Editing IPCC:AR6/WGI/Chapter-8 (section)

==== 8.3.1.7 Freshwater Reservoirs ====

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===== 8.3.1.7.1 Glaciers =====

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The AR5 and SROCC found, with ''very high confidence,'' a general decline in glaciers due to climate change in recent decades. There is ''very high confidence'' that during the decade 2010–2019 glaciers lost more mass than in any other decade since the beginning of the observational record (Sections 2.3.2.3 and 9.5.1). Human influence is ''very likely'' the main driver of the global, near-universal retreat of glaciers since the 1990s ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.3.1|Section 3.4.3.1]] ). In Table 9.5, the contribution of glaciers to sea level rise for different periods is presented; in 1971 – 2018 glacier mass loss contributed 20.9 [10.0 to 31.7] mm or 22.2% of the sea level rise during that period. The highest mass loss rates are observed in the southern Andes, New Zealand, Alaska, Central Europe and Iceland while the largest mass loss are observed in Alaska, the periphery of Greenland and Arctic Canada ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] and Figure 9.20). Predominantly '','' runoff from small glaciers such as in Canada has decreased because of glacier mass loss, while runoff from larger glaciers such as in Alaska has typically increased ( [[#Bolch--2010|Bolch et al., 2010]] ; [[#Thomson--2011|Thomson et al., 2011]] ; [[#Tennant--2012|Tennant et al., 2012]] ; [[#WGMS--2017|WGMS, 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ). Asia contains the largest concentration of glaciers outside the polar regions where the total glacier mass change is –16.3 ± 3.5 Gt yr <sup>–1</sup> over 2000 – 2016 with considerable intra-regional variability ( [[#Brun--2017|Brun et al., 2017]] ). Mass losses of glaciers in Asia between 2000 and 2018 are – 19.0 ± 2.5 Gt yr <sup>–1</sup> ( [[#Shean--2020|Shean et al., 2020]] ). The most negative changes were found in Nyainqentanglha with −4.0 ± 1.5 Gt yr <sup>–1</sup> , while glaciers in Kunlun, northern Tibetan Plateau, slightly gained mass at 1.4 ± 0.8 Gt yr <sup>–1</sup> .

There is some evidence that an increase of precipitation over high mountains can offset glacier ablation (melt; [[#Farinotti--2020|Farinotti et al., 2020]] ). However, this process has only been described from the Karakoram region in the north-western Himalaya, where it is thought to be partly responsible to the advances of glacier changes in the last two decades, referred to as the ‘Karakoram Anomaly’ ( [[#Farinotti--2020|Farinotti et al., 2020]] ). In the Himalaya, [[#Maurer--2019|Maurer et al. (2019)]] observed faster ice loss during 2000–2016 (7.5 ± 2.3 Gt yr <sup>–1</sup> ) compared to 1975–2000 (–3.9 ± 2.2 Gt yr <sup>–1</sup> ). In the Southern Hemisphere, the rate of glacier mass lost in South America is estimated at 19.4 ± 0.6 Gt yr <sup>–1</sup> based on surface elevation changes over 2000 – 2011, which include the North and South Patagonian Icefields of South America ( [[#Braun--2019|Braun et al., 2019]] ), and at −22.9 ± 5.9 Gt yr <sup>–1</sup> over 2000 – 2018 ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ).

In summary, human-induced global warming has been the primary driver of a global glacier recession since the early 20th century ( ''high confidence'' ). Most glaciers have lost mass more rapidly since the 1960s and in an unprecedented way over the last decade, thereby contributing to increased glacier runoff, especially from larger glaciers until a maximum is reached, which tends to occur later in basins with larger glaciers and higher ice-cover fractions ( ''high co'' ''nfidence'' ).

