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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-23-2333-2023</article-id><title-group><article-title>Low-regret climate change adaptation in coastal megacities –
evaluating large-scale flood protection and small-scale rainwater detention
measures for Ho Chi Minh City, Vietnam</article-title><alt-title>Low-regret climate change adaptation in coastal megacities</alt-title>
      </title-group><?xmltex \runningtitle{Low-regret climate change adaptation in coastal megacities}?><?xmltex \runningauthor{L. Scheiber et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Scheiber</surname><given-names>Leon</given-names></name>
          <email>scheiber@lufi.uni-hannover.de</email>
        <ext-link>https://orcid.org/0000-0001-7989-7639</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff3">
          <name><surname>David</surname><given-names>Christoph Gabriel</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-6733-0288</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Hoballah Jalloul</surname><given-names>Mazen</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Visscher</surname><given-names>Jan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4 aff5">
          <name><surname>Nguyen</surname><given-names>Hong Quan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6 aff7">
          <name><surname>Leitold</surname><given-names>Roxana</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6 aff7">
          <name><surname>Revilla Diez</surname><given-names>Javier</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2065-1380</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Schlurmann</surname><given-names>Torsten</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Ludwig-Franzius-Institute for Hydraulics, Estuarine and Coastal
Engineering,<?xmltex \hack{\break}?> Leibniz University Hannover, 30167 Hanover, Germany</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Division of Hydromechanics, Coastal and Ocean Engineering, Leichtweiß-Institute for Hydraulic Engineering and Water
Resources, Technische Universität Braunschweig, 38106 Braunschweig, Germany</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Junior Research Group “Future Urban Coastlines”, Leichtweiß-Institute for Hydraulic Engineering and Water
Resources, Technische Universität Braunschweig, 38106 Braunschweig, Germany</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Institute for Circular Economy Development, Vietnam National
University Ho Chi Minh City,<?xmltex \hack{\break}?> 700000 Ho Chi Minh City, Vietnam</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Institute for Environment and Resources, Vietnam National
University Ho Chi Minh City,<?xmltex \hack{\break}?> 700000 Ho Chi Minh City, Vietnam</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Institute of Geography, University of Cologne, 50923 Cologne, Germany</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>Global South Studies Center, University of Cologne, 50923 Cologne, Germany</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Leon Scheiber (scheiber@lufi.uni-hannover.de)</corresp></author-notes><pub-date><day>26</day><month>June</month><year>2023</year></pub-date>
      
      <volume>23</volume>
      <issue>6</issue>
      <fpage>2333</fpage><lpage>2347</lpage>
      <history>
        <date date-type="received"><day>19</day><month>September</month><year>2022</year></date>
           <date date-type="rev-request"><day>13</day><month>October</month><year>2022</year></date>
           <date date-type="rev-recd"><day>5</day><month>May</month><year>2023</year></date>
           <date date-type="accepted"><day>13</day><month>May</month><year>2023</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2023 </copyright-statement>
        <copyright-year>2023</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/.html">This article is available from https://nhess.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e193">Urban flooding is a major challenge for many megacities
in low-elevation coastal zones (LECZs), especially in Southeast Asia. In
these regions, the effects of environmental stressors overlap with rapid
urbanization, which significantly aggravates the hazard potential. Ho Chi
Minh City (HCMC) in southern Vietnam is a prime example of this set of
problems and therefore a suitable case study to apply the concept of
low-regret disaster risk adaptation as defined by the Intergovernmental
Panel on Climate Change (IPCC). In order to explore and evaluate potential
options of hazard mitigation, a hydro-numerical model was employed to
scrutinize the effectiveness of two adaptation strategies: (1) a classic
flood protection scheme including a large-scale ring dike as currently
constructed in HCMC and (2) the widespread installation of small-scale
rainwater detention as envisioned in the framework of the Chinese Sponge
City Program (SCP). A third adaptation scenario (3) assesses the combination of both approaches (1) and (2).</p>

      <p id="d1e196">From a hydrological point of view, the reduction in various flood intensity
proxies that were computed within this study suggests that large-scale flood protection is comparable but slightly more effective than small-scale
rainwater storage: for instance, the two adaptation options could reduce the normalized flood severity index (<inline-formula><mml:math id="M1" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>), which is a measure combining flood depth and duration, by 17.9 % and 17.7 %, respectively. The
number of flood-prone manufacturing firms that would be protected after
adaptation, in turn, is nearly 2 times higher for the ring dike than for
the Sponge City approach. However, the numerical results also reveal that
both response options can be implemented in parallel, not only without
reducing their individual effectiveness but also complementarily with
considerable added value. Additionally, from a governance perspective,
decentralized rainwater storage conforms ideally to the low-regret paradigm:
while the existing large-scale ring dike depends on a binary commitment (to
build or not to build), decentralized small- and micro-scale solutions can
be implemented gradually (for<?pagebreak page2334?> example through targeted subsidies) and add
technical redundancy to the overall system. In the end, both strategies are
highly complementary in their spatial and temporal reduction in flood
intensity. Local decision-makers may hence specifically seek combined
strategies, adding to singular approaches, and design multi-faceted
adaptation pathways in order to successfully prepare for a deeply uncertain
future.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Bundesministerium für Bildung und Forschung</funding-source>
<award-id>01LZ1703H</award-id>
</award-group>
<award-group id="gs2">
<funding-source>Deutsche Forschungsgemeinschaft</funding-source>
<award-id>UP 8/1</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e219">Between 1980 and 2009, floods caused more than half a million deaths and
affected another 2.8 billion people worldwide (Doocy et al., 2013). These
figures will further increase with global sea levels rising as a consequence
of climate change (IPCC, 2019) and more than half of all urban
agglomerations (<inline-formula><mml:math id="M2" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 100 000 habitants) being closer than 100 km
from the coastline (Barragán and de Andrés, 2015). Especially in
Southeast Asia, where many megacities are located in low-elevation coastal
zones (LECZs), the risk of storm surges will significantly increase
(McGranahan et al., 2007; Neumann et al., 2015; Kulp and Strauss, 2019).
Not only will these regions be affected by more frequent extreme storm
events, but rainfall volumes will also increase as a consequence of the
urban heat island (UHI) effect fueled by sustained urban growth (IPCC,
2021). Ranked fifth place in a global assessment of future exposure to
climate extremes, Ho Chi Minh City (HCMC; Fig. 1a, b) is a prime example of the complex physical and social
interactions that exacerbate the flood risk in many Southeast Asian
metropolises (Hanson et al., 2011; Hallegatte et al., 2013; Abidin et al.,
2015). Being the largest city in Vietnam and its major economical hub, HCMC
is subject to uncontrolled urban sprawl, which in turn deepens the exposure
to flooding for a growing and oftentimes highly vulnerable population (Huong
and Pathirana, 2013; Duy et al., 2018). Flood exposure, in this context,
results from the increasing number of settlements in low-lying areas (cf.
