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

==== 8.2.3.4 Direct Anthropogenic Influence on the Regional Water Cycle ====

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Human activities influence the regional water cycle directly through modifying and exploiting stores and flows from rivers, lakes and groundwater and by altering land cover characteristics. These actions alter surface energy and water balances through changes in permeability, surface albedo, evapotranspiration, surface roughness and leaf area. Direct redistribution of water by human activities for domestic, agricultural and industrial use of about 24,000 km <sup>3</sup> yr <sup>–1</sup> (Figure 8.1) is equivalent to half the global river discharge or double the global groundwater recharge each year ( [[#Abbott--2019|Abbott et al., 2019]] ). Since AR5, both modelling studies and observations have demonstrated that land use change can drive local and remote responses in precipitation and river flow by altering the surface energy balance, moisture advection and recycling, land – sea thermal contrast and associated wind patterns (Alter et al. , 2015; Wey et al. , 2015; De Vrese et al. , 2016; Pei et al. , 2016; Wang-Erlandsson et al. , 2018; Vicente-Serrano et al. , 2019) . There is ''robust evidence'' that a warming climate combined with direct human demand for groundwater will deplete groundwater resources in already dry regions ( [[#Wada--2014|Wada and Bierkens, 2014]] ; [[#D’Odorico--2018|D’Odorico et al., 2018]] ; [[#Jia--2019|Jia et al., 2019]] ).

The SRCCL presented evidence that extraction of water from the ground or river systems and intensive irrigation increases evaporation and atmospheric water vapour locally ( [[#Jia--2019|Jia et al., 2019]] ; [[#Mishra--2020|Mishra et al., 2020]] ). Irrigation can explain declining groundwater storage in some regions, including north-western India and North America ( [[#Asoka--2017|Asoka et al., 2017]] ; G. [[#Ferguson--2018|]] [[#Ferguson--2018|Ferguson et al., 2018]] ). Simulations spanning 1960–2010 indicate that approximately 30% of the present human water consumption is supplied from non-sustainable water resources ( [[#Wada--2014|Wada and Bierkens, 2014]] ). However, there is only ''limited evidence'' that groundwater extraction is lowering streamflow ( [[#Mukherjee--2018|Mukherjee et al., 2018]] ; [[#de%20Graaf--2019|de Graaf et al., 2019]] ). Model experiments show that irrigation can either aggravate or alleviate climate‐induced changes of surface or subsurface water (Lenget al., 2015). Widespread extraction of water from rivers can reduce flows and decrease the level and area of inland seas and lakes (Wurtsbaugh et al. , 2017; Torres-Batlló et al. , 2020; X. Wang et al. , 2020) . Between 1985 and 2015, about 139,000 km <sup>2</sup> of inland water areas have become land, while creation of dams has converted about 95,000 km <sup>2</sup> of land to water, particularly in the Amazon and Tibetan Plateau (Donchyts et al., 2016). Direct management of river flow is comparable in magnitude to climate change effects for snow-fed rivers at a continental scale based on a global analysis and a study of 96 Canadian catchments ( [[#Tan--2015|Tan and Gan, 2015]] ; [[#Arheimer--2017|Arheimer et al., 2017]] ).

The SRCCL assessed with ''medium confidence'' that mean and extreme precipitation is increased over and downwind of urban areas ( [[#Jia--2019|Jia et al., 2019]] ). There is ''medium confidence'' that altered thermodynamic and aerodynamic properties of the land surface from urbanization affects evaporation and increases precipitation over or downwind of cities (Box 10.3) due to altered stability and turbulence (Han et al. , 2014; Pathirana et al. , 2014; Jiang et al. , 2016; D’Odorico et al. , 2018; Sarangi et al. , 2018; Boyaj et al. , 2020) . However, reduced biogenic aerosol, but increased anthropogenic aerosol emissions modify cloud microphysics and precipitation processes ( Box 8.1; Schmidand Niyogi, 2017; D’Odorico et al. , 2018; Fan et al. , 2020; Zheng et al. , 2020) . Urbanization also decreases permeability of the surface, leading to increased surface runoff ( [[#Chen--2017|Chen et al., 2017]] ; [[#Jia--2019|Jia et al., 2019]] ). Large-scale infrastructure, such as the construction and operation of dikes, weirs, and hydropower plants, also alters surface energy and moisture fluxes, potentially influencing the regional water cycle. ''Limited'' modelling ''evidence'' suggests that large-scale solar and wind farms can increase precipitation locally (over the Sahel and North America) when dynamic vegetation responses are represented (Y. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Pryor--2020|Pryor et al., 2020]] ), with remote effects also possible ( [[#Lu--2021|Lu et al., 2021]] ).