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===== 8.3.1.7.2 Seasonal snow cover =====

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The AR5 assessed that Northern Hemisphere (NH) snow cover extent (SCE) has decreased since the late 1960s, especially in spring ( ''very high confidence'' ). This is confirmed by recent studies ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ; [[#Kunkel--2016|Kunkel et al., 2016]] ). AR6 assesses that NH spring snow cover has been decreasing since 1978 ( ''very high confidence'' ) and that this trend extends back to 1950 ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] ). Human-caused global warming is the dominant driver of this observed decline ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] ; [[#Estilow--2015|Estilow et al., 2015]] ). Model simulations suggest that surface temperature responses at hemispheric/regional scales explain between 40% and 85% of the SCE trend variability ( [[#Mudryk--2017|Mudryk et al., 2017]] ). A decreasing trend in snowfall has also been detected in the NH (Figure 8.1; [[#Rupp--2013|Rupp et al., 2013]] ). Snowfall as a proportion of precipitation has decreased significantly in recent years ( [[#Berghuijs--2014|Berghuijs et al., 2014]] ). However, a late-20th-century increase in snowfall in West Antarctica observed in ice cores has been linked to a combination of factors including the anthropogenically forced deepening of the Amundsen Sea Low ( [[#Thomas--2015|Thomas et al., 2015]] , 2017).

Observations show a rapid recent decrease of spring SCE in NH, mostly in Eurasia and North America, closely linked to temperature change, for example, March to April SCE is decreasing at 3.4% ± 1.1 % per decade (1979–2005; [[#Brown--2011|Brown and Robinson, 2011]] ; [[#Hernández-Henríquez--2015|Hernández-Henríquez et al., 2015]] ). An overall increasing annual trend of the NH SCE since the late 1980s has been observed, in contrast to decreasing trends over 1960s to 1980s that are dominated by the autumn and winter seasons ( [[#Barry--2020|Barry and Gan, 2020]] ). Such recent positive trends in snow cover extent are however at odds with other surface and satellite datasets and with the negative trends simulated by most CMIP5 and CMIP6 models ( [[#Mudryk--2017|Mudryk et al., 2017]] , 2020). [[#Hernández-Henríquez--2015|Hernández-Henríquez et al. (2015)]] also detected positive trends in October to November SCE in in the NOAA SCE Climate Data Record (NOAA-CDR), which are not replicated in other datasets ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] ). [[#Wu--2018|Wu et al. (2018)]] found slower snowmelt rates over the NH in 1980–2017, with higher ablation rates in locations with deep snow water equivalent (SWE), but due to the reduction of SWE in deep snowpacks, moderate/high ablation rates showed decreasing trends. [[#Santolaria-Otín--2020|Santolaria-Otín and Zolina (2020)]] reported weak but significant decline in SCE in autumn over northern Eurasia and North America during 1979 – 2005, and similarly for spring, except for northern Siberia which showed higher spring SCE. [[#Kapnick--2012|Kapnick and Hall (2012)]] detected significant loss of spring mountain snowpack in western USA in 1950 – 2008. For Canada, extensive decreasing snow depths, SCE and duration were detected since mid-1970s, especially in western Canada during winter and spring (DeBeer et al. , 2016). [[#Berghuijs--2014|Berghuijs et al. (2014)]] show that across the continental USA, catchments with more snowfall than rainfall generally have higher mean streamflow, which will probably decrease with smaller fractions of precipitation falling as snow because of climate warming.

In summary, a decline in the spring NH snow cover extent, snow depth and duration has been observed since the late 1960s and has been attributed to human influence ( ''high confidence'' ). Depending on the region and season, there is ''low-to-medium confidence'' in the main drivers of snow cover changes, although various regions exhibit a shortening of the snow cover season which is consistent with global warming. A more detailed assessment of observed changes in seasonal snow cover is provided in [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] .

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===== 8.3.1.7.3 Wetlands and lakes =====

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Wetlands and lakes affect the climate through their impact on carbon and methane budgets ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ; e.g., [[#Saunois--2016|Saunois et al., 2016]] ; [[#Zhan--2019|Zhan et al., 2019]] ) and on surface heat fluxes, with coupled weather and climate effects(e.g., [[#Zhan--2019|Zhan et al., 2019]] ). Although these features are also affected by human activities and by climate change, AR5 did not specifically report on wetlands and lakes.