Fig. 1c) which continuously expand due to the
humanmade problem of land subsidence resulting from soil compaction and
groundwater exploitation (Kaneko and Toyota, 2011; Erkens et al., 2015;
Duffy et al., 2020).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e231">Study area. <bold>(a, b)</bold> Located in proximity to the
Mekong Delta, Ho Chi Minh City (HCMC) is the largest city in Vietnam and a
major economical hub of Southeast Asia. Administrative boundaries and
waterbodies derived from © OpenStreetMap contributors 2022,
distributed under the Open Data Commons Open Database License (ODbL) v1.0.
<bold>(c)</bold> Informal settlements are the epitome of the uncontrolled urban sprawl that exacerbates flood risk in this region due to an interplay of increased natural hazards, exposure and vulnerability. Picture taken by the
corresponding author in September 2019.</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/2333/2023/nhess-23-2333-2023-f01.jpg"/>

      </fig>

      <p id="d1e246">In conjunction with globally rising sea levels and amplified tidal ranges in
the adjacent Saigon River, HCMC regularly experiences backwater effects in
the antiquated and, in many regards, deficient drainage system causing
widespread inundations (Phi, 2007​​​​​​​; Downes and Storch, 2014; Tran Ngoc et al.,
2016; MONRE, 2016). Apart from the immanent social implications, urban flood
events cause major and frequent impediments to the local economy, which is
estimated to cause losses of USD 48.3 billion of the gross domestic product
(GDP) in the period from 2006 to 2050 (ADB, 2010). Especially manufacturing
firms (owning, among others, lots of immovable machinery and facilities) are
expected to bear the brunt of damage and loss due to environmental hazards
(Neise et al., 2018). Already in 2015, studies suggested that more than
15 % of the manufacturing firms in HCMC were located in current or future
inundation areas. Many of these were small and medium-sized enterprises
(SMEs) that engage about 37 % of all national employees (Leitold and
Revilla Diez, 2019). Even if the city has prospered for several decades and
flood control is no longer a question of preventing casualties due to
extreme events, suitable adaptation strategies are required to reduce risks
and associated financial losses from those recurring floods in particular.</p>
      <p id="d1e250">According to the Intergovernmental Panel on Climate Change (IPCC), there
are, in general, five categories of possible adaptation options to future
risks: retreat, protection, accommodation, advance and ecosystem-based
adaptation (<ext-link xlink:href="https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/4-1synthesis/4-1-2future-sea-level-rise-and-implications-for-responses/ipcc-srocc-ch_4_box_4_3_figure_1/">https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/4-1synthesis/4-1-2future-sea-level-rise-and-implications-for-responses/ipcc-srocc-ch_4_box_4_3_figure_1/</ext-link>, last access: 12 June 2023)​​​​​​​; IPCC, 2019). These strategies can
also be differentiated into classic “grey” infrastructure and nature-based
“green” solutions (Dong et al., 2017; Morris et al., 2018). One recent
example of the latter approach is the Chinese Sponge City Program (SCP), a
framework which refines established concepts like water-sensitive urban
drainage (WSUD) and low-impact development (LID) (Jia et al., 2017; Qi et
al., 2020; Sun et al., 2020; Li and Zhang, 2022). To address the predicament
of increasing natural hazards in expanding urbanized areas, Sponge Cities
make use of the natural hydrological cycle to effectively reduce urban
flooding, harvest rainwater and improve water quality as well as restore
ecological values (Köster, 2021; Jia et al., 2022). In practice, this
can be accomplished by medium- and small-scale elements that allow for water
storage (detention basins and rain tanks), infiltration (permeable pavements
and infiltration wells) or both (public parks and rooftop/rain gardens)
(Nguyen et al., 2019; Kumar et al., 2021). Since its introduction to the
public in 2013, the Sponge City Program has proved its feasibility for
multiple real-world examples across China (Hawken et al., 2021; Yin et al.,
2022). Yet, especially its adaptability and possible small-scale
implementation make it highly attractive to other rapidly growing megacities
in Southeast Asia. Nevertheless, the aim of both grey and green adaptation
strategies should be to offer a solution that is of low regret. In the
context of the IPCC, the term “low-regret” does not necessarily refer to
financial aspects but rather aims at solutions that allow for coping with
current challenges and hazards without impairing future options to deal with
climate change effects (IPCC, 2012). In contrast, actions that alleviate
current adaptation needs but have a negative effect on future adaptation
pathways are “maladaptive” (IPCC, 2014). From a sociopolitical
perspective, adverse or deliberately false decisions leading to (recurrent)
maladaptive actions are called maldevelopment (David et al., 2021).
Maldevelopment is followed by continuously degrading<?pagebreak page2335?> conditions with the
risk of reaching and exceeding anthropogenic tipping points – critical
thresholds, from which changes to a system cannot be reversed (Duvat and
Magnan, 2019). Given these significant consequences of actions and the wide
range of climate change projections, suitable means of adaptation are
preceded by robust decisions. With regard to low-regret adaptation, robust
decision-making does not aim at providing solutions for all future
scenarios but rather lays the foundation and does not neglect to account
for a variety of possible impacts (Marchau et al., 2019). In this context,
climate adaptation builds on a more flexible strategy, including
scenario-based pathways and an “agree-on-decisions” approach rather than
the more traditional “predict-then-act” approach (Lempert, 2019). With
regard to flooding in Southeast Asian metropolises, low-regret flood
adaptation should be based on multiple options which consider the
trajectories of natural hazards, like sea level rise and the concentration
of extreme rainfall, on the one hand, and anthropogenic factors, such as
uncontrolled urban growth and humanmade land subsidence, on the other.</p>
      <p id="d1e256">For the particular case of HCMC, few authors investigated the potential of
available, grey or green, flood adaptation<?pagebreak page2336?> options within the last years.