Changes in land use from forest to agriculture can exert profound regional effects on the water cycle (FAQ 8.1) by modifying the surface energy balance and moisture recycling (Krishnan et al. , 2016; Paul et al. , 2016; Llopart et al. , 2018; Singh et al. , 2019) . There is ''medium evidence'' from modelling and observations over the Amazon and East Africa that deforestation drives increased streamflow (Dos Santos et al., 2018; [[#Guzha--2018|Guzha et al., 2018]] ; [[#Levy--2018|Levy et al., 2018]] ) but ''limited evidence'' that increases in global runoff due to deforestation are counterbalanced by decreases resulting from irrigation (Hoegh-Guldberg et al., 2018). Total Amazon deforestation drives reductions in precipitation but with a large 90% confidence range ( – 38 to +5 %) based on 44 primarily pre-AR5 climate model simulations (Spracklen and Garcia-Carreras, 2015) with smaller reductions ( – 2.3 to – 1.3 %) attributed to observed Amazon deforestation up to 2010. Climate model development has reduced this uncertainty range but has not altered the median change ( [[#Lejeune--2015|Lejeune et al., 2015]] ). Large-scale global deforestation (20 million km <sup>2</sup> ) simulated by 9 CMIP6 models confirms a large range in precipitation amount reduction of – 37 ± 54 mm yr <sup>–1</sup> over the deforested regions ( [[#Boysen--2020|Boysen et al., 2020]] ). However, small-scale deforestation can increase precipitation locally (Lawrence and Vandecar, 2015). A 50–60% deforestation rate corresponded to a wet season delay of about one week and greater chance of dry spells of eight days or longer based on correlation analysis of rain gauge and land-use data for South America (Leite-Filhoet al., 2019). Forest and grassland fires can also modify hydrological response at the watershed scale (Havel et al., 2018). Afforestation or reforestation aimed at removing CO <sub>2</sub> from the atmosphere can also alter the water cycle at the regional scale ( [[#8.4|Section 8.4.3]] and Cross-Chapter Box 5.1).

In summary, there is ''high confidence'' that land-use change and water extraction for irrigation drive local, regional and remote responses in the water cycle. Large-scale deforestation is ''likely'' to decrease precipitation over the deforested regions but there is ''low confidence'' in the effects of limited deforestation. There is ''medium confidence'' that deforestation drives increased streamflow relative to the responses caused by climate change. Urbanization can increase local precipitation ( ''medium confidence'' ) and resulting runoff intensity ( ''high confidence'' ). A warming climate combined with direct human demand for water is expected to deplete groundwater resources in dry regions ( ''high co'' ''nfidence'' ).

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'''Box 8.1 | Role of Anthropogenic Aerosols in Water Cyc''' '''le Changes'''

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Aerosols affect precipitation in two major pathways, by altering the shortwave and longwave radiation and influencing cloud microphysical properties.

'''Aerosol radiative effects on precipitation'''
Aerosols scatter and absorb solar radiation which reduces the energy available for surface evaporation and subsequent precipitation. In addition, cooling is incurred by the radiation that is reflected back to space directly by the aerosols and indirectly by the aerosol effect on cloud brightening. Northern Hemisphere (NH) station data indicate decreasing precipitation trends during the 1950s to the 1980s, which have since partially recovered ( [[#Wild--2012|Wild, 2012]] ; [[#Bonfils--2020|Bonfils et al., 2020]] ). These changes are attributable with ''high confidence'' to anthropogenic aerosol emissions from North America and Europe causing dimming through reduced surface solar radiation. This peaked during the late-1970s and partially recovered thereafter following improved air quality regulations (Section 6.2.1; Box 8.1, Figure 1).

