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

=== 8.2.3 Local-scale Physical Processes Affecting the Water Cycle ===

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Processes operating at local scales are capable of substantially modifying the regional water cycle. This section assesses the development in understanding of processes affecting the atmosphere, surface and subsurface, including cryosphere and biosphere interactions and the direct impacts of human activities.

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==== 8.2.3.1 Hydrological Processes Related to Ice and Snow ====

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Declining ice-sheet mass, glacier extent and Northern Hemisphere (NH) sea ice, snow cover and permafrost ( [[#Collins--2013|Collins et al., 2013]] ; [[#Vaughan--2013|Vaughan et al., 2013]] ) is an expected consequence of a warming climate (Sections 2.3.2, 3.4, 4.3.2.1 and 9.3 – 9.5). A decline in mountain snow cover and increased snow and glacier melt will alter the amount and timing of seasonal runoff in mountain regions (Sections 3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter and spring snowmelt (X. [[#Zeng--2018|Zeng et al., 2018]] ) can reduce summer and autumn runoff in snow-dominated river basins of mid–high latitudes of the NH (Rhoades et al., 2018; [[#Blöschl--2019|Blöschl et al., 2019]] ). Since AR5, an earlier but less rapid snowmelt has been explained by reduced winter snowfall and less intense solar radiation earlier in the season (Musselman et al. , 2017; Wu et al. , 2018; Grogan et al. , 2020). Reduced snow cover also increases energy available for evaporation, which can dominate declining river discharge based on modelling of the Colorado River ( [[#Milly--2020|Milly and Dunne, 2020]] ). An increase in the fraction of precipitation falling as rain compared with snow can lead to declines in both streamflow and groundwater storage in regions where snowmelt is the primary source of recharge ( [[#Earman--2011|Earman and Dettinger, 2011]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ). Such regions include western South America and western North America, semi-arid regions which rely on snowmelt from high mountain chains ( [[#Ragettli--2016|Ragettli et al., 2016]] ; [[#Milly--2020|Milly and Dunne, 2020]] ). Rain-on-snow melt events reduce at lower altitudes due to declining snow cover but increase at higher altitudes where snow tends to be replaced by rain based on observations and modelling (Musselman et al., 2018; [[#Pall--2019|Pall et al., 2019]] ), thereby altering seasonal and regional characteristics of flooding ( [[IPCC:Wg1:Chapter:Chapter-11#11.5|Section 11.5]] ).

Seasonal melt water from high mountain glaciers in Asia (see Cross-Chapter Box 10.4) supply the basic needs of 221 ± 97 million people (Pritchard, 2019; [[#Immerzeel--2020|Immerzeel et al., 2020]] ). Glacier-melt in response to warming can initially lead to increased runoff volumes, especially in peak summer flows, but they will eventually decline as most glaciers continue to shrink. SROCC concluded there is ''high confidence'' that the peak runoff has already been passed for some smaller glaciers ( [[#Hock--2019a|Hock et al., 2019a]] ). Increased precipitation and glacier-melt can also contribute to rising lake levels and flood hazards in regions such as the inner Tibetan Plateau, Patagonia, Peru, Alaska and Greenland (Lei et al. , 2017; Shugar et al. , 2020; Stuart-Smith et al. , 2020) . Since AR5, evidence from multiple locations (New Zealand, Greenland, Antarctica) shows that intrusions of warm, moist air are important in controlling glacier mass balance, the likelihood of extreme ablation or snowfall events depending on air temperature (Gorodetskaya et al. , 2014; Mackintosh et al. , 2017; Mattingly et al. , 2018; Little et al. , 2019; Oltmanns et al. , 2019; Wille et al. , 2019; Adusumilli et al. , 2021) . Sensible heating from warm air and increased longwave radiation from atmospheric moisture and low clouds drive melt events (Stuecker et al., 2018).