Most prominently, the Vietnam Climate Adaptation Partnership (VCAPS)
consortium presented an extensive flood protection scheme that comprises
various, mostly classic, adaptation measures (VCAPS, 2013). Supported by
multiple flood gates and six major pumping stations, a large-scale ring dike
prevents water from the Saigon River from entering the inner-city canal
system and, thus, literally decouples the city center from the natural
hydrological regime of the Saigon River. On this basis, annual damage for
five different extreme events under the aforementioned adaptation strategy
could be estimated by implementing the individual infrastructural elements
into a hydro-numerical model of one particularly flood-prone area (Lasage et
al., 2014). A comprehensive evaluation of the suggested adaptation measures
and their combination was subsequently published modeling present and
future storm surges for the complete city area and providing a detailed
cost–benefit analysis (Scussolini et al., 2017). The study concludes with a
map of proposed adaptation pathways for Ho Chi Minh City. Construction of
the ring dike was, in the meantime, ordered by the Vietnamese Ministry of
Agriculture and Rural Development (MARD) in 2016. Besides infrastructural
flood responses, the importance of governance decisions such as
reasonable land use planning was emphasized to minimize the exposure and
allow for the vulnerability of the ever-growing urban population (Downes and
Storch, 2014). Another recent study focused on surveying and discussing
examples of ecosystem-based flood adaptation in terms of sustainable urban
drainage systems (SUDSs) (Loc et al., 2015).</p>
      <p id="d1e259">In summary, nearly all suggested flood adaptations for HCMC originate from
recent work of the VCAPS consortium, and their effectiveness has been
thoroughly tested for the case of extreme storm surges with return periods
of up to 1000 years. However, hardly any research has been conducted to
evaluate the performance of the proposed grey solutions, such as the ring
dike, or any ecosystem-based approaches with respect to typical
“every-year” events, which is in stark contrast to their 100-fold
estimated GDP loss expected by the Asian Development Bank (ADB, 2010).
Moreover, heavy rainfall has been completely disregarded in the majority of
studies, although precipitation-based damage should demonstrably be an
essential part of all urban flood risk assessments (Rözer et al., 2019).
Finally, only a few working groups have scrutinized the combination of grey
and green flood adaptation measures (Hamel and Tan, 2022).</p>
      <p id="d1e262">To address this gap in current research, the presented study scrutinizes the
hydraulic effectiveness of grey and green flood adaptation measures under
the influence of a representative flood event, i.e., tidal water levels
between <inline-formula><mml:math id="M3" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.6 and <inline-formula><mml:math id="M4" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>1.2 m above sea level in combination with the official
design storm with a 1-year return period and 3 h duration (<inline-formula><mml:math id="M5" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> <inline-formula><mml:math id="M6" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 1 year; <inline-formula><mml:math id="M7" display="inline"><mml:mi>D</mml:mi></mml:math></inline-formula> <inline-formula><mml:math id="M8" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 180 min). By employing a simple yet robust surface runoff model, the focus is set on the following objectives:
<list list-type="bullet"><list-item>
      <p id="d1e310">to explore the feasibility and juxtapose the hydraulic effects
of the two aforementioned adaptation concepts given the case of HCMC;</p></list-item><list-item>
      <p id="d1e314">to quantify the potential reduction in flood intensity,
expressed through conventional and integrated proxies, that is achievable by each adaptation strategy and their combined implementation;</p></list-item><list-item>
      <p id="d1e318">to evaluate their low-regret character (including tentative
co-benefits) and discuss the implications for decision-makers.</p></list-item></list>
Classic, grey adaptation measures in this study follow the concept of
the large-scale ring dike as envisioned by VCAPS. Decentralized, green flood
adaptation, on the other hand, is represented by small-scale rainwater
detention as realized in the context of the Chinese Sponge City Program. Not
only do these two approaches differ in terms of their size and hydraulic
working principles, but the large-scale ring dike is also characterized by
its binary realization, while the Sponge City concept is inherently modular.
It is the aim of this study to juxtapose these two strategies both from a
hydrological and a governance point of view, elucidating the practical
validity of the IPCC's low-regret paradigm for flood protection and
disaster risk reduction in associated rainfall events.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Material and methods</title>
      <?pagebreak page2337?><p id="d1e330">To assess the hydraulic effectiveness of various adaptation strategies, a
simplified 2D flow model, designed in HEC-RAS (Hydrologic Engineering Center River Analysis System) version 6.0 by
the U.S. Army Corps of Engineers (USACE, 2021), was used to simulate
rainwater-induced inundations in HCMC. The key principle behind the employed
modeling scheme is that precipitation volumes typically accumulate across
the urban topography and finally discharge into the local canal system
(physically based rainfall runoff modeling). HEC-RAS was first introduced
in 1995 and, since then, has been validated in numerous case studies in
comparable settings (Patel et al., 2017; Muthusamy et al., 2019; Rangari et
al., 2019; Yalcin, 2020). As a consequence of the availability of local data, the
complete setup of the utilized model is based on open-access input data in
the form of freely available topography and hydro-meteorological conditions.
The computational grid comprises about 1.6 million rectangular cells of ca.
35 m <inline-formula><mml:math id="M9" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 35 m resolution with combined elevation data from the Shuttle Radar
Topography Mission (Farr et al., 2007) and the CoastalDEM (digital elevation model; Kulp and Strauss, 2018).
On this basis, the model domain is defined by the local terrain as it
contains all catchments that potentially contribute to surface runoff within
the city boundaries (cf. Fig. 2a). Upstream
influxes are given by the long-term mean discharges of the Saigon
(MQ <inline-formula><mml:math id="M10" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 54 m<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>) and Dong Nai
(MQ <inline-formula><mml:math id="M13" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 890 m<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>) rivers, respectively (Tran Ngoc et
al., 2016). The downstream boundary condition in the south of the city was
obtained by extrapolating coastal water levels from a global database
(Caldwell et al., 2015) in accordance with hydrological reports for this
catchment (Gugliotta et al., 2019). The model boundaries are complemented by
uniformly distributed rainfall with a hyetograph that follows an official
3 h design storm and can be readily adapted for arbitrary return
periods (cf. Fig. 2b). As there is hardly any
public information about the quality and condition of the existing drainage
system in HCMC – except that its capacity is regularly overloaded during
extreme events – all rainfall is assumed to become gradient-controlled
surface runoff as is a common conservative estimate (e.g., Scussolini et al.,
2017). The resulting flow paths resemble the course of the physical drainage
system, including site-specific backwater effects, so that flooding hotspots
can be reproduced reliably. The runoff model was validated against locally
reported inundations from 25 different locations across HCMC (documenting a
storm event in June 2010). Although verification data from 2010 might seem
outdated in the meantime, the practical choice for this event had to be based on
the co-occurrence of all boundary conditions (including discharge, tidal
water levels and precipitation) with credible reports of urban inundations
at a considerable number of locations. In combination with the goal of using
open-access data alone, suitable events were limited to a handful of
options. While three of these data sets (from 2010 and 2013) served as calibration
data, the remaining event with the most data points (2010) was used for
validation. In this context, the comparison of simulated and observed flood
depths yielded a root mean square error of RMSE <inline-formula><mml:math id="M16" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.03 m and a correlation coefficient of <inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M18" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.75, respectively, making the
modeling approach trustworthy enough for all intended tasks, i.e., the
direct comparison of flood intensities under different adaptation scenarios.
