Editing IPCC:AR6/WGI/Chapter-11 (section)

=== 11.6.1 Mechanisms and Drivers ===

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Similar to many other extreme events, droughts occur as a combination of thermodynamic and dynamic processes (Box 11.1). Thermodynamic processes contributing to drought, which are modified by greenhouse gas forcing both at global and regional scales, are mostly related to heat and moisture exchanges, and are also partly modulated by plant coverage and physiology. They affect, for instance, atmospheric humidity, temperature, and radiation, which in turn affect precipitation and/or evapotranspiration in some regions and time frames. However, dynamic processes are particularly important to explain drought variability on different time scales, from a few weeks (flash droughts) to multiannual (megadroughts). There is ''low confidence'' in the effects of greenhouse gas forcing on changes in atmospheric dynamic ( [[IPCC:Wg1:Chapter:Chapter-2#2.4|Section 2.4]] ; [[IPCC:Wg1:Chapter:Chapter-4#4.3.3|Section 4.3.3]] ), and on associated changes in drought occurrence. Thermodynamic processes are thus the main driver of drought changes in a warming climate ( ''hig'' ''h confidence'' ).

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==== 11.6.1.1 Precipitation Deficits ====

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Lack of precipitation is generally the main factor controlling drought onset. There is ''high confidence'' that atmospheric dynamics, which vary on interannual, decadal and longer time scales, is the dominant contributor to variations in precipitation deficits in the majority of world regions ( [[#Dai--2013|Dai, 2013]] ; [[#Miralles--2014b|Miralles et al., 2014b]] ; [[#Seager--2014|Seager and Hoerling, 2014]] ; [[#Burgman--2015|Burgman and Jang, 2015]] ; [[#Dong--2015|Dong and Dai, 2015]] ; [[#Schubert--2016|Schubert et al., 2016]] ; [[#Raymond--2018|Raymond et al., 2018]] ; [[#Baek--2019|Baek et al., 2019]] ; [[#Drumond--2019|Drumond et al., 2019]] ; [[#Herrera-Estrada--2019|Herrera-Estrada et al., 2019]] ; [[#Gimeno--2020|Gimeno et al., 2020]] ; [[#Mishra--2020|Mishra, 2020]] ). Precipitation deficits are driven by dynamic mechanisms taking place on different spatial scales, including synoptic processes – atmospheric rivers and extratropical cyclones, blocking and ridges ( [[#11.7|Section 11.7]] ; [[#Sousa--2017|Sousa et al., 2017]] ), dominant large-scale circulation patterns ( [[#Kingston--2015|Kingston et al., 2015]] ), and global ocean–atmosphere coupled patterns such as inter-decadal Pacific Oscillation (IPO), Atlantic Multi-decadal Oscillation (AMO) and El Niño–Southern Oscillation (ENSO; [[#Dai--2017|Dai and Zhao, 2017]] ). These various mechanisms occur on different scales, are not independent, and substantially interact with one another. Also regional moisture recycling and land–atmosphere feedbacks play an important role for some precipitation anomalies (see below).

There is ''high confidence'' that land–atmosphere feedbacks play a substantial or dominant role in affecting precipitation deficits in someregions (SREX, Chapter 3; [[#Koster--2011|Koster et al., 2011]] ; [[#Gimeno--2012|Gimeno et al., 2012]] ; [[#Taylor--2012|Taylor et al., 2012]] ; [[#Guillod--2015|Guillod et al., 2015]] ; [[#Tuttle--2016|Tuttle and Salvucci, 2016]] ; [[#Santanello%20Jr.--2018|Santanello Jr. et al., 2018]] ; [[#Haslinger--2019|Haslinger et al., 2019]] ; [[#Herrera-Estrada--2019|Herrera-Estrada et al., 2019]] ). The sign of the feedbacks can be either positive or negative, as well as local or non-local ( [[#Taylor--2012|Taylor et al., 2012]] ; [[#Guillod--2015|Guillod et al., 2015]] ; [[#Tuttle--2016|Tuttle and Salvucci, 2016]] ). Earth system models (ESMs) tend to underestimate non-local negative soil-moisture–precipitation feedbacks ( [[#Taylor--2012|Taylor et al., 2012]] ) and also show high variations in their representation in some regions ( [[#Berg--2017b|Berg et al., 2017b]] ). Soil-moisture–precipitation feedbacks contribute to changes in precipitation in climate model projections in some regions, but ESMs display substantial uncertainties in their representation, and there is thus only ''low confidence'' in these contributions ( [[#Berg--2017b|Berg et al., 2017b]] ; [[#Vogel--2017|Vogel et al., 2017]] , 2018).

