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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-21-2145-2021</article-id><title-group><article-title>Selecting and analysing climate change adaptation measures at six research
sites across Europe</article-title><alt-title>Selecting and analysing climate change adaptation measures</alt-title>
      </title-group><?xmltex \runningtitle{Selecting and analysing climate change adaptation measures}?><?xmltex \runningauthor{H.-J.~van Alphen et~al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>van Alphen</surname><given-names>Henk-Jan</given-names></name>
          <email>henk-jan.van.alphen@kwrwater.nl</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Strehl</surname><given-names>Clemens</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Vollmer</surname><given-names>Fabian</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Interwies</surname><given-names>Eduard</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Petersen</surname><given-names>Anasha</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Görlitz</surname><given-names>Stefan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Locatelli</surname><given-names>Luca</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-3859-3553</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Martinez Puentes</surname><given-names>Montse</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Guerrero Hidalga</surname><given-names>Maria</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-4550-5013</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6">
          <name><surname>Giannakis</surname><given-names>Elias</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1779-9811</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff7">
          <name><surname>Spek</surname><given-names>Teun</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Scheibel</surname><given-names>Marc</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Kristvik</surname><given-names>Erle</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff10">
          <name><surname>Rocha</surname><given-names>Fernanda</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Bergsma</surname><given-names>Emmy</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>KWR Water Research Institute, Nieuwegein, 3433 PE, the Netherlands</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>IWW Water Centre, 45476 Mülheim an der Ruhr, Germany</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>InterSus  –  Sustainability Services, 10405 Berlin, Germany</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Aquatec (Suez Spain), 08038 Barcelona, Spain</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>CETAQUA Water Technology Centre, 08940 Barcelona, Spain</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>The Cyprus Institute, Energy Environment and Water Research Center,
Nicosia, 2121, Cyprus</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>Province of Gelderland, Arnhem, 6811CG, the Netherlands</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>Wupperverband, 42289 Wuppertal, Germany</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>Norwegian University of Science and Technology, 7491 Trondheim,
Norway</institution>
        </aff>
        <aff id="aff10"><label>10</label><institution>Laboratório Nacional de Engenharia Civil, 1700-066 Lisbon,
Portugal</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Henk-Jan van Alphen (henk-jan.van.alphen@kwrwater.nl)</corresp></author-notes><pub-date><day>13</day><month>July</month><year>2021</year></pub-date>
      
      <volume>21</volume>
      <issue>7</issue>
      <fpage>2145</fpage><lpage>2161</lpage>
      <history>
        <date date-type="received"><day>29</day><month>May</month><year>2020</year></date>
           <date date-type="accepted"><day>18</day><month>May</month><year>2021</year></date>
           <date date-type="rev-recd"><day>13</day><month>May</month><year>2021</year></date>
           <date date-type="rev-request"><day>26</day><month>June</month><year>2020</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2021 Henk-Jan van Alphen et al.</copyright-statement>
        <copyright-year>2021</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/21/2145/2021/nhess-21-2145-2021.html">This article is available from https://nhess.copernicus.org/articles/21/2145/2021/nhess-21-2145-2021.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/21/2145/2021/nhess-21-2145-2021.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/21/2145/2021/nhess-21-2145-2021.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e272">As Europe is faced with increasing droughts and extreme precipitation,
countries are taking measures to adapt to these changes. It is challenging,
however, to navigate through the wide range of possible measures, taking into
account the efficacy, economic impact and social justice aspects of these
measures, as well as the governance requirements for implementing them. This
article presents the approach of selecting and analysing adaptation measures
to increasing extreme weather events caused by ongoing climate change that was
developed and applied in the H2020 project BINGO (Bringing Innovation to
Ongoing Water Management). The purpose of this project is (a) to develop an
integrated participatory approach for selecting and evaluating adaptation
measures, (b) to apply and evaluate the approach across six case-study river
basins across Europe, and (c) to support decision-making towards adaptation
capturing the diversity, the different circumstances and challenges river
basins face across Europe. It combines three analyses: governance,
socio-economic and social justice The governance analysis focuses on the
requirements associated with the measures and the extent to which these
requirements are met at the research sites. The socio-economic impact focuses
on the efficacy of the measures in reducing the risks and the broad range of
tools available to compare the measures on their societal impact. Finally, a
tentative social justice analysis focuses on the distributive impacts of the
adaptation measures. In the summary of results, we give an overview of the
outcome of the different analyses. In the conclusion, we briefly assess the
main pros and cons of the different analyses that were conducted. The main
conclusion is that although the research sites were very different in both the
challenges and the institutional context, the approach presented here yielded
decision-relevant outcomes.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e284">Along the process of adapting to climate change, finding and defining
appropriate adaptation measures is a complex task. Moreover, it is the key
activity to increase the resilience to future climate-change-induced risks
(Dogulu and Kentel, 2015). In addition, good practice in selecting adaptation
measures is a fundamental task in adjusting water infrastructure to climate
change, which is globally needed (Wilby,<?pagebreak page2146?> 2019). Part of this good practice is
to analyse the impact of potential adaptation measures, not only in terms of
hazard risk reduction, but also in terms of socio-economic effects, social
justice or governance needs for implementation. For example, Zhou
et al. (2012) combine climate modelling and an economic cost–benefit
assessment in analysing climate adaptation measures for pluvial flooding in
urban areas. Harrison et al. (2013) combine climate change scenarios with
socio-economic scenarios in a digital platform to allow stakeholders to
explore adaptation options within the context of varying futures. European
research projects such as ECONADAPT and BASE have also focused on the
economics of climate adaptation to support adaptation planning (Watkiss
