Editing IPCC:AR6/SRCCL/Chapter-3 (section)

== 3.2 Observations of desertification ==

<span id="status-and-trends-of-desertification"></span>
=== 3.2.1 Status and trends of desertification ===

<div id="section-3-2-1-status-and-trends-of-desertification-block-1"></div>

Current estimates of the extent and severity of desertification vary greatly due to missing and/or unreliable information (Gibbs and Salmon 2015 <sup>[[#fn:r213|213]]</sup> ). The multiplicity and complexity of the processes of desertification make its quantification difficult (Prince 2016 <sup>[[#fn:r214|214]]</sup> ; Cherlet et al. 2018 <sup>[[#fn:r215|215]]</sup> ). The most common definition for the drylands is based on defined thresholds of the AI (Figure 3.1; UNEP 1992 <sup>[[#fn:r216|216]]</sup> ). While past studies have used the AI to examine changes in desertification or extent of the drylands (Feng and Fu 2013 <sup>[[#fn:r217|217]]</sup> ; Zarch et al. 2015 <sup>[[#fn:r218|218]]</sup> ; Ji et al. 2015 <sup>[[#fn:r219|219]]</sup> ; Spinoni et al. 2015 <sup>[[#fn:r220|220]]</sup> ; Huang et al. 2016 <sup>[[#fn:r221|221]]</sup> ; Ramarao et al. 2018 <sup>[[#fn:r222|222]]</sup> ), this approach has several key limitations: (i) the AI does not measure desertification, (ii) the impact of changes in climate on the land surface and systems is more complex than assumed by AI, and (iii) the relationship between climate change and changes in vegetation is complex due to the influence of CO <sub>2</sub> . Expansion of the drylands does not imply desertification by itself, if there is no long-term loss of at least one of the following: biological productivity, ecological integrity, or value to humans.

The use of the AI to define changing aridity levels and dryland extent in an environment with changing atmospheric CO <sub>2</sub> has been strongly challenged (Roderick et al. 2015 <sup>[[#fn:r223|223]]</sup> ; Milly and Dunne 2016 <sup>[[#fn:r224|224]]</sup> ; Greve et al. 2017 <sup>[[#fn:r225|225]]</sup> ; Liu et al. 2017 <sup>[[#fn:r226|226]]</sup> ). The suggestion that most of the world has become more arid, since the AI has decreased, is not supported by changes observed in precipitation, evaporation or drought ( ''medium confidence'' ) (Sheffield et al. 2012 <sup>[[#fn:r227|227]]</sup> ; Greve et al. 2014 <sup>[[#fn:r228|228]]</sup> ). A key issue is the assumption in the calculation of potential evapotranspiration that stomatal conductance remains constant, which is invalid if atmospheric CO <sub>2</sub> changes. Given that atmospheric CO <sub>2</sub> has been increasing over the last century or more, and is projected to continue increasing, this means that AI with constant thresholds (or any other measure that relies on potential evapotranspiration) is not an appropriate way to estimate aridity or dryland extent (Donohue et al. 2013 <sup>[[#fn:r229|229]]</sup> ; Roderick et al. 2015 <sup>[[#fn:r230|230]]</sup> ; Greve et al. 2017 <sup>[[#fn:r231|231]]</sup> ). This issue helps explain the apparent contradiction between the drylands becoming more arid according to the AI and also becoming greener according to satellite observations (Fensholt et al. 2012 <sup>[[#fn:r232|232]]</sup> ; Andela et al. 2013 <sup>[[#fn:r233|233]]</sup> ) (Figure 3.5). Other climate type classifications based on various combinations of temperature and precipitation (Köppen-Trewartha, Köppen-Geiger) have also been used to examine historical changes in climate zones, finding a tendency toward drier climate types (Feng et al. 2014 <sup>[[#fn:r234|234]]</sup> ; Spinoni et al. 2015 <sup>[[#fn:r235|235]]</sup> ).

The need to establish a baseline when assessing change in the land area degraded has been extensively discussed in Prince et al. (2018). Desertification is a process, not a state of the system, hence an ‘absolute’ baseline is not required; however, every study uses a baseline defined by the start of their period of interest.

Depending on the definitions applied and methodologies used in evaluation, the status and extent of desertification globally and regionally still show substantial variations ( ''high confidence'' ) (D’Odorico et al. 2013 <sup>[[#fn:r236|236]]</sup> ). There is ''high confidence'' that the range and intensity of desertification has increased in some dryland areas over the past several decades (Sections 3.2.1.1 and 3.2.1.2). The three methodological approaches applied for assessing the extent of desertification: expert judgement, satellite observation of net primary productivity, and use of biophysical models, together provide a relatively holistic assessment but none on its own captures the whole picture (Gibbs and Salmon 2015 <sup>[[#fn:r237|237]]</sup> ; Vogt et al. 2011 <sup>[[#fn:r238|238]]</sup> ; Prince 2016 <sup>[[#fn:r239|239]]</sup> ) (Section 4.2.4).

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<span id="figure-3.5"></span>
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'''Figure 3.5'''

<span id="mean-annual-maximum-ndvi-19822015-global-inventory-modelling-and-mapping-studies-ndvi3g-v1.-non-dryland-regions-aridity-index-0.65-are-masked-in-grey."></span>
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'''Mean annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1). Non-dryland regions (aridity index &gt;0.65) are masked in grey.'''
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[[File:03f6170bc9c14bea64f0ba7d915860e3 Figure-3.5.png]]

Mean annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1). Non-dryland regions (aridity index &gt;0.65) are masked in grey.

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<div id="section-3-2-1-1-global-scale"></div>

<span id="global-scale"></span>
==== 3.2.1.1 Global scale ====

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Complex human–environment interactions, coupled with biophysical, social, economic and political factors unique to any given location, render desertification difficult to map at a global scale (Cherlet et al. 2018 <sup>[[#fn:r240|240]]</sup> ). Early attempts to assess desertification focused on expert knowledge in order to obtain global coverage in a cost-effective manner. '''Expert judgement''' continues to play an important role because degradation remains a subjective feature whose indicators are different from place to place (Sonneveld and Dent 2007 <sup>[[#fn:r241|241]]</sup> ). GLASOD (Global Assessment of Human-induced Soil Degradation) estimated nearly 2000 million hectares (Mha) (15.3% of the total land area) had been degraded by the early 1990s since the mid-20th century. GLASOD was criticised for perceived subjectiveness and exaggeration (Helldén and Tottrup 2008 <sup>[[#fn:r242|242]]</sup> ; Sonneveld and Dent 2007 <sup>[[#fn:r243|243]]</sup> ). Dregne and Chou (1992) <sup>[[#fn:r244|244]]</sup> found 3000 Mha in drylands (i.e. about 50% of drylands) were undergoing degradation. Significant improvements have been made through the efforts of WOCAT (World Overview of Conservation Approaches and Technologies), LADA (Land Degradation Assessment in Drylands) and DESIRE (Desertification Mitigation and Remediation of Land) who jointly developed a mapping tool for participatory expert assessment, with which land experts can estimate current area coverage, type and trends of land degradation (Reed et al. 2011 <sup>[[#fn:r245|245]]</sup> ).

