next section but they can also be a process: for example, if repeated fires deplete seed sources, they can affect regeneration and succession of forest ecosystems. The responses to land degradation follow the logic of the LDN concept: avoiding, reducing and reversing land degradation (Orr et al. 2017 <sup>[[#fn:r136|136]]</sup> ; Cowie et al. 2018 <sup>[[#fn:r137|137]]</sup> ).

In research on land degradation, climate and climate variability are often intrinsic factors. The role of climate change, however, is less articulated. Depending on what conceptual framework is used, climate change is understood either as a process or a driver of land degradation, and sometimes both.

<span id="processes-of-land-degradation"></span>
=== 4.2.1 Processes of land degradation ===

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A large array of interactive physical, chemical, biological and human processes lead to what we define in this report as land degradation (Johnson and Lewis 2007 <sup>[[#fn:r138|138]]</sup> ). The biological productivity, ecological integrity (which encompasses both functional and structural attributes of ecosystems) or the human value (which includes any benefit that people get from the land) of a given territory can deteriorate as the result of processes triggered at scales that range from a single furrow (e.g., water erosion under cultivation) to the landscape level (e.g., salinisation through raising groundwater levels under irrigation). While pressures leading to land degradation are often exerted on specific components of the land systems (i.e., soils, water, biota), once degradation processes start, other components become affected through cascading and interactive effects. For example, different pressures and degradation processes can have convergent effects, as can be the case of overgrazing leading to wind erosion, landscape drainage resulting in wetland drying, and warming causing more frequent burning; all of which can independently lead to reductions of the soil organic matter (SOM) pools as a second-order process. Still, the reduction of organic matter pools is also a first-order process triggered directly by the effects of rising temperatures (Crowther et al. 2016 <sup>[[#fn:r139|139]]</sup> ) as well as other climate changes such as precipitation shifts (Viscarra Rossel et al. 2014 <sup>[[#fn:r140|140]]</sup> ). Beyond this complexity, a practical assessment of the major land degradation processes helps to reveal and categorise the multiple pathways in which climate change exerts a degradation pressure (Table 4.1).

Conversion of freshwater wetlands to agricultural land has historically been a common way of increasing the area of arable land. Despite the small areal extent – about 1% of the earth’s surface (Hu et al. 2017 <sup>[[#fn:r141|141]]</sup> ; Dixon et al. 2016 <sup>[[#fn:r142|142]]</sup> ) – freshwater wetlands provide a very large number of ecosystem services, such as groundwater replenishment, flood protection and nutrient retention, and are biodiversity hotspots (Reis et al. 2017 <sup>[[#fn:r143|143]]</sup> ; Darrah et al. 2019 <sup>[[#fn:r144|144]]</sup> ; Montanarella et al. 2018 <sup>[[#fn:r145|145]]</sup> ). The loss of wetlands since 1900 has been estimated at about 55% globally (Davidson 2014 <sup>[[#fn:r146|146]]</sup> ) ( ''low confidence'' ) and 35% since 1970 (Darrah et al. 2019 <sup>[[#fn:r147|147]]</sup> ) ( ''medium confidence'' ) which in many situations pose a problem for adaptation to climate change. Drainage causes loss of wetlands, which can be exacerbated by climate change, further reducing the capacity to adapt to climate change (Barnett et al. 2015 <sup>[[#fn:r148|148]]</sup> ; Colloff et al. 2016 <sup>[[#fn:r149|149]]</sup> ; Finlayson et al. 2017 <sup>[[#fn:r150|150]]</sup> ) ( ''high confidence'' ).

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==== 4.2.1.1 Types of land degradation processes ====

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Land degradation processes can affect the soil, water or biotic components of the land as well as the reactions between them (Table 4.1). Across land degradation processes, those affecting the soil have received more attention. The most widespread and studied land degradation processes affecting soils are water and wind erosion, which have accompanied agriculture since its onset and are still dominant (Table 4.1). Degradation through erosion processes is not restricted to soil loss in detachment areas but includes impacts on transport and deposition areas as well (less commonly, deposition areas can have their soils improved by these inputs). Larger-scale degradation processes related to the whole continuum of soil erosion, transport and deposition include dune field expansion/ displacement, development of gully networks and the accumulation of sediments in natural and artificial water-bodies (siltation) (Poesen and Hooke 1997 <sup>[[#fn:r151|151]]</sup> ; Ravi et al. 2010 <sup>[[#fn:r152|152]]</sup> ). Long-distance sediment transport during erosion events can have remote effects on land systems, as documented for the fertilisation effect of African dust on the Amazon (Yu et al. 2015 <sup>[[#fn:r153|153]]</sup> ).

Coastal erosion represents a special case among erosional processes, with reports linking it to climate change. While human interventions in coastal areas (e.g., expansion of shrimp farms) and rivers (e.g., upstream dams cutting coastal sediment supply), and economic activities causing land subsidence (Keogh and Törnqvist 2019 <sup>[[#fn:r154|154]]</sup> ; Allison et al. 2016 <sup>[[#fn:r155|155]]</sup> ) are dominant human drivers, storms and sea-level rise have already left a significant global imprint on coastal erosion (Mentaschi et al. 2018 <sup>[[#fn:r156|156]]</sup> ). Recent projections that take into account geomorphological and socioecological feedbacks suggest that coastal wetlands may not be reduced by sea level rise if their inland growth is accommodated with proper management actions (Schuerch et al. 2018 <sup>[[#fn:r157|157]]</sup> ).

Other physical degradation processes in which no material detachment and transport are involved include soil compaction, hardening, sealing and any other mechanism leading to the loss of porous space crucial for holding and exchanging air and water (Hamza and Anderson 2005 <sup>[[#fn:r158|158]]</sup> ). A very extreme case of degradation through pore volume loss, manifested at landscape or larger scales, is ground subsidence. Typically caused by the lowering of groundwater or oil levels, subsidence involves a sustained collapse of the ground

surface, which can lead to other degradation processes such as salinisation and permanent flooding. Chemical soil degradation processes include relatively simple changes, like nutrient depletion resulting from the imbalance of nutrient extraction on harvested products and fertilisation, and more complex ones, such as acidification and increasing metal toxicity. Acidification in croplands is increasingly driven by excessive nitrogen fertilisation and, to a lower extent, by the depletion of cation like calcium, potassium or magnesium through exports in harvested biomass (Guo et al. 2010 <sup>[[#fn:r159|159]]</sup> ). One of the most relevant chemical degradation processes of soils in the context of climate change is the depletion of its organic matter pool. Reduced in agricultural soils through the increase of respiration rates by tillage and the decline of below-ground plant biomass inputs, SOM pools have been diminished also by the direct effects of warming, not only in cultivated land, but also under natural vegetation (Bond-Lamberty et al. 2018 <sup>[[#fn:r160|160]]</sup> ). Debate persists, however, on whether in more humid and carbon-rich ecosystems the simultaneous stimulation of decomposition and productivity may result in the lack of effects on soil carbon (Crowther et al. 2016 <sup>[[#fn:r161|161]]</sup> ; van Gestel et al. 2018 <sup>[[#fn:r162|162]]</sup> ). In the case of forests, harvesting – particularly if it is exhaustive, as in the case of the use of residues for energy generation – can also lead to organic matter declines (Achat et al. 2015 <sup>[[#fn:r163|163]]</sup> ). Many other degradation processes (e.g., wildfire increase, salinisation) have negative effects on other pathways of soil degradation (e.g., reduced nutrient availability, metal toxicity). SOM can be considered a ‘hub’ of degradation processes and a critical link with the climate system (Minasny et al. 2017 <sup>[[#fn:r164|164]]</sup> ).

