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

== 11.5 Floods ==

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Floods are the inundation of normally dry land, and are classified into types (e.g., pluvial floods, flash floods, river floods, groundwater floods, surge floods, coastal floods) depending on the space and time scales and the major factors and processes involved ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.2|Section 8.2.3.2]] ; [[#Nied--2014|Nied et al., 2014]] ; [[#Aerts--2018|Aerts et al., 2018]] ). Flooded area is difficult to measure or quantify and, for this reason, many of the existing studies on changes in floods focus on streamflow. Thus, this section assesses changes in flow as a proxy for river floods, in addition to some types of flash floods. Pluvial and urban floods – types of flash floods resulting from the precipitation intensity exceeding the capacity of natural and artificial drainage systems – are directly linked to extreme precipitation. Because of this link, changes in extreme precipitation are the main proxy for inferring changes in pluvial and urban floods (see also [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ), assuming there is no additional change in the surface condition. Changes in these types of floods are not assessed in this section, but can be inferred from the assessment of changes in heavy precipitation in [[#11.4|Section 11.4]] . Coastal floods due to extreme sea levels and flood changes at regional scales are assessed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] .

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=== 11.5.1 Mechanisms and Drivers ===

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Since AR5, the number of studies on understanding how floods may have changed, and will change in the future, has substantially increased. Floods are a complex interplay of hydrology, climate, and human management, and the relative importance of these factors varies for different flood types and regions.

In addition to the amount and intensity of precipitation, the main factors for river floods include antecedent soil moisture ( [[#Paschalis--2014|Paschalis et al., 2014]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Grillakis--2016|Grillakis et al., 2016]] ; [[#Woldemeskel--2016|Woldemeskel and Sharma, 2016]] ) and snow water-equivalent in cold regions ( [[#Sikorska--2015|Sikorska et al., 2015]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ). Other factors are also important, including stream morphology ( [[#Borga--2014|Borga et al., 2014]] ; [[#Slater--2015|Slater et al., 2015]] ), river and catchment engineering ( [[#Pisaniello--2012|Pisaniello et al., 2012]] ; [[#Nakayama--2013|Nakayama and Shankman, 2013]] ; [[#Kim--2016|Kim and Sanders, 2016]] ), land-use and land-cover characteristics ( [[#Aich--2016|Aich et al., 2016]] ; [[#Rogger--2017|Rogger et al., 2017]] ) and changes ( [[#Knighton--2019|Knighton et al., 2019]] ), and feedbacks between climate, soil, snow, vegetation, etc. ( [[#Hall--2014|Hall et al., 2014]] ; [[#Ortega--2014|Ortega et al., 2014]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Buttle--2016|Buttle et al., 2016]] ; [[#Teufel--2019|Teufel et al., 2019]] ). Water regulation and management have, in general, increased resilience to flooding ( [[#Formetta--2019|Formetta and Feyen, 2019]] ), masking effects of an increase in extreme precipitation on flood probability in some regions, even though they do not eliminate very extreme floods ( [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ). This means that an increase in precipitation extremes may not always result in an increase in river floods ( [[#Sharma--2018|Sharma et al., 2018]] ; [[#Do--2020|Do et al., 2020]] ). Yet, as very extreme precipitation can become a dominant factor for river floods, there can be some correspondence in the changes in very extreme precipitation and river floods ( [[#Ivancic--2015|Ivancic and Shaw, 2015]] ; [[#Wasko--2017|Wasko and Sharma, 2017]] ; [[#Wasko--2019|Wasko and Nathan, 2019]] ). This has been observed in the western Mediterranean ( [[#Llasat--2016|Llasat et al., 2016]] ), in China (Q. [[#Zhang--2015a|]] [[#Zhang--2015|Zhang et al., 2015]] a ) and in the USA ( [[#Peterson--2013b|Peterson et al., 2013b]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Slater--2016|Slater and Villarini, 2016]] ).

