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  <front>
    <journal-meta><journal-id journal-id-type="publisher">NHESS</journal-id><journal-title-group>
    <journal-title>Natural Hazards and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">NHESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Nat. Hazards Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1684-9981</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/nhess-19-1319-2019</article-id><title-group><article-title>Flood risk in a range of spatial perspectives – <?xmltex \hack{\break}?> from global to local scales</article-title><alt-title>Flood risk in a range of spatial perspectives – from global to local scales</alt-title>
      </title-group><?xmltex \runningtitle{Flood risk in a range of spatial perspectives -- from global to local scales}?><?xmltex \runningauthor{Z.~W.~Kundzewicz et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2 aff3">
          <name><surname>Kundzewicz</surname><given-names>Zbigniew W.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff4">
          <name><surname>Su</surname><given-names>Buda</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Wang</surname><given-names>Yanjun</given-names></name>
          <email>yjwang78@163.com</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Guojie</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Wang</surname><given-names>Guofu</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Huang</surname><given-names>Jinlong</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Jiang</surname><given-names>Tong</given-names></name>
          <email>jiangtong@nuist.edu.cn</email>
        <ext-link>https://orcid.org/0000-0001-8254-4236</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Institute for Disaster Risk Management (iDRM), School of Geographical Sciences, <?xmltex \hack{\break}?> Nanjing University of Information
Science and Technology (NUIST), Nanjing, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Institute for Agricultural and Forest Environment, Polish Academy
of Sciences, Poznań, Poland</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Potsdam Institute for Climate Impact Research, Potsdam, Germany</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>National Climate Center, China Meteorological Administration,
Beijing, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Tong Jiang (jiangtong@nuist.edu.cn) and Yanjun Wang (yjwang78@163.com)</corresp></author-notes><pub-date><day>5</day><month>July</month><year>2019</year></pub-date>
      
      <volume>19</volume>
      <issue>7</issue>
      <fpage>1319</fpage><lpage>1328</lpage>
      <history>
        <date date-type="received"><day>9</day><month>November</month><year>2018</year></date>
           <date date-type="rev-request"><day>4</day><month>December</month><year>2018</year></date>
           <date date-type="rev-recd"><day>30</day><month>April</month><year>2019</year></date>
           <date date-type="accepted"><day>8</day><month>June</month><year>2019</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2019 Zbigniew W. Kundzewicz et al.</copyright-statement>
        <copyright-year>2019</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019.html">This article is available from https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e161">The present paper examines flood risk (composed of hazard, exposure, and
vulnerability) in a range of spatial perspectives – from the global to the
local scale. It deals with observed records, noting that flood damage has
been increasing. It also tackles projections for the future, related to
flood hazard and flood losses. There are multiple factors driving flood
hazard and flood risk and there is a considerable uncertainty in our
assessments, and particularly in projections for the future. Further, this
paper analyses options for flood risk reduction in several spatial
dimensions, from global framework to regional to local scales. It is
necessary to continue examination of the updated records of flood-related
indices, trying to search for changes that influence flood hazard and flood
risk in river basins.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e173">River flooding is a major natural disaster, manifesting itself at a range of
spatial and temporal scales – from floods on large international rivers
conveying huge masses of water (cubic kilometres) lasting over weeks or
months to potentially violent, destructive, and killing inundations in
small, often urban basins lasting hours. It is estimated that, globally,
floods constitute 43 % of the total number of natural disasters and 47 %
of all weather-related disasters, affecting 2.3 billion people in 1995–2015,
with total damage of the order of USD 662 billion. About 800 million
people worldwide are currently living in flood-prone areas and about 70 million of those people are, on average, exposed to floods each year
(UNISDR, 2015).</p>
      <p id="d1e176">The nature of disastrous floods seems to have changed, in recent decades,
with increasing frequency and amplitude of heavy precipitation and flash and
urban floods, as well as acute riverine and coastal flooding. The climate
track in flood hazard is complex and not ubiquitous (see Sect. 2).
Urbanization and sealing of ground surface have significantly increased
surface water runoff in many areas. In some countries, recurrent flooding of
cropland has taken a heavy toll in terms of lost agricultural production,
food shortages, interrupted food supplies, and undernutrition. However, some
deleterious impacts of floods are preventable or at least can be reduced
because of the opportunity of primary prevention through existing, and – in
many places – affordable, technologies such as early warning systems and
some flood defences, while awareness raising and education can also be
effective in protecting people from adverse impact of floods.</p>
      <p id="d1e179">The spatial perspective on floods ranges from a global view by
multinational stakeholders, international organizations, reinsurance
institutions, and think tanks, interested in global affairs to regional
(group of countries and river basins which cross national borders, where 40 %
of the global population live and where transboundary water issues should be
addressed), national, and subnational (river basins) scales. The local
point of view is, for instance, that of the family of a person who lost their
life in the flood, of a family<?pagebreak page1320?> that lost their house or workplace in the
flood, or of persons responsible for local flood protection. The local scale
pertains to the locality and community in flood-prone areas, where flood
damage incurred and/or where implementation of a flood risk reduction
measure is planned. The global consideration may include aggregation of
observation records, model-based projections, and international
policies aimed at flood risk reduction.</p>
      <p id="d1e182">In the present paper, reviewing flood risk in a range of spatial
perspectives (from global to local), we start from examination of observed
records, noting that flood damage has been increasing. Further, we discuss
projections for the future – flood hazard and flood losses, and then review
flood risk reduction strategies, starting from the global framework to
regional to local scales.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Observed records – flood damage has been increasing</title>
      <p id="d1e193">European Academies' Science Advisory Council (see EASAC, 2018) presented
the trends in the number of different types of natural catastrophes
worldwide in 1980–2016 (with 1980 levels set at 100 %), based on the data
from Munich Re NatCatSERVICE. The number of hydrological events (floods and
mass movements) has increased much more than the number of geophysical,
meteorological and climatic events. The number of hydrological events in an
average year has now more than quadrupled since 1980 (exceeds 500 % in
some years). Global damage caused by “hydrological events”, according to Munich Re, has been growing, albeit with strong inter-annual variability (Fig. 1).