Land degradation processes can also start from alterations in the hydrological system that are particularly important in the context of climate change. Salinisation, although perceived and reported in soils, is typically triggered by water table-level rises, driving salts to the surface under dry to sub-humid climates (Schofield and Kirkby 2003 <sup>[[#fn:r165|165]]</sup> ). While salty soils occur naturally under these climates (primary salinity), human interventions have expanded their distribution, secondary salinity with irrigation without proper drainage being the predominant cause of salinisation (Rengasamy 2006 <sup>[[#fn:r166|166]]</sup> ). Yet, it has also taken place under non-irrigated conditions where vegetation changes (particularly dry forest clearing and cultivation) have reduced the magnitude and depth of soil water uptake, triggering water table rises towards the surface. Changes in evapotranspiration and rainfall regimes can exacerbate this process (Schofield and Kirkby 2003 <sup>[[#fn:r167|167]]</sup> ). Salinisation can also result from the intrusion of sea water into coastal areas, both as a result of sea level rise and ground subsidence (Colombani et al. 2016 <sup>[[#fn:r168|168]]</sup> ).

Recurring flood and waterlogging episodes (Bradshaw et al. 2007 <sup>[[#fn:r169|169]]</sup> ; Poff 2002 <sup>[[#fn:r170|170]]</sup> ), and the more chronic expansion of wetlands over dryland ecosystems, are mediated by the hydrological system, on occasions aided by geomorphological shifts as well (Kirwan et al. 2011 <sup>[[#fn:r171|171]]</sup> ). This is also the case for the drying of continental water bodies and wetlands, including the salinisation and drying of lakes and inland seas (Anderson et al. 2003 <sup>[[#fn:r172|172]]</sup> ; Micklin 2010 <sup>[[#fn:r173|173]]</sup> ; Herbert et al. 2015 <sup>[[#fn:r174|174]]</sup> ). In the context of climate change, the degradation of peatland ecosystems is particularly relevant given their very high carbon storage and their sensitivity to changes in soils, hydrology and/or vegetation (Leifeld and Menichetti 2018 <sup>[[#fn:r175|175]]</sup> ). Drainage for land-use conversion together with peat mining are major drivers of peatland degradation, yet other factors such as the extractive use of their natural vegetation and the interactive effects of water table levels and fires (both sensitive to climate change) are important (Hergoualc’h et al. 2017a <sup>[[#fn:r176|176]]</sup> ; Lilleskov et al. 2019 <sup>[[#fn:r177|177]]</sup> ).

The biotic components of the land can also be the focus of degradation processes. Vegetation clearing processes associated with land-use changes are not limited to deforestation but include other natural and seminatural ecosystems such as grasslands (the most cultivated biome on Earth), as well as dry steppes and shrublands, which give place to croplands, pastures, urbanisation or just barren land. This clearing process is associated with net carbon losses from the vegetation and soil pool. Not all biotic degradation processes involve biomass losses. Woody encroachment of open savannahs involves the expansion of woody plant cover and/or density over herbaceous areas and often limits the secondary productivity of rangelands (Asner et al. 2004 <sup>[[#fn:r178|178]]</sup> ; Anadon et al. 2014 <sup>[[#fn:r179|179]]</sup> ). These processes have accelerated since the mid-1800s over most continents (Van Auken 2009 <sup>[[#fn:r180|180]]</sup> ). Change in plant composition of natural or semi-natural ecosystems without any significant vegetation structural changes is another pathway of degradation affecting rangelands and forests. In rangelands, selective grazing and its interaction with climate variability and/or fire can push ecosystems to new compositions with lower forage value and a higher proportion of invasive species (Illius and O ́Connor 1999 <sup>[[#fn:r181|181]]</sup> ; Sasaki et al. 2007 <sup>[[#fn:r182|182]]</sup> ), in some cases with higher carbon sequestration potential, yet with very complex interactions between vegetation and soil carbon shifts (Piñeiro et al. 2010 <sup>[[#fn:r183|183]]</sup> ). In forests, extractive logging can be a pervasive cause of degradation, leading to long-term impoverishment and, in extreme cases, a full loss of the forest cover through its interaction with other agents such as fires (Foley et al. 2007 <sup>[[#fn:r184|184]]</sup> ) or progressive intensification of land use. Invasive alien species are another source of biological degradation. Their arrival into cultivated systems is constantly reshaping crop production strategies, making agriculture unviable on occasions. In natural and seminatural systems such as rangelands, invasive plant species not only threaten livestock production through diminished forage quality, poisoning and other deleterious effects, but have cascading effects on other processes such as altered fire regimes and water cycling (Brooks et al. 2004 <sup>[[#fn:r185|185]]</sup> ). In forests, invasions affect primary productivity and nutrient availability, change fire regimes, and alter species composition, resulting in long-term impacts on carbon pools and fluxes (Peltzer et al. 2010 <sup>[[#fn:r186|186]]</sup> ).

Other biotic components of ecosystems have been shown as a focus of degradation processes. Invertebrate invasions in continental waters can exacerbate other degradation processes such as eutrophication, which is the over-enrichment of nutrients, leading to excessive algal growth (Walsh et al. 2016a <sup>[[#fn:r187|187]]</sup> ). Shifts in soil microbial and mesofaunal composition – which can be caused by pollution with pesticides or nitrogen deposition and by vegetation or disturbance regime shifts – alter many soil functions, including respiration rates and carbon release to the atmosphere (Hussain et al. 2009 <sup>[[#fn:r188|188]]</sup> ; Crowther et al. 2015 <sup>[[#fn:r189|189]]</sup> ). The role of the soil biota in modulating the effects of climate change on soil carbon has been recently demonstrated (Ratcliffe et al. 2017 <sup>[[#fn:r190|190]]</sup> ), highlighting the importance of this lesser-known component of the biota as a focal point of land degradation. Of special relevance as both indicators and agents of land degradation recovery are mycorrhiza, which are root-associated fungal organisms (Asmelash et al. 2016 <sup>[[#fn:r191|191]]</sup> ; Vasconcellos et al. 2016 <sup>[[#fn:r192|192]]</sup> ). In natural dry ecosystems, biological soil crusts composed of a broad range of organisms, including mosses, are a particularly sensitive focus for degradation (Field et al. 2010 <sup>[[#fn:r193|193]]</sup> ) with evidenced sensitivity to climate change (Reed et al. 2012 <sup>[[#fn:r194|194]]</sup> ).

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==== 4.2.1.2 Land degradation processes and climate change ====

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While the subdivision of individual processes is challenged by their strong interconnectedness, it provides a useful setting to identify the most important ‘focal points’ of climate change pressures on land degradation. Among land degradation processes, those responding more directly to climate change pressures include all types of erosion and SOM declines (soil focus), salinisation, sodification and permafrost thawing (soil/water focus), waterlogging of dry ecosystems and drying of wet ecosystems (water focus), and a broad group of biologically-mediated processes like woody encroachment, biological invasions, pest outbreaks (biotic focus), together with biological soil crust destruction and increased burning (soil/biota focus) (Table 4.1). Processes like ground subsidence can be affected by climate change indirectly through sea level rise (Keogh and Törnqvist 2019 <sup>[[#fn:r195|195]]</sup> ).

Even when climate change exerts a direct pressure on degradation processes, it can be a secondary driver subordinated to other overwhelming human pressures. Important exceptions are three processes in which climate change is a dominant global or regional pressure and the main driver of their current acceleration. These are: coastal erosion as affected by sea level rise and increased storm frequency/intensity ( ''high agreement, medium evidence'' ) (Johnson et al. 2015 <sup>[[#fn:r196|196]]</sup> ; Alongi 2015 <sup>[[#fn:r197|197]]</sup> ; Harley et al. 2017 <sup>[[#fn:r198|198]]</sup> ; Nicholls et al. 2016 <sup>[[#fn:r199|199]]</sup> ); permafrost thawing responding to warming ( ''high agreement, robust evidence'' ) (Liljedahl et al. 2016 <sup>[[#fn:r200|200]]</sup> ; Peng et al. 2016 <sup>[[#fn:r201|201]]</sup> ; Batir et al. 2017 <sup>[[#fn:r202|202]]</sup> ); and increased burning responding to warming and altered precipitation regimes ( ''high agreement, robust evidence'' ) (Jolly et al. 2015 <sup>[[#fn:r203|203]]</sup> ; Abatzoglou and Williams 2016 <sup>[[#fn:r204|204]]</sup> ; Taufik et al. 2017 <sup>[[#fn:r205|205]]</sup> ; Knorr et al. 2016 <sup>[[#fn:r206|206]]</sup> ). The previous assessment highlights the fact that climate change not only exacerbates many of the well-acknowledged ongoing land degradation processes of managed ecosystems (i.e., croplands and pastures), but becomes a dominant pressure that introduces novel degradation pathways in natural and seminatural ecosystems. Climate change has influenced species invasions and the degradation that they cause by enhancing the transport, colonisation, establishment, and ecological impact of the invasive species, and also by impairing their control practices ( ''medium agreement, medium evidence'' ) (Hellmann et al. 2008 <sup>[[#fn:r207|207]]</sup> ).