et al., 2015; Garotte et al., 2016; Meyer et al., 2015).</p>
      <p id="d1e287">Another part of this good practice is to involve stakeholders in selecting and
analysing these adaptation measures. Involving local stakeholders in these
analyses, not just through consultation, but through co-production, enhances
their relevance, usability, legitimacy and credibility (Palutikof et al.,
2019). For example, Bhave et al. (2014) combine top-down climate modelling
with bottom-up (involving stakeholders) prioritisation of adaptation measures
but do not analyse socio-economic effect of measures or governance
requirements for implementation. Andersson-Sköld et al. (2015) use focus
group interviews with stakeholders to gauge the perceptions of adaptation
measures, as part of a broader integrated framework to analyse the impact of
climate adaptation measures. On the other hand, Singh et al. (2020) develop
and apply a broad framework to assess the feasibility of adaptation measures,
including political, economic and social indicators, but not specific to local
conditions and not as part of a participatory framework. This study
contributes to the literature by integrating three different analyses
(governance, socio-economic and social justice) in a participatory framework,
where most other studies capture only one or two of the above-mentioned
dimensions (Verkerk et al., 2017; Bojovic et al., 2018; O'Sullivan et al.,
2020).</p>
      <p id="d1e290">This article presents the approach of selecting and analysing adaptation
measures to increasing extreme weather events caused by ongoing climate change
that was developed and applied in the H2020 project BINGO (Bringing Innovation
to Ongoing Water Management). The purpose of this project is (a) to develop an
integrated participatory approach for selecting and evaluating adaptation
measures, (b) to apply and evaluate the approach across six case-study river
basins across Europe, and (c) to support decision-making towards adaptation
capturing the diversity, different circumstances and challenges river
basins face across Europe. The project was conducted by over 20 project
partners at six research sites in Europe: (1) the city of Badalona (Spain),
which faces the risk of flash floods and combined sewer overflows (CSOs) due
to increased precipitation; (2) the city of Bergen (Norway), also facing the
risk of floods and CSOs due to increased precipitation; (3) Veluwe
(Netherlands), a Natura 2000 site where long-term drought may affect the
groundwater system; (4) the Troodos mountains (Cyprus), where decreasing
precipitation causes water shortages for farmers and communities; (5) the
Wupper River basin, which is divided into two sub-cases, one about flood risk
due extreme weather events and one about decreasing water levels in the main
water reservoir due to decreasing precipitation; and (6) the Sorraia Valley
(Portugal) where farmers are confronted with water shortages due to decreasing
precipitation.</p>
      <p id="d1e293">The BINGO project followed a comprehensive approach from decadal predictions
of weather events, to hydrological analysis of the impact of the weather events
on water systems, to risk analysis and risk treatment. The work presented in
this article focuses on the treatment of risks following extreme precipitation
or drought. Risk treatment in project BINGO was organised as a collaborative
process between scientists and local stakeholders, through communities of
practice (CoPs) (Freitas et al., 2018). These CoPs consisted of
representatives of local and regional governments, organisations involved in
climate adaptation, and research partners. CoPs provide a social context in
which researchers and stakeholders can engage in formal and informal
interactions and co-analyse and co-produce the contextual knowledge that is
necessary for climate change adaptation (Iyalomhe et al., 2013). The CoPs in
the BINGO project were locally created and externally supported by the
scientific project partners, which is found to be a necessary condition for a
sustainable CoP (Vincent et al. 2018)</p>
      <p id="d1e297">Based on the risks that were identified and analysed in the risk analysis, the
CoPs selected and analysed adaptation measures, with the goal of informing
decision makers about the expected efforts and gains from the implementation
of these measures. The approach applied in the BINGO project is in line with
steps formulated in the Adaptation Support Tool developed as part of the
Climate-ADAPT initiative of the European Union
(<uri>https://climate-adapt.eea.europa.eu/knowledge/tools/adaptation-support-tool</uri>, last access: 8 July 2021).  More resources from the BINGO project can be
found on the project website (<uri>http://www.projectbingo.eu</uri>, last access: 8 July 2021).</p>
      <p id="d1e306">The next sections describe the process of selecting and analysing promising
adaptation measures in the order as conducted within the BINGO project for all
cases: (1) collecting and selecting adaptation measures, (2) governance
analysis of selected adaptation measures, (3) analysis of socio-economic
implications and (4) social justice analysis. These steps are illustrated with
examples from the case study in the city of Badalona as well as from other
sites in brief. A summary of the results of the analysis is provided,
comparing different types of measures. Finally, conclusions are drawn on the
application of the different methods.</p>
</sec>
<?pagebreak page2147?><sec id="Ch1.S2">
  <label>2</label><title>Collecting and selecting adaptation measures</title>
      <p id="d1e317">Two approaches were applied to collect potential adaptation measures suitable
to the climate change risks identified at the six research sites, namely a
desk study of previous adaptation research and consultation of stakeholders
involved in the local CoPs. For the desk study, the primary sources for
adaptation measures were two previous EU research projects CarpathCC
(<uri>http://www.carpathcc.eu/</uri>, last access: 8 July 2021)
and PREPARED (<uri>https://cordis.europa.eu/project/id/244232</uri>, last access: 8 July 2021). From both projects databases were available with adaptation
measures, including a brief analysis of their potential impact and risk
reduction potential. From these databases the BINGO research partners selected
measures that were (a) potentially relevant for the hazards the research sites
are facing and (b) relevant for the main characteristics of the research site
(e.g. urban area, agricultural area, natural area). At the same time, in each
of the six research sites the first CoP meeting was organised. In this
meeting, local stakeholders discussed and identified potential future climate
hazards for their research site and identified measures that were either
already planned or considered suitable.</p>
      <p id="d1e326">These measures were collected as part of workshop reports (van Alphen et al.,
2017a) and compiled, together with the measures from the desk research that
were selected by the research sites. In total, 91 measures were collected. In
many cases, research sites reported similar measures with slightly different
wording, or very specific measures could be placed in a broader
category. Through this reduction, 44 measures were compiled in a portfolio of
adaptation measures (van Alphen et al., 2017b). The portfolio of adaptation
measures is now available as an online tool
(<uri>http://beta.tools.watershare.eu/bingo/\$/</uri>, last access: 8 July 2021). In the portfolio, different types of measures are
distinguished: informational measures (e.g. raising awareness for behavioural
change), financial measures (e.g. insurance and subsidies), regulatory
measures (e.g. standards and legal bans) and infrastructural measures (e.g.