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==== 11.6.1.2 Atmospheric Evaporative Demand ====

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Atmospheric evaporative demand (AED) quantifies the maximum amount of actual evapotranspiration (ET) that can happen from land surfaces if they are not limited by water availability (Table 11.A.1). AED is affected by radiative and aerodynamic components. For this reason, the atmospheric dryness, often quantified with the relative humidity or the vapour pressure deficit (VPD), is not equivalent to the AED, as other variables are also highly relevant, including solar radiation and wind speed ( [[#Hobbins--2012|Hobbins et al., 2012]] ; [[#McVicar--2012a|McVicar et al., 2012a]] ; [[#Sheffield--2012|Sheffield et al., 2012]] ). AED can be estimated using different methods ( [[#McMahon--2013|McMahon et al., 2013]] ), and those solely based on air temperature (e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of magnitude and temporal trends ( [[#Sheffield--2012|Sheffield et al., 2012]] ), in particular, in the context of substantial background warming. Physically-based combination methods such as the Penman-Monteith equation are more adequate and recommended since 1998 by the United Nations Food and Agriculture Oganization ( [[#Pereira--2015|Pereira et al., 2015]] ). For this reason, the assessment of this Chapter, when considering atmospheric-based drought indices, only includes AED estimates using the latter (see also [[#11.9|Section 11.9]] ). AED is generally higher than ET, since AED represents an upper bound for ET. Hence, an AED increase does not necessarily lead to increased ET ( [[#Milly--2016|Milly and Dunne, 2016]] ), in particular under drought conditions given soil moisture limitation ( [[#Bonan--2014|Bonan et al., 2014]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Konings--2017|Konings et al., 2017]] ; [[#Stocker--2018|Stocker et al., 2018]] ). In general, AED is highest in regions where ET is lowest (e.g., desert areas), further illustrating the decoupling between the two variables under limited soil moisture.

The influence of AED on drought depends on the drought type, background climate, the environmental conditions and the moisture availability ( [[#Hobbins--2016|Hobbins et al., 2016]] , 2017; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ). This influence also includes effects not related to increased ET. Under low soil moisture conditions, increased AED increases plant stress, enhancing the severity of agricultural and ecological droughts ( [[#Williams--2013|Williams et al., 2013]] ; [[#Allen--2015|Allen et al., 2015]] ; [[#McDowell--2016|McDowell et al., 2016]] ; [[#Grossiord--2020|Grossiord et al., 2020]] ). Moreover, high VPD impacts overall plant physiology; it affects the leaf and xylem safety margins, and decreases the sap velocity and plant hydraulic conductance ( [[#Fontes--2018|Fontes et al., 2018]] ). VPD also affects the plant metabolism of carbon and, if prolonged, it may cause plant mortality via carbon starvation ( [[#Breshears--2013|Breshears et al., 2013]] ; [[#Hartmann--2015|Hartmann, 2015]] ). Drought projections based exclusively on AED metrics overestimate changes in soil moisture and runoff deficits. Nevertheless, AED also directly impacts hydrological drought, as ET from surface waters is not limited ( [[#Wurbs--2014|Wurbs and Ayala, 2014]] ; [[#Friedrich--2018|Friedrich et al., 2018]] ; [[#Hogeboom--2018|Hogeboom et al., 2018]] ; K. [[#Xiao--2018|]] [[#Xiao--2018|Xiao et al., 2018]] ), and this effect increases under climate change projections (W. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#Althoff--2020|Althoff et al., 2020]] ). In addition, high AED increases crop water consumptions in irrigated lands ( [[#García-Garizábal--2014|García-Garizábal et al., 2014]] ), contributing to intensifying hydrological droughts downstream ( [[#Fazel--2017|Fazel et al., 2017]] ; [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ).