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'''Table 4.1'''

<span id="major-land-degradation-processes-and-their-connections-with-climate-change."></span>
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'''Major land degradation processes and their connections with climate change.''''

For each process a ‘focal point’ (soil, water, biota) on which degradation occurs in the first place is indicated, acknowledging that most processes propagate to other land components and cascade into or interact with some of the other processes listed below. The impact of climate change on each process is categorised based on the proximity (very direct = high, very indirect = low) and dominance (dominant = high, subordinate to other pressures = low) of effects. The major effects of climate change on each process are highlighted together with the predominant pressures from other drivers. Feedbacks of land degradation processes on climate change are categorised according to the intensity (very intense = high, subtle = low) of the chemical (GHG emissions or capture) or physical (energy and momentum exchange, aerosol emissions) effects. Warming effects are indicated in red and cooling effects in blue. Specific feedbacks on climate change are highlighted.
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[[File:bd0f83353a4426d84d576f7ceb6c2d56 table-4.1-c.png]] [[File:ae07a84259ab2deab04b25b77f1c14ae table-4.1-b.png]] [[File:24c4ed677f4e9364ae9b543b983f25d6 table-4.1-d.png]] [[File:7ea1721e1692d75bd6f0ecd7035b6c96 table-4.1-a.png]] [[File:78948ffb54cb13fc5094f6433b7237e8 table-4.1-e.png]] [[File:91e0690e53ee286b5279765eec2c017f table-4.1-f.png]]

References in Table 4.1: (1) Bärring et al. 2003 <sup>[[#fn:r1580|1580]]</sup> ; Munson et al. 2011 <sup>[[#fn:r1581|1581]]</sup> ; Sheffield et al. 2012 <sup>[[#fn:r1582|1582]]</sup> , (2) Nearing et al. 2004 <sup>[[#fn:r1583|1583]]</sup> ; Shakesby 2011 <sup>[[#fn:r1584|1584]]</sup> ; Panthou et al. 2014 <sup>[[#fn:r1585|1585]]</sup> , (3) Johnson et al. 2015 <sup>[[#fn:r1586|1586]]</sup> ; Alongi 2015 <sup>[[#fn:r1587|1587]]</sup> ; Harley et al. 2017 <sup>[[#fn:r1588|1588]]</sup> , (4) Bond-Lamberty et al. 2018 <sup>[[#fn:r1589|1589]]</sup> ; Crowther et al. 2016 <sup>[[#fn:r1590|1590]]</sup> ; van Gestel et al. 2018 <sup>[[#fn:r1591|1591]]</sup> , (5) Colombani et al. 2016 <sup>[[#fn:r1592|1592]]</sup> , (6) Schofield and Kirkby 2003 <sup>[[#fn:r1593|1593]]</sup> ; Aragüés et al. 2015 <sup>[[#fn:r1594|1594]]</sup> ; Benini et al. 2016 <sup>[[#fn:r1595|1595]]</sup> , (7) Jobbágy et al. 2017 <sup>[[#fn:r1596|1596]]</sup> , (8) Liljedahl et al. 2016 <sup>[[#fn:r1597|1597]]</sup> ; Peng et al. 2016 <sup>[[#fn:r1598|1598]]</sup> ; Batir et al. 2017 <sup>[[#fn:r1599|1599]]</sup> , (9) Piovano et al. 2004 <sup>[[#fn:r1600|1600]]</sup> ; Osland et al. 2016 <sup>[[#fn:r1601|1601]]</sup> , (10) Burkett and Kusler 2000 <sup>[[#fn:r1602|1602]]</sup> ; Nielsen and Brock 2009 <sup>[[#fn:r1603|1603]]</sup> ; Johnson et al. 2015 <sup>[[#fn:r1604|1604]]</sup> ; Green et al. 2017 <sup>[[#fn:r1605|1605]]</sup> , (11) Panthou et al. 2014 <sup>[[#fn:r1606|1606]]</sup> ; Arnell and Gosling 2016 <sup>[[#fn:r1607|1607]]</sup> ; Vitousek et al. 2017 <sup>[[#fn:r1608|1608]]</sup> , (12) Van Auken 2009 <sup>[[#fn:r1609|1609]]</sup> ; Wigley et al. 2010 <sup>[[#fn:r1610|1610]]</sup> , (13) Vincent et al. 2014 <sup>[[#fn:r1611|1611]]</sup> ; Gonzalez et al. 2010 <sup>[[#fn:r1612|1612]]</sup> ; Scheffers et al. 2016 <sup>[[#fn:r1613|1613]]</sup> , (14) Pritchard 2011 <sup>[[#fn:r1614|1614]]</sup> ; Ratcliffe et al. 2017 <sup>[[#fn:r1615|1615]]</sup> , (15) Reed et al. 2012 <sup>[[#fn:r1616|1616]]</sup> ; Maestre et al. 2013 <sup>[[#fn:r1617|1617]]</sup> , (16) Hellmann et al. 2008 <sup>[[#fn:r1618|1618]]</sup> ; Hulme 2017 <sup>[[#fn:r1619|1619]]</sup> , (17) Pureswaran et al. 2015 <sup>[[#fn:r1620|1620]]</sup> ; Cilas et al. 2016 <sup>[[#fn:r1621|1621]]</sup> ; Macfadyen et al. 2018 <sup>[[#fn:r1622|1622]]</sup> , (18) Jolly et al. 2015 <sup>[[#fn:r1623|1623]]</sup> ; Abatzoglou and Williams 2016 <sup>[[#fn:r1624|1624]]</sup> ; Taufik et al. 2017 <sup>[[#fn:r1625|1625]]</sup> ; Knorr et al. 2016 <sup>[[#fn:r1626|1626]]</sup> , (19) Davin et al. 2010 <sup>[[#fn:r1627|1627]]</sup> ; Pinty et al. 2011 <sup>[[#fn:r1628|1628]]</sup> , (20) Wang et al. 2017b <sup>[[#fn:r1629|1629]]</sup> ; Chappell et al. 2016 <sup>[[#fn:r1630|1630]]</sup> , (21) Pendleton et al. 2012 <sup>[[#fn:r1631|1631]]</sup> , (22) Oertel et al. 2016 <sup>[[#fn:r1632|1632]]</sup> , (23) Houghton et al. 2012 <sup>[[#fn:r1633|1633]]</sup> ; Eglin et al. 2010 <sup>[[#fn:r1634|1634]]</sup> , (24) Schuur et al. 2015 <sup>[[#fn:r1635|1635]]</sup> ; Christensen et al. 2004 <sup>[[#fn:r1636|1636]]</sup> ; Walter Anthony et al. 2016 <sup>[[#fn:r1637|1637]]</sup> ; Abbott et al. 2016 <sup>[[#fn:r1638|1638]]</sup> , (25) Belnap, Walker, Munson &amp; Gill, 2014 <sup>[[#fn:r1639|1639]]</sup> ; Rutherford et al. 2017 <sup>[[#fn:r1640|1640]]</sup> , (26) Page et al. 2002 <sup>[[#fn:r1641|1641]]</sup> ; Pellegrini et al. 2018 <sup>[[#fn:r1642|1642]]</sup> .

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=== 4.2.2 Drivers of land degradation ===

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Drivers of land degradation and land improvement are many and they interact in multiple ways. Figure 4.2 illustrates how some of the most important drivers interact with the land users. It is important to keep in mind that natural and human factors can drive both degradation and improvement (Kiage 2013 <sup>[[#fn:r208|208]]</sup> ; Bisaro et al. 2014 <sup>[[#fn:r209|209]]</sup> ).