The named hurricanes, such as the most costly three that occurred in the
North Atlantic in just 4 weeks, Harvey in August 2017 as well as Irma
and Maria (September 2017), are counted as meteorological events.
However, the vast majority of the total damage (approximately USD 95 billion) caused by Hurricane Harvey was related to flooding. This hurricane,
which counts as the second-costliest on record (after Katrina), dropped record
levels of rain that inundated the city of Houston, Texas, USA. If the damage
caused by flooding related to Harvey were counted in Fig. 1, the year 2017
would likely be the outstanding one, with the highest flood damage ever.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><label>Figure 1</label><caption><p id="d1e198">Global damage by hydrological events, in billions of US dollars (source:
Munich Re NatCatSERVICE).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019-f01.png"/>

      </fig>

      <p id="d1e207">Flood risk can be assumed to depend on flood hazard, flood exposure, and
flood vulnerability, which, in turn, are driven by a complex interplay of
climate system, terrestrial and hydrological system, and
socio-economic system (Fig. 2). Kundzewicz et al. (2014) indicated that
increasing exposure of population and assets has been primarily responsible
for the recent increase in flood losses.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><label>Figure 2</label><caption><p id="d1e213">Conceptual sketch of components of flood risk and its drivers (after Kundzewicz et al., 2018c, modified).</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/19/1319/2019/nhess-19-1319-2019-f02.png"/>

      </fig>

      <p id="d1e222">Economic losses in monetary units (adjusted for inflation and PPP,
i.e. purchase power parity) caused by floods have been on the rise at any spatial
scale. They are higher, in absolute terms, in industrialized countries,
while relative economic losses expressed as a proportion of gross domestic product (GDP) and fatality
rates are higher in less developed countries. This has grave security
implications. This observation holds for natural disasters in general. From 1970 to 2008, over 95 % of natural-disaster-related deaths occurred in
developing countries (Field et al., 2012).</p>
      <p id="d1e225">Typically, disaster losses associated with hydrological extremes can be well
buffered in high-income countries (accounting for less than 0.1 % of GDP),
while being much higher, considerably exceeding 1 % of GDP, in small
exposed and less developed countries (Field et al., 2012).</p>
      <p id="d1e228">Several factors may explain a perceived increase in flood risk:
<list list-type="bullet"><list-item>
      <p id="d1e233">higher frequency and/or intensity of flood events;</p></list-item><list-item>
      <p id="d1e237">increased exposure of population and assets;</p></list-item><list-item>
      <p id="d1e241">increase in property value;</p></list-item><list-item>
      <p id="d1e245">generally, degraded awareness about natural risks, due to a less natural lifestyle;</p></list-item><list-item>
      <p id="d1e249">increased vulnerability; and</p></list-item><list-item>
      <p id="d1e253">improved and expanded reporting of disasters (sometimes called the “CNN effect”).</p></list-item></list></p>
      <p id="d1e256">We listed vulnerability increase as one of factors that may explain risk
increase, but this holds for some areas only. In general, there is a
significant decrease in vulnerability at the global scale (see Kundzewicz et
al., 2014; Jongman et al., 2015), largely due to developments in China, and
“vertical urbanization” in particular. Many examples of decreasing
vulnerability at the local scale have been reported (e.g. Di Baldassarre
et al., 2015; Mechler and Bouwer, 2015; Wind et al., 1999; Kreibich et
al., 2017).</p>
      <p id="d1e259">There are countries in the world (see Kundzewicz et al., 2014), where more
than 10 % of the population and/or more than 10 % of the gross domestic
product (GDP) were exposed to floods in an average year. In absolute terms,
the highest number of people exposed was in India and Bangladesh (over 10 million each), and then in China, Vietnam, and Cambodia, while the highest amount
of GDP exposed was in the USA and China (over USD 10 billion per year in
each country), while in India and Bangladesh, it was nearly USD 10 billion. In relative terms, the highest percentage of people exposed was in
Bangladesh and Cambodia (each over 10 % of the total population), and then in
Vietnam, while the highest relative share of economy exposed to floods was
estimated in Cambodia and Bangladesh (over 10 % in each country), and then in
Vietnam.</p>
      <p id="d1e263">Dartmouth Flood Observatory (<uri>http://floodobservatory.colorado.edu/</uri>, last access: 28 June 2019) has
been compiling information about large floods, worldwide, since 1985. A
short list of the most deadly floods (including coastal surges) according to the
Dartmouth Flood Observatory is presented in Table 1. Among the main causes
of the most destructive<?pagebreak page1321?> floods (with more than 1000 fatalities per event)
were tropical and extra-tropical cyclones, monsoonal rains, tropical
storms, torrential rains, heavy rains, tsunamis, coastal surges, and typhoons.