On subseasonal to decadal scales, temporal variations in AED are strongly controlled by circulation variability ( [[#Williams--2014|Williams et al., 2014]] ; [[#Chai--2018|Chai et al., 2018]] ; [[#Martens--2018|Martens et al., 2018]] ), but thermodynamic processes also play a fundamental role and, under human-induced climate change, dominate the changes in AED. Atmospheric warming due to increased atmospheric CO <sub>2</sub> concentrations increases AED by means of enhanced VPD in the absence of other influences ( [[#Scheff--2015|Scheff and Frierson, 2015]] ). Because of the greater warming over land than over oceans (Sections 2.3.1.1 and 11.3), the saturation pressure of water vapour increases more over land than over oceans; oceanic air masses advected over land thus contain insufficient water vapour to keep pace with the greater increase in saturation vapour pressure over land ( [[#Sherwood--2014|Sherwood and Fu, 2014]] ; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ; [[#Findell--2019|Findell et al., 2019]] ). Land–atmosphere feedbacks are also important in affecting atmospheric moisture content and temperature, with resulting effects on relative humidity and VPD (Box 11.1; [[#Berg--2016|Berg et al., 2016]] ; [[#Haslinger--2019|Haslinger et al., 2019]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ).

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==== 11.6.1.3 Soil Moisture Deficits ====

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Soil moisture shows an important correlation with precipitation variability ( [[#Khong--2015|Khong et al., 2015]] ; [[#Seager--2019|Seager et al., 2019]] ), but ET also plays a substantial role in further depleting moisture from soils, in particular in humid regions during periods of precipitation deficits ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Padrón--2020|Padrón et al., 2020]] ). In addition, soil moisture plays a role in drought self-intensification under dry conditions in which ET is decreased and leads to higher AED ( [[#Miralles--2019|Miralles et al., 2019]] ), an effect that can also contribute to triggering flash droughts ( [[#Otkin--2016|Otkin et al., 2016]] , 2018; [[#DeAngelis--2020|DeAngelis et al., 2020]] ; [[#Pendergrass--2020|Pendergrass et al., 2020]] ). If soil moisture becomes limited, ET is reduced, which may decrease the rate of soil drying, but can also lead to further atmospheric dryness through various feedback loops ( [[#Seneviratne--2010|Seneviratne et al., 2010]] ; [[#Miralles--2014a|Miralles et al., 2014a]] , 2019; [[#Teuling--2018|Teuling, 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ; [[#Liu--2020|Liu et al., 2020]] ). The process is complex since vegetation cover plays a role in modulating albedo and in providing access to deeper stores of water (both in the soil and groundwater). Also, changes in land cover and in plant phenology may alter ET ( [[#Sterling--2013|Sterling et al., 2013]] ; [[#Woodward--2014|Woodward et al., 2014]] ; [[#Frank--2015|Frank et al., 2015]] ; [[#Döll--2016|Döll et al., 2016]] ; [[#Ukkola--2016|Ukkola et al., 2016]] ; [[#Trancoso--2017|Trancoso et al., 2017]] ; [[#Hao--2019|Hao et al., 2019]] ; [[#Lian--2020|Lian et al., 2020]] ). Snow depth has strong and direct impacts on soil moisture in many systems ( [[#Gergel--2017|Gergel et al., 2017]] ; [[#Williams--2020|Williams et al., 2020]] ).