Inventories of surface water bodies are not systematically produced at national or regional levels. However, assessments are undertaken at the global scale ( [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ). Merging observations from multiple satellite sensors makes it possible to detect surface water even under vegetation and clouds over about 25 years, but with low spatial resolution ( [[#Prigent--2016|Prigent et al., 2016]] ). Most recent multi-satellite products from visible, infrared, and microwave measurements, estimate a surface water area of about 12 to 14 million km <sup>2</sup> (including permanent and transitory surfaces, e.g., [[#Aires--2018|Aires et al., 2018]] ; [[#Davidson--2018|Davidson et al., 2018]] ), which is much higher than those provided by optical imagery (about 3 million km <sup>2</sup> ). Inventories show a strong decrease in natural surface water of about 0.8% yr <sup>–1</sup> in total from 1970 to the present ( [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ) but the sites are not evenly distributed. Multi-satellite estimates show a strong interannual variability in surface water extent over the period 1992 – 2015 with no clear long-term trend ( [[#Prigent--2020|Prigent et al., 2020]] ).

Human-made water bodies represent approximately 10% of the total continental water surfaces (Figure 8.1; [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ) and consist mainly of reservoirs and rice paddies. High resolution optical imagery over the period 1984 – 2015 ( [[#Donchyts--2016|Donchyts et al., 2016]] ; [[#Pekel--2016|Pekel et al., 2016]] ) shows a net increase of about 0.1 million km <sup>2</sup> in artifical water surfaces, mainly due to the construction of reservoirs. Surfaces of rice paddies are also increasing, especially in South East Asia ( [[#Davidson--2018|Davidson et al., 2018]] ).

In summary, there is ''high confidence'' that the extent of human-made surface water has increased over the 20th and early 21st centuries. In contrast, due to ''low agreement'' in the observational records at the global scale, there is only ''low confidence'' in the observed decline of the natural surface water extent in recent years (see also SRCCL).

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===== 8.3.1.7.4 Groundwater =====

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As the world’s most widespread store of freshwater (R.G. [[#Taylor--2013|Taylor et al., 2013]] a), groundwater is estimated to supply between a quarter and a third of the world’s annual freshwater withdrawals to meet agricultural, industrial and domestic demands ( [[#Döll--2012|Döll et al., 2012]] ; [[#Wada--2014|Wada et al., 2014]] ; [[#Hanasaki--2018|Hanasaki et al., 2018]] ).

Attribution of changes in groundwater storage, observed locally through piezometry (Figure 8.10; R.G. [[#Taylor--2013|Taylor et al., 2013]] a) or estimated from GRACE satellite measurements ( [[#Rodell--2018|Rodell et al., 2018]] ) at regional scales (&gt;100,000 km <sup>2</sup> ), is often complicated by non-climate influences that include land-use change ( [[#Favreau--2009|Favreau et al., 2009]] ) and human withdrawals ( [[#Bierkens--2019|Bierkens and Wada, 2019]] ).

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[[File:0f716d182560d9bc8edaeede0f45dd62 IPCC_AR6_WGI_Figure_8_10.png]]