Reductions in snow, freshwater ice and permafrost affect terrestrial hydrology. Permafrost degradation reduces soil ice and alters the extent of thermokarst lake coverage ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ; M. [[#Meredith--2019|]] [[#Meredith--2019|Meredith et al., 2019]] ). A lag between current climate change and permafrost degradation is expected, given the slow response rates in frozen ground and the fact that snow cover insulates soil from sensible heat exchanges with the air above (Hoegh-Guldberg et al. , 2018; García-García et al. , 2019; Soong et al. , 2020) . Post‐wildfire areas are also linked with permafrost degradation in the Arctic based on satellite observations ( [[#Yanagiya--2020|Yanagiya and Furuya, 2020]] ). An increase in spring rainfall can increase heat advection by infiltration, exacerbating permafrost thaw and leading to increased methane emissions ( [[IPCC:Wg1:Chapter:Chapter-5#5.4.7|Section 5.4.7]] ; [[#Neumann--2019|Neumann et al., 2019]] ). Increased heat transport by Arctic rivers can also contribute to earlier sea ice melt ( [[#Park--2020|Park et al., 2020]] ).

In summary, it is ''virtually certain'' that warming will cause a loss of frozen water stores, except in areas where temperatures remain below 0°C for most of the year. There is ''high confidence'' that warming and reduced snow volume drives an earlier snowmelt, leading to seasonally dependent changes in streamflow. There is ''medium confidence'' that weaker sunlight earlier in the season can reduce the rate of snowmelt. Melting of snowpack or glaciers can increase streamflow in high-latitude and high-altitude catchments until frozen water reserves are depleted ( ''high confidence'' ). There is ''high confidence'' that warm, moist airflows and associated precipitation dominate glacier mass balance in some regions (New Zealand, Greenland, Antarctica).

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==== 8.2.3.2 Processes Determining Heavy Precipitation and Flooding ====

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Evidence that heavy precipitation events (from sub-daily up to seasonal time scales) intensify as the planet warms has strengthened since AR5 ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Box 11.1 and Cross-Chapter Box 3.2) based on improved physical understanding, extensive modelling and increasing observational corroboration ( [[#O’Gorman--2015|O’Gorman, 2015]] ; [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Neelin--2017|Neelin et al., 2017]] ). There is ''robust evidence'' , with ''medium agreement'' across a range of modelling and observational studies, of thermodynamic intensification of wet seasons ( [[#Chou--2013|Chou et al., 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Lan--2019|Lan et al., 2019]] ; [[#Zhang--2019|Zhang and Fueglistaler, 2019]] ). Extreme daily precipitation is expected to increase at close to the 7% °C <sup>–1</sup> increase in the near-surface atmospheric moisture-holding capacity determined by the Clausius–Clapeyron equation ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Figure 8.4), with ''limited evidence'' that higher rates apply for shorter duration precipitation events ( [[#Formayer--2017|Formayer and Fritz, 2017]] ; Lenderink et al. , 2017; Ali et al. , 2018; Guerreiro et al. , 2018; Burdanowitz et al. , 2019; W. Zhang et al. , 2019a) . However, observed estimates sample multiple synoptic weather states, mixing thermodynamic and dynamic factors, so are not directly relatable to climate change responses ( [[#Bao--2017|Bao et al., 2017]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ). The contrasting spatial scales sampled by the observations and models (from global to cloud resolving) explain the large range of daily and sub-daily precipitation scaling with temperature assessed in Figure 8.4.