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<span id="figure-4.2"></span>
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'''Figure 4.2'''

<span id="schematic-representation-of-the-interactions-between-the-human-h-and-environmental-e-components-of-the-land-system-showing-decision-making-and-ecosystem-services-as-the-key-linkages-between-the-components-moderated-by-an-effective-system-of-local-and-scientific-knowledge-and-indicating-how-the-rates-of-change-and-the-way-these-linkages-operate-must-be-kept"></span>
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'''Schematic representation of the interactions between the human (H) and environmental (E) components of the land system showing decision-making and ecosystem services as the key linkages between the components (moderated by an effective system of local and scientific knowledge), and indicating how the rates of change and the way these linkages operate must be kept […]'''
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[[File:e0f1f8ac699bbfa5ad615b2d1c345fd9 Figure-4.2-1024x589.jpg]]

Schematic representation of the interactions between the human (H) and environmental (E) components of the land system showing decision-making and ecosystem services as the key linkages between the components (moderated by an effective system of local and scientific knowledge), and indicating how the rates of change and the way these linkages operate must be kept broadly in balance for functional coevolution of the components. Modified with permission from Stafford Smith et al. (2007) <sup>[[#fn:r1643|1643]]</sup> .

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<div id="section-4-2-2-drivers-of-land-degradation-block-3"></div>

Land degradation is driven by the entire spectrum of factors, from very short and intensive events, such as individual rain storms of 10 minutes removing topsoil or initiating a gully or a landslide (Coppus and Imeson 2002 <sup>[[#fn:r210|210]]</sup> ; Morgan 2005b <sup>[[#fn:r211|211]]</sup> ) to century-scale slow depletion of nutrients or loss of soil particles (Johnson and Lewis 2007, pp. 5–6). But, instead of focusing on absolute temporal variations, the drivers of land degradation can be assessed in relation to the rates of possible recovery. Unfortunately, this is impractical to do in a spatially explicit way because rates of soil formation are difficult to measure due to the slow rate, usually &lt;5mm/century (Delgado and Gómez 2016 <sup>[[#fn:r212|212]]</sup> ). Studies suggest that erosion rates of conventionally tilled agricultural fields exceed the rate at which soil is generated by one to two orders of magnitude (Montgomery 2007a <sup>[[#fn:r213|213]]</sup> ).

The landscape effects of gully erosion from one short intensive rainstorm can persist for decades and centuries (Showers 2005 <sup>[[#fn:r214|214]]</sup> ). Intensive agriculture under the Roman Empire in occupied territories in France is still leaving its marks and can be considered an example of irreversible land degradation (Dupouey et al. 2002 <sup>[[#fn:r215|215]]</sup> ).

The climate-change-related drivers of land degradation are gradual changes of temperature, precipitation and wind, as well as changes of the distribution and intensity of extreme events (Lin et al. 2017 <sup>[[#fn:r216|216]]</sup> ). Importantly, these drivers can act in two directions: land improvement and land degradation. Increasing CO <sub>2</sub> level in the atmosphere is a driver of land improvement, even if the net effect is modulated by other factors, such as the availability of nitrogen (Terrer et al. 2016 <sup>[[#fn:r217|217]]</sup> ) and water (Gerten et al. 2014 <sup>[[#fn:r218|218]]</sup> ; Settele et al. 2015 <sup>[[#fn:r219|219]]</sup> ; Girardin et al. 2016 <sup>[[#fn:r220|220]]</sup> ).

The gradual and planetary changes that can cause land degradation/ improvement have been studied by global integrated models and Earth observation technologies. Studies of global land suitability for agriculture suggest that climate change will increase the area suitable for agriculture by 2100 in the Northern high latitudes by 16% (Ramankutty et al. 2002 <sup>[[#fn:r221|221]]</sup> ) or 5.6 million km <sup>2</sup> (Zabel et al. 2014 <sup>[[#fn:r222|222]]</sup> ), while tropical regions will experience a loss (Ramankutty et al. 2002 <sup>[[#fn:r223|223]]</sup> ; Zabel et al. 2014 <sup>[[#fn:r224|224]]</sup> ).

Temporal and spatial patterns of tree mortality can be used as an indicator of climate change impacts on terrestrial ecosystems. Episodic mortality of trees occurs naturally even without climate change, but more widespread spatio-temporal anomalies can be a sign of climate-induced degradation (Allen et al. 2010 <sup>[[#fn:r225|225]]</sup> ). In the absence of systematic data on tree mortality, a comprehensive meta-analysis of 150 published articles suggests that increasing tree mortality around the world can be attributed to increasing drought and heat stress in forests worldwide (Allen et al. 2010 <sup>[[#fn:r226|226]]</sup> ).

Other and more indirect drivers can be a wide range of factors such as demographic changes, technological change, changes of consumption patterns and dietary preferences, political and economic changes, and social changes (Mirzabaev et al. 2016 <sup>[[#fn:r227|227]]</sup> ). It is important to stress that there are no simple or direct relationships between underlying drivers and land degradation, such as poverty or high population density, that are necessarily causing land degradation (Lambin et al. 2001 <sup>[[#fn:r228|228]]</sup> ). However, drivers of land degradation need to be studied in the context of spatial, temporal, economic, environmental and cultural aspects (Warren 2002 <sup>[[#fn:r229|229]]</sup> ). Some analyses suggest an overall negative correlation between population density and land degradation (Bai et al. 2008 <sup>[[#fn:r230|230]]</sup> ) but we find many local examples of both positive and negative relationships (Brandt et al. 2018a, 2017 <sup>[[#fn:r231|231]]</sup> ). Even if there are correlations in one or the other direction, causality is not always the same.

Land degradation is inextricably linked to several climate variables, such as temperature, precipitation, wind, and seasonality. This means that there are many ways in which climate change and land degradation are linked. The linkages are better described as a web of causality rather than a set of cause–effect relationships.

<span id="attribution-in-the-case-of-land-degradation"></span>
=== 4.2.3 Attribution in the case of land degradation ===

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The question here is whether or not climate change can be attributed to land degradation and vice versa. Land degradation is a complex phenomenon often affected by multiple factors such as climatic (rainfall, temperature, and wind), abiotic ecological factors (e.g., soil characteristics and topography), type of land use (e.g., farming of various kinds, forestry, or protected area), and land management practices (e.g., tilling, crop rotation, and logging/thinning). Therefore, attribution of land degradation to climate change is extremely challenging. Because land degradation is highly dependent on land management, it is even possible that climate impacts would trigger land management changes reducing or reversing land degradation, sometimes called transformational adaptation (Kates et al. 2012 <sup>[[#fn:r232|232]]</sup> ). There is not much research on attributing land degradation explicitly to climate change, but there is more on climate change as a threat multiplier for land degradation. However, in some cases, it is possible to infer climate change impacts on land degradation, both theoretically and empirically. Section 4.2.3.1 outlines the potential direct linkages of climate change on land degradation based on current theoretical understanding of land degradation processes and drivers. Section 4.2.3.2 investigates possible indirect impacts on land degradation.

<div id="section-4-2-3-1-direct-linkages-with-climate-change"></div>

<span id="direct-linkages-with-climate-change"></span>
==== 4.2.3.1 Direct linkages with climate change ====

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The most important direct impacts of climate change on land degradation are the results of increasing temperatures, changing rainfall patterns, and intensification of rainfall. These changes will, in various combinations, cause changes in erosion rates and the processes driving both increases and decreases of soil erosion. From an attribution point of view, it is important to note that projections of precipitation are, in general, more uncertain than projections of temperature changes (Murphy et al. 2004 <sup>[[#fn:r233|233]]</sup> ; Fischer and Knutti 2015 <sup>[[#fn:r234|234]]</sup> ; IPCC 2013a <sup>[[#fn:r235|235]]</sup> ). Precipitation involves local processes of larger complexity than temperature, and projections are usually less robust than those for temperature (Giorgi and Lionello 2008 <sup>[[#fn:r236|236]]</sup> ; Pendergrass 2018 <sup>[[#fn:r237|237]]</sup> ).