Floods with a heavy human toll were recorded in many locations in Asia
(India, China, Bangladesh, Philippines, Afghanistan, Pakistan, Japan,
Myanmar), Central and South America (Honduras, Venezuela, Dominican
Republic, Haiti, Salvador, Nicaragua, Costa Rica), and Africa (Tanzania and
Sudan).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e272">Six most deadly floods (including coastal surges) worldwide since 1985. Information from Dartmouth Flood Observatory.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Countries</oasis:entry>
         <oasis:entry colname="col2">Flood</oasis:entry>
         <oasis:entry colname="col3">Flood end</oasis:entry>
         <oasis:entry colname="col4">Dead</oasis:entry>
         <oasis:entry colname="col5">Main cause</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">beginning</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M1" display="inline"><mml:mo>(</mml:mo></mml:math></inline-formula>thousand<inline-formula><mml:math id="M2" display="inline"><mml:mo>)</mml:mo></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5"/>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Thailand</oasis:entry>
         <oasis:entry colname="col2">26 Dec 2004</oasis:entry>
         <oasis:entry colname="col3">29 Dec 2004</oasis:entry>
         <oasis:entry colname="col4">160</oasis:entry>
         <oasis:entry colname="col5">Coastal surge</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Bangladesh</oasis:entry>
         <oasis:entry colname="col2">29 Apr 1991</oasis:entry>
         <oasis:entry colname="col3">10 May 1991</oasis:entry>
         <oasis:entry colname="col4">138</oasis:entry>
         <oasis:entry colname="col5">Tropical cyclone</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Burma</oasis:entry>
         <oasis:entry colname="col2">3 May 2008</oasis:entry>
         <oasis:entry colname="col3">25 May 2008</oasis:entry>
         <oasis:entry colname="col4">100</oasis:entry>
         <oasis:entry colname="col5">Tropical cyclone</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Venezuela, Colombia</oasis:entry>
         <oasis:entry colname="col2">15 Dec 1999</oasis:entry>
         <oasis:entry colname="col3">20 Dec 1999</oasis:entry>
         <oasis:entry colname="col4">20</oasis:entry>
         <oasis:entry colname="col5">Brief torrential rain</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Honduras, Panama</oasis:entry>
         <oasis:entry colname="col2">24 Oct 1998</oasis:entry>
         <oasis:entry colname="col3">5 Nov 1998</oasis:entry>
         <oasis:entry colname="col4">11</oasis:entry>
         <oasis:entry colname="col5">Brief torrential rain</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">India</oasis:entry>
         <oasis:entry colname="col2">29 Oct 1999</oasis:entry>
         <oasis:entry colname="col3">12 Nov 1999</oasis:entry>
         <oasis:entry colname="col4">9.8</oasis:entry>
         <oasis:entry colname="col5">Tropical cyclone</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e451">Frequency and intensity of heavy precipitation have grown in many, but not
all, areas of the globe. However, no gauge-based evidence has been found so
far for a clear, widespread, and consistent change in the magnitude and/or
frequency of river floods (see Kundzewicz et al., 2005; Madsen et al.,
2014). Lins and Slack (1999) found that, hydrologically, the conterminous USA had been getting wetter, but less extreme. Later, they (Lins and Slack,
2005) confirmed the pattern of increasing discharge in the low to moderate
range of river flows, without a concomitant increase in flooding. Relatively
few trends in the annual maximum flow were detected. Hodgkins et al. (2017)
examined climate-driven variability in the occurrence of major floods across
North America and Europe, in minimally altered catchments (to eliminate
major non-climatic effects), finding that the number of significant trends
was approximately equal to the number expected due to chance alone. Shaw and
Riha (2011) studied three watersheds in different physiographic regions of
New York state, USA, and concluded that 20 % or less of annual maximum
streamflows were associated with the annual maximum rainfall events, another
20 % with the annual maximum snowmelt events, and 60 % with
moderate rainfall amounts and very wet soil conditions. Noting that it has
not been possible to find ubiquitous flood hazard changes in observation
records in Europe, so far, Kundzewicz et al. (2018c) detected an increasing
trend in the number of large floods, even if the natural variability dominates. It is likely that temporally varying connections exist between
indices of climate variability and variability of the likelihood of
destructive abundance of water. Blöschl et al. (2017) noted no
“consistent climate change signal in flood magnitudes” in Europe, while
Di Baldassarre et al. (2010) reported a similar finding for Africa.</p>
      <p id="d1e454">Blöschl et al. (2017) found climate-induced patterns of change in
observed flood timing in Europe, at the continental scale. They detected
earlier spring snowmelt floods throughout northeastern Europe (warming-driven change),
later winter floods around the North Sea and part of the Mediterranean coast
(related to polar warming), and earlier winter floods in western Europe (reflecting
advancement of soil moisture maxima). In contrast, Lins and Slack (2005)
detected no systematic shift in the timing of the maximum flow in any US region on a monthly timescale.</p>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Projections for the future – flood hazard and flood damage</title>
      <p id="d1e465">Climate projections show ubiquitous warming for all seasons and most models
project an increase in intense precipitation. Seneviratne et al. (2012)
presented regional projections of<?pagebreak page1322?> 20-year 24 h precipitation, noting
increases over virtually all regions of the globe.</p>
      <p id="d1e468">There have been several global studies of model-based projections of flood
hazard, starting from Milly et al. (2002), who covered selected basins
worldwide, and Hirabayashi et al. (2008), who covered the global scale. It
is worthwhile to compare four more recent papers, published since 2013 by
Hirabayashi et al. (2013), Dankers et al. (2014), Arnell and Gosling (2014),
and Giuntoli et al. (2015). Table 2 presents assumptions made in the global
projection endeavours that considerably differ among studies (there are also
slightly different reference periods).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2" specific-use="star"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e474">Assumptions made in model-based global flood hazard projection
studies.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="6">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:colspec colnum="4" colname="col4" align="left"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:colspec colnum="6" colname="col6" align="left"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Paper</oasis:entry>
         <oasis:entry colname="col2">Number of</oasis:entry>
         <oasis:entry colname="col3">Number of</oasis:entry>
         <oasis:entry colname="col4">Variable</oasis:entry>
         <oasis:entry colname="col5">Time</oasis:entry>
         <oasis:entry colname="col6">Emission</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">climate</oasis:entry>