For a more detailed explanation regarding the processing of input data as
well as the calibration and validation of the employed model, please refer
to the independent publication by Scheiber et al. (2023), which
discusses the general validity of open-access data in numerical analyses
more profoundly.</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e424">Model setup. A bias-corrected digital elevation model was
derived from the combination of SRTM and CoastalDEM; the local catchments
that result from the local topography define the modeling domain (<bold>a</bold>, topographic boundaries). While tidal water levels were extrapolated from coastal data, average river discharges were taken from the literature and precipitation was forced on the model boundary in accordance with the valid design storm focusing on a return period of 1 year (<bold>b</bold>, hydro-meteorological boundaries; all elevation data displayed using scientific color maps; Crameri, 2021). Two adaptation strategies were
investigated by either (1) implementing changed elevations due to a large-scale
ring dike in combination with six parameterized pumping stations or (2)
reproducing the hydraulic effects of decentralized water detention according
to a Sponge City approach in the form of an attenuated hyetograph,
respectively (<bold>c</bold>, flood adaptation strategies). The flood simulations
resulted in spatiotemporal data on water surface elevations revealing local
maximum flood depths and durations of significant flooding (<bold>d</bold>, conventional flood intensity). Finally, the local flood severity was estimated via the normalized flood severity index (<inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) and in terms of manufacturing firms that were still affected by significant flooding
after implementation of the individual adaptation strategies (<bold>e</bold>, integrated flood intensity; all numerical results displayed in monochromatic colors for illustration purposes).</p></caption>
        <?xmltex \igopts{width=361.35pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/2333/2023/nhess-23-2333-2023-f02.jpg"/>

      </fig>

      <p id="d1e460">The described surface runoff model was employed to assess four different
adaptation scenarios:
<list list-type="custom"><list-item><label>(0)</label>
      <p id="d1e465">a base case scenario without any technical adaptations to mitigate urban flooding;</p></list-item><list-item><label>(1)</label>
      <p id="d1e469">a large-scale protection project including a ring dike, multiple sluice gates and pumping stations protecting the central districts of HCMC from storm surges (and, in parts, pluvial flooding);</p></list-item><list-item><label>(2)</label>
      <p id="d1e473">a small-scale rainwater detention scheme as implemented in the Sponge City Program, mitigating the peak of rain-induced surface runoff; and</p></list-item><list-item><label>(3)</label>
      <p id="d1e477">a combined application of strategies (1) and (2).</p></list-item></list>
Apart from a reference simulation without any adaption options (0), the
first strategy (1) originates from the large-scale development project
envisioned by VCAPS that was initiated by the Vietnamese Ministry of
Agriculture and Rural Development (MARD) in 2016. In the framework of the
numerical model, the central ring dike was implemented by locally fitting
digital elevations to a designated height of 2.3 m above sea level (see
Figs. 2c, left, and 3c), thus, directly affecting the simulated surface runoff in this area. The pumping stations, on the other hand, were realized by a customary HEC-RAS feature (since version 6.0), which allows for cell-specific discharges according to a user-defined rating curve. The six pumping stations were placed vis-à-vis the flood gates, i.e., at all crossings between the ring dike and a major inner-city canal (see Figs. 2c, left, and 3c). In contrast, the six
large-scale sluice gates (and multiple smaller ones) that are also part of
the underlying “MARD plan variant” (Phi et al., 2015) were not implemented
in this study as these facilities would be locked and therefore without
hydraulic effect in the considered flood events. While the structural
solutions of the first adaptation strategy were integrated directly into the
hydraulic model, the decentralized approach of the second strategy (2) had to be
implemented in a parameterized manner. All rainwater detention measures –
be it public basins, green roofs or private rain barrels – generally
procure a similar alteration of surface runoff: while the effective volume
stays the same, the local maxima in the hydrograph are attenuated and
converted into (throttled) discharge of manageable magnitude and time.
According to this volumetric analysis, the comprehensive application of
small-scale rainwater detention as a realization of the Sponge City concept
was represented by directly changing the hyetograph of the regular design
storm, thereby translating the hydraulic effect of decentralized water storage
into attenuated and prolonged rainfall (see Fig. 2c, right). In this connection, an approximate assessment based on
Sentinel-2 images revealed that about one-third of the included area is
covered by roofs. Optimistically estimating the potential of household level
rain detention, it was assumed that a maximum of one-half of this roof-area
could be utilized for water storage (i.e., connected to rainwater detention
measures), leaving about 15 % of the city's total area to be considered in
the parametrization scheme. The remaining scenario (3) finally combines the
large-scale ring dike project with small-scale rainwater detention in order
to assess the complementarity of these seemingly adverse strategies. All
simulations yield spatiotemporal output data describing the water surface
elevation at every cell of the modeling domain over time. To understand the
resulting risks, flood intensity was further assessed through four proxies
in two steps: the flood height (Fig. 2d, left),
which quite intuitively represents time-independent maximum inundation
depths at every point, and the flood duration
(Fig. 2d, right) defined by the time span a
certain point experiences a significant inundation, i.e., an inundation depth
greater than 10 cm. These two parameters are the most common proxies for
flood intensity and can directly be derived from the model outputs.