Theoretically the intensification of the hydrological cycle as a result of human-induced climate change is well established (Guerreiro et al. 2018 <sup>[[#fn:r238|238]]</sup> ; Trenberth 1999 <sup>[[#fn:r239|239]]</sup> ; Pendergrass et al. 2017 <sup>[[#fn:r240|240]]</sup> ; Pendergrass and Knutti 2018 <sup>[[#fn:r241|241]]</sup> ) and also empirically observed (Blenkinsop et al. 2018 <sup>[[#fn:r242|242]]</sup> ; Burt et al. 2016a <sup>[[#fn:r243|243]]</sup> ; Liu et al. 2009 <sup>[[#fn:r244|244]]</sup> ; Bindoff et al. 2013 <sup>[[#fn:r245|245]]</sup> ). AR5 WGI concluded that heavy precipitation events have increased in frequency, intensity, and/or amount since 1950 ( ''likely'' ) and that further changes in this direction are ''likely'' to very ''likely'' during the 21st century (IPCC 2013 <sup>[[#fn:r246|246]]</sup> ). The IPCC Special Report on 1.5°C concluded that human-induced global warming has already caused an increase in the frequency, intensity and/or amount of heavy precipitation events at the global scale (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r247|247]]</sup> ). As an example, in central India, there has been a threefold increase in widespread extreme rain events during 1950–2015 which has influenced several land degradation processes, not least soil erosion (Burt et al. 2016b <sup>[[#fn:r248|248]]</sup> ). In Europe and North America, where observation networks are dense and extend over a long time, it is ''likely'' that the frequency or intensity of heavy rainfall have increased (IPCC 2013b <sup>[[#fn:r1644|1644]]</sup> ). It is also expected that seasonal shifts and cycles such as monsoons and El Niño–Southern Oscillation (ENSO) will further increase the intensity of rainfall events (IPCC 2013 <sup>[[#fn:r249|249]]</sup> ).

When rainfall regimes change, it is expected to drive changes in vegetation cover and composition, which may be a cause of land degradation in and of itself, as well as impacting on other aspects of land degradation. Vegetation cover, for example, is a key factor in determining soil loss through water (Nearing et al. 2005 <sup>[[#fn:r250|250]]</sup> ) and wind erosion (Shao 2008 <sup>[[#fn:r251|251]]</sup> ). Changing rainfall regimes also affect below-ground biological processes, such as fungi and bacteria (Meisner et al. 2018 <sup>[[#fn:r252|252]]</sup> ; Shuab et al. 2017 <sup>[[#fn:r253|253]]</sup> ; Asmelash et al. 2016 <sup>[[#fn:r254|254]]</sup> ).

Changing snow accumulation and snow melt alter volume and timing of hydrological flows in and from mountain areas (Brahney et al. 2017 <sup>[[#fn:r255|255]]</sup> ; Lutz et al. 2014 <sup>[[#fn:r256|256]]</sup> ), with potentially large impacts on downstream areas. Soil processes are also affected by changing snow conditions with partitioning between evaporation and streamflow and between subsurface flow and surface runoff (Barnhart et al. 2016 <sup>[[#fn:r257|257]]</sup> ). Rainfall intensity is a key climatic driver of soil erosion. Early modelling studies and theory suggest that light rainfall events will decrease while heavy rainfall events increase at about 7% per degree of warming (Liu et al. 2009 <sup>[[#fn:r258|258]]</sup> ; Trenberth 2011 <sup>[[#fn:r259|259]]</sup> ). Such changes result in increased intensity of rainfall, which increases the erosive power of rainfall (erosivity) and hence enhances the likelihood of water erosion. Increases in rainfall intensity can even exceed the rate of increase of atmospheric moisture content (Liu et al. 2009 <sup>[[#fn:r260|260]]</sup> ; Trenberth 2011 <sup>[[#fn:r261|261]]</sup> ). Erosivity is highly correlated to the product of total rainstorm energy and the maximum 30-minute rainfall intensity of the storm (Nearing et al. 2004 <sup>[[#fn:r262|262]]</sup> ) and increased erosivity will exacerbate water erosion substantially (Nearing et al. 2004 <sup>[[#fn:r263|263]]</sup> ). However, the effects will not be uniform, but highly variable across regions (Almagro et al. 2017 <sup>[[#fn:r264|264]]</sup> ; Mondal et al. 2016 <sup>[[#fn:r265|265]]</sup> ). Several empirical studies around the world have shown the increasing intensity of rainfall (IPCC 2013b <sup>[[#fn:r266|266]]</sup> ; Ma et al. 2015 <sup>[[#fn:r267|267]]</sup> , 2017 <sup>[[#fn:r268|268]]</sup> ) and also suggest that this will be accentuated with future increased global warming (Cheng and AghaKouchak 2015 <sup>[[#fn:r269|269]]</sup> ; Burt et al. 2016b <sup>[[#fn:r270|270]]</sup> ; O’Gorman 2015 <sup>[[#fn:r271|271]]</sup> ).

The very comprehensive database of direct measurements of water erosion presented by García-Ruiz et al. (2015) <sup>[[#fn:r272|272]]</sup> contains 4377 entries (North America: 2776, Europe: 847, Asia: 259, Latin America: 237, Africa: 189, Australia and Pacific: 67), even though not all entries are complete (Figure 4.3).

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'''Figure 4.3'''

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'''Map of observed soil erosion rates in database of 4,377 entries by García-Ruiz et al. (2015). The map was published by Li and Fang (2016).'''
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[[File:31fbf90f85fcb5e6021810a5e93836c1 Figure-4.3-1024x576.jpg]]

Map of observed soil erosion rates in database of 4,377 entries by García-Ruiz et al. (2015) <sup>[[#fn:r1645|1645]]</sup> . The map was published by Li and Fang (2016) <sup>[[#fn:r1646|1646]]</sup> .

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<div id="section-4-2-3-1-direct-linkages-with-climate-change-block-3"></div>

An important finding from that database is that almost any erosion rate is possible under almost any climatic condition (García-Ruiz et al. 2015 <sup>[[#fn:r273|273]]</sup> ). Even if the results show few clear relationships between erosion and land conditions, the authors highlighted four observations (i) the highest erosion rates were found in relation to agricultural activities – even though moderate erosion rates were also found in agricultural settings, (ii) high erosion rates after forest fires were not observed (although the cases were few), (iii) land covered by shrubs showed generally low erosion rates, (iv) pasture land showed generally medium rates of erosion. Some important findings for the link between soil erosion and climate change can be noted from erosion measurements: erosion rates tend to increase with increasing mean annual rainfall, with a peak in the interval of 1000 to 1400 mm annual rainfall (García-Ruiz et al. 2015 <sup>[[#fn:r274|274]]</sup> ) ( ''low confidence'' ). However, such relationships are overshadowed by the fact that most rainfall events do not cause any erosion, instead erosion is caused by a few high-intensity rainfall events (Fischer et al. 2016 <sup>[[#fn:r275|275]]</sup> ; Zhu et al. 2019 <sup>[[#fn:r276|276]]</sup> ). Hence, mean annual rainfall is not a good predictor of erosion (Gonzalez-Hidalgo et al. 2012, 2009 <sup>[[#fn:r277|277]]</sup> ). In the context of climate change, it means that the tendency for rainfall patterns to change towards more intensive precipitation events is serious. Such patterns have already been observed widely, even in cases where the total rainfall is decreasing (Trenberth 2011 <sup>[[#fn:r278|278]]</sup> ). The findings generally confirm the strong consensus about the importance of vegetation cover as a protection against soil erosion, emphasising how extremely important land management is for controlling erosion.

In the Mediterranean region, the observed and expected decrease in annual rainfall due to climate change is accompanied by an increase of rainfall intensity, and hence erosivity (Capolongo et al. 2008 <sup>[[#fn:r279|279]]</sup> ). In tropical and sub-tropical regions, the on-site impacts of soil erosion dominate, and are manifested in very high rates of soil loss, in some cases exceeding 100 t ha–1 yr–1 (Tadesse 2001 <sup>[[#fn:r280|280]]</sup> ; García-Ruiz et al. 2015 <sup>[[#fn:r281|281]]</sup> ). In temperate regions, the off-site costs of soil erosion are often a greater concern, for example, siltation of dams and ponds, downslope damage to property, roads and other infrastructure (Boardman 2010). In cases where water erosion occurs, the downstream effects, such as siltation of dams, are often significant and severe in terms of environmental and economic damages (Kidane and Alemu 2015 <sup>[[#fn:r282|282]]</sup> ; Reinwarth et al. 2019 <sup>[[#fn:r283|283]]</sup> ; Quiñonero-Rubio et al. 2016 <sup>[[#fn:r284|284]]</sup> ; Adeogun et al. 2018 <sup>[[#fn:r285|285]]</sup> ; Ben Slimane et al. 2016 <sup>[[#fn:r286|286]]</sup> ).