Soil moisture directly affects plant water stress and ET. Soil moisture is the primary factor that controls xylem hydraulic conductance – that is, water uptake in plants ( [[#Sperry--2016|Sperry et al., 2016]] ; [[#Hayat--2019|Hayat et al., 2019]] ; X. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ). For this reason, soil moisture deficits are the main driver of xylem embolism, the primary cause of plant mortality ( [[#Anderegg--2012|Anderegg et al., 2012]] , 2016; [[#Rowland--2015|Rowland et al., 2015]] ). Also carbon assimilation by plants strongly depends on soil moisture ( [[#Hartzell--2017|Hartzell et al., 2017]] ), with implications for carbon starvation and plant dying if soil moisture deficits are prolonged ( [[#Sevanto--2014|Sevanto et al., 2014]] ). These mechanisms explain that soil moisture deficits are usually more relevant than AED excess to explain gross primary production anomalies and vegetation stress, mostly in sub-humid and semi-arid regions ( [[#Stocker--2018|Stocker et al., 2018]] ; [[#Liu--2020|Liu et al., 2020]] ). High CO <sub>2</sub> concentrations are shown to potentially decrease plant ET and increase plant water-use efficiency, affecting soil moisture levels, but this effect interacts with other CO <sub>2</sub> physiological and radiative effects ( [[#11.6.5.2|Section 11.6.5.2]] and Cross-Chapter Box 5.1), and has less relevance under low soil moisture ( [[#Morgan--2011|Morgan et al., 2011]] ; Z. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ; [[#Nackley--2018|Nackley et al., 2018]] ; [[#Dikšaitytė--2019|Dikšaitytė et al., 2019]] ). ESMs represent both surface (around 10cm) and total column soil moisture, whereby total soil moisture is of more direct relevance for root water uptake, in particular by trees. There is evidence that surface soil moisture projections are substantially drier than total soil moisture projections, and may overestimate drying of relevance for most vegetation ( [[#Berg--2017a|Berg et al., 2017a]] ).

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==== 11.6.1.4 Hydrological Deficits ====

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Drivers of streamflow and surface water deficits are complex and strongly depend on the hydrological system analysed (e.g., streamflows in the headwaters, medium course of the rivers, groundwater, highly regulated hydrological basins). Soil hydrological processes, which control the propagation of meteorological droughts throughout different parts of the hydrological cycle ( [[#Van%20Loon--2012|Van Loon and Van Lanen, 2012]] ), are spatially and temporally complex ( [[#Herrera-Estrada--2017|Herrera-Estrada et al., 2017]] ; S. [[#Huang--2017|Huang et al., 2017]] b) and difficult to quantify ( [[#Van%20Lanen--2016|Van Lanen et al., 2016]] ; [[#Apurv--2017|Apurv et al., 2017]] ; [[#Caillouet--2017|Caillouet et al., 2017]] ; [[#Konapala--2017|Konapala and Mishra, 2017]] ; [[#Hasan--2019|Hasan et al., 2019]] ). The physiographic characteristics of the basins also affect how droughts propagate throughout the hydrological cycle ( [[#Van%20Loon--2012|Van Loon and Van Lanen, 2012]] ; [[#Van%20Lanen--2013|Van Lanen et al., 2013]] ; [[#Van%20Loon--2015|Van Loon, 2015]] ; [[#Konapala--2020|Konapala and]] [[#Mishra--2020|Mishra, 2020]] ; Veettil and [[#Mishra--2020|Mishra, 2020]] ). In addition, the assessment of groundwater deficits is very difficult given the complexity of processes that involve natural and human-driven feedbacks and interactions with the climate system ( [[#Taylor--2013|Taylor et al., 2013]] ). Streamflow and surface water deficits are affected by land cover, groundwater and soil characteristics ( [[#Van%20Lanen--2013|Van Lanen et al., 2013]] ; [[#Van%20Loon--2015|Van Loon and Laaha, 2015]] ; [[#Barker--2016|Barker et al., 2016]] ; [[#Tijdeman--2018|Tijdeman et al., 2018]] ), as well as human activities (water management and demand, damming) and land-use changes ( [[#11.6.4.3|Section 11.6.4.3]] ; [[#Van%20Loon--2016|Van Loon et al., 2016]] ; [[#He--2017|He et al., 2017]] ; [[#Veldkamp--2017|Veldkamp et al., 2017]] ; J. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ; Y. [[#Xu--2019|]] [[#Xu--2019|Xu et al., 2019]] ; [[#Jehanzaib--2020|Jehanzaib et al., 2020]] ). Finally, snow and glaciers are relevant for water resources in some regions. For instance, warming affects snowpack levels ( [[#Dierauer--2019|Dierauer et al., 2019]] ; [[#Huning--2020|Huning and AghaKouchak, 2020]] ), as well as the timing of snow melt, thus potentially affecting the seasonality and magnitude of low flows ( [[#Barnhart--2016|Barnhart et al., 2016]] ).