flood control infrastructure). The complete set of measures can be filtered by
type of risk, sector or adaptation objective. Since the portfolio was first
created to support the work in BINGO, the broad risk categories reflected the
risks first identified in the six case studies: (1) decrease in water quantity
due to decreased precipitation, (2) decrease in water quality due to decreased
precipitation, (3) floods due to increased precipitation and (4) decrease in
water quality due to increased precipitation. The sectors reflected the
sectors represented in the case studies: (1) agriculture, (2) flood
management, (3) public water supply, (4) urban drainage, (5) water governance and
(6) water resource management. This design was chosen so project partners and
future users can easily find measures suited to their own circumstances. For
each measure an analysis of the governance needs for implementation was given,
based on the three-layer framework presented below. This analysis was done by
research partners and was not based on specific conditions at the research
sites, but on desk research.</p>
      <p id="d1e332">After compiling this broad portfolio, a more specific assessment of potential
risks at the research site was made and discussed with stakeholders. Local
stakeholders could make a selection of adaptation measures from the long
list
provided by the project team and the measures that were developed
locally. This first selection of measures was accompanied by a discussion on
the following governance aspects related to the measures: (1) responsibility
for implementation, (2) participation/division of roles, (3) availability of
necessary resources and (4) potential challenges. During the CoP meetings at the
six research sites, these aspects were discussed for the different measures,
and a selection was made either through scoring or through voting. The
measures were selected for the purpose of further analysis. For instance, in
the case of Cyprus, measures were first scored on relevance and feasibility
and then voted on by the stakeholders. In some cases, stakeholders decided to
analyse measures that were not part of the portfolio but came up in the
stakeholder process after the portfolio was already compiled. Table 1 shows
the selection of measures for each research site.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e339">Overview of adaptation measures selected by the research sites.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.88}[.88]?><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="40mm"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="40mm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="29mm"/>
     <oasis:colspec colnum="4" colname="col4" align="justify" colwidth="40mm"/>
     <oasis:colspec colnum="5" colname="col5" align="justify" colwidth="30mm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Research site – climate risk</oasis:entry>
         <oasis:entry colname="col2">Technical infrastructure measures</oasis:entry>
         <oasis:entry colname="col3">Blue/green measures</oasis:entry>
         <oasis:entry colname="col4">Behavioural measures</oasis:entry>
         <oasis:entry colname="col5">Socio-economic analysis applied (also see Fig. 1)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1"><italic>Wupper River basin, Germany</italic>  <?xmltex \hack{\hfill\break}?>Insufficient reservoir storage due to drought</oasis:entry>
         <oasis:entry colname="col2">Water transport between reservoir catchments <?xmltex \hack{\hfill\break}?>Alternative water source (horizontal well)</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">Water-saving <?xmltex \hack{\hfill\break}?>reduction of low water<?xmltex \hack{\hfill\break}?>elevation</oasis:entry>
         <oasis:entry colname="col5">Cost–effectiveness<?xmltex \hack{\hfill\break}?>analysis (CEA)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Flood risk due to increased precipitation</oasis:entry>
         <oasis:entry colname="col2">Technical protection measures for property <?xmltex \hack{\hfill\break}?>Alignment protection <?xmltex \hack{\hfill\break}?>Retention basin</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5">CEA with multi- criteria analysis (MCA)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"><italic>Veluwe, the Netherlands</italic> <?xmltex \hack{\hfill\break}?>Decreasing groundwater levels due to drought</oasis:entry>
         <oasis:entry colname="col2">Artificial infiltration</oasis:entry>
         <oasis:entry colname="col3">Land use change<?xmltex \hack{\hfill\break}?>(pine to broadleaf)</oasis:entry>
         <oasis:entry colname="col4">Agricultural water restrictions</oasis:entry>
         <oasis:entry colname="col5">MCA</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"><italic>Sorraia Valley (Tagus basin), Portugal</italic> <?xmltex \hack{\hfill\break}?>Decreasing groundwater levels due to drought</oasis:entry>
         <oasis:entry colname="col2">Rehabilitation and modernisation of irrigation networks</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">Tagus water resources management model</oasis:entry>
         <oasis:entry colname="col5">CEA</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"><italic>Troodos, Cyprus</italic> <?xmltex \hack{\hfill\break}?>Constraints on public water <?xmltex \hack{\hfill\break}?>supply and irrigation due to<?xmltex \hack{\hfill\break}?>drought</oasis:entry>
         <oasis:entry colname="col2">Desalination <?xmltex \hack{\hfill\break}?>Use of treated sewage water for irrigation <?xmltex \hack{\hfill\break}?>Maintenance of groundwater <?xmltex \hack{\hfill\break}?>recharge systems <?xmltex \hack{\hfill\break}?>Irrigation scheduling technologies</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5">CEA with MCA</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"><italic>Bergen, Norway</italic> <?xmltex \hack{\hfill\break}?>Combined sewer overflow due to increased precipitation</oasis:entry>
         <oasis:entry colname="col2">Sewer separation <?xmltex \hack{\hfill\break}?>Safe flood ways</oasis:entry>
         <oasis:entry colname="col3">Sustainable urban<?xmltex \hack{\hfill\break}?>drainage systems<?xmltex \hack{\hfill\break}?>(SUDS)</oasis:entry>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5">CEA</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><italic>Badalona, Spain</italic> <?xmltex \hack{\hfill\break}?>Combined sewer overflow and flash floods due to increased precipitation</oasis:entry>
         <oasis:entry colname="col2">Increase in sewer capacity</oasis:entry>
         <oasis:entry colname="col3">SUDS</oasis:entry>
         <oasis:entry colname="col4">Early warning system</oasis:entry>
         <oasis:entry colname="col5">Cost–benefit analysis (CBA)</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Governance analysis of selected adaptation measures</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Three-layer framework</title>
      <p id="d1e573">The three-layer framework for water governance, a tool for assessing water
governance practices (Havekes et al., 2016), was used to analyse the
governance needs of the adaptation measures. The framework builds on the work
done by the Organisation for Economic Co-operation and Development (OECD 2011)
on governance gaps in water governance and elaborates on these gaps with
building blocks for good water governance identified by the Dutch Water
Governance Centre. The framework distinguishes between three layers of