         <oasis:entry colname="col3">hydrological</oasis:entry>
         <oasis:entry colname="col4">of interest</oasis:entry>
         <oasis:entry colname="col5">horizon of</oasis:entry>
         <oasis:entry colname="col6">scenario</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">model</oasis:entry>
         <oasis:entry colname="col3">models</oasis:entry>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5">concern</oasis:entry>
         <oasis:entry colname="col6"/>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">scenarios</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Arnell and</oasis:entry>
         <oasis:entry colname="col2">21 GCMs</oasis:entry>
         <oasis:entry colname="col3">One: Mac-PDM.09</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M3" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mn mathvariant="normal">100</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5">2050s</oasis:entry>
         <oasis:entry colname="col6">SRES A1B</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Gosling (2014)</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Dankers et al.</oasis:entry>
         <oasis:entry colname="col2">Five GCMs</oasis:entry>
         <oasis:entry colname="col3">Nine GHMs</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M4" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mn mathvariant="normal">30</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5">2070–2099</oasis:entry>
         <oasis:entry colname="col6">RCP8.5</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">(2014)</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Giuntoli et al.</oasis:entry>
         <oasis:entry colname="col2">Five GCMs</oasis:entry>
         <oasis:entry colname="col3">Six GHMs</oasis:entry>
         <oasis:entry colname="col4">Frequency</oasis:entry>
         <oasis:entry colname="col5">2066–2099</oasis:entry>
         <oasis:entry colname="col6">RCP8.5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">(2015)</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">of high-flow</oasis:entry>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">days</oasis:entry>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Hirabayashi et</oasis:entry>
         <oasis:entry colname="col2">11 GCMs</oasis:entry>
         <oasis:entry colname="col3">One CaMa-Flood</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M5" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mn mathvariant="normal">100</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5">2071–2100</oasis:entry>
         <oasis:entry colname="col6">RCP8.5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">al. (2013)</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">model</oasis:entry>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e784">Projections by Hirabayashi et al. (2013) indicate that what used to be a
100-year flood in the control period in many areas is likely to occur much
more frequently in the future, under changed climate, with a return period of
50 years and less. Hirabayashi et al. (2013) project an increase in hazard
(<inline-formula><mml:math id="M6" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mn mathvariant="normal">100</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>) in most of Asia (except for western Asia) and in particular eastwards of 80<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E. They also project flood hazard to increase in
central Africa in the latitude range 20<inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> S–10<inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N and in Central and
South America from 20<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N to 40<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> S, as well as in the north of North
America and on the east coast of the USA. For most of Europe, a decrease in flood
hazard is projected. Results of Dankers et al. (2014) referring to a
different index, <inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mn mathvariant="normal">30</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (30-year 5 d peak flow), are broadly similar to those
reported by Hirabayashi et al. (2013) as to the direction of change, except
for a large area of decrease in hazard in South America. In turn, Giuntoli
et al. (2015) project more frequent days with high river flow conditions
over much of the north, from 50<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N northwards. However, over most of the
area of continents, they projected rather small changes, with an absolute value
less than 5 % (i.e. from <inline-formula><mml:math id="M14" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> % to <inline-formula><mml:math id="M15" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> %).</p>
      <p id="d1e884">Studies of large-scale projections of changes in flood hazard illustrate a
considerable degree of uncertainty. This is no wonder, as projections were
determined for different assumptions (see Table 2). They may differ with
respect to (see Kundzewicz et al., 2018a, b)
<list list-type="bullet"><list-item>
      <p id="d1e889">greenhouse gas emissions scenarios (SRES, RCP);</p></list-item><list-item>
      <p id="d1e893">driving climate models, general circulation models (GCMs), and regional climate models (RCMs);</p></list-item><list-item>
      <p id="d1e897">downscaling techniques and bias correction methods;
<?xmltex \hack{\newpage}?></p></list-item><list-item>
      <p id="d1e902">performance of large-scale hydrological models, i.e. global hydrological models (GHMs) and regional hydrological models (RHMs);</p></list-item><list-item>
      <p id="d1e906">climate and hydrological model resolution;</p></list-item><list-item>
      <p id="d1e910">time horizons of future projections;</p></list-item><list-item>
      <p id="d1e914">reference (historic) intervals;</p></list-item><list-item>
      <p id="d1e918">return period (recurrence interval) of concern;</p></list-item><list-item>
      <p id="d1e922">low-temperature effects, e.g. snow and ice component in models;</p></list-item><list-item>
      <p id="d1e926">general problems related to simulation of extremes and extreme value
techniques applied to time series that are not long enough.</p></list-item></list></p>
      <p id="d1e929">The implications of the changing flood hazard for human society depend on the
size of the population at risk of flooding. Under the assumption of a fixed
population (at the level of the scenario from 2005), it was projected that
annual global flood exposure would increase by a factor of <inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:mn mathvariant="normal">4</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:math></inline-formula> (under
RCP2.6), <inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:mn mathvariant="normal">7</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> (RCP4.5), <inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:mn mathvariant="normal">7</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">6</mml:mn></mml:mrow></mml:math></inline-formula> (RCP6.0), and <inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:mn mathvariant="normal">14</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula> (RCP8.5) from the 20th to 21st century (Hirabayashi et al., 2013).