The distribution of wet and dry spells also affects land degradation, although uncertainties remain depending on resolution of climate models used for prediction (Kendon et al. 2014 <sup>[[#fn:r287|287]]</sup> ). Changes in timing of rainfall events may have significant impacts on processes of soil erosion through changes in wetting and drying of soils (Lado et al. 2004 <sup>[[#fn:r288|288]]</sup> ).

Soil moisture content is affected by changes in evapotranspiration and evaporation, which may influence the partitioning of water into surface and subsurface runoff (Li and Fang 2016 <sup>[[#fn:r289|289]]</sup> ; Nearing et al. 2004 <sup>[[#fn:r290|290]]</sup> ). This portioning of rainfall can have a decisive effect on erosion (Stocking et al. 2001 <sup>[[#fn:r291|291]]</sup> ).

Wind erosion is a serious problem in agricultural regions, not only in drylands (Wagner 2013 <sup>[[#fn:r292|292]]</sup> ). Near-surface wind speeds over land areas have decreased in recent decades (McVicar and Roderick 2010 <sup>[[#fn:r293|293]]</sup> ), partly as a result of changing surface roughness (Vautard et al. 2010 <sup>[[#fn:r294|294]]</sup> ). Theoretically (Bakun 1990 <sup>[[#fn:r295|295]]</sup> ; Bakun et al. 2015 <sup>[[#fn:r296|296]]</sup> ) and empirically (Sydeman et al. 2014 <sup>[[#fn:r297|297]]</sup> ; England et al. 2014 <sup>[[#fn:r298|298]]</sup> ) average winds along coastal regions worldwide have increased with climate change ( ''medium evidence, high agreement'' ). Other studies of wind and wind erosion have not detected any long-term trend, suggesting that climate change has altered wind patterns outside drylands in a way that can significantly affect the risk of wind erosion (Pryor and Barthelmie 2010 <sup>[[#fn:r299|299]]</sup> ; Bärring et al. 2003 <sup>[[#fn:r300|300]]</sup> ). Therefore, the findings regarding wind erosion and climate change are inconclusive, partly due to inadequate measurements.

Global mean temperatures are rising worldwide, but particularly in the Arctic region ( ''high confidence'' ) (IPCC 2018a <sup>[[#fn:r301|301]]</sup> ). Heat stress from extreme temperatures and heatwaves (multiple days of hot weather in a row) have increased markedly in some locations in the last three decades ( ''high confidence'' ), and are ''virtually certain'' to continue during the 21st century (Olsson et al. 2014a <sup>[[#fn:r302|302]]</sup> ). The IPCC Special Report on Global Warming of 1.5°C concluded that human-induced global warming has already caused more frequent heatwaves in most of land regions, and that climate models project robust differences between present-day and global warming up to 1.5°C and between 1.5°C and 2°C (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r303|303]]</sup> ). Direct temperature effects on soils are of two kinds. Firstly, permafrost thawing leads to soil degradation in boreal and high-altitude regions (Yang et al. 2010 <sup>[[#fn:r304|304]]</sup> ; Jorgenson and Osterkamp 2005 <sup>[[#fn:r305|305]]</sup> ). Secondly, warming alters the cycling of nitrogen and carbon in soils, partly due to impacts on soil microbiota (Solly et al. 2017 <sup>[[#fn:r306|306]]</sup> ). There are many studies with particularly strong experimental evidence, but a full understanding of cause and effect is contextual and elusive (Conant et al. 2011a <sup>[[#fn:r307|307]]</sup> ,b <sup>[[#fn:r308|308]]</sup> ; Wu et al. 2011 <sup>[[#fn:r309|309]]</sup> ). This is discussed comprehensively in Chapter 2.

Climate change, including increasing atmospheric CO <sub>2</sub> levels, affects vegetation structure and function and hence conditions for land degradation. Exactly how vegetation responds to changes remains a research task. In a comparison of seven global vegetation models under four representative concentration pathways, Friend et al. (2014) <sup>[[#fn:r310|310]]</sup> found that all models predicted increasing vegetation carbon storage, however, with substantial variation between models. An important insight compared with previous understanding is that structural dynamics of vegetation seems to play a more important role for carbon storage than vegetation production (Friend et al. 2014 <sup>[[#fn:r311|311]]</sup> ). The magnitude of CO <sub>2</sub> fertilisation of vegetation growth, and hence conditions for land degradation, is still uncertain (Holtum and Winter 2010 <sup>[[#fn:r312|312]]</sup> ), particularly in tropical rainforests (Yang et al. 2016 <sup>[[#fn:r313|313]]</sup> ). For more discussion on this topic, see Chapter 2 in this report.

In summary, rainfall changes attributed to human-induced climate change have already intensified drivers of land degradation ( ''robust evidence, high agreement'' ) but attributing land degradation to climate change is challenging because of the importance of land management ( ''medium evidence, high agreement'' ). Changes in climate variability modes, such as in monsoons and El Niño–Southern Oscillation (ENSO) events, can also affect land degradation ( ''low evidence, low agreement'' ).

<div id="section-4-2-3-2-indirect-and-complex-linkages-with-climate-change"></div>

<span id="indirect-and-complex-linkages-with-climate-change"></span>
==== 4.2.3.2 Indirect and complex linkages with climate change ====

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Many important indirect linkages between land degradation and climate change occur via agriculture, particularly through changing outbreaks of pests (Rosenzweig et al. 2001 <sup>[[#fn:r314|314]]</sup> ; Porter et al. 1991 <sup>[[#fn:r315|315]]</sup> ; Thomson et al. 2010 <sup>[[#fn:r316|316]]</sup> ; Dhanush et al. 2015 <sup>[[#fn:r317|317]]</sup> ; Lamichhane et al. 2015 <sup>[[#fn:r318|318]]</sup> ), which is covered comprehensively in Chapter 5. More negative impacts have been observed than positive ones (IPCC 2014b <sup>[[#fn:r319|319]]</sup> ). After 2050, the risk of yield loss increases as a result of climate change in combination with other drivers ( ''medium confidence'' ) and such risks will increase dramatically if global mean temperatures increase by about 4°C ( ''high confidence'' ) (Porter et al. 2014). The reduction (or plateauing) in yields in major production areas (Brisson et al. 2010 <sup>[[#fn:r320|320]]</sup> ; Lin and Huybers 2012 <sup>[[#fn:r321|321]]</sup> ; Grassini et al. 2013 <sup>[[#fn:r322|322]]</sup> ) may trigger cropland expansion elsewhere, either into natural ecosystems, marginal arable lands or intensification on already cultivated lands, with possible consequences for increasing land degradation.

Precipitation and temperature changes will trigger changes in land and crop management, such as changes in planting and harvest dates, type of crops, and type of cultivars, which may alter the conditions for soil erosion (Li and Fang 2016 <sup>[[#fn:r323|323]]</sup> ).

Much research has tried to understand how plants are affected by a particular stressor, for example, drought, heat, or waterlogging, including effects on below-ground processes. But less research has tried to understand how plants are affected by several simultaneous stressors – which of course is more realistic in the context of climate change (Mittler 2006 <sup>[[#fn:r324|324]]</sup> ; Kerns et al. 2016 <sup>[[#fn:r325|325]]</sup> ) and from a hazards point of view (Section 7.2.1). From an attribution point of view, such a complex web of causality is problematic if attribution is only done through statistically-significant correlation. It requires a combination of statistical links and theoretically informed causation, preferably integrated into a model. Some modelling studies have combined several stressors with geomorphologically explicit mechanisms – using the Water Erosion Prediction Project (WEPP) model – and realistic land-use scenarios, and found severe risks of increasing erosion from climate change (Mullan et al. 2012 <sup>[[#fn:r326|326]]</sup> ; Mullan 2013 <sup>[[#fn:r327|327]]</sup> ). Other studies have included various management options, such as changing planting and harvest dates (Zhang and Nearing 2005 <sup>[[#fn:r328|328]]</sup> ; Parajuli et al. 2016 <sup>[[#fn:r329|329]]</sup> ; Routschek et al. 2014 <sup>[[#fn:r330|330]]</sup> ; Nunes and Nearing 2011 <sup>[[#fn:r331|331]]</sup> ), type of cultivars (Garbrecht and Zhang 2015 <sup>[[#fn:r332|332]]</sup> ), and price of crops (Garbrecht et al. 2007 <sup>[[#fn:r333|333]]</sup> ; O’Neal et al. 2005 <sup>[[#fn:r334|334]]</sup> ) to investigate the complexity of how new climate regimes may alter soil erosion rates.