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[[File:e222ac727181417ec565ebd134ba5d39 IPCC_AR6_WGI_Box_8_1_Figure_1.png]]

'''Box 8.1, Figure 1 |'''

'''Northern Hemisphere surface downward radiation anomalies (W m''' <sup>–2</sup> '''; a) and precipitation anomalies (mm day''' <sup>–1</sup> '''; b) for''' '''1951–2014''' '''for summer season (May–September) monsoon region (Polson et al. , 2014)''' '''from CMIP6 DAMIP experiments.''' Observed solar radiation anomalies are from GEBA global data from 1961–2014 ( [[#Wild--2017|Wild et al., 2017]] ) and observed precipitation anomalies are from GPCC and CRU. CMIP6 multi-model mean anomalies are from all-forcings (ALL), greenhouse gas forcing (GHG) and anthropogenic aerosol forcing (AER) experiments. Anomalies are with respect to 1961–1990 and smoothed with a 11-year running mean. Red shading shows the ensemble spread of ALL forcing experiment (5–95% range). Models are masked to the GPCC data set. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Dimming over the NH causes a relative cooling, compared to the Southern Hemisphere (SH), which induces a southward shift of the northern edge of the tropical rain belt ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ; [[#Allen--2014|Allen et al., 2014]] ; [[#Brönnimann--2015|Brönnimann et al., 2015]] ). CMIP5 simulations show that most of the cooling is caused by the aerosol cloud-mediated effect ( [[#Chung--2017|Chung and Soden, 2017]] ). Dimming also weakens monsoon flow and precipitation, offsetting or even overcoming the expected precipitation increase due to increased GHGs ( [[#Ayantika--2021|Ayantika et al., 2021]] ). The oceanic response to a weakened monsoon cross-equatorial flow can further weaken the South Asian monsoon through an amplifying feedback loop ( [[#Swapna--2012|Swapna et al., 2012]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Patil--2019|Patil et al., 2019]] ). These processes partially explain ( ''medium confidence'' ) the southward shift of the NH tropical edge of the tropical rain belt from the 1950s to the 1980s ( [[#Allen--2014|Allen et al., 2014]] ; [[#Brönnimann--2015|Brönnimann et al., 2015]] ) and the severe drought in the Sahel that peaked in the mid-1980s ( [[#Rotstayn--2002|Rotstayn et al., 2002]] ; [[#Undorf--2018b|Undorf et al., 2018b]] ). These processes also explain ( ''high confidence'' ) the observed decrease of South East Asian monsoon precipitation during the second half of the 20th century (Figure 8.7; [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Sanap--2015|Sanap et al., 2015]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Lau--2017|Lau and Kim, 2017]] ; [[#Lin--2018|Lin et al., 2018]] ; [[#Undorf--2018b|Undorf et al., 2018b]] ).

Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y. [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ). Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding ( [[#Fan--2015|Fan et al., 2015]] ; [[#Lee--2016|Lee et al., 2016]] ) and severe convective storms ( ''medium confidence'' ) ( [[#Saide--2015|Saide et al., 2015]] ). In particular, aerosols induce intense convection at the Himalaya foothills during the pre-monsoon season, which generates a regional convergence there ( ''medium confidence'' ). This mechanism is termed the ‘elevated heat pump hypothesis’ ( [[#Lau--2006|Lau and Kim, 2006]] ; [[#D’Errico--2015|D’Errico et al., 2015]] ).

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[[File:02db3d5a52af3c474c907c7a475bf2f3 IPCC_AR6_WGI_Box_8_1_Figure_2.png]]

'''Box 8.1, Figure 2 |'''

'''Schematic depiction of the atmospheric effects of light-absorbing aerosols on convection and cloud formation: (a) without and (b) with the presence of absorbing aerosols in the planetary boundary layer.''' The dashed and solid blue lines correspond to the vertical temperature profiles in the absence and presence of the absorbing aerosol layer, respectively, and the solid and dashed red lines denote the dry and moist adiabats, respectively. Absorbing aerosols result in an increasing temperature in the atmosphere but a reduced temperature at the surface. The reduced surface temperature and the increased temperature aloft led to a larger negative energy associated with convective inhibition (–) and a higher convection condensation level (CCL) under the polluted conditions. On the other hand, the absorbing aerosol layer induces a larger convective available potential energy (+) above CCL, facilitating more intensive vertical development of clouds, if lifting is sufficient to overcome the larger convective inhibition. Figure from Y. [[#Wang--2013|Wang et al. (2013)]] .