However, such results have to be interpreted with caution, especially
considering changing adaptation and risk reduction capacity.</p>
      <p id="d1e980">Where both rain floods and snow floods (as well as ice-jam floods) can
influence projections, relevant processes and different mechanisms have to
be examined, for present and future conditions.</p>
      <p id="d1e983">In addition, future flood risk in coastal zones will increase due to the sea
level rise (Paprotny and Terefenko, 2017). Taking into account both the
socioeconomic pathways and climate change but in absence of further
investments in adaptation, Vousdoukas et al. (2018) projected the annual
damage caused by coastal flooding in Europe to increase from the current EUR 1.25 billion to EUR 93–961 billion by the end of the 21st
century, and the exposed population to increase from the current level of
0.1 million to 1.52–3.65 million.</p><?xmltex \hack{\newpage}?>
</sec>
<?pagebreak page1323?><sec id="Ch1.S4">
  <label>4</label><title>Flood risk reduction – global framework</title>
      <p id="d1e995">Efforts on flood risk reduction are embedded in the general global
framework, including major documents – Hyogo Framework for Action and
Sendai Framework for Disaster Risk Reduction.</p>
      <p id="d1e998">“Tragedies will continue to be repeated if we do not address water and
disaster issues at all levels”, stated Han Seung-soo, the founding
chair of High-Level Experts and Leaders' Panel on Water and Disaster (HELP) (<uri>https://www.unisdr.org/archive/58108</uri>, last access: 28 June 2019), while the UN special
representative for disaster risk reduction, Mami Mizutori, remarked that
floods which now account for half of all weather-related disasters
highlight how disaster risk reduction is both a long-term development issue
and a necessary strategy to prevent disasters and save lives in the short to
medium term.</p>
      <p id="d1e1004">The World Conference on Disaster Reduction held in Hyogo, Japan, in 2005,
promoting a strategic and systematic approach to reducing vulnerabilities
and risks to hazards, adopted the Framework for Action 2005–2015,
identifying ways of building the resilience of nations and communities to
disasters (UNISDR, 2007).</p>
      <p id="d1e1007">Disaster loss has been on the rise with grave adverse consequences for the
survival, dignity, and livelihood of people, particularly of the poor, and
for hard-won development gains. Disaster risk is increasingly of global
concern and a flood occurrence in one region can have an impact on risk in
another one (e.g. via broken production links that manifested themselves
during and after the 2011 Thailand flood). The Hyogo Framework identified
specific gaps and challenges in the following main areas:
organizational, legal, and policy frameworks in governance; risk identification,
assessment, monitoring, and early warning; knowledge management and
education; reduction of underlying risk factors; and preparedness for effective
response and recovery.</p>
      <p id="d1e1011"><?xmltex \hack{\newpage}?>Disaster risk reduction can be regarded as a cross-cutting issue in the
realm of sustainable development and therefore an important element for the
achievement of internationally agreed Millennium Development Goals.</p>
      <p id="d1e1015">The global plan for reducing disaster losses, the Sendai Framework for
Disaster Risk Reduction, 2015–2030, was adopted by UN member states in 2015,
at the Third UN World Conference on Disaster Risk Reduction in Sendai, Japan
(<uri>https://www.unisdr.org/we/coordinate/sendai-framework</uri>, last access: 28 June 2019). It is a voluntary,
non-binding agreement aimed at a substantial reduction of disaster risk and
losses in lives, livelihoods, health, and assets. It emphasizes the
importance of risk-informed investment in critical infrastructure, including
water facilities, to avoid the creation of new risk. Disaster risk reduction
and prevention should be integrated in long-term national planning and
education on disaster risk must be advanced. Recognizing the state's primary
role to reduce disaster risk but also noting that responsibility should be
shared with stakeholders, the Sendai Framework agreement, aiming to make a
difference for poverty, health, and resilience, is the major document of the
recent development agenda, embracing seven targets and four priorities for
action.</p>
      <p id="d1e1021">The global targets include substantial reduction of mortality in flood
disasters and the number of affected people and reduction of direct economic
loss and damage to critical infrastructure as well as disruption of basic
services (among them health and educational facilities), including
enhancing resilience (recovery). The targets also include work on national and
local disaster risk reduction strategies, international cooperation, and
increasing the availability of and access to early warning systems (also
dedicated to multiple hazards) and disaster risk information and
assessments. Timelines for achieving these targets and reference intervals
for measuring the progress were defined.</p>
      <?pagebreak page1324?><p id="d1e1024">The priorities for action refer to understanding of disaster risk in its
dimensions of vulnerability, capacity, exposure of persons and assets,
hazard characteristics, and the environment. Such knowledge can be used for
risk assessment, as well as for various flood risk reduction strategies
prevention, mitigation, preparedness and response, recovery and
rehabilitation (see Dieperink et al., 2016; Driessen et al., 2016; Hegger et
al., 2016; Kundzewicz et al., 2018b). Strengthening disaster risk
governance at a range of levels (national, regional, and global) is another
priority. Also investing in disaster risk reduction to enhance the economic,
social, health, and cultural resilience of persons, communities, countries,
and their assets as well as the environment is an identified priority. So
is also enhancing disaster preparedness for effective response and