Secondly, a more integrated view on the potential reduction in flood risk is
provided by the normalized flood severity index (<inline-formula><mml:math id="M20" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)
(Fig. 2e, left) and the number of affected
manufacturing firms (Fig. 2e, right). The
<inline-formula><mml:math id="M21" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> combines flood depth and duration into one dimensionless
parameter. For a given pair of coordinates, the <inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is calculated as
the product of these two conventional flood intensity proxies divided by the
product of the 95th percentiles of the same proxies as follows:
          <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M23" display="block"><mml:mrow><?xmltex \hack{\hbox\bgroup\fontsize{8.9}{8.9}\selectfont$\displaystyle}?><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub><mml:mfenced close=")" open="("><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced><mml:mfenced close=")" open="("><mml:mi mathvariant="italic">%</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mo>max⁡</mml:mo></mml:msub><mml:mfenced close=")" open="("><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">cm</mml:mi></mml:mrow></mml:msub><mml:mfenced open="(" close=")"><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mo>max⁡</mml:mo><mml:mo>,</mml:mo><mml:mn mathvariant="normal">95</mml:mn><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="italic">%</mml:mi></mml:mrow></mml:msub><mml:mfenced close=")" open="("><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">cm</mml:mi><mml:mo>,</mml:mo><mml:mn mathvariant="normal">95</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="italic">%</mml:mi></mml:mrow></mml:msub><mml:mfenced close=")" open="("><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>×</mml:mo><mml:mn mathvariant="normal">100</mml:mn><mml:mo>,</mml:mo><?xmltex \hack{$\egroup}?></mml:mrow></mml:math></disp-formula>
        where <inline-formula><mml:math id="M24" display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mo>max⁡</mml:mo></mml:msub><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> refers to the maximum simulated flood depth
over time at coordinates <inline-formula><mml:math id="M25" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M26" display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">cm</mml:mi></mml:mrow></mml:msub><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> refers to the inundation duration over the predefined threshold of 10 cm at the same coordinates. Using the 95th spatial percentiles in the denominator
eliminates the distorting effects of potential numerical artifacts. As a
qualitative first estimate, this easy-to-apply index emphasizes those areas
which are exposed to significant flooding over a significant time and, thus,
are expected to experience the most severe damage across a given study area.
A detailed explanation of the rationale behind the <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and its
validation can be found in an independent publication by Scheiber et al. (2023). The location of manufacturing firms in HCMC is based on information from the Vietnamese 2017 Economic Census collected by the General Statistics
Office of Vietnam. These data were previously utilized in a study which
investigated the constraints that affect firm decisions in undertaking flood
adaptation measures based on SRTM elevation data (Leitold et al., 2021). In
contrast to traditional cost–benefit analyses that require sophisticated
damage–loss models, the avail of assessing only the number of affected firms
is that all companies are valued equally, thereby avoiding an
underestimation of (non-monetary) SME values (Kind et al., 2020; Hino and
Nance, 2021). On this basis, the hydraulic effectiveness of each adaptation
strategy was expressed through difference maps highlighting the local
reduction in each of the described proxies in comparison with the base case,
allowing for a qualitative estimation of the flood reduction that each of the
hypothetical strategies offers and, what is more, facilitating a direct
juxtaposition of their hydraulic effectiveness.</p>
</sec>
<?pagebreak page2339?><sec id="Ch1.S3">
  <label>3</label><title>Results</title>
      <p id="d1e721">Allowing for the highly dynamic behavior of surface runoff volumes across
the HCMC model, all spatiotemporal output data were converted into heat
maps for the first three flood intensity proxies. The potential flood
reduction in a given adaptation strategy was then illustrated by subtracting
the respective base case heat map. The benefits of this methodology become
clearer when studying the distribution of flood severity (<inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)
reductions as depicted in Fig. 3. Analogous maps
of the potential changes in maximum flood depth and duration can be found in
the Supplement. Figure 3a and b,
corresponding to strategies (1) and (2), visualize the reduction in flood
severity due to the implementation of the VCAPS ring dike (including sluice
gates and pumping stations) and the comprehensive application of water
detention, respectively. When comparing the two strategies, areas with
reduced flood severity (highlighted in green) significantly exceed
after implementation of the first adaptation strategy
(Fig. 3a), although they form certain clusters
along the low-lying areas close to the major canals. This seems obvious
given that the dike eradicates any tidal influence and the heavy-duty pumps
are capable of emptying these crucial elements of urban drainage within
hours. Then again, there are several areas (highlighted in blue) that
experience increased flood severity, especially across the western, riverine
parts of the municipal city of Thu Duc (hatched area), which lies east of
the northernmost pumping station. Given their proximity to the Saigon
River, these “new” flooding hotspots are very likely a consequence of
excess water being pumped into the river from inside the ring dike. Flood
reduction for the second adaptation strategy (Fig. 3b), on the other hand, is less pronounced but more uniformly
distributed, likewise addressing low- and high-lying areas. In addition,
this strategy hardly affects flood severity except for the exact spots that
are alleviated by the ring dike strategy, which already hints at the
benefits of using both strategies in combination. Changes in flood severity
that can be expected for a combination of both strategies are depicted in
the lower panel (Fig. 3c). At first sight, most
flood severity reductions visible in Fig. 3a can also be found for the combined approach in Fig. 3c. When looking at the increases in flood severity, however,
several of the inundations, which are induced by the implementation of a
ring dike, diminish in the case of additional rainwater detention. This applies
particularly to the areas east of the Saigon River, suggesting that certain
detrimental effects of excessive pumping could be mitigated by a combined
adaptation strategy. Complementing Fig. 3, the
Supplement includes a more detailed frequency analysis further
quantifying the changes in specific value ranges.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e737">Simulation results. Spatial changes in flood intensity in
terms of decreases (green) and increases (blue) in normalized flood severity
(<inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). <bold>(a)</bold> While the large-scale ring dike strategy
improves the flooding situation in the low-lying areas along the major
canals but worsens the situation for the western parts of the municipal
city of Thu Duc (hatched area), <bold>(b)</bold> the decentralized water detention scheme shows more uniformly distributed improvements. <bold>(c)</bold> The combination of both approaches yields even better results as increases and decreases in flood severity overlap for the former two strategies in a highly complementary way. All <inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reductions displayed using scientific color maps (Crameri, 2021).</p></caption>
        <?xmltex \igopts{width=355.659449pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/23/2333/2023/nhess-23-2333-2023-f03.png"/>

      </fig>

      <p id="d1e777">Adding to these findings, the following quantitative results also underline
that both grey and green adaptation strategies are, in general, capable of
alleviating the average intensity of local flooding in HCMC. This can be
concluded, for instance, from average values of maximum flood depths and
flood durations inside the ring dike, which are significantly reduced in
relation to the reference case without adaptations
(Table 1). In direct comparison, it becomes evident
that (1) the currently constructed ring dike (including flood gates and pumping
stations) would reduce average flood depths nearly as effectively as (2) the
implementation of widespread water detention, whereas the potential
improvements from (3) a combined approach suggest reductions in flood depth
are even higher than the sum of the single components of strategies (1) and (2). The numerical simulations suggest not only that the considered
strategies can be implemented without limiting their individual benefits
but also that they are highly complementary with respect to their hydraulic
effects. This is corroborated by the integrated flood intensity proxies
(Table 2). For example, the <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is reduced by
<inline-formula><mml:math id="M33" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>17.9 % for strategy 1 and <inline-formula><mml:math id="M34" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>17.7 % for strategy 2, respectively, in comparison to the base case without adaptation. The combination of both, however, would yield a <inline-formula><mml:math id="M35" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>38.3 % reduction in normalized flood severity (<inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). Further evidence for this complementarity can be found when assessing how the different adaptation strategies influence the number of manufacturing firms affected by significant flooding (with a depth of more than 10 cm). While the classic strategy (1) protects 33.8 % of the listed
companies, decentralized detention measures alone (2) could also reduce the
current number by <inline-formula><mml:math id="M37" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>18.0 % after all. But again, the combined approach (3) would protect more manufacturing firms than the individual measures, yielding a 58 % higher effectiveness compared to the ring dike scheme<?pagebreak page2341?> alone. Even though these numerical results already indicate that small-scale
measures can support future flood adaptation concepts in an effective and
straightforward manner, they do not (yet) allow for the strategic low-regret
qualities of the assessed options and the implications that arise for local
decision-makers.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e835">Potential reduction in flood intensity for three adaptation