governance: the <italic>content layer</italic>, the <italic>institutional layer</italic> and
the <italic>relational layer</italic>. First, the content layer looks into
the substance of adaptation measures. Measures are characterised by the risk
that they address (such as from floods, CSOs or droughts) and the type of
intervention (informational, financial, regulatory, infrastructural). Also,
the content layer addresses the type of knowledge and expertise needed to
implement the measure (technical knowledge, administrative knowledge,
knowledge about interest and preferences). Second, the institutional
layer deals with the broad range of organisational requirements for the
implementation of adaptation measures.  This entails (1) the involvement of
the necessary actors and a clear division of roles and responsibilities
between them; (2) the administrative resources to implement the measure, such
as staff, accounting and monitoring capacities, regulatory capacity, and
knowledge infrastructure; (3) the legal requirements and the connection with
EU regulation, policy and directives; and finally (4) the<?pagebreak page2148?> financial
requirements and the way these funds can be generated. Third, the
relational layer of the framework refers to the requirements placed
on the wider governance context of adaptation to climate change. This entails
(1) the potential cultural or ethical issues that may support or obstruct
implementation of adaptation measures and (2) the requirements with regard to
public accountability, communication and participation.</p>
      <p id="d1e585">Based on this three-layer framework, a questionnaire was developed to assess
each individual measure selected by the CoPs. The questions address the
different layers and their elements. Examples of questions are as follows. Which
(constellation of) actors should be involved in the development and
implementation of the adaptation measure? Are the necessary actors currently
involved sufficiently? Which cultural or ethical issues either support or
obstruct the implementation of the adaptation measure? The questionnaires were
filled in by the research partners or in a collaborative effort with experts
and local stakeholders.</p>
</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Application in the Badalona case</title>
      <p id="d1e596">Following the methodology outlined above, three adaptation measures were
selected for the Badalona research site with the objective of reducing
urban floods and CSOs or reducing the impact thereof. These include
conventional urban drainage grey infrastructure (e.g. new or larger drainage
conduits, new detention tanks, new surface drains), the development of
sustainable urban drainage systems (SUDSs) and the implementation of an early warning system (EWS).</p>
      <p id="d1e599">For each one of the adaptation measures a governance assessment was
performed by following the expert analysis of the three-layer framework. The
results of the analysis demonstrate that (1) the structural measure
(increase in sewer capacity) meets the knowledge and legal requirements
(this measure was already included in the drainage master plan of Badalona for
2012) but does not have the financial, organisational and relational
requirements for its implementation; (2) the SUDS development meets the
technical and relational requirements (it has quite a lot of support given it is a
“green solution”) but does not meet the financial, legal and
organisational requirements to foster its implementation; (3) the<?pagebreak page2149?> early
warning system meets almost all the requirements except from the relational
layer regarding public accountability, communication and participation.</p>
      <p id="d1e602">This governance assessment (together with the socio-economic assessment
explained next) has allowed the Badalona City Council to have a clear roadmap
to support decisions towards urban adaptation.</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Analysis of socio-economic implications</title>
<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>Guidance in selecting fitting analysis frameworks</title>
      <p id="d1e621">Achieving a viable adaptation to climate change is a complex task that is
highly dependent on factors such as the financial means of involved
stakeholders and the social impacts of the implementation of a measure. For
decision makers it is key to define all necessary indicators and acquire the
necessary data for the evaluation. Guidance is needed to find the framework
that best fits the specific case, depending on the need to include not
only monetary but also non-monetary decision indicators (Markanday et al.,
2019; Dogulu and Kentel, 2015).</p>
      <p id="d1e624">Within the BINGO project a toolbox was compiled that summarises the
state of the art of suitable methods for evaluating and comparing alternative
strategies and measures for climate change adaptation (Koti et al.,
2017). This toolbox has been used as a background framework to analyse and
prioritise fitting risk reduction measures for the six research sites,
customised to local stakeholders' needs. The work conducted in the BINGO
project resulted in the preparation of a decision tree that supports
stakeholders to identify suitable assessment methods, depending
on their requirements and preferences for the analysis process. Complementing
the comprehensive BINGO toolbox, the decision tree in Fig. 1 focuses on those
analysis frameworks applied in the BINGO case studies.</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="d1e629">Decision tree supporting the definition process of a fitting
analytical framework to evaluate socio-economic implications of climate
change adaptation measures.</p></caption>
          <?xmltex \igopts{width=483.69685pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2145/2021/nhess-21-2145-2021-f01.png"/>

        </fig>

      <p id="d1e639">The application of the decision tree presupposes the definition of potential
adaptation measures. This is due to the fact that the provided methods aim to
support the analyst in prioritising a set of potential adaptation
measures. The work conducted in the BINGO case studies showed that the nature
of potential adaptation measures (e.g. infrastructural measures, behavioural
measures) can have a major influence on the requirements of the analysis
methods and relevant indicators, underlying the need for a case-specific
analysis method. Furthermore, a risk assessment of expected climate change
hazards and their magnitude needs to be conducted beforehand. This is
important in formulating a baseline (expected future without any adaptation
measure). In this way the risk reduction potential compared to that baseline
can be assessed for all alternative adaptation measures, in order to evaluate
the potential risk reduction of each measure. This is a mandatory data set to
compare alternative adaptation measures with one of the methods presented in
the framework above. This risk reduction potential should be used as a primary
indicator. For example, in the application of a cost–effectiveness analysis
(CEA) it can serve as input to compare the costs to the risk reduction
effectiveness.</p>
      <p id="d1e642">In selecting an evaluation framework by using the decision tree, the
participation of all stakeholders that are affected by the adaptation measures
turned out to be of high importance. These stakeholders might be water
authorities, local or regional governments, NGOs, farmers, or local