A number of studies have used '''satellite-based remote sensing''' to investigate long-term changes in the vegetation and thus identify parts of the drylands undergoing desertification. Satellite data provides information at the resolution of the sensor, which can be relatively coarse (up to 25 km), and interpretations of the data at sub-pixel levels are challenging. The most widely used remotely sensed vegetation index is the NDVI, providing a measure of canopy greenness that is related to the quantity of standing biomass (Bai et al. 2008 <sup>[[#fn:r246|246]]</sup> ; de Jong et al. 2011 <sup>[[#fn:r247|247]]</sup> ; Fensholt et al. 2012 <sup>[[#fn:r248|248]]</sup> ; Andela et al. 2013 <sup>[[#fn:r249|249]]</sup> ; Fensholt et al. 2015 <sup>[[#fn:r250|250]]</sup> ; Le et al. 2016 <sup>[[#fn:r251|251]]</sup> ) (Figure 3.5). A main challenge associated with NDVI is that although biomass and productivity are closely related in some systems, they can differ widely when looking across land uses and ecosystem types, giving a false positive in some instances (Pattison et al. 2015 <sup>[[#fn:r252|252]]</sup> ; Aynekulu et al. 2017 <sup>[[#fn:r253|253]]</sup> ). For example, bush encroachment in rangelands and intensive monocropping with high fertiliser application gives an indication of increased productivity in satellite data though these could be considered as land degradation. According to this measure there are regions undergoing desertification, however the drylands are greening on average (Figure 3.6).

<div id="section-3-2-1-1-global-scale-block-2"></div>

<span id="figure-3.6"></span>
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'''Figure 3.6'''

<span id="trend-in-the-annual-maximum-ndvi-19822015-global-inventory-modelling-and-mapping-studies-ndvi3g-v1-calculated-using-the-theilsen-estimator-which-is-a-median-based-estimator-and-is-robust-to-outliers.-non-dryland-regions-aridity-index-0.65-are-masked-in-grey"></span>
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'''Trend in the annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1) calculated using the Theil–Sen estimator which is a median based estimator, and is robust to outliers. Non-dryland regions (aridity index &gt;0.65) are masked in grey'''
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[[File:8db6a9b42f7bb9dc07a519c001b7e633 Figure-3.6.png]]

Trend in the annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1) calculated using the Theil–Sen estimator which is a median based estimator, and is robust to outliers. Non-dryland regions (aridity index &gt;0.65) are masked in grey

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<div id="section-3-2-1-1-global-scale-block-3"></div>

A simple linear trend in NDVI is an unsuitable measure for dryland degradation for several reasons (Wessels et al. 2012 <sup>[[#fn:r254|254]]</sup> ; de Jong et al. 2013 <sup>[[#fn:r255|255]]</sup> ; Higginbottom and Symeonakis 2014 <sup>[[#fn:r256|256]]</sup> ; Le et al. 2016 <sup>[[#fn:r257|257]]</sup> ). NDVI is strongly coupled to precipitation in drylands where precipitation has high inter-annual variability. This means that NDVI trend can be dominated by any precipitation trend and is sensitive to wet or dry periods, particularly if they fall near the beginning or end of the time series. Degradation may only occur during part of the time series, while NDVI is stable or even improving during the rest of the time series. This reduces the strength and representativeness of a linear trend. Other factors such as CO <sub>2</sub> fertilisation also influence the NDVI trend. Various techniques have been proposed to address these issues, including the residual trends (RESTREND) method to account for rainfall variability (Evans and Geerken 2004 <sup>[[#fn:r258|258]]</sup> ), time-series break point identification methods to find major shifts in the vegetation trends (de Jong et al. 2013 <sup>[[#fn:r259|259]]</sup> ; Verbesselt et al. 2010a <sup>[[#fn:r260|260]]</sup> ), and methods to explicitly account for the effect of CO <sub>2</sub> fertilisation (Le et al. 2016 <sup>[[#fn:r261|261]]</sup> ).

Using the RESTREND method, Andela et al. (2013) <sup>[[#fn:r262|262]]</sup> found that human activity contributed to a mixture of improving and degrading regions in drylands. In some locations these regions differed substantially from those identified using the NDVI trend alone, including an increase in the area being desertified in southern Africa and northern Australia, and a decrease in southeast and western Australia and Mongolia. De Jong et al. (2013) <sup>[[#fn:r263|263]]</sup> examined the NDVI time series for major shifts in vegetation activity and found that 74% of drylands experienced such a shift between 1981 and 2011. This suggests that monotonic linear trends are unsuitable for accurately capturing the changes that have occurred in the majority of the drylands. Le et al. (2016) <sup>[[#fn:r264|264]]</sup> explicitly accounted for CO <sub>2</sub> fertilisation effect and found that the extent of degraded areas in the world is 3% larger when compared to the linear NDVI trend.

Besides NDVI, there are many vegetation indices derived from satellite data in the optical and infrared wavelengths. Each of these datasets has been derived to overcome some limitation in existing indices. Studies have compared vegetation indices globally (Zhang et al. 2017 <sup>[[#fn:r265|265]]</sup> ) and specifically over drylands (Wu 2014 <sup>[[#fn:r266|266]]</sup> ). In general, the data from these vegetation indices are available only since around 2000, while NDVI data is available since 1982. With less than 20 years of data, the trend analysis remains problematic with vegetation indices other than NDVI. However, given the various advantages in terms of resolution and other characteristics, these newer vegetation indices will become more useful in the future as more data accumulates.