“building back better”. Disaster risk reduction has to be integrated into
sustainable development measures.</p>
      <p id="d1e1027">Willner et al. (2018) computed the increase in flood protection that would
be required worldwide for subnational administrative units, in order to maintain
the historic high-end fluvial flood risk in the next 25 years. They found
that most of the USA, central Europe, and northeast and west
Africa, as well as large parts of India and Indonesia, require strong
adaptation effort. For example, according to the results of this paper,
flood protection needs to at least double over more than half of the United
States within the next 2 decades.</p>
      <p id="d1e1030">However, the increase in flood protection levels to meet the requirements
posed by Willner et al. (2018) would lead to having even more levees, which
attract even more people and assets in flood-prone areas (that are often
assumed to be perfectly safe by inhabitants). Since the seminal work of
Gilbert White in the 1940s (White, 1945), many authors reported on
the safe-development paradox, residual risk, and adverse levee effects
(e.g. Kates et al., 2006; Ludy and Kondolf, 2012; Di Baldassarre et al., 2014). It
has been shown that the introduction or reinforcement of structural
protection measures is often associated with negative effects. Such effects
include increasing exposure to flooding (Kates et al., 2006) and increasing
vulnerability to flooding (as protected flood-prone areas are perceived as
safer, so that inhabitants have fewer incentives to take individual
precautionary measures; see Ludy and Kondolf, 2012). There is a social
injustice effect – structural flood protection measures may alter the
spatial distribution of risk in a way that affects less privileged social
groups (Di Baldassarre et al., 2014). People in structurally protected areas
are less willing to relocate from risky areas (Mård et al., 2018).
Furthermore, levees that prevent natural inundation of floodplains also
adversely affect biodiversity and ecological functions (Auerswald et al.,
2019), e.g. via elimination of a “flood pulse”.</p>
</sec>
<sec id="Ch1.S5">
  <label>5</label><title>Flood risk reduction – from regional to local scales</title>
      <p id="d1e1042">There is no doubt that flood risk has grown in many places and is likely to
grow further in the future, due to a combination of anthropogenic and
climatic factors. Intense precipitation grows in the warming climate.
However, reliable and detailed quantification of aggregate flood statistics
is very difficult to obtain for the past to present and is virtually
impossible to obtain for the future. Nevertheless, despite the lack of
reliable projections, flood risk reduction endeavours have been carried out
at a range of scales, from regional (multi-national) to national,
subnational, and local.</p>
      <p id="d1e1045">At the subcontinental scale, the European Union (EU) passed a dedicated
directive, 2007/60/EC, on the assessment and management of flood risks (EU, 2007), which required all EU member states (28 at present) to identify areas
at risk from flooding, to map the flood extent as well as assets and humans
at risk in these areas, and to take adequate and coordinated measures to
reduce this flood risk. This directive also reinforces the rights of the
public to access information and to participate in the planning process. The
directive aims to reduce and manage the risks that floods pose to human
health, economic activity, environment, and cultural heritage. The directive
required EU member states to establish flood risk management plans focused
on prevention, protection, and preparedness by 2015.</p>
      <p id="d1e1048">Presence of people and wealth in flood-prone areas can be regarded like an
illness. One can prevent flooding (“illness”) by keeping the destructive water away
from people and proceeding with flood defences. This cures the
symptoms of the illness. One can also keep people away from the destructive
water by way of zoning and banning floodplain development. This cures the source of the illness. However, it is also necessary to prepare for living
with floods. This embraces flood mitigation – keeping water where it falls,
flood preparation – forecasting, warning, and preparation for
evacuation and post-flood recovery (see Dieperink et al., 2016; Driessen
et al., 2016; Hegger et al., 2016; Nieland and Mushtaq, 2016, Kundzewicz et
al., 2018b).</p>
      <p id="d1e1051">Since it is naïve to expect availability of trustworthy quantitative
projections of future flood hazard (as some practitioners clearly do), in
order to reduce flood risk, one should focus attention on identification of
existing risk and vulnerability hotspots and improve the situation in areas
where such hotspots occur (Kundzewicz et al., 2017).</p>
      <p id="d1e1055">The prerequisite for flood risk reduction is to examine long time series of
reliable records on flood-related information. Koç and Thieken (2018)
carried out a comparative national review of information on floods in Turkey
from three sources: Turkey Disaster Database (TABB), the Emergency Events
Database (EM-DAT), and the Global Active Archive of Large Flood
Events – Dartmouth Flood Observatory. They found large mismatches in the
flood data for Turkey, related to the number of events, the number of
affected people, and the economic loss.</p>
      <p id="d1e1058">Flood protection, i.e. adaptation to huge variability of discharge, has been
developed in China for 4 millennia, since the quasi-legendary Emperor Yu,
who established the Xia dynasty, marking the beginning of Chinese
civilization. He<?pagebreak page1325?> succeeded in taming a long-lasting and disastrous flood in
the Yellow River basin by dredging and channelling the rivers to drain the
floodwaters.</p>
      <p id="d1e1061">Flood protection has always been important in China, where hundreds of
millions of people live in river valleys. Structural measures, both dikes