In regions with a seasonal snow cover, snowmelt is the main cause of extreme river flooding over large areas ( [[#Pall--2019|Pall et al., 2019]] ). Extensive snowmelt combined with heavy and/or long-duration precipitation can cause significant floods (D. [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Krug--2020|Krug et al., 2020]] ). Changes in floods in these regions can be uncertain because of the compounding and competing effects of the responses of snow and rain to warming that affect snowpack size: warming results in an increase in precipitation, but also a reduction in the time period of snowfall accumulation ( [[#Teufel--2019|Teufel et al., 2019]] ). An increase in atmospheric CO <sub>2</sub> enhances water-use efficiency by plants ( [[#Roderick--2015|Roderick et al., 2015]] ; [[#Milly--2016|Milly and Dunne, 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Swann--2018|Swann, 2018]] ); this could reduce evapotranspiration and contribute to the maintenance of soil moisture and streamflow levels under enhanced atmospheric CO <sub>2</sub> concentrations ( [[#Yang--2019|Yang et al., 2019]] ). This mechanism would suggest an increase in the magnitude of some floods in the future ( [[#Kooperman--2018|Kooperman et al., 2018]] ). But this effect is uncertain as an increase in leaf area index, and vegetation coverage could also result in overall larger water consumption ( [[#Mátyás--2014|Mátyás and Sun, 2014]] ; [[#Mankin--2019|Mankin et al., 2019]] ; [[#Teuling--2019|Teuling et al., 2019]] ), and there are also other CO <sub>2</sub> -related mechanisms that come into play (Cross-Chapter Box 5.1).

Various factors, such as extreme precipitation ( [[#Cho--2016|Cho et al., 2016]] ; [[#Archer--2018|Archer and Fowler, 2018]] ), glacier lake outbursts ( [[#Schneider--2014|Schneider et al., 2014]] ; [[#Schwanghart--2016|Schwanghart et al., 2016]] ), or dam breaks ( [[#Biscarini--2016|Biscarini et al., 2016]] ) can cause flash floods. Very intense rainfall, along with a high fraction of impervious surfaces can result in flash floods in urban areas ( [[#Hettiarachchi--2018|Hettiarachchi et al., 2018]] ). Because of this direct connection, changes in very intense precipitation can translate to changes in urban flood potential ( [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ), though there can be a spectrum of urban flood responses to this flood potential ( [[#Smith--2013|Smith et al., 2013]] ), as many factors, such as the overland flow rate and the design of urban ( [[#Falconer--2009|Falconer et al., 2009]] ) and storm water drainage systems ( [[#Maksimović--2009|Maksimović et al., 2009]] ), can play an important role. Nevertheless, changes in extreme precipitation are the main proxy for inferring changes in some types of flash floods, (which are addressed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ), given the relation between extreme precipitation and pluvial floods, the very limited literature on urban and pluvial floods (e.g., [[#Skougaard%20Kaspersen--2017|Skougaard Kaspersen et al., 2017]] ), and limitations of existing methodologies for assessing changes in floods ( [[#Archer--2016|Archer et al., 2016]] ).

In summary, there is not always a one-to-one correspondence between an extreme precipitation event and a flood event, or between changes in extreme precipitation and changes in floods, because floods are affected by many factors in addition to heavy precipitation ( ''high confidence'' ). Changes in extreme precipitation may be used as a proxy to infer changes in some types of flash floods that are more directly related to extreme precipitation ( ''high'' ''confidence'' ).

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=== 11.5.2 Observed Trends ===

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The SREX ( [[#Seneviratne--2012|Seneviratne et al., 2012]] ) assessed ''low confidence'' for observed changes in the magnitude or frequency of floods at the global scale. This assessment was confirmed by AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ). The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) found increases in flood frequency and extreme streamflow in some regions, but decreases in other regions. While the number of studies on flood trends has increased since AR5, and there were also new analyses after the release of SR1.5 ( [[#Berghuijs--2017|Berghuijs et al., 2017]] ; [[#Blöschl--2019|Blöschl et al., 2019]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ), hydrological literature on observed flood changes is heterogeneous, focusing at regional and sub-regional basin scales, making it difficult to synthesize at the global and sometimes regional scales. The vast majority of studies focus on river floods using streamflow as a proxy, with limited attention to urban floods. Streamflow measurements are not evenly distributed over space, with gaps in spatial coverage, and their coverage in many regions of Africa, South America, and parts of Asia is poor (e.g., [[#Do--2017|Do et al., 2017]] ), leading to difficulties in detecting long-term changes in floods ( [[#Slater--2017|Slater and Villarini, 2017]] ). See also [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.5|Section 8.3.1.5]] .