A major shortcoming of these studies based on vegetation datasets derived from satellite sensors is that they do not account for changes in vegetation composition, thus leading to inaccuracies in the estimation of the extent of degraded areas in drylands. For example, drylands of eastern Africa currently face growing encroachment of invasive plant species, such as Prosopis juliflora (Ayanu et al. 2015 <sup>[[#fn:r267|267]]</sup> ), which constitutes land degradation since it leads to losses in economic productivity of affected areas but appears as a greening in the satellite data. Another case study in central Senegal found degradation manifested through a reduction in species richness despite satellite observed greening (Herrmann and Tappan 2013 <sup>[[#fn:r268|268]]</sup> ). A number of efforts to identify changes in vegetation composition from satellites have been made (Brandt et al. 2016a <sup>[[#fn:r269|269]]</sup> , b <sup>[[#fn:r270|270]]</sup> ; Evans and Geerken 2006 <sup>[[#fn:r271|271]]</sup> ; Geerken 2009 <sup>[[#fn:r272|272]]</sup> ; Geerken et al. 2005 <sup>[[#fn:r273|273]]</sup> ; Verbesselt et al. 2010a <sup>[[#fn:r274|274]]</sup> , b <sup>[[#fn:r275|275]]</sup> ). These depend on well-identified reference NDVI time series for particular vegetation groupings, can only differentiate vegetation types that have distinct spectral phenology signatures, and require extensive ground observations for validation. A recent alternative approach to differentiating woody from herbaceous vegetation involves the combined use of optical/infrared-based vegetation indices, indicating greenness, with microwave based Vegetation Optical Depth (VOD) which is sensitive to both woody and leafy vegetation components (Andela et al. 2013 <sup>[[#fn:r276|276]]</sup> ; Tian et al. 2017 <sup>[[#fn:r277|277]]</sup> ).

Vegetation Optical Depth (VOD) has been available since the 1980s. VOD is based on microwave measurements and is related to total above-ground biomass water content. Unlike NDVI, which is only sensitive to green canopy cover, VOD is also sensitive to water in woody parts of the vegetation and hence provides a view of vegetation changes that can be complementary to NDVI. Liu et al. (2013) <sup>[[#fn:r278|278]]</sup> used VOD trends to investigate biomass changes and found that VOD was closely related to precipitation changes in drylands. To complement their work with NDVI, Andela et al. (2013) <sup>[[#fn:r279|279]]</sup> also applied the RESTREND method to VOD. By interpreting NDVI and VOD trends together they were able to differentiate changes to the herbaceous and woody components of the biomass. They reported that many dryland regions are experiencing an increase in the woody fraction often associated with shrub encroachment and suggest that this was aided by CO <sub>2</sub> fertilisation.

'''Biophysical models''' use global datasets that describe climate patterns and soil groups, combined with observations of land use, to define classes of potential productivity and map general land degradation (Gibbs and Salmon 2015 <sup>[[#fn:r280|280]]</sup> ). All biophysical models have their own set of assumptions and limitations that contribute to their overall uncertainty, including: model structure; spatial scale; data requirements (with associated errors); spatial heterogeneities of socio-economic conditions; and agricultural technologies used. Models have been used to estimate the vegetation productivity potential of land (Cai et al. 2011 <sup>[[#fn:r281|281]]</sup> ) and to understand the causes of observed vegetation changes. Zhu et al. (2016) <sup>[[#fn:r282|282]]</sup> used an ensemble of ecosystem models to investigate causes of vegetation changes from 1982–2009, using a factorial simulation approach. They found CO <sub>2</sub> fertilisation to be the dominant effect globally, though climate and land-cover change were the dominant effects in various dryland locations. Borrelli et al. (2017) <sup>[[#fn:r283|283]]</sup> modelled that about 6.1% of the global land area experienced very high soil erosion rates (exceeding 10 Mg ha− <sup>1</sup> yr− <sup>1</sup> ) in 2012, particularly in South America, Africa, and Asia.

Overall, improved estimation and mapping of areas undergoing desertification are needed. This requires a combination of rapidly expanding sources of remotely sensed data, ground observations and new modelling approaches. This is a critical gap, especially in the context of measuring progress towards achieving the land degradation-neutrality target by 2030 in the framework of SDGs.

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==== 3.2.1.2 Regional scale ====

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While global-scale studies provide information for any region, there are numerous studies that focus on sub-continental scales, providing more in-depth analysis and understanding. Regional and local studies are important to detect location-specific trends in desertification and heterogeneous influences of climate change on desertification. However, these regional and local studies use a wide variety of methodologies, making direct comparisons difficult. For details of the methodologies applied by each study refer to the individual papers.

''Africa''

It is estimated that 46 of the 54 countries in Africa are vulnerable to desertification, with some already affected (Prăvălie 2016 <sup>[[#fn:r284|284]]</sup> ). Moderate or higher severity degradation over recent decades has been identified in many river basins including the Nile (42% of area), Niger (50%), Senegal (51%), Volta (67%), Limpopo (66%) and Lake Chad (26%) (Thiombiano and Tourino-Soto 2007 <sup>[[#fn:r285|285]]</sup> ).

The Horn of Africa is getting drier (Damberg and AghaKouchak 2014 <sup>[[#fn:r286|286]]</sup> ; Marshall et al. 2012 <sup>[[#fn:r287|287]]</sup> ) exacerbating the desertification already occurring (Oroda 2001 <sup>[[#fn:r288|288]]</sup> ). The observed decline in vegetation cover is diminishing ecosystem services (Pricope et al. 2013 <sup>[[#fn:r289|289]]</sup> ). Based on NDVI residuals, Kenya experienced persistent negative (positive) trends over 21.6% (8.9%) of the country, for the period 1992–2015 (Gichenje and Godinho 2018 <sup>[[#fn:r290|290]]</sup> ). Fragmentation of habitats, reduction in the range of livestock grazing, and higher stocking rates are considered to be the main drivers for vegetation structure loss in the rangelands of Kenya (Kihiu 2016 <sup>[[#fn:r291|291]]</sup> ; Otuoma et al. 2009 <sup>[[#fn:r292|292]]</sup> ).