residents. The BINGO case studies showed the importance of stakeholders
getting the chance to express their points of view and major concerns. This
holistic integration of stakeholder perspectives enabled the definition of
sets of indicators for prioritisation of adaptation measures, and that ensured
the eventual acceptance of the results by all stakeholders. An omission of
this broad stakeholder participation might lead to a lack of stakeholders'
acceptance of the analysis results and thus to major barriers in the
implementation of the adaptation measures. Limitations in the final choice of
an evaluation framework may arise due to insufficient data availability,
e.g. because required data do not exist or because the effort to get the
required data is incommensurate with the benefits gained.</p>
      <p id="d1e645">The following sections briefly highlight why and in which case studies of the
BINGO project the decision support frameworks have been applied. This is not a
comprehensive presentation of the results, since this would exceed limits of
this article. Details can be found throughout the documentation in BINGO
project reports (<uri>http://www.projectbingo.eu/resources</uri>, last access: 8 July 2021).</p>
</sec>
<sec id="Ch1.S4.SS2">
  <label>4.2</label><title>Cost–benefit analysis</title>
      <p id="d1e659">A CBA helps to obtain a rank of available options in monetary terms. It is a
commonly used approach to prioritise flood risk reduction measures for climate
change adaptation (Penning-Rowsell et al., 2010; Zhou et al., 2012).  Costs
represent the resources necessary to implement a certain measure. In this
context, benefits account for the expected reduction of monetary damages
brought by the measure implementation. In addition, co-benefits can be
included for measures that improve ecosystem service provision, such as green
infrastructure, which are evaluated in monetary terms by available valuation
methods (OECD, 2018; Gerner et al., 2018; Hanley and Barbier, 2009).</p>
      <p id="d1e662">A CBA was conducted for the Badalona case study, due to suitability with the
data available and general interest among stakeholders. The costs of the
measures under assessment contain (1) initial investments, included gradually
in a linear trend following the assumptions of future implementation times;
(2) operating costs for the time horizon of the analysis (set until 2100); and (3)
rehabilitation and disposal costs, considering technical assumptions on the
duration of the assets.</p>
      <p id="d1e665">Benefits were assessed using the avoided cost methods, consisting of the
estimation of the difference between<?pagebreak page2150?> estimated damages in the baseline
scenario and in each of the alternative scenarios. Expected annual damage
(EAD) was used as an indicator (Martinez-Gomariz et al., 2018) for flood
damages, calculated for Badalona using historical flood damage data provided
by the National Reinsurance Consortium (Consorcio de Compensación de
Seguros). In addition, for the green roof and other green areas proposed as
measures, ecosystem service benefits were identified as regulating (air
quality and temperature control), supporting (habitat creation) and cultural
(aesthetic) services. Monetisation of the changes on the environmental
variables were estimated using market prices for the marketed items
(e.g. reduction of electricity consumption from temperature control) and also
non-market prices for those items that do not have a market for trade
(e.g. increase in property value after green roof implementation). For
non-market prices, the benefit transfer method has been applied, using reference
studies and adapting the values in economic and size terms. For more details
on the methodologies and results, please refer to the deliverable D5.3 of the
project (Strehl et al., 2019a).</p>
</sec>
<sec id="Ch1.S4.SS3">
  <label>4.3</label><title>Cost–effectiveness analysis</title>
      <p id="d1e676">The core idea of a CEA is to relate the costs of a measure to its
effectiveness, like the technical performance (Levin and McEwan, 2001). Both
key figures, the costs and the effectiveness, which is measured with a
suitable indicator, need to be quantified to calculate the ratio. Within
BINGO, a CEA was used in the case study of the Große Dhünn reservoir
(Wupper River basin). The reservoir, operated and owned by the Wupperverband
(regional water board), usually stores up to 81 <inline-formula><mml:math id="M1" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">Mm</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> of water used for
drinking water production, supplying up to 1 million people. In this case
the risk assessment conducted in the project pointed out the potentially
hazardous event of more than 1000 <inline-formula><mml:math id="M2" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">d</mml:mi></mml:mrow></mml:math></inline-formula> with an insufficient reservoir
water storage (defined as less than 35 <inline-formula><mml:math id="M3" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">Mm</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> water storage) in the
worst-case decadal climate change projections. Therefore, the focus of this
case study was to explore infrastructural and non-infrastructural adaptation
measures that reduce the risk to an acceptable level.</p>
      <p id="d1e709">In this particular case, effectiveness was measured by a non-monetary
indicator, namely its technical performance, which was defined as the
additional amount of available water per year. The Wupperverband had the
capacity to simulate the additional amount of water based on the reduction of
the low water elevation (non-infrastructural measure) and by a transfer
pipeline from the so-called Kerspe reservoir to the Große Dhünn
reservoir (infrastructural measure). Moreover, the additional water
availability by a new horizontal well (infrastructural measure) and by water-saving devices coupled with water use restrictions as emergency action
(non-infrastructural measure) could be estimated. The data availability
allowed a cost estimation for all four measures. Thus, a cost–effectiveness
analysis was the best-fitting decision support method in this case, offering
the possibility to rank technically and/or organisationally feasible risk
reduction measures by their cost–effectiveness ratio, advising<?pagebreak page2151?> the
Wupperverband and other regional stakeholders in the prioritisation of climate
change adaptations for their regional situation. More details can be found in
Strehl et al. (2019a).</p>
</sec>
<sec id="Ch1.S4.SS4">
  <label>4.4</label><title>Multi-criteria analysis</title>
      <p id="d1e720">An MCA describes a class of analysis methods that consider a variety of
different criteria (synonym: indicators) to achieve a prioritisation of the
potential measures. A common application is the weighted sum method. Here,
first the stakeholders affected by the potential adaptation measures have to
agree on a set of relevant indicators to evaluate the impacts of the different
measures. Afterwards the stakeholders have to give a weight to each
indicator. In the subsequent step each indicator is evaluated by the
stakeholders with respect to its manifestation for each respective measure,
e.g. by applying a scale from 1 (negative manifestation) to 5 (positive
manifestation). Finally, the score for each measure is determined by summing
up the products of the weighting and the evaluation score of each measure.