strategies: (1) large-scale ring dike scheme including pumping stations, (2) comprehensive application of decentralized rainwater detention and (3) the combination of both approaches. Improvements are expressed through
decreasing mean values (inside the ring dike) of the conventional flood
intensity proxies, maximum flood depths in centimeters (left) and significant flood
duration with depths higher than 10 cm in hours (right), in comparison with
respective values for (0) the base case without adaptation measures.</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="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right" colsep="1"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Strategy</oasis:entry>
         <oasis:entry namest="col2" nameend="col5" align="center">Conventional flood intensity </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry rowsep="1" namest="col2" nameend="col5" align="center">proxies </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry namest="col2" nameend="col3" align="center" colsep="1">Flood depth </oasis:entry>
         <oasis:entry namest="col4" nameend="col5" align="center">Flood duration </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry rowsep="1" namest="col2" nameend="col3" align="center" colsep="1">(<inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mo>max⁡</mml:mo></mml:msub></mml:mrow></mml:math></inline-formula>) </oasis:entry>
         <oasis:entry rowsep="1" namest="col4" nameend="col5" align="center">(<inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi>d</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">cm</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>) </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">(cm)</oasis:entry>
         <oasis:entry colname="col3">(%)</oasis:entry>
         <oasis:entry colname="col4">(h)</oasis:entry>
         <oasis:entry colname="col5">(%)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">(1) Large-scale protection</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M40" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.9</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M41" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>14.4</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">5.5</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">(2) Small-scale detention</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M44" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.9</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M45" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>14.0</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M46" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.8</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M47" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>14.1</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">(3) Combination</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M48" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>2.5</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M49" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>37.4</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M50" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.1</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M51" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>18.1</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><?xmltex \gdef\@currentlabel{1}?></table-wrap>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e1079">Potential reduction in flood intensity for three adaptation
strategies: (1) large-scale ring dike scheme including pumping stations, (2) comprehensive application of decentralized rainwater detention and (3) the combination of both approaches. Improvements are expressed through
decreasing values (inside the ring dike) of two integrated flood intensity
proxies, namely the average normalized flood severity index
(<inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) (left) and the total number of manufacturing
firms that are affected by a flood depth greater than 10 cm (right), in
comparison with respective values for (0) the base case without adaptation
measures.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.92}[.92]?><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right" colsep="1"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Strategy</oasis:entry>
         <oasis:entry rowsep="1" namest="col2" nameend="col5" align="center">Integrated flood intensity proxies </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry namest="col2" nameend="col3" align="center" colsep="1">Flood severity </oasis:entry>
         <oasis:entry namest="col4" nameend="col5" align="center">Number of affected </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry rowsep="1" namest="col2" nameend="col3" align="center" colsep="1">(<inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi mathvariant="normal">NFS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) </oasis:entry>
         <oasis:entry rowsep="1" namest="col4" nameend="col5" align="center">manufacturing firms </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">(units)</oasis:entry>
         <oasis:entry colname="col3">(%)</oasis:entry>
         <oasis:entry colname="col4">(–)</oasis:entry>
         <oasis:entry colname="col5">(%)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">(1) Large-scale protection</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M54" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.0</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M55" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>17.9</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M56" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>14502</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M57" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>33.8</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">(2) Small-scale detention</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M58" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.0</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M59" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>17.7</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M60" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>7745</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M61" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>18.0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">(3) Combination</oasis:entry>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M62" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>2.1</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M63" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>38.3</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M64" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>22926</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M65" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>53.4</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table><?xmltex \gdef\@currentlabel{2}?></table-wrap>

</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Discussion and conclusions</title>
      <p id="d1e1308">Several studies have recently assessed the available flood adaptation
options of HCMC, with a special focus on resistance against extreme storm
surges and such hazards under future sea level rise scenarios (VCAPS, 2013;
Lasage et al., 2014; Scussolini et al., 2017). However, no attention was
paid to the more frequent, especially pluvial, events. But these incidents
demonstrably cause financial losses that are up to 10 times higher than
singular extremes (ADB, 2010) and affect small and medium-sized manufacturing
firms, which are the backbone of the national economy, to a
disproportionately high degree (Leitold and Revilla Diez, 2019). To address
this knowledge gap, a numerical model was employed to evaluate possible
responses to urban inundations resulting from heavy precipitation of an
annual return period. Two exemplary adaptation measures, (1) a classic
(grey) protection scheme in the form of a large-scale ring dike and (2) a
(green) Sponge City approach in terms of decentralized rainwater detention,
were implemented in a process-based hydrological model.</p>
      <p id="d1e1311">The fact that this model was exclusively built on freely accessible boundary
conditions introduces some uncertainties to the numerical setup: first of
all, the elevation data from SRTM and CoastalDEM typically come with a
site-specific vertical bias (Schumann and Bates, 2018; Kulp and Strauss,
2019; Vernimmen et al., 2020; Mukul et al., 2015). However, a comparison
with three major lidar samples suggests that, given the specific case of
HCMC, errors on the order of 0.5 m are to be expected (<inline-formula><mml:math id="M66" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula> <inline-formula><mml:math id="M67" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 165 km<inline-formula><mml:math id="M68" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>, ME <inline-formula><mml:math id="M69" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> <inline-formula><mml:math id="M70" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.45 m, SD <inline-formula><mml:math id="M71" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.81 m, RMSE <inline-formula><mml:math id="M72" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.93 m; Scheiber et al., 2023). Yet, although the results of the here-employed model are highly dependent on the topographic conditions across the
city as well, the influence of a vertical bias diminishes when simulation
runs with the same systematic error are directly compared. The same applies
to the (conservative) assumption that the local drainage system
malfunctions from the beginning of each model run. Furthermore, the impact
of local land subsidence was not considered in the assessed simulations of
present-day adaptation responses, but the extrapolation of such trends
should be a requisite for simulations that address options under future
climate change projections given their significant share in relative
sea level rise (Nicholls et al., 2021). Additional limitations arise from
the definition of a design storm and from the parametrization of rainwater
detention in the form of an attenuated hyetograph (cf. Sect. 2, “Material
and methods”). The precipitation boundary was determined in conjunction with
a systematic mapping of local policies and adaptation guidelines. It follows
a 180 min design storm specified by an official decree enforced in decision 752/QD-TTg of the HCMC government. This duration may be seen as a balance
point: while the large-scale ring dike and pumping stations are constructed
to cope with extraordinary precipitation volumes (mostly in combination with
spring tides), the suggested detention measures aim at complementing the
local drainage system during shorter cloudburst events. The same applies to
the return period, which certainly has a considerable impact on the
effectiveness and technical limitation of the compared strategies.