Despite desertification in the Sahel being a major concern since the 1970s, wetting and greening conditions have been observed in this region over the last three decades (Anyamba and Tucker 2005 <sup>[[#fn:r294|294]]</sup> ; Huber et al. 2011 <sup>[[#fn:r295|295]]</sup> ; Brandt et al. 2015 <sup>[[#fn:r296|296]]</sup> ; Rishmawi et al. 2016 <sup>[[#fn:r297|297]]</sup> ; Tian et al. 2016 <sup>[[#fn:r298|298]]</sup> ; Leroux et al. 2017 <sup>[[#fn:r299|299]]</sup> ; Herrmann et al. 2005 <sup>[[#fn:r300|300]]</sup> ; Damberg and AghaKouchak 2014 <sup>[[#fn:r301|301]]</sup> ). Cropland areas in the Sahel region of West Africa have doubled since 1975, with settlement area increasing by about 150% (Traore et al. 2014 <sup>[[#fn:r302|302]]</sup> ). Thomas and Nigam (2018) <sup>[[#fn:r303|303]]</sup> found that the Sahara expanded by 10% over the 20th century based on annual rainfall. In Burkina Faso, Dimobe et al. (2015) <sup>[[#fn:r304|304]]</sup> estimated that from 1984 to 2013, bare soils and agricultural lands increased by 18.8% and 89.7%, respectively, while woodland, gallery forest, tree savannahs, shrub savannahs and water bodies decreased by 18.8%, 19.4%, 4.8%, 45.2% and 31.2%, respectively. In Fakara region in Niger, a 5% annual reduction in herbaceous yield between 1994 and 2006 was largely explained by changes in land use, grazing pressure and soil fertility (Hiernaux et al. 2009 <sup>[[#fn:r305|305]]</sup> ). Aladejana et al. (2018) <sup>[[#fn:r306|306]]</sup> found that between 1986 and 2015, 18.6% of the forest cover around the Owena River basin was lost. For the period 1982–2003, Le et al. (2012) <sup>[[#fn:r307|307]]</sup> found that 8% of the Volta River basin’s landmass had been degraded, with this increasing to 65% after accounting for the effects of CO <sub>2</sub> (and NOx) fertilisation.

Greening has also been observed in parts of southern Africa but it is relatively weak compared to other regions of the continent (Helldén and Tottrup 2008 <sup>[[#fn:r308|308]]</sup> ; Fensholt et al. 2012 <sup>[[#fn:r309|309]]</sup> ). However, greening can be accompanied by desertification when factors such as decreasing species richness, changes in species composition and shrub encroachment are observed (Smith et al. 2013 <sup>[[#fn:r310|310]]</sup> ; Herrmann and Tappan 2013 <sup>[[#fn:r311|311]]</sup> ; Kaptué et al. 2015 <sup>[[#fn:r312|312]]</sup> ; Herrmann and Sop 2016 <sup>[[#fn:r313|313]]</sup> ; Saha et al. 2015) <sup>[[#fn:r314|314]]</sup> (Sections 3.1.4 and 3.7.3). In the Okavango river Basin in southern Africa, conversion of land towards higher utilisation intensities, unsustainable agricultural practises and overexploitation of the savanna ecosystems have been observed in recent decades (Weinzierl et al. 2016 <sup>[[#fn:r315|315]]</sup> ).

In the arid Algerian High Plateaus, desertification due to both climatic and human causes led to the loss of indigenous plant biodiversity between 1975 and 2006 (Hirche et al. 2011 <sup>[[#fn:r316|316]]</sup> ). Ayoub (1998) <sup>[[#fn:r317|317]]</sup> identified 64 Mha in Sudan as degraded, with the Central North Kordofan state being most affected. However, reforestation measures in the last decade sustained by improved rainfall conditions have led to low-medium regrowth conditions in about 20% of the area (Dawelbait and Morari 2012 <sup>[[#fn:r318|318]]</sup> ). In Morocco, areas affected by desertification were predominantly on plains with high population and livestock pressure (del Barrio et al. 2016 <sup>[[#fn:r319|319]]</sup> ; Kouba et al. 2018 <sup>[[#fn:r320|320]]</sup> ; Lahlaoi et al. 2017 <sup>[[#fn:r321|321]]</sup> ). The annual costs of soil degradation were estimated at about 1% of Gross Domestic Product (GDP) in Algeria and Egypt, and about 0.5% in Morocco and Tunisia (Réquier-Desjardins and Bied-Charreton 2006 <sup>[[#fn:r322|322]]</sup> ).

''Asia''

Prăvălie (2016) <sup>[[#fn:r323|323]]</sup> found that desertification is currently affecting 38 of 48 countries in Asia. The changes in drylands in Asia over the period 1982–2011 were mixed, with some areas experiencing vegetation improvement while others showed reduced vegetation (Miao et al. 2015a <sup>[[#fn:r324|324]]</sup> ). Major river basins undergoing salinisation include: Indo-Gangetic Basin in India (Lal and Stewart 2012 <sup>[[#fn:r325|325]]</sup> ), Indus Basin in Pakistan (Aslam and Prathapar 2006 <sup>[[#fn:r326|326]]</sup> ), Yellow River Basin in China (Chengrui and Dregne 2001 <sup>[[#fn:r327|327]]</sup> ), Yinchuan Plain in China (Zhou et al. 2013 <sup>[[#fn:r328|328]]</sup> ), Aral Sea Basin of Central Asia (Cai et al. 2003 <sup>[[#fn:r329|329]]</sup> ; Pankova 2016 <sup>[[#fn:r330|330]]</sup> ; Qadir et al. 2009 <sup>[[#fn:r331|331]]</sup> ).

Helldén and Tottrup (2008) <sup>[[#fn:r332|332]]</sup> highlighted a greening trend in East Asia between 1982 and 2003. Over the past several decades, air temperature and the rainfall increased in the arid and hyper-arid region of Northwest China (Chen et al. 2015 <sup>[[#fn:r333|333]]</sup> ; Wang et al. 2017 <sup>[[#fn:r334|334]]</sup> ). Within China, rainfall erosivity has shown a positive trend in dryland areas between 1961 and 2012 (Yang and Lu 2015 <sup>[[#fn:r335|335]]</sup> ). While water erosion area in Xinjiang, China, has decreased by 23.2%, erosion considered as severe or intense was still increasing (Zhang et al. 2015 <sup>[[#fn:r336|336]]</sup> ). Xue et al. (2017) <sup>[[#fn:r337|337]]</sup> used remote sensing data covering 1975 to 2015 to show that wind-driven desertified land in northern Shanxi in China had expanded until 2000, before contracting again. Li et al. (2012) <sup>[[#fn:r338|338]]</sup> used satellite data to identify desertification in Inner Mongolia, China and found a link between policy changes and the locations and extent of human-caused desertification. Several oasis regions in China have seen increases in cropland area, while forests, grasslands and available water resources have decreased (Fu et al. 2017 <sup>[[#fn:r339|339]]</sup> ; Muyibul et al. 2018 <sup>[[#fn:r340|340]]</sup> ; Xie et al. 2014 <sup>[[#fn:r341|341]]</sup> ). Between 1990 and 2011 15.3% of Hogno Khaan nature reserve in central Mongolia was subjected to desertification (Lamchin et al. 2016 <sup>[[#fn:r342|342]]</sup> ). Using satellite data Liu et al. (2013) <sup>[[#fn:r343|343]]</sup> found the area of Mongolia undergoing non-climatic desertification was associated with increases in goat density and wildfire occurrence.