These final scores serve as a ranking of the measures (Carrico et al., 2014).</p>
      <p id="d1e723">This method was applied in the Veluwe case study. The Veluwe is a region in
the Netherlands dealing with hazards of long-term droughts and warming/heat
stress. To reduce the risks connected to these hazards, three potential
adaptation measures were identified, namely the reduction of areas covered by
pine trees, the implementation of artificial surface water infiltration and
agricultural water restrictions. As a separate cost–effectiveness analysis was
conducted in the Veluwe case, an MCA was chosen as second decision support
that focused on 19 different non-monetary indicators that the group of
relevant stakeholders agreed on. This methodology enabled a focused
investigation of the manifestation of different non-monetary indicators besides
the cost–effectiveness analysis, allowing us to make a well-founded and holistic
decision for or against the respective adaptation measures (Strehl et al.,
2019a).</p>
</sec>
<sec id="Ch1.S4.SS5">
  <label>4.5</label><title>Cost comparison</title>
      <p id="d1e735">Cost comparison (CC) is a dynamic approach used to compare the costs.
Investment expenditures as well as operational expenditures for implementing
and operating an adaptation measure are accounted for along the lifetime of a
measure, also minding discounting (Götze et al., 2015; DWA, 2012). The
advantage of a CC in general is that it allows a straightforward comparison of
adaptation measures by one single common indicator. Thus, this method is a
viable approach to support decision making in climate change adaptation if
only cost data are available for potential adaptation measures, or if the costs
are the most important indicator and other indicators are negligible.</p>
      <p id="d1e738">Within the BINGO case studies, no sole CC was conducted as the data
availabilities in all case studies allowed a more complex analysis,
incorporating more than one single indicator for decision support analysis.
However, the underlying methodology for a CC was used in many of the case
studies, e.g. in the case study for the Große Dhünn reservoir (Wupper
River basin) to calculate the annual costs for adaptation measures.</p>
</sec>
<sec id="Ch1.S4.SS6">
  <label>4.6</label><title>Combining frameworks</title>
      <p id="d1e749">The decision tree explained above serves as a guide that is suitable for a
variety of cases where decisions for or against certain adaptation measures
need to be made. However, sometimes a combination of analysis frameworks
might be necessary or desired. Within the BINGO project, this was essential
for the case study of the Wupper River basin. The spatial boundaries of that
case study covered an area of approx. 8 <inline-formula><mml:math id="M4" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">km</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> around a small urban
water course called the Mirke creek.  The area is known as an endangered flood
zone (MKULNV, 2015), and recent flood damage events triggered the urgency of
involved stakeholders to act since flood risk might also be aggravated with
further climate change in the future.  The aim of the case study was to
compare potential flood risk reduction measures at several so-called critical
hotspots along a 6 <inline-formula><mml:math id="M5" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">km</mml:mi></mml:mrow></mml:math></inline-formula> long course of the creek. The explored measures
needed to be ranked by their cost effectiveness in order to advise
stakeholders where to spend time and financial resources first (Strehl et al.,
2019b).</p>
      <p id="d1e771">To capture all relevant socio-economic details, the customised approach for
Wuppertal had to combine some of the frameworks mentioned in Fig. 1 above. In
the Wupper River basin case, stakeholders stated from the beginning of the
project that non-monetary indicators are also relevant for this case study.
However, as stated above, the primary aim was to rank the solutions in order
to guide stakeholders on how to spend time and financial resources wisely,
beginning at a hotspot with the best cost effectiveness. This is why a CEA was
combined with an MCA framework.</p>
      <p id="d1e774">The MCA framework followed in the Wupper River basin case study was aligned to
the so-called analytical hierarchy process (AHP) based on Saaty (2008) and
Saaty (1987). Here, at first a weighing of the indicators was given by the
stakeholders by pairwise comparisons of the indicators, followed by an
evaluation of the indicators' manifestations themselves. Both values per
indicator were afterwards combined to a final value that indicates the
respective measure's effectiveness in non-monetary terms. The resulting single
value was related to the costs for each measure (as calculated by the
principles of a CC). Details on the subsequent approach and results of the case
can be found in the BINGO D5.3 report (Strehl et al., 2019a).</p><?xmltex \hack{\newpage}?>
</sec>
</sec>
<?pagebreak page2152?><sec id="Ch1.S5">
  <label>5</label><title>Social justice analysis</title>
<sec id="Ch1.S5.SS1">
  <label>5.1</label><title>Why a social justice analysis?</title>
      <p id="d1e795">Social justice and equity principles have been highlighted by the IPCC (2018)
as key aspects of a climate-resilient development of societies.  Adaptation to
climate change is difficult to regulate because the causes and effects of a
changing climate are spread both geographically and in time.  For
policy-making on climate adaptation to be legitimate and effective, it has to
take justice and equity principles into account (Gupta, 2005; Caney,
2005b). Adaptation policies also contribute to human well-being and social
capital and increase the overall adaptive capacity of societies (Reckien
et al., 2018).</p>
      <p id="d1e798">Until today, the debate on social justice and climate change has mainly
centred on the recognition of responsibility for global climate change (Pielke
et al., 2007), inter-generational justice (Caney, 2005a) and
distributional justice, especially in the context of vulnerability to impacts
of climate change (Adger 2006; Breil et al., 2018). It is only recently that