Nevertheless, the commitment to this specific design storm has to be seen in
a row of conceptual assumptions that were necessary to undertake a direct
comparison of these hydraulically unlike adaptation options.<?pagebreak page2342?> Furthermore,
even if a comparison of different technical solutions for this type of
decentralized rainwater storage is generally possible on an analytical basis
– investigating what detention measure would theoretically cause which
attenuation – their parameterized implementation in the model would nearly
be identical, as is their general working principle. As a consequence,
the experienced flooding would also be very similar in its pattern of depth and
duration for all realizations of the Sponge City concept. Even if presumable
heterogeneities in the realization of rainwater detention measures are
neglected by this uniform parametrization, it still allows for the fact that
subsidized micro-scale solutions may be implemented in a people-centered,
i.e., bottom-up, approach. In the first place, this new paradigm in adaptation
can complicate official monitoring but may render supervision by local
authorities unnecessary in the end. The estimation of the roofed area from
satellite imagery and corresponding detention capacities entails additional
uncertainties with respect to the assumed runoff attenuation. In reality,
the actual shape of this hyetograph depends on a multitude of factors
including technical details about the individual solutions (how much storage
volume per unit) as well as the degree of implementation (how many units per
area). Nevertheless, the presented approach is sufficiently descriptive for
a conceptual juxtaposition of the effects and performance of the two
mitigation strategies under consideration and demonstrates the general
working principles despite the underlying simplifications: the large-scale
pumping stations comprised in the classic protection scheme reduce flood
volumes along the inner-city canals and thus represent a line or even point
sink within the numerical model; the implementation of the Sponge City
concept, in contrast, is characterized by spatially uniform runoff
attenuation, which translates to an area sink for flood volumes across the
whole model domain. The aim of this study, i.e., to compare the working
principles behind these two seemingly adverse adaptation options, is
thoroughly accomplished by the employed conceptual approach. Although the
employed surface runoff model is based on these assumptions, its
implications still go beyond earlier DEM-based analyses (Dang and Kumar,
2017; Leitold and Revilla Diez, 2019) and its limitations are comparable to
other process-based models of this region (Scussolini et al., 2017). Even
though simulated urban inundations should not be mistaken for quantitative
forecasts (as needed by, for instance, insurance companies), the reduction
in flood intensity and especially the spatiotemporal differences between
the assessed strategies can help both researchers and decision-makers to
critically compare the hydraulic effects of available adaptation options. In
this context, the combination of conventional and integrated flood intensity
proxies allows for a holistic view of their potential in physical flood risk
reduction and avoids a singular focus on economic assets, which would
underestimate the social relevance of small and medium-sized enterprises (Kind et
al., 2020; Hino and Nance, 2021).</p>
      <p id="d1e1366">The obtained simulation results suggest that both adaptation measures are
generally capable of alleviating the flooding situation in HCMC, albeit on
very different spatial and temporal scales. The classic protection scheme
has a slightly better reduction rate than the projected rainwater storage
regarding flood depth but increases the average flood duration inside the
ring dike. Positive effects of the ring dike are also limited to the
low-lying areas in the vicinity of the inner-city canals, which are easily
drained in contrast to the more remote and high-lying districts. In
addition, the grey adaptation measure relies on sufficient capacities and
smooth operation of a few central pumping stations, which makes this concept
highly vulnerable in the case of technical or operational failure. The
importance of technical robustness and reliability becomes particularly
clear when considering the role of the existing drainage system. Outdated
and in many regards deficient, this system is one of the main drivers of
growing flood risk in HCMC and the epitome of critical dependence on grey
infrastructure and susceptible technical solutions. A Sponge City approach,
in contrast, is characterized by its modularity comprising a large number of
decentralized elements. This is a major advantage as it disperses the risk
of failure through redundancy and therefore increases the resilience of the
overall system. However, in urban settings, the implementation of
medium-scale solutions for rainwater storage, like dedicated detention
basins, may entail conflicts with other (mostly commercial) interests in
land use management due to the required consumption of limited public space.
Multiple administrative authorities, private land owners and developers
typically need to be involved to make decisions. Although arising conflicts
can be addressed through active participation by representatives of the
local citizens and businesses as well as experts from the administration in
a joint co-design process (Chen et al., 2021), their complexity can prolong
and in some cases even prevent implementation plans. Hence, the most
promising approach for integrating the Sponge City concept into a highly
urbanized area, like HCMC, is promoting small- and even micro-scale
solutions on a neighborhood and household or firm level. These are
inherently easier to realize than medium-scale solutions as they can make
use of existing residential spaces or vacant lots (not being subject to
commercial interests) and can be governed through incentives more easily.