In Central Asia, drying up of the Aral Sea is continuing to have negative impacts on regional microclimate and human health (Issanova and Abuduwaili 2017 <sup>[[#fn:r344|344]]</sup> ; Lioubimtseva 2015 <sup>[[#fn:r345|345]]</sup> ; Micklin 2016 <sup>[[#fn:r346|346]]</sup> ; Xi and Sokolik 2015 <sup>[[#fn:r347|347]]</sup> ). Half of the region’s irrigated lands, especially in the Amudarya and Syrdarya river basins, were affected by secondary salinisation (Qadir et al. 2009 <sup>[[#fn:r349|349]]</sup> ). Le et al. (2016) <sup>[[#fn:r1792|1792]]</sup> showed that about 57% of croplands in Kazakhstan and about 20% of croplands in Kyrgyzstan had reductions in their vegetation productivity between 1982 and 2006. Chen et al. (2019) <sup>[[#fn:r350|350]]</sup> indicated that about 58% of the grasslands in the region had reductions in their vegetation productivity between 1999 and 2015. Anthropogenic factors were the main driver of this loss in Turkmenistan and Uzbekistan, while the role of human drivers was smaller than that of climate-related factors in Tajikistan and Kyrgyzstan (Chen et al. 2019). The total costs of land degradation in Central Asia were estimated to equal about 6 billion USD annually (Mirzabaev et al. 2016 <sup>[[#fn:r351|351]]</sup> ).

Damberg and AghaKouchak (2014) <sup>[[#fn:r352|352]]</sup> found that parts of South Asia experienced drying over the last three decades. More than 75% of the area of northern, western and southern Afghanistan is affected by overgrazing and deforestation (UNEP-GEF 2008 <sup>[[#fn:r353|353]]</sup> ). Desertification is a serious problem in Pakistan with a wide range of human and natural causes (Irshad et al. 2007 <sup>[[#fn:r354|354]]</sup> ; Lal 2018 <sup>[[#fn:r355|355]]</sup> ). Similarly, desertification affects parts of India (Kundu et al. 2017 <sup>[[#fn:r356|356]]</sup> ; Dharumarajan et al. 2018 <sup>[[#fn:r357|357]]</sup> ; Christian et al. 2018 <sup>[[#fn:r358|358]]</sup> ). Using satellite data to map various desertification processes, Ajai et al. (2009) <sup>[[#fn:r359|359]]</sup> found that 81.4 Mha were subject to various processes of desertification in India in 2005, while salinisation affected 6.73 Mha in the country (Singh 2009 <sup>[[#fn:r360|360]]</sup> ).

Saudi Arabia is highly vulnerable to desertification (Ministry of Energy Industry and Mineral Resources 2016 <sup>[[#fn:r361|361]]</sup> ), with this vulnerability expected to increase in the north-western parts of the country in the coming decades. Yahiya (2012) <sup>[[#fn:r362|362]]</sup> found that Jazan, south-western Saudi Arabia, lost about 46% of its vegetation cover from 1987 to 2002. Droughts and frequent dust storms were shown to impose adverse impacts over Saudi Arabia, especially under global warming and future climate change (Hasanean et al. 2015 <sup>[[#fn:r363|363]]</sup> ). In north-west Jordan, 18% of the area was prone to severe to very severe desertification (Al-Bakri et al. 2016 <sup>[[#fn:r364|364]]</sup> ). Large parts of the Syrian drylands have been identified as undergoing desertification (Evans and Geerken 2004 <sup>[[#fn:r365|365]]</sup> ; Geerken and Ilaiwi 2004 <sup>[[#fn:r366|366]]</sup> ). Moridnejad et al. (2015) <sup>[[#fn:r367|367]]</sup> identified newly desertified regions in the Middle East based on dust sources, finding that these regions accounted for 39% of all detected dust source points. Desertification has increased substantially in Iran since the 1930s. Despite numerous efforts to rehabilitate degraded areas, it still poses a major threat to agricultural livelihoods in the country (Amiraslani and Dragovich 2011 <sup>[[#fn:r368|368]]</sup> ).

''Australia''

Damberg and AghaKouchak (2014) <sup>[[#fn:r369|369]]</sup> found that wetter conditions were experienced in northern Australia over the last three decades with widespread greening observed between 1981 and 2006 over much of Australia, except for eastern Australia where large areas were affected by droughts from 2002 to 2009 based on Advanced High Resolution Radiometer (AVHRR) satellite data (Donohue et al. 2009) <sup>[[#fn:r370|370]]</sup> . For the period 1982–2013, Burrell et al. (2017) <sup>[[#fn:r371|371]]</sup> also found widespread greening over Australia including eastern Australia over the post-drought period. This dramatic change in the trend found for eastern Australia emphasises the dominant role played by precipitation in the drylands. Degradation due to anthropogenic activities and other causes affects over 5% of Australia, particularly near the central west coast. Jackson and Prince (2016) used a local NPP scaling approach applied with MODIS derived vegetation data to quantify degradation in a dryland watershed in Northern Australia from 2000 to 2013. They estimated that 20% of the watershed was degraded. Salinisation has also been found to be degrading parts of the Murray-Darling Basin in Australia (Rengasamy 2006 <sup>[[#fn:r372|372]]</sup> ). Eldridge and Soliveres (2014) <sup>[[#fn:r373|373]]</sup> examined areas undergoing woody encroachment in eastern Australia and found that rather than degrading the landscape, the shrubs often enhanced ecosystem services.

''Europe''

Drylands cover 33.8% of northern Mediterranean countries: approximately 69% of Spain, 66% of Cyprus, and between 16% and 62% in Greece, Portugal, Italy and France (Zdruli 2011 <sup>[[#fn:r374|374]]</sup> ). The European Environment Agency (EEA) indicated that 14 Mha, that is 8% of the territory of the European Union (mostly in Bulgaria, Cyprus, Greece, Italy, Romania, Spain and Portugal), had a ‘very high’ and ‘high sensitivity’ to desertification (European Court of Auditors 2018 <sup>[[#fn:r375|375]]</sup> ). This figure increases to 40 Mha (23% of the EU territory) if ‘moderately’ sensitive areas are included (Prăvălie et al. 2017 <sup>[[#fn:r376|376]]</sup> ; European Court of Auditors 2018 <sup>[[#fn:r377|377]]</sup> ). Desertification in the region is driven by irrigation developments and encroachment of cultivation on rangelands (Safriel 2009 <sup>[[#fn:r378|378]]</sup> ) caused by population growth, agricultural policies, and markets. According to a recent assessment report (ECA 2018 <sup>[[#fn:r379|379]]</sup> ), Europe is increasingly affected by desertification leading to significant consequences on land use, particularly in Portugal, Spain, Italy, Greece, Malta, Cyprus, Bulgaria and Romania. Using the Universal Soil Loss Equation, it was estimated that soil erosion can be as high as 300 t ha– <sup>1</sup> yr– <sup>1</sup> (equivalent to a net loss of 18 mm yr– <sup>1</sup> ) in Spain (López-Bermúdez 1990 <sup>[[#fn:r380|380]]</sup> ). For the badlands region in south-east Spain, however, it was shown that biological soil crusts effectively prevent soil erosion (Lázaro et al. 2008 <sup>[[#fn:r381|381]]</sup> ). In Mediterranean Europe, Guerra et al. (2016) <sup>[[#fn:r382|382]]</sup> found a reduction of erosion due to greater effectiveness of soil erosion prevention between 2001 and 2013. Helldén and Tottrup (2008) <sup>[[#fn:r383|383]]</sup> observed a greening trend in the Mediterranean between 1982 and 2003, while Fensholt et al. (2012) <sup>[[#fn:r384|384]]</sup> also show a dominance of greening in Eastern Europe.