Peak flow trends are characterized by high regional variability and lack overall statistical significance of a decrease or an increase over the globe as a whole. Of more than 3500 streamflow stations in the USA, central and Northern Europe, Africa, Brazil, and Australia, 7.1% stations showed a significant increase, and 11.9% stations showed a significant decrease in annual maximum peak flow during 1961–2005 ( [[#Do--2017|Do et al., 2017]] ). This is in direct contrast to the global and continental scale intensification of short-duration extreme precipitation ( [[#11.4.2|Section 11.4.2]] ). There may be some consistency over large regions (see [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ), in high streamflows (&gt;90th percentile), including a decrease in some regions (e.g., in the Mediterranean) and an increase in others (e.g., northern Asia), but gauge coverage is often limited. On a continental scale, a decrease seems to dominate in Africa ( [[#Tramblay--2020|Tramblay et al., 2020]] ) and Australia ( [[#Ishak--2013|Ishak et al., 2013]] ; [[#Wasko--2019|Wasko and Nathan, 2019]] ), an increase in the Amazon ( [[#Barichivich--2018|Barichivich et al., 2018]] ), and trends are spatially variable in other continents (Q. [[#Zhang--2015b|]] [[#Zhang--2015|Zhang et al., 2015]] b ; [[#Bai--2016|Bai et al., 2016]] ; [[#Do--2017|Do et al., 2017]] ; [[#Hodgkins--2017|Hodgkins et al., 2017]] ). In Europe, flow trends have large spatial differences ( [[#Hall--2014|Hall et al., 2014]] ; [[#Mediero--2015|Mediero et al., 2015]] ; [[#Kundzewicz--2018|Kundzewicz et al., 2018]] ; [[#Mangini--2018|Mangini et al., 2018]] ), but there appears to be a pattern of increase in north-western Europe, and a decrease in southern and eastern Europe in annual peak flow during 1960–2000 ( [[#Blöschl--2019|Blöschl et al., 2019]] ). In North America, peak flow has increased in north-east USA and decreased in south-west USA ( [[#Peterson--2013b|Peterson et al., 2013b]] ; [[#Armstrong--2014|Armstrong et al., 2014]] ; [[#Mallakpour--2015|Mallakpour and Villarini, 2015]] ; [[#Archfield--2016|Archfield et al., 2016]] ; [[#Burn--2016|Burn and Whitfield, 2016]] ; [[#Wehner--2017|Wehner et al., 2017]] ; [[#Neri--2019|Neri et al., 2019]] ). There are important changes in the seasonality of peak flows in regions where snowmelt dominates, such as northern North America ( [[#Burn--2016|Burn and Whitfield, 2016]] ; [[#Dudley--2017|Dudley et al., 2017]] ) and Northern Europe ( [[#Blöschl--2017|Blöschl et al., 2017]] ), corresponding to strong winter and spring warming.

In summary, the seasonality of floods has changed in cold regions where snowmelt dominates the flow regime in response to warming ( ''high confidence'' ). There is ''low confidence'' about peak flow trends over past decades on the global scale '','' but there are regions experiencing increases, including parts of Asia, Southern South America, north-east USA, north-western Europe, and the Amazon, and regions experiencing decreases, including parts of the Mediterranean, Australia, Africa, and south-western USA.

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=== 11.5.3 Model Evaluation ===

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Hydrological models used to simulate floods are structurally diverse ( [[#Dankers--2014|Dankers et al., 2014]] ; [[#Mateo--2017|Mateo et al., 2017]] ; [[#Şen--2018|Şen, 2018]] ), often requiring extensive calibration since sub-grid processes and land-surface properties need to be parametrized, irrespective of the spatial resolutions ( [[#Döll--2016|Döll et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). The data used to drive and calibrate the models are usually of coarse resolution, necessitating the use of a wide variety of downscaling techniques ( [[#Muerth--2013|Muerth et al., 2013]] ). This adds uncertainty not only to the models but also to the reliability of the calibrations. The quality of the flood simulations also depends on the spatial scale, as flood processes are different for catchments of different sizes. It is more difficult to replicate flood processes for large basins, as water management and water use are often more complex for these basins.

Studies that use different regional hydrological models show a large spread in flood simulations ( [[#Dankers--2014|Dankers et al., 2014]] ; [[#Roudier--2016|Roudier et al., 2016]] ; [[#Trigg--2016|Trigg et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). Regional models reproduce moderate and high flows reasonably well (0.02–0.1 flow annual exceedance probabilities), but there are large biases for the most extreme flows (0–0.02 annual flow exceedance probability), independent of the climatic and physiographic characteristics of the basins (S. [[#Huang--2017|Huang et al., 2017]] a). Global-scale hydrological models have even more challenges, as they struggle to reproduce the magnitude of the flood hazard ( [[#Trigg--2016|Trigg et al., 2016]] ). Also, the ensemble mean of multiple models does not perform better than individual models ( [[#Zaherpour--2018|Zaherpour et al., 2018]] ).