Also, concerning future trends, both strategies differ in their degree of
sustainability: because the operation of grey flood protection measures, and
specifically the ring dike, is defined by absolute design heights, these
structures are constructed for one specific water level projection and
constant land subsidence rate. In contrast, these two components of relative
sea level rise hardly impair the potential of rainwater detention. Although
classic flood protection shows higher effectiveness in almost all assessed
flood intensity proxies, this study of HCMC shows that small-scale
solutions, as projected in the framework of a Sponge City approach, excel in
terms of technical resilience and that they are readily available<?pagebreak page2343?> options
for addressing residual flood risk. Especially the combination of both
strategies offers much-needed technical redundancy besides the obvious
gains in quantitative flood reduction, which, in the present case, are even
higher than if the solutions would be implemented individually.</p>
      <p id="d1e1369">With regard to the low-regret paradigm, flexible approaches and strategic
planning for the future are central elements of a successful disaster risk
response strategy (IPCC, 2012; Marchau et al., 2019). Typically, an
adaptation plan contains multiple adaptation pathways, visualizing the
impact and dependence of single adaptation measures on future capacities to
deal with climate change effects (Haasnoot et al., 2013; Kwakkel et al.,
2015; Haasnoot et al., 2019). Planning adaptation with a low-regret mindset
facilitates small-scale measures along the way, with the chance of avoiding
more drastic interference within the natural and social setting and
circumventing maladaptation or maldevelopment (David et al., 2021). With this
in mind, the ring dike in HCMC is a direct protection measure for the
low-lying parts of the inner city. Complemented by heavy-duty pumps, it
shields the central districts from harmful storm surges and eradicates the
tidal influence on the inner-city canals. However, even if parts of the
precipitation volumes remain in more highly elevated areas, unable to drain
through the deficient drainage system, this surplus of discharge still
raises the water levels of the gaining Saigon River. Like the general
dependence on a single technical structure, this fact shows a maladaptive
tendency as it can be detrimental to the outer reaches of HCMC (outside the
ring dike). These are increasingly inhabited by the poorer, more vulnerable
portions of the population (Duy et al., 2018), who now experience even
higher floods. To alleviate this tendency and prevent maldevelopment, the
currently constructed solution can be complemented with incentives for
societal and communal flood protection: decentralized rainwater detention
verifiably attenuates the runoff peak and allots discharges to a longer
period (Jiang et al., 2022). Considering an adaptation pathway plan for HCMC
from a governance perspective, the ring dike is a unique project based on a
binary decision (to build or not to build). It is designed to protect a
given domain from a clearly specified set of natural hazards. Rainwater
detention, on the other hand, is a modular category of individual measures
following the Sponge City concept which does not need further specification
and can be tailored to the specific demands of the local situation. This
makes this approach highly flexible as its spatiotemporal implementation
can be controlled by targeted subsidies, e.g., for purchasing and installing
private rain barrels. Such micro-scale solutions enable both individuals
and SMEs, who currently depend on top-down decisions, to actively
participate in (bottom-up) climate change adaptation on a community and
household level. Finally, this approach tackles flooding at its source and
does not interfere with other protection measures on site. These aspects –
flexibility, the capacity of local stakeholders to respond gradually and
in accordance with near-future precipitation projections, and the
ability to mitigate without impairing protection efforts on site – make the
Sponge City concept a prime addition to a successful low-regret adaptation
strategy.</p>
      <p id="d1e1373">As mentioned before, the construction of a ring dike, multiple flood gates
and pumping stations is currently underway in HCMC. This adaptation pathway
is an effective response to the current flooding situation as it protects
the central districts of the city from high tides and coastal storm surges.
However, frequent rain events cause major socio-economic disruptions and
induce significant financial losses to the local economy as well. To address
those areas that are not yet protected from pluvial flooding or even
adversely affected by the installation of the ring dike, pursuing a Sponge
City approach would be a second and highly complementary adaptation pathway.
In this context, decentralized rainwater detention in any form – be it
green roofs, rain barrels or detention basins – can be considered useful,
especially in view of the impending rise in relative sea levels through
global warming and local land subsidence. This may be even more beneficial
as many of these solutions can be implemented as multi-purpose structures or
possess valuable ecosystem services, first and foremost beneficial to
mitigating urban-heat-island effects (He et al., 2019). Although further
adding to the low-regret character of this concept, an assessment of the
co-benefits of individual Sponge City measures would go beyond the scope of
this study. In the end, it is highly desirable to consider this vital
concept when technically approaching the increasingly severe disaster risk
of low-elevation coastal zones in general and of Southeast Asian
metropolises in particular to prepare for a deeply uncertain
future.</p>
</sec>

      
      </body>
    <back><notes notes-type="codeavailability"><title>Code availability</title>

      <p id="d1e1381">No code was used in this research. Details about the general processing of numerical data are provided in the methods section or can be inquired from the corresponding author.</p>
  </notes><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e1387">The presented model setup is completely based on open-access data from third parties, which are clearly referenced in the companion paper and specified in the respective list of references. Modeling outputs will be made available in a decision support tool in the context of the research project DECIDER (<uri>https://www.decider-project.org</uri>, last access: 21 June 2023). All other data can be requested from the corresponding author.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e1393">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/nhess-23-2333-2023-supplement" xlink:title="pdf">https://doi.org/10.5194/nhess-23-2333-2023-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1402">LS, MHJ and JV designed the hydro-numerical model finally set up and operated by MHJ. JRD acquired the census data, from which RL extracted the
geolocation of manufacturing firms. MHJ processed the simulation results,
which LS, MHJ and JV interpreted. LS and CGD conceptualized the paper
outline. LS and CGD wrote the initial manuscript with input from<?pagebreak page2344?> MHJ, while
JV, JRD, HQN and TS reviewed and edited the final text. JV and TS
(co-)designed the overarching research project and were responsible for funding resources at the Ludwig Franzius Institute for Hydraulics, Estuarine and Coastal Engineering, as was JRD at the Global South Studies Center. All three provided guidance
throughout the entire study.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1408">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e1414">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e1420">This article is part of the special issue “Future risk and adaptation in coastal cities”. It is not associated with a conference.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1426">The list of inundation hotspots, forming the basis for the model validation
in this study, was generously provided by Nguyen Quy from the HCMC-based
engineering company EPT Ltd. Moreover, the authors wish to express their
gratitude to Friedrich Hilgenstock from WTM Engineers GmbH for his
expert guidance in technical flood adaptation options. Talia Schoonees
provided helpful comments on the writing style, which is highly appreciated.
Finally, sincere thanks go to the editor at <italic>Natural Hazards and Earth System Sciences</italic> for handling the
paper and three anonymous reviewers for their helpful comments.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e1434">This research has received funding from the DECIDER project sponsored by the German Federal Ministry of Education and Research (BMBF; grant no. 01LZ1703H). C. Gabriel David is funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's
Excellence Strategy (grant no. UP 8/1).<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>The publication of this article was funded by the open-access fund of Leibniz Universität Hannover.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1443">This paper was edited by Liang Emlyn Yang and reviewed by three anonymous referees.</p>
  </notes><ref-list>
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