In Russia, at the beginning of the 2000s, about 7% of the total area (that is, approximately 130 Mha) was threatened by desertification (Gunin and Pankova 2004 <sup>[[#fn:r385|385]]</sup> ; Kust et al. 2011 <sup>[[#fn:r386|386]]</sup> ). Turkey is considered highly vulnerable to drought, land degradation and desertification (Türkeş 1999 <sup>[[#fn:r387|387]]</sup> , 2003 <sup>[[#fn:r388|388]]</sup> ). About 60% of Turkey’s land area is characterised with hydro-climatological conditions favourable for desertification (Türkeş 2013 <sup>[[#fn:r389|389]]</sup> ). ÇEMGM (2017) <sup>[[#fn:r390|390]]</sup> estimated that about half of Turkey’s land area (48.6%) is prone to moderate-to-high desertification.

''North America''

Drylands cover approximately 60% of Mexico. According to Pontifes et al. (2018) <sup>[[#fn:r391|391]]</sup> , 3.5% of the area was converted from natural vegetation to agriculture and human settlements between 2002 and 2011. The region is highly vulnerable to desertification due to frequent droughts and floods (Méndez and Magaña 2010 <sup>[[#fn:r392|392]]</sup> ; Stahle et al. 2009 <sup>[[#fn:r393|393]]</sup> ; Becerril-Pina Rocio et al. 2015 <sup>[[#fn:r394|394]]</sup> ).

For the period 2000–2011 the overall difference between potential and actual NPP in different land capability classes in the south-western United States was 11.8% (Noojipady et al. 2015 <sup>[[#fn:r395|395]]</sup> ); reductions in grassland-savannah and livestock grazing area and forests were the highest. Bush encroachment is observed over a fairly wide area of grasslands in the western United States, including Jornada Basin within the Chihuahuan Desert, and is spreading at a fast rate despite grazing restrictions intended to curb the spread (Yanoff and Muldavin 2008 <sup>[[#fn:r396|396]]</sup> ; Browning and Archer 2011 <sup>[[#fn:r397|397]]</sup> ; Van Auken 2009 <sup>[[#fn:r398|398]]</sup> ; Rachal et al. 2012 <sup>[[#fn:r399|399]]</sup> ). In comparing sand dune migration patterns and rates between 1995 and 2014, Potter and Weigand (2016) <sup>[[#fn:r400|400]]</sup> established that the area covered by stable dune surfaces, and sand removal zones, decreased, while sand accumulation zones increased from 15.4 to 25.5 km <sup>2</sup> for Palen Dunes in the Southern California desert, while movement of Kelso Dunes is less clear (Lam et al. 2011 <sup>[[#fn:r401|401]]</sup> ). Within the United States, average soil erosion rates on all croplands decreased by about 38% between 1982 and 2003 due to better soil management practices (Kertis 2003 <sup>[[#fn:r402|402]]</sup> ).

''Central and South America''

Morales et al. (2011) <sup>[[#fn:r403|403]]</sup> indicated that desertification costs between 8% and 14% of gross agricultural product in many Central and South American countries. Parts of the dry Chaco and Caldenal regions in Argentina have undergone widespread degradation over the last century (Verón et al. 2017 <sup>[[#fn:r404|404]]</sup> ; Fernández et al. 2009 <sup>[[#fn:r405|405]]</sup> ). Bisigato and Laphitz (2009) <sup>[[#fn:r406|406]]</sup> identified overgrazing as a cause of desertification in the Patagonian Monte region of Argentina. Vieira et al. (2015) <sup>[[#fn:r407|407]]</sup> found that 94% of northeast Brazilian drylands were susceptible to desertification. It is estimated that up to 50% of the area was being degraded due to frequent prolonged droughts and clearing of forests for agriculture. This land-use change threatens the extinction of around 28 native species (Leal et al. 2005 <sup>[[#fn:r408|408]]</sup> ). In Central Chile, dryland forest and shrubland area was reduced by 1.7% and 0.7%, respectively, between 1975 and 2008 (Schulz et al. 2010 <sup>[[#fn:r409|409]]</sup> ).

<span id="attribution-of-desertification"></span>
=== 3.2.2 Attribution of desertification ===

<div id="section-3-2-2-attribution-of-desertification-block-1"></div>

Desertification is a result of complex interactions within coupled social-ecological systems. Thus, the relative contributions of climatic, anthropogenic and other drivers of desertification vary depending on specific socio-economic and ecological contexts. The high natural climate variability in dryland regions is a major cause of vegetation changes but does not necessarily imply degradation. Drought is not degradation as the land productivity may return entirely once the drought ends (Kassas 1995 <sup>[[#fn:r410|410]]</sup> ). However, if droughts increase in frequency, intensity and/or duration they may overwhelm the vegetation’s ability to recover (ecosystem resilience, Prince et al. 2018), causing degradation. Assuming a stationary climate and no human influence, rainfall variability results in fluctuations in vegetation dynamics which can be considered temporary, as the ecosystem tends to recover with rainfall, and desertification does not occur (Ellis 1995 <sup>[[#fn:r411|411]]</sup> ; Vetter 2005 <sup>[[#fn:r412|412]]</sup> ; von Wehrden et al. 2012 <sup>[[#fn:r413|413]]</sup> ). Climate change on the other hand, exemplified by a non-stationary climate, can gradually cause a persistent change in the ecosystem through aridification and CO <sub>2</sub> changes. Assuming no human influence, this ‘natural’ climatic version of desertification may take place rapidly, especially when thresholds are reached (Prince et al. 2018 <sup>[[#fn:r414|414]]</sup> ), or over longer periods of time as the ecosystems slowly adjust to a new climatic norm through progressive changes in the plant community composition. Accounting for this climatic variability is required before attributions to other causes of desertification can be made.