In summary, climate change increases the risk of land degradation, both in terms of likelihood and consequence, but the exact attribution to climate change is challenging due to several confounding factors. But since climate change exacerbates most degradation processes, it is clear that, unless land management is improved, climate change will result in increasing land degradation ( ''very high confidence'' ).

<span id="approaches-to-assessing-land-degradation"></span>
=== 4.2.4 Approaches to assessing land degradation ===

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In a review of different approaches and attempts to map global land degradation, Gibbs and Salmon (2015) <sup>[[#fn:r335|335]]</sup> identified four main approaches to map the global extent of degraded lands: expert opinions (Oldeman and van Lynden 1998 <sup>[[#fn:r336|336]]</sup> ; Dregne 1998 <sup>[[#fn:r337|337]]</sup> ; Reed 2005 <sup>[[#fn:r338|338]]</sup> ; Bot et al. 2000 <sup>[[#fn:r339|339]]</sup> ); satellite observation of vegetation greenness – for example, remote sensing of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Plant Phenology Index (PPI) – (Yengoh et al. 2015 <sup>[[#fn:r340|340]]</sup> ; Bai et al. 2008c <sup>[[#fn:r341|341]]</sup> ; Shi et al. 2017 <sup>[[#fn:r342|342]]</sup> ; Abdi et al. 2019 <sup>[[#fn:r343|343]]</sup> ; JRC 2018 <sup>[[#fn:r344|344]]</sup> ); biophysical models (biogeographical/ topological) (Cai et al. 2011b <sup>[[#fn:r345|345]]</sup> ; Hickler et al. 2005 <sup>[[#fn:r346|346]]</sup> ; Steinkamp and Hickler 2015 <sup>[[#fn:r347|347]]</sup> ; Stoorvogel et al. 2017 <sup>[[#fn:r348|348]]</sup> ); and inventories of land use/ condition. Together they provide a relatively complete evaluation, but none on its own assesses the complexity of the process (Vogt et al. 2011 <sup>[[#fn:r349|349]]</sup> ; Gibbs and Salmon 2015 <sup>[[#fn:r350|350]]</sup> ). There is, however, a robust consensus that remote sensing and field-based methods are critical to assess and monitor land degradation, particularly over large areas (such as global, continental and sub-continental) although there are still knowledge gaps to be filled (Wessels et al. 2007 <sup>[[#fn:r351|351]]</sup> , 2004 <sup>[[#fn:r352|352]]</sup> ; Prince 2016 <sup>[[#fn:r353|353]]</sup> ; Ghazoul and Chazdon 2017 <sup>[[#fn:r354|354]]</sup> ) as well as the problem of baseline values (Section 4.1.3).

Remote sensing can provide meaningful proxies of land degradation in terms of severity, temporal development, and areal extent. These proxies of land degradation include several indexes that have been used to assess land conditions, and monitoring changes of land conditions – for example, extent of gullies, severe forms of rill and sheet erosion, and deflation. The presence of open-access, quality controlled and continuously updated global databases of remote sensing data is invaluable, and is the only method for consistent monitoring of large areas over several decades (Sedano et al. 2016 <sup>[[#fn:r355|355]]</sup> ; Brandt et al. 2018b <sup>[[#fn:r356|356]]</sup> ; Turner 2014 <sup>[[#fn:r357|357]]</sup> ).The NDVI, as a proxy for Net Primary Production (NPP) (see Glossary), is one of the most commonly used methods to assess land degradation, since it indicates land cover, an important factor for soil protection. Although NDVI is not a direct measure of vegetation biomass, there is a close coupling between NDVI integrated over a season and in situ NPP ( ''high agreement, robust evidence'' ) (see Higginbottom et al. 2014 <sup>[[#fn:r358|358]]</sup> ; Andela et al. 2013 <sup>[[#fn:r359|359]]</sup> ; Wessels et al. 2012 <sup>[[#fn:r360|360]]</sup> ).

Distinction between land degradation/improvement and the effects of climate variation is an important and contentious issue (Murthy and Bagchi 2018 <sup>[[#fn:r361|361]]</sup> ; Ferner et al. 2018 <sup>[[#fn:r362|362]]</sup> ).There is no simple and straightforward way to disentangle these two effects. The interaction of different determinants of primary production is not well understood. A key barrier to this is a lack of understanding of the inherent interannual variability of vegetation (Huxman et al. 2004 <sup>[[#fn:r363|363]]</sup> ; Knapp and Smith 2001 <sup>[[#fn:r364|364]]</sup> ; Ruppert et al. 2012 <sup>[[#fn:r365|365]]</sup> ; Bai et al. 2008a <sup>[[#fn:r366|366]]</sup> ; Jobbágy and Sala 2000 <sup>[[#fn:r367|367]]</sup> ). One possibility is to compare potential land productivity modelled by vegetation models and actual productivity measured by remote sensing (Seaquist et al. 2009 <sup>[[#fn:r368|368]]</sup> ; Hickler et al. 2005 <sup>[[#fn:r369|369]]</sup> ; van der Esch et al. 2017 <sup>[[#fn:r370|370]]</sup> ), but the difference in spatial resolution, typically 0.5 degrees for vegetation models compared to 0.25–0.5 km for remote sensing data, is hampering the approach. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides higher spatial resolution (up to 0.25 km), delivers data for the EVI, which is calculated in the same way as NDVI, and has showed a robust approach to estimate spatial patterns of global annual primary productivity (Shi et al. 2017 <sup>[[#fn:r371|371]]</sup> ; Testa et al. 2018 <sup>[[#fn:r372|372]]</sup> ).

Another approach to disentangle the effects of climate and land use/ management is to use the Rain Use Efficiency (RUE), defined as the biomass production per unit of rainfall, as an indicator (Le Houerou 1984 <sup>[[#fn:r373|373]]</sup> ; Prince et al. 1998 <sup>[[#fn:r374|374]]</sup> ; Fensholt et al. 2015 <sup>[[#fn:r375|375]]</sup> ). A variant of the RUE approach is the residual trend (RESTREND) of a NDVI time series, defined as the fraction of the difference between the observed NDVI and the NDVI predicted from climate data (Yengoh et al. 2015 <sup>[[#fn:r376|376]]</sup> ; John et al. 2016 <sup>[[#fn:r377|377]]</sup> ). These two metrics aim to estimate the NPP, rainfall and the time dimensions. They are simple transformations of the same three variables: RUE shows the NPP relationship with rainfall for individual years, while RESTREND is the interannual change of RUE; also, both consider that rainfall is the only variable that affects biomass production. They are legitimate metrics when used appropriately, but in many cases they involve oversimplifications and yield misleading results (Fensholt et al. 2015 <sup>[[#fn:r378|378]]</sup> ; Prince et al. 1998 <sup>[[#fn:r379|379]]</sup> ).

Furthermore, increases in NPP do not always indicate improvement in land condition/reversal of land degradation, since this does not account for changes in vegetation composition. It could, for example, result from conversion of native forest to plantation, or due to bush encroachment, which many consider to be a form of land degradation (Ward 2005 <sup>[[#fn:r380|380]]</sup> ). Also, NPP may be increased by irrigation, which can enhance productivity in the short to medium term while increasing risk of soil salinisation in the long term (Niedertscheider et al. 2016 <sup>[[#fn:r381|381]]</sup> ).