'''Figure 8.10 |''' '''Trends in Terrestrial Water Storage (TWS; in centimetres per year, cm y''' '''r''' <sup>–1</sup> ''') obtained on the basis of GRACE observations from April 2002 to March 2016.''' The cause of the trend in each outlined study region is briefly explained and colour-coded by category. The trend map was smoothed with a 150 km radius Gaussian filter for the purpose of visualization. However, all calculations were performed at the native 3° resolution of the data product. Figure from [[#Rodell--2018|Rodell et al. (2018)]] . Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Following a global review of groundwater and climate change (R.G. [[#Taylor--2013|Taylor et al., 2013]] a) and AR5 WGII, evidence of an association between heavy or extreme precipitation and groundwater recharge has continued to grow, especially in tropical ( [[#Asoka--2018|Asoka et al., 2018]] ; [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ; [[#Kotchoni--2019|Kotchoni et al., 2019]] ) and subtropical regions ( [[#Meixner--2016|Meixner et al., 2016]] ). Stable-isotope ratios of oxygen and hydrogen at 14 of 15 sites across the tropics trace groundwater recharge to intensive monthly rainfall, commonly exceeding the 70th intensity percentile, approximately ( [[#Jasechko--2015|Jasechko and]] [[#Taylor--2015|Taylor, 2015]] ). Further, heavy rainfall recharging groundwater resources is often influenced by climate variability such as ENSO and PDO (R.G. [[#Taylor--2013|Taylor et al., 2013]] b; [[#Kuss--2014|Kuss and Gurdak, 2014]] ; [[#Asoka--2017|Asoka et al., 2017]] ; [[#Cuthbert--2019b|Cuthbert et al., 2019b]] ; [[#Kolusu--2019|Kolusu et al., 2019]] ; [[#Shamsudduha--2020|Shamsudduha and Taylor, 2020]] ). Additionally, increases in groundwater storage estimated from GRACE for 37 of the world’s large-scale aquifer systems from 2002 to 2016 are generally found to result from episodic recharge associated with extreme (&gt;90th percentile) annual precipitation.

The overall underestimation of precipitation intensities in global climate models ( [[#Wehner--2010|Wehner et al., 2010]] , 2020; [[#Goswami--2017|Goswami and Goswami, 2017]] ) and of their sensitivity to warming temperatures ( [[#Borodina--2017|Borodina et al., 2017]] ) may lead to underestimates of their recharging effect on groundwater ( [[#Mileham--2009|Mileham et al., 2009]] ; [[#Cuthbert--2019b|Cuthbert et al., 2019b]] ). The limited ability of global climate models to represent key controls on regional rainfall variability like ENSO (Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] and [[IPCC:Wg1:Chapter:Chapter-3#3.7.3|Section 3.7.3]] ; R. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ) may also underestimate observed recharge from such events that are of particular importance in drylands (R.G. Taylor et al. , 2013b; Cuthbert et al. , 2019b) . Numerical representations of the impact of precipitation intensification on groundwater recharge in large-scale models remain constrained by the challenges of including key recharge pathways that consider preferential flowpaths in soils ( [[#Beven--2018|Beven, 2018]] ) and focused recharge through leakage from surface waters ( [[#Döll--2014|Döll et al., 2014]] ).

Increasing global freshwater withdrawals, primarily associated with the expansion of irrigated agriculture in drylands, have led to global groundwater depletion that has an estimated range of about 100 and about 300 km <sup>3</sup> yr <sup>–1</sup> from hydrological models and volumetric-based calculations ( [[#Bierkens--2019|Bierkens and Wada, 2019]] ). The magnitude of this change is such that its estimated contribution to global sea level rise is in the order of 0.3 to 0.9 mm yr <sup>−1</sup> (Wada et al. , 2010; [[#Konikow--2011|Konikow, 2011]] ; Döll et al. , 2014; Pokhrel et al. , 2015; de Graaf et al. , 2017; Hanasaki et al. , 2018) . Groundwater depletion has been observed regionally in The USA High Plains, California’s Central Valley ( [[#Scanlon--2012|Scanlon et al., 2012]] ), north-west India (Rodell et al. , 2009; Asoka et al. , 2017), Upper Ganges in India ( [[#MacDonald--2016|MacDonald et al., 2016]] ), North China Plain ( [[#Feng--2013|Feng et al., 2013]] ), north-central Middle East region of Tigris–Euphrates–Western Iran ( [[#Voss--2013|Voss et al., 2013]] ), Central Asia ( [[#Hu--2019|Hu et al., 2019]] ), and North Africa ( [[#Bouchaou--2013|Bouchaou et al., 2013]] ). The regional contribution of agricultural irrigation to groundwater depletion was previously highlighted by SRCCL but no formal assessment of observed changes in global or regional groundwater featured in AR5.