Since AR5, advances in understanding the expected changes in intense rainfall at the sub-daily time scale ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Figure 8.4) are provided by idealized or high resolution model experiments and observations ( [[#Westra--2014|Westra et al., 2014]] ; [[#Fowler--2021|Fowler et al., 2021]] ). There is ''robust evidence'' from simplified calculations, convection resolving models and observations that thermodynamics drives an increase in convective available potential energy (CAPE) with warming and therefore the intensity of convective storms ( [[#Singh--2013|Singh and O’Gorman, 2013]] ; [[#Romps--2016|Romps, 2016]] ; [[#Barbero--2019|Barbero et al., 2019]] ). Also, declining relative humidity over land (Sections 2.3.1.3.2 and 8.2.2.1) increases lifting condensation level, thereby delaying but intensifying convective systems ( [[#Louf--2019|Louf et al., 2019]] ; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). Larger systems are linked with increasing tropopause height ( [[#Lenderink--2017|Lenderink et al., 2017]] ) that can also amplify storm precipitation ( [[#Prein--2017|Prein et al., 2017]] ). However, the heaviest rainfall is not necessarily associated with the most intense (deepest) storms based on satellite data ( [[#Hamada--2015|Hamada et al., 2015]] ; [[#Hamada--2018|Hamada and Takayabu, 2018]] ). Precipitation intensification can exceed thermodynamic expectations where and when additional latent heating invigorates individual storms ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.1|Section 11.4.1]] ) as implied by ''medium agreement'' across modelling and observational studies (Berg et al. , 2013; Molnar et al. , 2015; Scoccimarro et al. , 2015; Prein et al. , 2017; [[#Zhou--2017|Zhou and Wang, 2017]] ; Nie et al. , 2018; Kendon et al. , 2019; Z. Zhang et al. , 2019) . This intensification depends on time of day, based on convection-permitting simulations (E.P. [[#Meredith--2019|]] [[#Meredith--2019|Meredith et al., 2019]] ).

Intensification of sub-daily rainfall is inhibited in regions and seasons where available moisture is limited ( [[#Prein--2017|Prein et al., 2017]] ). However, a fixed threshold temperature above which precipitation is limited by moisture availability is not supported by modelling evidence ( [[#Neelin--2017|Neelin et al., 2017]] ; [[#Prein--2017|Prein et al., 2017]] ). Enhanced latent heating within storms can also suppress convection at larger scales due to atmospheric stabilization as demonstrated with high resolution, idealized and large ensemble modelling studies (Loriaux et al. , 2017; Chan et al. , 2018; Nie et al. , 2018; Tandon et al. , 2018; Kendon et al. , 2019) . Stability is also increased by the direct radiative heating effect of higher CO <sub>2</sub> concentrations ( [[#Baker--2018|Baker et al., 2018]] ) and influenced by aerosol effects on the atmospheric energy budget and cloud development (Box 8.1). Since AR5, modelling evidence shows increases in convective precipitation extremes are limited by droplet/ice fall speeds (Singh and [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Sandvik--2018|Sandvik et al., 2018]] ) but these processes are only crudely represented ( [[#Tapiador--2019a|Tapiador et al., 2019a]] ). Idealized regional and coupled global models combined with ''limited'' observational ''evidence'' shows that instantaneous precipitation extremes are sensitive to microphysical processes, while daily extremes are determined more by the degree of convective aggregation ( [[#Bao--2019|Bao and Sherwood, 2019]] ; [[#Pendergrass--2020a|Pendergrass, 2020a]] ).