Recent progress and expanding time series of canopy characterisations based on passive microwave satellite sensors have offered rapid progress in regional and global descriptions of forest degradation and recovery trends (Tian et al. 2017 <sup>[[#fn:r382|382]]</sup> ). The most common proxy is vertical optical depth (VOD) and has already been used to describe global forest/savannah carbon stock shifts over two decades, highlighting strong continental contrasts (Liu et al. 2015a <sup>[[#fn:r383|383]]</sup> ) and demonstrating the value of this approach to monitor forest degradation at large scales. Contrasting with NDVI, which is only sensitive to vegetation ‘greenness’, from which primary production can be modelled, VOD is also sensitive to water in woody parts of the vegetation and hence provides a view of vegetation dynamics that can be complementary to NDVI. As well as the NDVI, VOD also needs to be corrected to take into account the rainfall variation (Andela et al. 2013 <sup>[[#fn:r384|384]]</sup> ).

Even though remote sensing offers much potential, its application to land degradation and recovery remains challenging as structural changes often occur at scales below the detection capabilities of most remote-sensing technologies. Additionally, if the remote sensing is based on vegetation index data, other forms of land degradation, such as nutrient depletion, changes of soil physical or biological properties, loss of values for humans, among others, cannot be inferred directly by remote sensing. The combination of remotely sensed images and field-based approach can give improved estimates of carbon stocks and tree biodiversity (Imai et al. 2012 <sup>[[#fn:r385|385]]</sup> ; Fujiki et al. 2016 <sup>[[#fn:r386|386]]</sup> ).

Additionally, the majority of trend techniques employed would be capable of detecting only the most severe of degradation processes, and would therefore not be useful as a degradation early-warning system (Higginbottom et al. 2014 <sup>[[#fn:r387|387]]</sup> ; Wessels et al. 2012 <sup>[[#fn:r388|388]]</sup> ). However, additional analyses using higher-resolution imagery, such as the Landsat and SPOT satellites, would be well suited to providing further localised information on trends observed (Higginbottom et al. 2014 <sup>[[#fn:r389|389]]</sup> ). New approaches to assess land degradation using high spatial resolution are developing, but the need for time series makes progress slow. The use of synthetic aperture radar (SAR) data has been shown to be advantageous for the estimation of soil surface characteristics, in particular, surface roughness and soil moisture (Gao et al. 2017 <sup>[[#fn:r390|390]]</sup> ; Bousbih et al. 2017 <sup>[[#fn:r391|391]]</sup> ), and detecting and quantifying selective logging (Lei et al. 2018 <sup>[[#fn:r392|392]]</sup> ). Continued research effort is required to enable full assessment of land degradation using remote sensing.

Computer simulation models can be used alone or combined with the remote sensing observations to assess land degradation. The Revised Universal Soil Loss Equation (RUSLE) can be used, to some extent, to predict the long-term average annual soil loss by water erosion. RUSLE has been constantly revisited to estimate soil loss based on the product of rainfall–runoff erosivity, soil erodibility, slope length and steepness factor, conservation factor, and support practice parameter (Nampak et al. 2018 <sup>[[#fn:r393|393]]</sup> ). Inherent limitations of RUSLE include data-sparse regions, inability to account for soil loss from gully erosion or mass wasting events, and that it does not predict sediment pathways from hillslopes to water bodies (Benavidez et al. 2018 <sup>[[#fn:r394|394]]</sup> ). Since RUSLE models only provide gross erosion, the integration of a further module in the RUSLE scheme to estimate the sediment yield from the modelled hillslopes is needed. The spatially distributed sediment delivery model, WaTEM/SEDEM, has been widely tested in Europe (Borrelli et al. 2018 <sup>[[#fn:r395|395]]</sup> ). Wind erosion is another factor that needs to be taken into account in the modelling of soil erosion (Webb et al. 2017a <sup>[[#fn:r396|396]]</sup> , 2016 <sup>[[#fn:r397|397]]</sup> ). Additional models need to be developed to include the limitations of the RUSLE models.

Regarding the field-based approach to assess land degradation, there are multiple indicators that reflect functional ecosystem processes linked to ecosystem services and thus to the value for humans. These indicators are a composite set of measurable attributes from different factors, such as climate, soil, vegetation, biomass, management, among others, that can be used together or separately to develop indexes to better assess land degradation (Allen et al. 2011 <sup>[[#fn:r398|398]]</sup> ; Kosmas et al. 2014 <sup>[[#fn:r399|399]]</sup> ).

Declines in vegetation cover, changes in vegetation structure, decline in mean species abundances, decline in habitat diversity, changes in abundance of specific indicator species, reduced vegetation health and productivity, and vegetation management intensity and use, are the most common indicators in the vegetation condition of forest and woodlands (Stocking et al. 2001 <sup>[[#fn:r400|400]]</sup> ; Wiesmair et al. 2017 <sup>[[#fn:r401|401]]</sup> ; Ghazoul and Chazdon 2017 <sup>[[#fn:r402|402]]</sup> ; Alkemade et al. 2009 <sup>[[#fn:r403|403]]</sup> ).

Several indicators of the soil quality (SOM, depth, structure, compaction, texture, pH, C:N ratio, aggregate size distribution and stability, microbial respiration, soil organic carbon, salinisation, among others) have been proposed (Schoenholtz et al. 2000 <sup>[[#fn:r404|404]]</sup> ) (Section 2.2). Among these, SOM directly and indirectly drives the majority of soil functions. Decreases in SOM can lead to a decrease in fertility and biodiversity, as well as a loss of soil structure, causing reductions in water-holding capacity, increased risk of erosion (both wind and water) and increased bulk density and hence soil compaction (Allen et al. 2011 <sup>[[#fn:r405|405]]</sup> ; Certini 2005 <sup>[[#fn:r406|406]]</sup> ; Conant et al. 2011a <sup>[[#fn:r407|407]]</sup> ). Thus, indicators related with the quantity and quality of the SOM are necessary to identify land degradation (Pulido et al. 2017 <sup>[[#fn:r408|408]]</sup> ; Dumanski and Pieri 2000 <sup>[[#fn:r409|409]]</sup> ). The composition of the microbial community is very likely to be positive impacted by both climate change and land degradation processes (Evans and Wallenstein 2014 <sup>[[#fn:r410|410]]</sup> ; Wu et al. 2015 <sup>[[#fn:r411|411]]</sup> ; Classen et al. 2015 <sup>[[#fn:r412|412]]</sup> ), thus changes in microbial community composition can be very useful to rapidly reflect land degradation (e.g., forest degradation increased the bacterial alpha-diversity indexes) (Flores-Rentería et al. 2016 <sup>[[#fn:r413|413]]</sup> ; Zhou et al. 2018 <sup>[[#fn:r414|414]]</sup> ). These indicators might be used as a set of site-dependent indicators, and in a plant-soil system (Ehrenfeld et al. 2005 <sup>[[#fn:r415|415]]</sup> ).

Useful indicators of degradation and improvement include changes in ecological processes and disturbance regimes that regulate the flow of energy and materials and that control ecosystem dynamics under a climate change scenario. Proxies of dynamics include spatial and temporal turnover of species and habitats within ecosystems (Ghazoul et al. 2015 <sup>[[#fn:r416|416]]</sup> ; Bahamondez and Thompson 2016 <sup>[[#fn:r417|417]]</sup> ). Indicators in agricultural lands include crop yield decreases and difficulty in maintaining yields (Stocking et al. 2001 <sup>[[#fn:r418|418]]</sup> ). Indicators of landscape degradation/improvement in fragmented forest landscapes include the extent, size and distribution of remaining forest fragments, an increase in edge habitat, and loss of connectivity and ecological memory (Zahawi et al. 2015 <sup>[[#fn:r419|419]]</sup> ; Pardini et al. 2010 <sup>[[#fn:r420|420]]</sup> ).

In summary, as land degradation is such a complex and global process, there is no single method by which land degradation can be estimated objectively and consistently over large areas ( ''very high confidence'' ). However, many approaches exist that can be used to assess different aspects of land degradation or provide proxies of land degradation. Remote sensing, complemented by other kinds of data (i.e., field observations, inventories, expert opinions), is the only method that can generate geographically explicit and globally consistent data over time scales relevant for land degradation (several decades).

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