Quantification of changes in groundwater storage from GRACE is currently constrained by uncertainty in the estimation of changes in other terrestrial water stores using uncalibrated, global-scale Land Surface Models (Döll et al. , 2014; Scanlon et al. , 2018) and the limited duration of the period of GRACE observations (2002 to 2016). Centennial-scale piezometry in north-west India reveals that recent groundwater depletion traced by GRACE ( [[#Rodell--2009|Rodell et al., 2009]] ; [[#Chen--2014|Chen et al., 2014]] ), follows more than a century of groundwater accumulation through canal leakage ( [[#MacDonald--2016|MacDonald et al., 2016]] ). Further, groundwater depletion is often localized occurring below the footprint (200,000 km <sup>2</sup> ) of GRACE, as has been well demonstrated by detailed modelling studies in the California Central Valley ( [[#Scanlon--2012|Scanlon et al., 2012]] ) and North China Plain ( [[#Cao--2016|Cao et al., 2016]] ).

Climate variability and drought affect groundwater depletion mainly due to amplified groundwater withdrawals. For instance, the depletion rate in Central Valley aquifer in the USA from 2006 to 2010 is estimated to range from 6 to 8 km <sup>3</sup> yr <sup>–1</sup> using GRACE data ( [[#Scanlon--2012|Scanlon et al., 2012]] ). In India, [[#Asoka--2017|Asoka et al. (2017)]] show contrasting trends in groundwater storage in the north (declining at 2 cm yr <sup>–1</sup> ) and south (increasing at 1–2 cm yr <sup>–1</sup> ) that is explained by variations in human withdrawals and precipitation linked to Indian Ocean sea surface temperature variability.

Changes in meltwater regimes from glaciers and seasonal snow packs tend to reduce the seasonal duration and magnitude of recharge ( [[#Tague--2009|Tague and Grant, 2009]] ). Aquifers in mountain valleys show shifts in the timing and magnitude of: (i) peak groundwater levels due to an earlier spring melt; and (ii) low groundwater levels associated with lower baseflow periods ( [[#Allen--2010|Allen et al., 2010]] ; [[#Dierauer--2018|Dierauer et al., 2018]] ; [[#Hayashi--2020|Hayashi, 2020]] ). The effects of receding alpine glaciers on groundwater systems are not well understood but long-term loss of glacier storage is estimated to reduce summer baseflow ( [[#Gremaud--2009|Gremaud et al., 2009]] ). In permafrost regions, coupling between surface water and groundwater systems may be particularly enhanced by warming ( [[#Lamontagne-Hallé--2018|Lamontagne-Hallé et al., 2018]] ; [[#Lemieux--2020|Lemieux et al., 2020]] ). In areas of seasonal or perennial ground frost, increased recharge is expected despite a decrease in absolute snow volume (Okkonen and Kløve, 2011; Walvoord and Kuryl yk, 2016) .

Coastal aquifers are the interface between the oceanic and terrestrial hydrological systems. Global sea level rise (SLR) causes interfaces between freshwater and saline-water to move inland. The extent of seawater intrusion into coastal aquifers depends on a variety of factors including coastal topography, recharge, and groundwater abstraction from coastal aquifers ( [[#Comte--2016|Comte et al., 2016]] ). Modelling results suggest that the impact of SLR on seawater intrusion is negligible compared to that of groundwater abstraction (Ferguson and Gleeson, 2012; [[#Yu--2019|Yu and Michael, 2019]] ) . Coastal aquifers under very low hydraulic gradients, such as the Asian mega-deltas, are theoretically sensitive to SLR but, according to evidence from [[#Akter--2019|Akter et al. (2019)]] in the Ganges-Brahmaputra-Megna basin, may be more severely and widely affected by changes in upstream river discharge. They argue further that saltwater inundation from storm surges will have the greatest localized effects.

In summary, there is ''medium confidence'' that increased precipitation intensities, partly due to human influence, have enhanced groundwater recharge, most notably in the tropics. There is ''high confidence'' that groundwater depletion has occurred since at least the start of the 21st century as a consequence of groundwater withdrawals for irrigation in some of the world’s most productive agricultural areas in drylands (e.g., southern High Plains and California Central Valley in the USA, the North China Plain, north-west India).

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