and dams of different sizes, have a very long tradition in China (a term
“hydraulic civilization” was coined by Wittfogel, 1956) and continue to
play a vital role in flood prevention today, and in the foreseeable
future. The multi-objective, massive Three Gorges Dam on the river Yangtze,
the world's greatest engineering work, has flood protection as the principal
objective. Many large reservoirs, also with flood protection as the main
objective, have been built in China, with a total storage capacity in excess
of <inline-formula><mml:math id="M20" display="inline"><mml:mrow><mml:mn mathvariant="normal">0.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mn mathvariant="normal">12</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> m<inline-formula><mml:math id="M21" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula>, accounting for over one-fifth of the
total estimated annual runoff from the land areas (Guo et al., 2004).
Typically, water storage reservoirs serve multiple purposes: flood control,
hydropower, irrigation, water supply, navigation, etc. The total number of
large dams has increased significantly since 1960, when only five large dams
(higher than 100 m) existed in China. The number of large dams grew 10-fold
in 2000 (Xu et al., 2010). In the second half of the 20th century, more than
200 000 km of dikes have been strengthened for alleviating the
impacts of floods in China (Zhang et al., 2002).</p>
      <p id="d1e1088">The level of expenditure on flood protection in China has grown considerably
in recent decades. However, despite the massive efforts, it is becoming
abundantly clear that complete flood control is not possible. Even if there
are powerful levees along the rivers in China, they may not provide
satisfactory protection of riparian habitats during large floods (see Kundzewicz
and Xia, 2004). Recently, large flood damage has been increasingly occurring
on medium- and small-size rivers. Hence, improvement of flood risk
management is needed in the country and ambitious and vigorous attempts to
improve flood preparedness have already been undertaken, by both structural
(“hard”) and non-structural (“soft”) measures. The former refer to such
defences as dikes, dams and flood control reservoirs, diversions, etc. The
latter include implementing watershed management (source control), zoning,
insurance, flood forecasting–warning systems, and awareness raising
(Surminski et al., 2015; Nieland and Mushtaq, 2016; Adelekan and Asiyanbi,
2016). The coping capacities at a local level can influence the robustness
of flood warning systems (Daupras et al., 2015).</p>
      <p id="d1e1091">In many countries, flood protection is distributed among several agencies,
hence effective cooperation and communication among federal, state, and local
stakeholders is essential. This is inherently difficult, but progress has
been achieved in China in flood forecasting integration, data sharing, and
collaborative problem solving. The China Meteorological Administration (CMA)
collects observations of precipitation and other meteorological variables
and prepares precipitation forecasts. The Ministry of Water Resources (MWR)
of China collects hydrological observations (e.g. of river levels and
discharges) and is responsible for flood forecasting and dissemination of
the forecast. River basin commissions in China (altogether seven
commissions, including the Yangtze River Basin Commission) are agencies of
the MWR. The Flood Prevention Law of 2007 laid out principles and
responsibilities for flood prevention planning in China. There is a national
standard (GB50201-94) drafted by the Ministry of Water Resources and issued
by the Ministry of Construction in 1994 dealing with flood return periods
for different categories of location (Gemmer et al., 2011). In 2010, flood
hazard mapping guidelines were published as a professional standard by the
Ministry of Water Resources.</p>
      <p id="d1e1094">Gemmer et al. (2011) reviewed climate change adaptation in China, the
National Climate Change Programme and China's White Paper
“China's Policies and Actions for Addressing Climate Change”. All
34 provinces of China produced a climate change adaptation plan, including
flood risk reduction.</p>
      <p id="d1e1098">It is a well-established observation that occurrence of a disastrous flood
event in a country or a region improves awareness and triggers investment in
flood risk reduction as well as funding of relevant research. In fact, there
are many case studies that report social learning effects, one of the
findings being that the negative impact of an extreme flood tends to be
lower if such an event occurs shortly after another one (e.g. in Jongman et
al., 2015; Di Baldassarre et al., 2015; Mechler and Bouwer, 2015; Wind et
al., 1999 and Kreibich et al., 2017). Di Baldassarre et al. (2015) show
adaptation effects in study areas around the world, while Mechler and Bouwer
(2015) noted a decreasing number of flood fatalities in Bangladesh over the
past decades. Wind et al. (1999) reported that the economic losses of the
1995 Meuse River flooding were much lower than those in 1993, even though
the magnitudes of the two events were comparable. Kreibich et al. (2017)
illustrated the learning dynamics by way of multiregional, paired, flood
event studies. However, sometimes deficiencies in learning show up. Marks
and Thomalla (2017) studied consequences of the great 2011 flood in
Thailand, noting that only minor efforts to reduce flood risk were made. The
sociopolitical transformation needed to reduce system vulnerability has not
occurred. The focus was on structural defences – building flood walls to
reduce risk to large-scale enterprises, and this results in redistribution
of risk to unprotected areas.</p>
</sec>
<sec id="Ch1.S6" sec-type="conclusions">
  <label>6</label><title>Concluding remarks</title>
      <p id="d1e1109">Many studies of flood hazard projections demonstrate the likely rise of
flood hazard in the future. Plausible climate change scenarios indicate the