The use of hydrological models for assessing changes in floods, especially for future projections, adds another dimension of uncertainty on top of uncertainty in the driving climate projections, including emissions scenarios, and in the driving climate models (both RCMs and GCMs) ( [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Hundecha--2016|Hundecha et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). The differences in hydrological models ( [[#Roudier--2016|Roudier et al., 2016]] ; [[#Thober--2018|Thober et al., 2018]] ), as well as post-processing of climate model output for the hydrological models ( [[#Muerth--2013|Muerth et al., 2013]] ; [[#Maier--2018|Maier et al., 2018]] ), add to uncertainty for flood projections.

In summary, there is ''medium confidence'' that simulations for the most extreme flows by regional hydrological models can have large biases. Global-scale hydrological models still struggle with reproducing the magnitude of floods. Projections of future floods are hampered by these difficulties and cascading uncertainties, including uncertainties in emissions scenarios and the climate models that generate inputs.

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=== 11.5.4 Detection and Attribution, Event Attribution ===

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There are very few studies focused on the attribution of long-term changes in floods, but there are studies on changes in flood events. Most of the studies focus on flash floods and urban floods, which are closely related to intense precipitation events ( [[#Hannaford--2015|Hannaford, 2015]] ). In other cases, event attribution focused on runoff using hydrological models, and examples include river basins in the UK ( [[#11.4.4|Section 11.4.4]] ; [[#Schaller--2016|Schaller et al., 2016]] ; [[#Kay--2018|Kay et al., 2018]] ), the Okavango River in Africa ( [[#Wolski--2014|Wolski et al., 2014]] ), and the Brahmaputra River in Bangladesh ( [[#Philip--2019|Philip et al., 2019]] ). Findings about anthropogenic influences vary between different regions and basins. For some flood events, the probability of high floods in the current climate is lower than in a climate without an anthropogenic influence ( [[#Wolski--2014|Wolski et al., 2014]] ), while in other cases anthropogenic influence leads to more intense floods ( [[#Cho--2016|Cho et al., 2016]] ; [[#Pall--2017|Pall et al., 2017]] ; [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ; [[#Philip--2018a|Philip et al., 2018a]] ; [[#Teufel--2019|Teufel et al., 2019]] ). Factors such as land-cover change and river management can also increase the probability of high floods ( [[#Ji--2020|Ji et al., 2020]] ). These, along with model uncertainties and the lack of studies overall, suggest a ''low confidence'' in general statements to attribute changes in flood events to anthropogenic climate change. A few individual regions have been well studied, which allows for ''high confidence'' in the attribution of increased flooding in these cases. For example, flooding in the UK following increased winter precipitation ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Kay--2018|Kay et al., 2018]] ) can be attributed to anthropogenic climate change ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Vautard--2016|Vautard et al., 2016]] ; [[#Yiou--2017|Yiou et al., 2017]] ; [[#Otto--2018b|Otto et al., 2018b]] ).

Attributing changes in heavy precipitation to anthropogenic activities ( [[#11.4.4|Section 11.4.4]] ) cannot be readily translated to attributing changes in floods to human activities, because precipitation is only one of the multiple factors, albeit an important one, that affect floods. For example, [[#Teufel--2017|Teufel et al. (2017)]] showed that, while human influence increased the odds of the flood-producing rainfall for the 2013 Alberta flood in Canada, it was not detected to have influenced the probability of the flood itself. [[#Schaller--2016|Schaller et al. (2016)]] showed that human influence on the increase in the probability of heavy precipitation translated linearly into an increase in the resulting river flow of the Thames in the UK in winter 2014, but its contribution to the inundation was inconclusive.

[[#Gudmundsson--2021|Gudmundsson et al. (2021)]] compared the spatial pattern of the observed regional trends in high river flows (&gt;90th percentile) over 1971–2010 with that simulated by global hydrological models. The hydrological models were driven by outputs of climate model simulations under all historical forcing and pre-industrial forcing conditions. They found complex spatial patterns of extreme river flow trends. They also found the observed spatial patterns of trends can be reproduced only if anthropogenic climate change is considered, and that simulated effects of water and land management cannot reproduce the observed spatial pattern of trends. As there is only one study and multiple caveats associated with the study, including relatively poor observational data coverage, there is ''low confidence'' about human influence on the changes in high river flows on the global scale.