For attributing vegetation changes to climate versus other causes, rain use efficiency (RUE – the change in net primary productivity (NPP) per unit of precipitation) and its variations in time have been used (Prince et al. 1998 <sup>[[#fn:r415|415]]</sup> ). Global applications of RUE trends to attribute degradation to climate or other (largely human) causes have been performed by Bai et al. (2008) <sup>[[#fn:r416|416]]</sup> and Le et al. (2016) <sup>[[#fn:r417|417]]</sup> (Section 3.2.1.1). The RESTREND (residual trend) method analyses the correlation between annual maximum NDVI (or other vegetation index as a proxy for NPP) and precipitation by testing accumulation and lag periods for the precipitation (Evans and Geerken 2004 <sup>[[#fn:r418|418]]</sup> ). The identified relationship with the highest correlation represents the maximum amount of vegetation variability that can be explained by the precipitation, and corresponding RUE values can be calculated. Using this relationship, the climate component of the NDVI time series can be reconstructed, and the difference between this and the original time series (the residual) is attributed to anthropogenic and other causes.

The RESTREND method, or minor variations of it, have been applied extensively. Herrmann and Hutchinson (2005) <sup>[[#fn:r419|419]]</sup> concluded that climate was the dominant causative factor for widespread greening in the Sahel region from 1982–2003, and anthropogenic and other factors were mostly producing land improvements or no change. However, pockets of desertification were identified in Nigeria and Sudan. Similar results were also found from 1982–2007 by Huber et al. (2011) <sup>[[#fn:r420|420]]</sup> . Wessels et al. (2007) <sup>[[#fn:r421|421]]</sup> applied RESTREND to South Africa and showed that RESTREND produced a more accurate identification of degraded land than RUE alone. RESTREND identified a smaller area undergoing desertification due to non-climate causes compared to the NDVI trends. Liu et al. (2013) <sup>[[#fn:r430|430]]</sup> extended the climate component of RESTREND to include temperature and applied this to VOD observations of the cold drylands of Mongolia. They found the area undergoing desertification due to non-climatic causes is much smaller than the area with negative VOD trends. RESTREND has also been applied in several other studies to the Sahel (Leroux et al. 2017 <sup>[[#fn:r422|422]]</sup> ), Somalia (Omuto et al. 2010) <sup>[[#fn:r423|423]]</sup> , West Africa (Ibrahim et al. 2015) <sup>[[#fn:r424|424]]</sup> , China (Li et al. 2012 <sup>[[#fn:r425|425]]</sup> ; Yin et al. 2014 <sup>[[#fn:r426|426]]</sup> ), Central Asia (Jiang et al. 2017 <sup>[[#fn:r427|427]]</sup> ), Australia (Burrell et al. 2017 <sup>[[#fn:r428|428]]</sup> ) and globally (Andela et al. 2013 <sup>[[#fn:r429|429]]</sup> ). In each of these studies the extent to which desertification can be attributed to climate versus other causes varies across the landscape.

These studies represent the best regional, remote-sensing based attribution studies to date, noting that RESTREND and RUE have some limitations (Higginbottom and Symeonakis 2014 <sup>[[#fn:r431|431]]</sup> ). Vegetation growth (NPP) changes slowly compared to rainfall variations and may be sensitive to rainfall over extended periods (years), depending on vegetation type. Detection of lags and the use of weighted antecedent rainfall can partially address this problem, though most studies do not do this. The method addresses changes since the start of the time series; it cannot identify whether an area is already degraded at the start time. It is assumed that climate, particularly rainfall, is a principal factor in vegetation change which may not be true in more humid regions.

Another assumption in RESTREND is that any trend is linear throughout the period examined. That is, there are no discontinuities (break points) in the trend. Browning et al. (2017) <sup>[[#fn:r432|432]]</sup> have shown that break points in NDVI time series reflect vegetation changes based on long-term field sites. To overcome this limitation, Burrell et al. (2017) <sup>[[#fn:r433|433]]</sup> introduced the Time Series Segmentation-RESTREND (TSS-RESTREND) which allows a breakpoint or turning point within the period examined (Figure 3.7). Using TSS-RESTREND over Australia they identified more than double the degrading area than could be identified with a standard RESTREND analysis. The occurrence and drivers of abrupt change (turning points) in ecosystem functioning were also examined by Horion et al. (2016) <sup>[[#fn:r434|434]]</sup> over the semi-arid Northern Eurasian agricultural frontier. They combined trend shifts in RUE, field data and expert knowledge, to map environmental hotspots of change and attribute them to climate and human activities. One-third of the area showed significant change in RUE, mainly occurring around the fall of the Soviet Union (1991) or as the result of major droughts. Recent human-induced turning points in ecosystem functioning were uncovered around Volgograd (Russia) and around Lake Balkhash (Kazakhstan), attributed to recultivation, increased salinisation, and increased grazing.

Attribution of vegetation changes to human activity has also been done within modelling frameworks. In these methods ecosystem models are used to simulate potential natural vegetation dynamics, and this is compared to the observed state. The difference is attributed to human activities. Applied to the Sahel region during the period of 1982–2002, it showed that people had a minor influence on vegetation changes (Seaquist et al. 2009 <sup>[[#fn:r435|435]]</sup> ). Similar model/observation comparisons performed globally found that CO <sub>2</sub> fertilisation was the strongest forcing at global scales, with climate having regionally varying effects (Mao et al. 2013 <sup>[[#fn:r436|436]]</sup> ; Zhu et al. 2016 <sup>[[#fn:r437|437]]</sup> ). Land-use/ land-cover change was a dominant forcing in localised areas. The use of this method to examine vegetation changes in China (1982–2009) attributed most of the greening trend to CO <sub>2</sub> fertilisation and nitrogen (N) deposition (Piao et al. 2015). However in some parts of northern and western China, which includes large areas of drylands, Piao et al. (2015) <sup>[[#fn:r438|438]]</sup> found climate changes could be the dominant forcing. In the northern extratropical land surface, the observed greening was consistent with increases in greenhouse gases (notably CO <sub>2</sub> ) and the related climate change, and not consistent with a natural climate that does not include anthropogenic increase in greenhouse gases (Mao et al. 2016 <sup>[[#fn:r439|439]]</sup> ). While many studies found widespread influence of CO <sub>2</sub> fertilisation, it is not ubiquitous; for example, Lévesque et al. (2014) found little response to CO <sub>2</sub> fertilisation in some tree species in Switzerland/northern Italy.