social justice has emerged as a central concept to guide decision making for
adaptation policy. In the face of climate change, the scope of the transition
ahead calls for a high degree of support from all parts of society. The
successful implementation of adaptation action thus depends on transparent and
legitimate decision-making processes as well as a systematic consideration of
equity principles (Patterson et al., 2018). A social justice analysis of
adaptation measures, especially with an advanced methodology to introduce the
topic into adaptation decision making, has great potential to assess the
probable acceptability of proposed measures, to inform their context-adequate
design and to enhance the legitimacy of the planning process with a view to
the long-term support by the wider public.</p>
</sec>
<sec id="Ch1.S5.SS2">
  <label>5.2</label><title>The concept of social justice in BINGO</title>
      <p id="d1e809">There is not a commonly agreed definition of social justice or equity in the
context of adaptation (Breil et al., 2018), and the prioritisation of
principles and values varies according to the specific regional context (EEA,
2018). In essence, social justice theorises about fair allocations of burdens
and benefits among different members of a society (Rawls, 1971).  According to
Miller (1999) social justice thus concerns the question of “how the basic
structure of a society distributes advantages and disadvantages to its
members”. These distributions are often based on, and legitimised through,
“distributive” or “equity” principles (Buchanan, 1972; Cook, 1987). The
BINGO social justice analysis seeks to map the distributions of costs or
negative impacts and benefits of the adaptation measures among different
actors or groups in society in the specific context of each research
site. This was done using a standardised questionnaire (see
Fig. 2). Participants also received a short introduction paper, highlighting
the concept of social justice to them as well. The questionnaire was developed
based on three equity principles generally distinguished in the
environmental–philosophical literature (Shue, 1999; Low and Gleeson, 1998;
Paavola and Adger, 2002; Ikeme, 2003; Anand, 2004). (1) The egalitarian
principle is based on Mill's and Bentham's utilitarian “greatest happiness
principle”. Distributions aim to maximise the positive effects and minimise
the negative effects for society as a whole. An example of this principle in
adaptation governance is the upcoming international weather insurances and
bonds, which pay out after a certain weather disaster irrespective of the
needs of the victims (Dlugolecki and Keykhah, 2002). (2) The solidarity
principle aims to neutralise “involuntary inequalities” between people.
Distributions follow Rawls' “maximin” principle which involves maximising
the well-being of those who are worst off. A practical example of the
operation of this principle in adaptation governance is the United Nations
Adaptation Fund that finances adaptation projects in developing countries
(Person and Remling, 2014). (3) The deontological principle is based on Kant's
notion that people are rational and act intentionally and can therefore be
held responsible for their choices and actions. Nozick elaborated on this
notion in his “entitlement theory”, which holds that any “patterned”
redistributions focused on outcomes are unjust and (re)distributions should
always put individual rights and liberties at the basis. The “polluter pays”
principle is a practical example of this principle (Tol and Verheyen,
2004).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e814">Questionnaire for social justice analysis.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/21/2145/2021/nhess-21-2145-2021-f02.png"/>

        </fig>

      <p id="d1e823">As the evaluation of social justice is highly context dependent, the analysis
does not present a conclusive result for each measure but rather presents a
qualitative summary of distributional impacts for decision makers to consider
in addition to the rating which is produced in the socio-economic assessment.</p>
</sec>
<sec id="Ch1.S5.SS3">
  <label>5.3</label><title>The application of social justice analysis in BINGO  –  the Badalona case
study</title>
      <p id="d1e834">In the BINGO case study of Badalona, the application of the social justice
analysis for the three selected adaptation measures shows the following. (1) All
adaptation measures will have positive impacts on Badalona's citizens. The
general public will benefit from the reduction of flooding and combined sewer
overflows and the social perception in the municipality's efficiency will
increase. (2) None of the adaptation measures are likely to incur negative
side effects; on the contrary, the implementation of nature-based solutions
will incur social co-benefits such as enhanced public amenity, enhanced air
quality, increase in ecosystem services and the reduction of the “heat island
effect”. (3) Regarding equity principles, both the deontological and
egalitarian principles may apply in the case of climate change adaptation
given that, on the one hand, Badalona's citizens are paying for the proper
performance of the urban drainage system and at the same time<?pagebreak page2153?> the society as a
whole receives the positive consequences of such adaptation.</p>
</sec>
<sec id="Ch1.S5.SS4">
  <label>5.4</label><title>Limitations</title>
      <p id="d1e845">Pre-existing inequalities or specific vulnerabilities of certain groups of the
respective municipalities could only be considered to a limited extent
(question 7 of the questionnaire). However, the analysis of specific social
vulnerabilities at the level of the municipality is advisable when designing
adaptation measures as well as the participation of vulnerable groups in the
planning process to ensure that the contextual and procedural equity are also
taken into account (Breil et al., 2018).</p>
</sec>
</sec>
<sec id="Ch1.S6">
  <label>6</label><title>Summary of results</title>
      <p id="d1e857">In total, 22 measures were selected and analysed using the methods described
above (Table 1). A majority of measures are technical or “grey” measures.