'''Aerosol cloud microphysical effects'''

Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce into raindrops and to freeze into ice hydrometeors at temperatures below 0°C. Adding CCN suppresses light rainfall from shallow and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air invigorates them by more efficient vapour condensation on the added drop surfaces ( [[#Koren--2014|Koren et al., 2014]] ; [[#Fan--2018|Fan et al., 2018]] ). Clouds forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain ( [[#Freud--2012|Freud and Rosenfeld, 2012]] ; [[#Konwar--2012|Konwar et al., 2012]] ; [[#Campos%20Braga--2017|Campos Braga et al., 2017]] ). This leads to larger amount of cloud water evaporating aloft while cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration ( [[#Dagan--2017|Dagan et al., 2017]] ; [[#Chua--2020|Chua and Ming, 2020]] ), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop

Box 8.1

( [[#Abbott--2021|Abbott and Cronin, 2021]] ). In addition, delaying rain initiation until greater altitudes are reached transports more cloud water above the 0°C altitude and leads to additional release of latent heat of freezing and/or vapour deposition, which in combination with the added latent heat of condensation enhances the cloud updrafts ( [[#Fan--2018|Fan et al., 2018]] ). The stronger updrafts invigorate mixed-phase precipitation and the resultant hail and cloud electrification (Rosenfeldet al., 2008; [[#Thornton--2017|Thornton et al., 2017]] ). This includes the outer convective rainbands of tropical cyclones. There is ''medium confidence'' that air pollution enhances flood hazard associated with the outer rain bands at the expense of the inner rain bands ( [[#Wang--2014|Wang et al., 2014]] ; [[#Zhao--2018|]] [[#Zhao--2018|]] [[#Zhao--2018|C. Zhao et al., 2018]] ; [[#Souri--2020|Souri et al., 2020]] ).

The aerosol effect on invigoration and rainfall from deep convective clouds peaks at moderate levels (aerosol optical depth of 0.2 to 0.3), but reverses into suppression with more aerosols (H. [[#Liu--2019|]] [[#Liu--2019|Liu et al., 2019]] ). More generally, the microphysical aerosol-related processes often compensate or buffer each other ( [[#Stevens--2009|Stevens and Feingold, 2009]] ). For example, suppressed rain by slowing drop coalescence enhances mixed-phase precipitation. Therefore, despite the potentially large aerosol influence on the precipitation forming processes, the net outcome of aerosol microphysical effects on precipitation amount has generally ''low confidence'' , especially when evaluated with respect to the background of high natural variability in precipitation ( [[#Tao--2012|Tao et al., 2012]] ).

Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for efficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds ( [[#Creamean--2013|Creamean et al., 2013]] ). Most INPs are desert and soil dust particles, rather than air pollution aerosols ( [[#DeMott--2010|DeMott et al., 2010]] ). Biogenic particles from terrestrial and marine origin are more rare, but important at temperatures above about – 15°C ( [[#Murray--2012|Murray et al., 2012]] ; [[#DeMott--2016|DeMott et al., 2016]] ). Dust particles from long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California ( [[#Creamean--2013|Creamean et al., 2013]] ; [[#Fan--2014|Fan et al., 2014]] ). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which enhanced orographic supercooled clouds with ''medium confidence'' of success ( [[#French--2018|French et al., 2018]] ; [[#Rauber--2019|Rauber et al., 2019]] ; [[#Friedrich--2020|Friedrich et al., 2020]] ). There are still major gaps in understanding the effects of INPs mainly on deep convective clouds ( [[#Kanji--2017|Kanji et al., 2017]] ; [[#Stanford--2017|Stanford et al., 2017]] ; [[#Korolev--2020|Korolev et al., 2020]] ).

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