In summary there is ''low confidence'' in the human influence on the changes in high river flows on the global scale. In general, there is ''low confidence'' in attributing changes in the probability or magnitude of flood events to human influence because of a limited number of studies, differences in the results of these studies and large modelling uncertainties.

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=== 11.5.5 Future Projections ===

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The SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) stressed the low availability of studies on flood projections under different emissions scenarios, and concluded that there was ''low confidence'' in projections of flood events given the complexity of the mechanisms driving floods at the regional scale. The AR5 WGII report (Chapter 3, [[#Jimenez%20Cisneros--2014|Jimenez Cisneros et al., 2014]] ) assessed with ''medium confidence'' the pattern of future flood changes, including flood hazards increasing over about half of the globe (parts of southern and South East Asia, tropical Africa, north-east Eurasia, and South America) and flood hazards decreasing in other parts of the world, despite uncertainties in GCMs and their coupling to hydrological models. The SR1.5 (Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed with ''medium confidence'' that global warming of 2°C would lead to an expansion of the fraction of global area affected by flood hazards, compared to conditions at 1.5°C of global warming, as a consequence of changes in heavy precipitation.

The majority of new studies that produce future flood projections based on hydrological models do not typically consider aspects that are also important to actual flood severity or damages, such as flood prevention measures ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Şen--2018|Şen, 2018]] ), flood control policies ( [[#Barraqué--2017|Barraqué, 2017]] ), and future changes in land cover (see also [[IPCC:Wg1:Chapter:Chapter-8#8.4.1.5|Section 8.4.1.5]] ). At the global scale, [[#Alfieri--2017|Alfieri et al. (2017)]] used downscaled projections from seven GCMs as input to drive a hydrodynamic model. They found successive increases in the frequency of high floods in all continents except Europe, associated with increasing levels of global warming (1.5°C, 2°C, 4°C). These results are supported by [[#Paltan--2018|Paltan et al. (2018)]] , who applied a simplified runoff aggregation model forced by outputs from four GCMs. S. [[#Huang--2018|]] [[#Huang--2018|Huang et al. (2018)]] used three hydrological models forced with bias-adjusted outputs from four GCMs to produce projections for four river basins including the Rhine, Upper Mississippi, Upper Yellow, and Upper Niger under 1.5°C, 2°C, and 3°C global warming. This study found diverse projections for different basins, including a shift towards earlier flooding for the Rhine and the Upper Mississippi, a substantial increase in flood frequency in the Rhine only under the 1.5°C and 2°C scenarios, and a decrease in flood frequency in the Upper Mississippi under all scenarios.

At the continental and regional scales, the projected changes in floods are uneven in different parts of the world, but there is a larger fraction of regions with an increase than with a decrease over the 21st century ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Dankers--2014|Dankers et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Döll--2018|Döll et al., 2018]] ). These results suggest ''medium confidence'' in flood trends at the global scale, but ''low confidence'' in projected regional changes. Increases in flood frequency or magnitude are identified for south-eastern and northern Asia and India ( ''high agreement'' across studies), eastern and tropical Africa, and the high latitudes of North America ( ''medium agreement'' ), while decreasing frequency or magnitude is found for central and eastern Europe and the Mediterranean ( ''high confidence'' ), and parts of South America, southern and central North America, and south-west Africa ( ''low confidence'' ) ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Dankers--2014|Dankers et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Döll--2018|Döll et al., 2018]] ). Over South America, most studies based on global and regional hydrological models show an increase in the magnitude and frequency of high flows in the western Amazon ( [[#Guimberteau--2013|Guimberteau et al., 2013]] ; [[#Langerwisch--2013|Langerwisch et al., 2013]] ; [[#Sorribas--2016|Sorribas et al., 2016]] ; [[#Zulkafli--2016|Zulkafli et al., 2016]] ) and the Andes ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Bozkurt--2018|Bozkurt et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] provides a detailed assessment of regional flood projections.

In summary, global hydrological models project a larger fraction of land areas to be affected by an increase in river floods than by a decrease in river floods ( ''medium confidence'' ). There is ''medium confidence'' that river floods will increase in the western Amazon, the Andes, and south-eastern and northern Asia. Regional changes in river floods are more uncertain than changes in pluvial floods because complex hydrological processes and forcings are involved, including land cover change and human water management.

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