Using multiple extreme-event attribution methodologies, Uhe et al. (2018) <sup>[[#fn:r440|440]]</sup> shows that the dominant influence for droughts in eastern Africa during the October–December ‘short rains’ season is the prevailing tropical SST patterns, although temperature trends mean that the current drought conditions are hotter than they would have been without climate change. Similarly, Funk et al. (2019) <sup>[[#fn:r441|441]]</sup> found that 2017 March–June East African drought was influenced by Western Pacific SST, with high SST conditions attributed to climate change.

There are numerous local case studies on attribution of desertification, which use different periods, focus on different land uses and covers, and consider different desertification processes. For example, two-thirds of the observed expansion of the Sahara Desert from 1920–2003 has been attributed to natural climate cycles (the cold phase of Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation) (Thomas and Nigam 2018 <sup>[[#fn:r442|442]]</sup> ). Some studies consider drought to be the main driver of desertification in Africa (e.g., Masih et al. 2014 <sup>[[#fn:r443|443]]</sup> ). However, other studies suggest that although droughts may contribute to desertification, the underlying causes are human activities (Kouba et al. 2018 <sup>[[#fn:r444|444]]</sup> ). Brandt et al. (2016a) found that woody vegetation trends are negatively correlated with human population density. Changes in land use, water pumping and flow diversion have enhanced drying of wetlands and salinisation of freshwater aquifers in Israel (Inbar 2007 <sup>[[#fn:r445|445]]</sup> ). The dryland territory of China has been found to be very sensitive to both climatic variations and land-use/land-cover changes (Fu et al. 2000 <sup>[[#fn:r446|446]]</sup> ; Liu and Tian 2010 <sup>[[#fn:r447|447]]</sup> ; Zhao et al. 2013, 2006 <sup>[[#fn:r448|448]]</sup> ). Feng et al. (2015) shows that socio-economic factors were dominant in causing desertification in north Shanxi, China, between 1983 and 2012, accounting for about 80% of desertification expansion. Successful grass establishment has been impeded by overgrazing and nutrient depletion leading to the encroachment of shrubs into the northern Chihuahuan Desert (USA) since the mid-19th century (Kidron and Gutschick 2017 <sup>[[#fn:r449|449]]</sup> ). Human activities led to rangeland degradation in Pakistan and Mongolia during 2000–2011 (Lei et al. 2011 <sup>[[#fn:r450|450]]</sup> ). More equal shares of climatic (temperature and precipitation trends) and human factors were attributed for changes in rangeland condition in China (Yang et al. 2016 <sup>[[#fn:r451|451]]</sup> ).

This kaleidoscope of local case studies demonstrates how attribution of desertification is still challenging for several reasons. Firstly, desertification is caused by an interaction of different drivers which vary in space and time. Secondly, in drylands, vegetation reacts closely to changes in rainfall so the effect of rainfall changes on biomass needs to be ‘removed’ before attributing desertification to human activities. Thirdly, human activities and climatic drivers impact vegetation/ ecosystem changes at different rates. Finally, desertification manifests as a gradual change in ecosystem composition and structure (e.g., woody shrub invasion into grasslands). Although initiated at a limited location, ecosystem change may propagate throughout an extensive area via a series of feedback mechanisms. This complicates the attribution of desertification to human and climatic causes, as the process can develop independently once started.

<div id="section-3-2-2-attribution-of-desertification-block-2"></div>

<span id="figure-3.7"></span>
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'''Figure 3.7'''

<span id="the-drivers-of-dryland-vegetation-change.-the-mean-annual-change-in-ndvimax-between-1982-and-2015-see-figure-3.6-for-total-change-using-global-inventory-modelling-and-mapping-studies-ndvi3g-v1-dataset-attributable-toaco2-fertilisationbclimate-and-c-land-use.-the-change-attributable-to-co2-fertilisation-was-calculated-using-the-co2-fertilisation-relationship-described-in-franks"></span>
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'''The drivers of dryland vegetation change. The mean annual change in NDVImax between 1982 and 2015 (see Figure 3.6 for total change using Global Inventory Modelling and Mapping Studies NDVI3g v1 dataset) attributable to(a)CO2 fertilisation(b)climate and (c) land use. The change attributable to CO2 fertilisation was calculated using the CO2 fertilisation relationship described in Franks […]'''
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[[File:1575a316e0081c3ad0d78e507d3e945c Figure-3.7.png]]

The drivers of dryland vegetation change. The mean annual change in NDVImax between 1982 and 2015 (see Figure 3.6 for total change using Global Inventory Modelling and Mapping Studies NDVI3g v1 dataset) attributable to(a)CO <sub>2</sub> fertilisation(b)climate and (c) land use. The change attributable to CO <sub>2</sub> fertilisation was calculated using the CO <sub>2</sub> fertilisation relationship described in Franks et al. 2013 <sup>[[#fn:r1793|1793]]</sup> . The Time Series Segmented Residual Trends (TSS-RESTREND) method (Burrell et al. 2017 <sup>[[#fn:r1794|1794]]</sup> ) applied to the CO <sub>2</sub> -adjusted NDVI was used to separate Climate and Land Use. A multi-climate dataset ensemble was used to reduce the impact of dataset errors (Burrell et al. 2018 <sup>[[#fn:r1795|1795]]</sup> ). Non-dryland regions (aridity index &gt;0.65) are masked in dark grey. Areas where the change did not meet the multi-run ensemble significance criteria, or are smaller than the error in the sensors (±0.00001) are masked in white

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<div id="section-3-2-2-attribution-of-desertification-block-3"></div>

Rasmussen et al. (2016) <sup>[[#fn:r452|452]]</sup> studied the reasons behind the overall lack of scientific agreement in trends of environmental changes in the Sahel, including their causes. The study indicated that these are due to differences in conceptualisations and choice of indicators, biases in study site selection, differences in methods, varying measurement accuracy, differences in time and spatial scales. High-resolution, multi-sensor airborne platforms provide a way to address some of these issues (Asner et al. 2012 <sup>[[#fn:r453|453]]</sup> ).

The major conclusion of this section is that, with all the shortcomings of individual case studies, relative roles of climatic and human drivers of desertification are location-specific and evolve over time ( ''high confidence'' ). Biophysical research on attribution and socio-economic research on drivers of land degradation have long studied the same topic, but in parallel, with little interdisciplinary integration. Interdisciplinary work to identify typical patterns, or typologies, of such interactions of biophysical and human drivers of desertification (not only of dryland vulnerability), and their relative shares, done globally in comparable ways, will help in the formulation of better informed policies to address desertification and achieve land degradation neutrality.

<span id="desertification-feedbacks-to-climate"></span>