Dynamical changes modify and can dominate thermodynamic drivers of local rainfall and flood hazard change (Box 11.1). For example, increased land – ocean temperature gradients ( [[#8.2.2.2|Section 8.2.2.2]] ) explain more intense rain from convective systems over the Sahel based on satellite data since the 1980s ( [[#Taylor--2017|Taylor et al., 2017]] ) and dynamical feedbacks can invigorate active to break phase transition over India ( [[#Karmakar--2017|Karmakar et al., 2017]] ; [[#Roxy--2017|Roxy et al., 2017]] ). Satellite data shows long-lived, organized mesoscale convective systems contribute disproportionally to extreme tropical precipitation ( [[#Roca--2020|Roca and Fiolleau, 2020]] ). Since AR5, the spatial variability in soil moisture has been linked with the timing and location of convective rainfall by altering the partitioning between latent and sensible heating. This was demonstrated for the Sahel, Europe and India in observations (C.M. [[#Taylor--2013|Taylor et al., 2013]] ; [[#Taylor--2015|Taylor, 2015]] ; [[#Petrova--2018|Petrova et al., 2018]] ; [[#Barton--2020|Barton et al., 2020]] ; [[#Klein--2020|Klein and Taylor, 2020]] ) but depends on the moisture-convergence regime ( [[#Welty--2020|Welty et al., 2020]] ). Only high-resolution convection-permitting models can capture the sub-grid scale mechanisms for convective initiation ( C.M. Taylor et al. , 2013; H. Moon et al. , 2019 ). There is ''medium evidence'' that greater tropical cyclone rainfall totals can be caused by dynamical feedbacks ( [[#Chauvin--2017|Chauvin et al., 2017]] ) and slower propagation speed as tropical circulation weakens ( [[#Kossin--2018|Kossin, 2018]] ). These processes amplify the thermodynamic intensification of rainfall ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ), yet observational support is weak ( [[#Chan--2019|Chan, 2019]] ; [[#Lanzante--2019|Lanzante, 2019]] ; I.J. Moon et al. , 2019; Knutson et al. , 2020) . Slower decay following landfall, explained by larger stores of heat and moisture at higher SSTs, can also amplify rainfall amount based on observations and modelling ( [[#Li--2020|Li and Chakraborty, 2020]] ). Rainfall intensity from the outer rain bands of tropical cyclones is also increased by aerosol – cloud interactions (Box 8.1).

The amount and intensity of rainfall within extratropical storms is expected to increase with atmospheric moisture. This is particularly evident for atmospheric rivers (see Glossary) and research since AR5 has confirmed their link with flooding and terrestrial water storage ( [[#Froidevaux--2016|Froidevaux and Martius, 2016]] ; Paltan et al. , 2017; [[#Waliser--2017|Waliser and Guan, 2017]] ; Adusumilli et al. , 2019; Ionita et al. , 2020; Payne et al. , 2020) . There is ''robust evidence'' based on simple physics and detailed modelling that extratropical cyclone rainfall, including atmospheric river events, will intensify through increased atmospheric moisture flux ( [[#Lavers--2013|Lavers et al., 2013]] ; [[#Ramos--2016|Ramos et al., 2016]] ; [[#Yettella--2017|Yettella and Kay, 2017]] ; V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al., 2018]] ; [[#Algarra--2020|Algarra et al., 2020]] ; [[#Xu--2020|Xu et al., 2020]] ; [[#Zavadoff--2020|Zavadoff and Kirtman, 2020]] ; [[#Zhao--2020|Zhao, 2020]] ), although changes in dynamical aspects will modify responses regionally ( [[#8.4.2.8|Section 8.4.2.8]] ). For example, stronger latitudinal temperature gradients in the high-latitude upper troposphere drive increased extratropical storm speed around 30°N – 70°N based on CMIP5 simulations ( [[#Dwyer--2017|Dwyer and O’Gorman, 2017]] ), causing reduced precipitation accumulation.