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==== 11.6.1.5 Atmospheric-based Drought Indices ====

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Given the difficulties of drought quantification and data constraints, atmospheric-based drought indices combining both precipitation and AED have been developed, as they can be derived from meteorological data that is available in most regions (with few exceptions). These demand/supply indices are not intended to be metrics of soil moisture, streamflow or vegetation water stress. Because of their reliance on precipitation and AED, they are mostly related to the actual water balance in humid regions, in which ET is not limited by soil moisture and tends towards AED. In water-limited regions and in dry periods everywhere, they constitute an upper bound for overall water-balance deficits (e.g., of surface waters) but are also related to conditions conducive to vegetation stress, particularly under soil moisture limitation ( [[#11.6.1.2|Section 11.6.1.2]] ).

Although there are many atmospheric-based drought indices, two are assessed in this chapter: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The PDSI has been widely used to monitor and quantify drought severity ( [[#Dai--2018|Dai et al., 2018]] ), but is affected by some constraints (SREX Chapter 3; [[#Mukherjee--2018a|Mukherjee et al., 2018a]] ). Although the calculation of the PDSI is based on a soil water budget, the PDSI is essentially a climate drought index that mostly responds to the precipitation and the AED ( [[#van%20der%20Schrier--2013|van der Schrier et al., 2013]] ; [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ; [[#Dai--2018|Dai et al., 2018]] ). The SPEI also combines precipitation and AED, being equally sensitive to these two variables ( [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ). The SPEI is more sensitive to AED than the PDSI ( [[#Cook--2014a|Cook et al., 2014a]] ; [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ), although under humid and normal precipitation conditions, the effects of AED on the SPEI are small ( [[#Tomas-Burguera--2020|Tomas-Burguera et al., 2020]] ). Given the limitations associated with temperature-based AED estimates ( [[#11.6.1.2|Section 11.6.1.2]] ), only studies using the Penman-Monteith-based SPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this assessment and in the regional tables in [[#11.9|Section 11.9]] .

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==== 11.6.1.6 Relation of Assessed Variables and Metrics for Changes in Different Drought Types ====

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This Chapter assesses changes in meteorological drought, agricultural and ecological droughts, and hydrological droughts. Precipitation-based indices are used for the estimation of changes in meteorological droughts, such as the Standardized Precipitation Index (SPI) and the number of consecutive dry days (CDD). Changes in total soil moisture and soil moisture-based drought events are used for the estimation of changes in agricultural and ecological droughts, complemented by changes in surface soil moisture, water-balance estimates (precipitation minus ET), and SPEI-PM and PDSI-PM. For hydrological droughts, changes in low flows are assessed, sometimes complemented by changes in mean streamflow.

In summary, different drought types exist and they are associated with different impacts and respond differently to increasing greenhouse gas concentrations. Precipitation deficits and changes in evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased atmospheric evaporative demand, result in agricultural and ecological drought. Lack of runoff and surface water result in hydrological drought. Drought events are the result of dynamic and/or thermodynamic processes, with thermodynamic processes being the main driver of drought changes under human-induced climate change ( ''hig'' ''h confidence'' ).

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