possibility of increases in both the frequency and the magnitude of flooding
events in many regions. Yet there has been no conclusive and general finding
as to how climate change affects flood behaviour, in light<?pagebreak page1326?> of data
observed so far, except some indications of regional changes in timing of
floods observed in some areas, with increasing late autumn and winter floods
(caused by rain) and fewer ice-jam-related floods, e.g. in Europe. The
natural variability in observation records is overwhelming.</p>
      <p id="d1e1112">The flood risk depends on a combination of anthropogenic and natural
factors, such as climate, land use, and population density and wealth
(hence damage potential) in flood risk areas and development of flood
defences. Owing to the growing population pressure, activities like
deforestation, agricultural land expansion, urbanization (and increasing
sealing of the ground surface), construction of roads, and
reclamation of wetlands and lakes have been progressing. This has reduced
the available water storage capacity in river basins, increased the value of
the runoff coefficient, and aggravated flood hazard and flood risk. Flood
potential has ubiquitously increased – there is simply more to lose.</p>
      <p id="d1e1115">There are multiple factors driving flood hazard and flood risk and there is
a considerable uncertainty in our assessments, and in particular projections
for the future. In many places flood risk is likely to grow, due to a
combination of anthropogenic and climatic factors. However, in general, it
is difficult to disentangle the climatic change component in maximum river
flow or flood hazard records from strong natural variability and direct,
synthetic, environmental changes. There is a large difference between flood
hazard projections obtained by using different scenarios and different
models. Therefore, one should be careful with flat-rate statements on
changes in flood hazard and flood risk, and on climate change impact in
particular. The impact of climate forcing on flood risk is complex and
depends on the flood generation mechanism. Indeed, higher and more intense
precipitation has already been observed in many (but not all) areas of the
globe and this trend is expected to strengthen in the warmer world, directly
impacting flood risk. Therefore, common-sense changes to design rules,
aimed at flood risk reduction, have been introduced in some countries of
Europe, based more on precautionary principle rather than on robust science.
The design flood was adjusted upward in light of projections for the warmer
climate.</p>
      <p id="d1e1118">However, it is a robust statement that, in general, today's climate models
are still not good enough at producing local climate extremes due to, inter alia,
inadequate (coarse) resolution. There is hope that, with improving
resolution, models will be able to grasp details of extreme events in a more
accurate and reliable way (Kundzewicz and Schellnhuber, 2004).</p>
      <p id="d1e1122">It is necessary to continue examination of the updated records of
flood-related indices, trying to search for changes that influence flood
hazard and flood risk in river basins. Possibly, there have been and will
continue to be changes in intense precipitation, changes in cyclone track,
changes in land use, and changes in exposure and vulnerability. Early
detection and attribution of changes at any spatial scale would be of vast
practical importance.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e1129">The co-authors used publicly available data from Munich Re NatCatSERVICE and from Dartmouth Floods Observatory.</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1135">ZWK and JT conceived the study, ZWK drafted the paper, and all co-authors jointly worked on enriching and developing the draft, also in reaction to reviewers' recommendations.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1141">The authors declare that they have no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1147">This study was supported by the National Key Research and Development Program of
China MOST (2018FY100501) and a bilateral cooperation project between the
Natural Science Foundation of China and the Pakistan Science Foundation (41661144027). The CMA Special Fund on Climatic Change Research (CCSF 201810, CCSF 201924) offered a PhD student job. The authors are thankful
for the support by the High-level Talent Recruitment Program of the Nanjing
University of Information Science and Technology (NUIST). Thanks are also
due to Munich Re NatCatSERVICE for provision of global flood loss data. The
review by the two anonymous referees, who provided many constructive and useful
comments, proved to be valuable and allowed us to enrich this paper.</p></ack><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1152">This paper was edited by Maria-Carmen Llasat and reviewed by two anonymous referees.</p>
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    <!--<article-title-html>Flood risk in a range of spatial perspectives –  from global to local scales</article-title-html>
<abstract-html><p>The present paper examines flood risk (composed of hazard, exposure, and
vulnerability) in a range of spatial perspectives – from the global to the
local scale. It deals with observed records, noting that flood damage has
been increasing. It also tackles projections for the future, related to
flood hazard and flood losses. There are multiple factors driving flood
hazard and flood risk and there is a considerable uncertainty in our
assessments, and particularly in projections for the future. Further, this
paper analyses options for flood risk reduction in several spatial
dimensions, from global framework to regional to local scales. It is
necessary to continue examination of the updated records of flood-related
indices, trying to search for changes that influence flood hazard and flood
risk in river basins.</p></abstract-html>
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