The response of flood hazard to changing rainfall characteristics depends on time and space scale and the nature of the land surface ( [[IPCC:Wg1:Chapter:Chapter-11#11.5.1|Section 11.5.1]] and FAQ 8.2). Sustained and heavy rainfall can lead to widespread flooding and landslides while intensification of short-duration intense rainfall can increase the severity and frequency of flash flooding ( [[#Marengo--2013|Marengo et al., 2013]] ; [[#Chan--2016|Chan et al., 2016]] ; [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Sandvik--2018|Sandvik et al., 2018]] ). Flooding events in many tropical regions (e.g., north-western South America, southern Africa and Australasia) are associated with ENSO variability ( [[#Emerton--2017|Emerton et al., 2017]] ; [[#Takahashi--2019|Takahashi and Martínez, 2019]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ) and amplified by thermodynamic increases in water vapour. Flood hazard from heavy rainfall is modulated by snowmelt ( [[#8.2.3.1|Section 8.2.3.1]] ), vegetation characteristics ( [[#Page--2020|Page et al., 2020]] ; [[#Murphy--2021|Murphy et al., 2021]] ) and direct human intervention (Sections 8.2.3.4 and FAQ 8.2) but also can be compounded by sea level rise (Sections 4.3.2.2 and 9.6.4) in coastal and delta regions ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ; [[#Ganguli--2019|Ganguli and Merz, 2019]] ; [[#Eilander--2020|Eilander et al., 2020]] ). Antecedent soil moisture conditions are an important modulator of flooding ( [[IPCC:Wg1:Chapter:Chapter-11#11.5.1|Section 11.5.1]] ) but become less important for smaller catchments and for more severe floods ( [[#Wasko--2019|Wasko and Nathan, 2019]] ). Depleted soil moisture after more intense dry seasons ( [[#8.2.2.1|Section 8.2.2.1]] ) can allow greater uptake of wet season rainfall before soils saturate. Since AR5, evidence confirms that more intense rainfall increases the proportion of runoff and reservoir recharge relative to infiltration into the soil ( [[#Eekhout--2018|Eekhout et al., 2018]] ; [[#Yin--2018|Yin et al., 2018]] ). More intense but less frequent storms ( [[#Kendon--2019|Kendon et al., 2019]] ) favour focused groundwater recharge through leakage from surface waters (R.G. [[#Taylor--2013|Taylor et al., 2013]] a; [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ) and runoff and flash flooding where the percolation capacity of the soil is exceeded ( [[#Yin--2018|Yin et al., 2018]] ).

Increased severity of flooding on larger, more slowly-responding rivers is expected as precipitation accumulations increase during persistent wet events over a season. This can occur where atmospheric blocking patterns repeatedly steer extratropical cyclones across large river catchments, as identified for NH mid-latitudes and Asia ( [[#Takahashi--2015|Takahashi et al., 2015]] ; [[#Pfleiderer--2018|Pfleiderer et al., 2018]] ; [[#Zhou--2018|Zhou et al., 2018]] ; [[#Blöschl--2019|Blöschl et al., 2019]] ; [[#Lenggenhager--2019|Lenggenhager et al., 2019]] ; [[#Nikumbh--2019|Nikumbh et al., 2019]] ; [[#Zanardo--2019|Zanardo et al., 2019]] ), although groundwater flooding and antecedent conditions including soil moisture and snowmelt also play a role ( [[#Muchan--2015|Muchan et al., 2015]] ; [[#Berghuijs--2019|Berghuijs et al., 2019]] ). Increased atmospheric moisture amplifies the severity of these events when they occur in a warmer climate, yet drivers of change in the occurrence of blocking patterns, stationary waves and jet stream position are not well understood ( [[#8.2.2.2|Section 8.2.2.2]] and Cross-Chapter Box 10.1).

In summary, there is ''very high confidence'' that heavy precipitation events will become more intense in a warming climate. There is ''high confidence'' that increased moisture and its convergence within extratropical and tropical cyclones and storms will increase rainfall totals during wet events at close to the 7% °C <sup>–1</sup> Thermodynamic response, with ''low confidence'' of higher rates for sub-daily intensities. There is ''medium confidence'' that more intense but less frequent rainfall increases the proportion of rainfall leading to surface runoff and focused groundwater recharge from temporary water bodies. There is ''low confidence'' in how the frequency of flooding will change regionally as it is strongly dependent on catchment characteristics, antecedent conditions and how atmospheric circulation systems respond to climate change, which is less certain than thermodynamic drivers ( [[IPCC:Wg1:Chapter:Chapter-11#11.5|Section 11.5]] ). However, there is ''high confidence'' that increases in precipitation intensity and amount during very wet events (from sub-daily up to seasonal time scales) will intensify severe flooding when these extremes occur.

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==== 8.2.3.3 Drivers of Aridity and Drought ====

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Regional changes in aridity – broadly defined as a deficit of moisture – are expected to occur in response to anthropogenic forcings as a consequence of shifting precipitation patterns, warmer temperatures, changes in cloudiness (affecting solar radiation), declining snowpack, changes in winds and humidity, and vegetation cover (Figure 8.6). Evapotranspiration (see Annex VII: Glossary) is a key component of aridity, and is composed of two main processes: evaporation from soil, water and vegetation surfaces; and transpiration, the exchange of moisture between plants and atmosphere through plant stomata. On a global level, warmer temperatures increase evaporative demand in the atmosphere, and thus (assuming sufficient soil moisture is available) increase moisture loss from evapotranspiration ( ''high confidence'' ) (Dai et al., 2018; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). On a regional level, aridity is further modulated by seasonal rainfall patterns, runoff, water storage, and interactions with vegetation.

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'''Figure 8.6 |''' '''Climatic drivers of drought, effects on water availability, and impacts.''' Plus and minus signs denote the direction of change that drivers have on factors such as snowpack, evapotranspiration, soil moisture, and water storage. The three main types of drought are listed, along with some possible environmental and socio-economic impacts of drought (bottom).

Vegetation is a crucial interface between subsurface water storage (in soil moisture and groundwater) and the atmosphere. Plants alter evapotranspiration and the surface energy balance, and thus can have a large influence on regional aridity ( [[#Lemordant--2018|Lemordant et al., 2018]] ). SRCCL concluded there is ''high confidence'' that higher atmospheric CO <sub>2</sub> increases the ratio of plant CO <sub>2</sub> uptake to water loss (water-use efficiency; WUE) through the combined enhancement of photosynthesis and stomatal regulation ( Section 5.4.1; DeKauwe et al. , 2013; C.D. Jones et al. , 2013; Deryng et al. , 2016; Swann et al. , 2016; Cheng et al. , 2017; Knauer et al. , 2017; Peters et al. , 2018; Guerrieri et al. , 2019) . Modelling studies suggest that increasing WUE can partly counteract water losses from increased evaporative demand in a warmer atmosphere, potentially mitigating aridification (Milly and Dunne, 2016; Bonfils et al. , 2017; Cook et al. , 2018; Y. Yang et al. , 2018) . However, observational studies suggest that this effect may be counter-balanced by the increase in plant growth in response to elevated CO <sub>2</sub> , which results in increased water consumption (De Kauwe et al. , 2013; Donohue et al. , 2013; Ukkola et al. , 2016b; Yang et al. , 2016; Guerrieri et al. , 2019; Mankin et al. , 2019; A. Singh et al. , 2020) . In semi-arid regions, increased plant water consumption can reduce streamflow and exacerbate aridification (Ukkola et al. , 2016b; Mankin et al. , 2019; A. Singh et al. , 2020) . Thus, there is ''low confidence'' that increased WUE in plants can counterbalance increased evaporative demand (Cross-Chapter Box 5.1).

A drought is a period of abnormally dry weather that persists for long enough to cause a serious hydrological imbalance (Glossary; Wilhite and Glantz, 1985; [[#Wilhite--2000|Wilhite, 2000]] ; [[#Cook--2018|Cook et al., 2018]] ). Most droughts begin as persistent precipitation deficits (‘meteorological drought’) that propagate over time into deficits in soil moisture, streamflow, and water storage (Figure 8.6), leading to a reduction in water supply (‘hydrological drought’). Increased atmospheric evaporative demand increases plant water stress, leading to ‘agricultural and ecological drought’ (Williams et al. , 2013; C.D. Allen et al. , 2015; Anderegg et al. , 2016; McDowell et al. , 2016; Grossiord et al. , 2020) . Evaporative demand affects plants in two ways. It increases evapotranspiration, depleting soil moisture and stressing plants through lack of water ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Sperry--2016|Sperry et al., 2016]] ), and also directly affects plant physiology, causing a decline in hydraulic conductance and carbon metabolism, leading to mortality (Figure 8.6; [[#Breshears--2013|Breshears et al., 2013]] ; [[#Hartmann--2015|Hartmann, 2015]] ; [[#McDowell--2015|McDowell and Allen, 2015]] ; [[#Fontes--2018|Fontes et al., 2018]] ). While droughts are traditionally viewed as ‘slow moving’ disasters that typically take months or years to develop, rapidly evolving and often unpredictable ''flash droughts'' can also occur ( [[#Otkin--2016|Otkin et al., 2016]] , 2018). ''Flash droughts'' can develop within a few weeks, causing substantial disruption to agriculture and water resources ( [[#Pendergrass--2020|Pendergrass et al., 2020]] ). Conversely, droughts that persist for a long time (usually a decade or more) are called ''megadroughts'' . Droughts span a large range of spatial and temporal scales, arise through a variety of climate system dynamics (e.g., internal atmospheric variability, ocean teleconnections), and can be amplified or alleviated by a variety of physical and biological processes. As such, droughts occupy a unique space within the framework of extreme climate and weather events, possessing no singular definition.

While the role of precipitation in droughts is obvious, other climatic drivers are also important, such as temperature, radiation, wind, and humidity (Figure 8.6). These factors have a strong influence on atmospheric evaporative demand, which affects evapotranspiration and soil moisture (Figure 8.6). In snow-dominated regions, high temperatures increase the fraction of precipitation falling as rain instead of snow and advance the timing of spring snowmelt ( ''high confidence'' ) (Vincent et al. , 2015; Mote et al. , 2016, 2018; [[#Berg--2017|Berg and Hall, 2017]] ; Solander et al. , 2018) . This can result in lower than normal snowpack levels (a ‘snow drought’), and thus reduced streamflow, even if total precipitation is at or above normal for the cold season ( [[#Harpold--2017|Harpold et al., 2017]] ). Plants also affect the severity of droughts by modulating evapotranspiration (Figure 8.6). As discussed above, the effect of elevated CO <sub>2</sub> on plants has the potential to both increase and reduce water loss through evapotranspiration via enhanced WUE and plant growth, respectively (Figure 8.6), but there is ''low confidence'' in whether one process dominates over another at the global scale.

Drought severity also depends on human activities and decision-making (AghaKouchak et al. , 2015; Van Loon et al. , 2016; Pendergrass et al. , 2020) . Societies have developed a variety of strategies to manipulate the water cycle to increase resiliency in the face of water scarcity, including irrigation, creation of artificial reservoirs, and groundwater pumping. While potentially buffering water resource capacity, in some cases these interventions may unexpectedly increase vulnerability ( ''medium confidence'' ). For example, while increased irrigation efficiency may ensure more water is available to crops, the corresponding reduction in runoff and subsurface recharge may exacerbate hydrologic drought ( [[#Grafton--2018|Grafton et al., 2018]] ). Furthermore, while building dams and increasing surface reservoir capacity can boost water resources, they may actually increase drought vulnerability if demands rise to take advantage of the increased supply or if over-reliance on these surface reservoirs is encouraged (Di Baldassarre et al., 2018). Interactions between adaptation, vulnerability, and drought impacts are discussed further in WGII (Chapters 2 and 4).

In summary, there is ''high confidence'' that a warming climate drives an increase in atmospheric evaporative demand, decreasing available soil moisture. There is ''high confidence'' that higher atmospheric CO <sub>2</sub> increases plant water-use efficiency, but ''low confidence'' that this physiological effect can counterbalance water losses. Since drought can be defined in a number of ways, there are potentially different responses under a warming climate depending on drought type. Beyond a lack of precipitation, changes in evapotranspiration are critical components of drought, because these can lead to soil moisture declines ( ''high confidence'' ). Under very dry soil conditions, evapotranspiration becomes restricted and plants experience water stress in response to increased atmospheric demand ( ''medium confidence'' ). Human activities and decision-making have a critical impact on drought severity ( ''high co'' ''nfidence'' ).

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==== 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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