This may be explained by the familiarity of the stakeholders and end users
with this type of measure. The governance analysis shows that the knowledge
and administrative resources for implementation of these measures are present
at the sites, and implementation generally does not require the involvement of
a broad range of stakeholders. Also, the effectiveness of these measures can
often be modelled and is less uncertain than for instance behavioural
measures. This is in line with Dhakal and Chevalier (2017), who find that, in
the case of urban storm water management, technical solutions remain preferable
throughout the world. However, the socio-economic analysis shows that these
technical infrastructure measures are often expensive, particularly when
compared with blue–green solutions or behavioural measures.</p>
      <p id="d1e860">In the case of Badalona, the grey infrastructure proposed has the highest
level of risk reduction but is also much more expensive than the SUDS and the
EWS. In fact, the cost–benefit analysis shows that the investment and
operational costs are not compensated for by the socio-economic benefits
considered. The proposed SUDSs have a lower potential for flood and CSO risk
reduction (also because the measures analysed only covered a small area of the
city), but the improvements they bring, for instance to habitat creation, and
enhanced aesthetic and recreational value (Locatelli et al., 2020) give them
a higher net benefit. The EWS was the most cost-effective measure,
significantly reducing flood risk.</p>
      <p id="d1e863">When the measures are compared by their governance needs, we see a different
picture. In Badalona, the measures<?pagebreak page2154?> that propose an increase in sewer capacity
are part of an already existing urban drainage master plan. That means that
the knowledge and competencies to implement these measures are readily
available. This is labelled by Dhakal and Chevalier (2017) as a “pro-grey
arrangement”. The main barriers to implementation are funding, political
decision making and disturbance due to construction works.  While the SUDSs
require a smaller budget, there is limited experience on how to implement them,
and technical expertise and standards/guidelines are currently lacking,
although relevant knowledge can be obtained from regional examples or local
research partners. SUDSs require the collaboration of a broad range of
stakeholders, which requires coordination by the Badalona City Council. This
makes implementation significantly more complicated than the proposed
technical measures. These barriers are all acknowledged in other cases as well
(Dhakal and Chevalier, 2017). The governance needs for the EWS are mostly met;
the main challenge is to develop and implement the required protocols for the
response to the “warnings” that the EWS gives.</p>
      <p id="d1e866">In terms of social justice, as stated above, all measures have a positive
impact on Badalona's citizens due to the decrease in the risk of floods and
CSOs. The SUDSs have an increased benefit, due to their many positive side
effects, but some of them are local and depend on where the measures are
implemented.</p>
      <p id="d1e870">The case of Bergen shows similar results. The sewer separation (a traditional
engineering measure) shows the highest potential for risk reduction,
particularly in cases of extreme rainfall. However, they are also very
expensive in relation to the risk reduction achievable. The proposed SUDS
measures are relatively low priced compared to their overall risk reduction
potential, but they do not have the potential to reduce the risk of extreme
events. When combined with using the roads as safe flood ways (a clever way of
repurposing the grey infrastructure), they are able to handle peak flows in
urban drainage at lower costs than sewer separation. The combination of
blue–green–grey measures has been proven successful in other studies as well
(Alves et al., 2020; Depietri and McPhearson, 2017).</p>
      <p id="d1e873">In the case of Bergen, all the governance needs for implementation of sewer
separation are met. With regard to SUDSs, there is still additional knowledge
required on the performance of SUDSs in cold climates. The BINGO project was
instrumental in involving the required stakeholders and so meeting the
organisational needs. However, there were too few incentives for private
property owners to implement the required measures (see also Dhakal and
Chevalier, 2017). This can be circumvented by first implementing the SUDS at
municipally owned properties. The implementation of safe flood ways is a less
traditional technical solution. It adds a new functionality to roads that fall
outside the responsibility of the road authorities and thus require
coordination between different municipal authorities, an example of fragmented
governance (Dhakal and Chevalier, 2017). Also, the broader impact on public
safety when running water with high velocity through the streets needs to be
assessed (Skrede et al., 2020).</p>
      <p id="d1e876">All measures in Bergen benefit the general public because of the reduction of
the risk of CSOs. As in the Badalona case, the SUDSs can provide many side
benefits that have additional positive impacts. Negative side effects mostly
involve construction and maintenance activities and resulting disturbances.
Most measures are financed at the municipal level, reflecting the egalitarian
or solidarity principle. SUDS or sewer separation implemented at private
properties has to be financed privately, following the “polluter pays”
principle (Strehl et al., 2019a). In the Veluwe case, the artificial
retention measure, which involves constructing a large water transportation
pipe from a nearby lake to the Veluwe, is the most expensive measure. It is
also the measure with the highest potential for risk reduction, in this case
measured as the additional groundwater recharge in the Veluwe groundwater
system (approximately 30 <inline-formula><mml:math id="M6" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">Mm</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>). The additional
recharge for the green measures (change in vegetation) ranges from
2–20 <inline-formula><mml:math id="M7" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">Mm</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>, but at much lower costs. Agricultural water restrictions are less
expensive than the other measures. Most expenses go to helping farmers change
their farming practices (or buying them out), since water restrictions will
force them to change crops. However, the amount of water saved is relatively
small (0.2–0.3 <inline-formula><mml:math id="M8" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">Mm</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>).</p>
      <p id="d1e939">At the Veluwe, the implementation of artificial retention is relatively easy,
because the required knowledge is available and the required coordination
between actors is limited and can be achieved through existing
institutions. The implementation of land use change is much more
complicated. It requires the collaboration between stakeholders outside of
current arrangements and with diverging interests. The BINGO CoP has already
been successful in establishing this cooperation. More importantly, changing
land use has a huge impact on public opinion, since the Veluwe is well
protected (Natura 2000) and a cherished spot for recreation. Changing its
vegetation at the required scale would require a public debate on forest
management at the national level (van Alphen et al., 2019). Agricultural water
restrictions require a locally embedded stakeholder process to be initiated,
involving farmers, municipalities, water authorities and the province. It
requires farmers to change their crops and farming practices, which are often
considered part of the cultural heritage as well.</p>
      <p id="d1e942">Artificial infiltration improves the sustainability of the drinking water
supply and helps preserve the groundwater system. These benefits are
distributed equally among water users in the region, who, through fees, also
bear the costs. The negative side effects are mostly a decrease in
attractiveness of the environment due to additional water (and energy)
infrastructure. These negative effects disproportionally impact people who
live near these infrastructures. Mitigation activities include minimisation
of visibility and ecological effects. Land use change has a number of<?pagebreak page2155?> positive
effects (sustainable drinking water supply, preservation of groundwater
system, a more diverse and robust landscape, increased biodiversity) that
impact the general public. Cost for these measures is borne mostly by land
owners, who will be compensated by either the province or through water
fees. Negative effects mostly have to do with the loss of wildlife and plants
specific to pine forests (although overall bio-diversity will
increase). During the transition period, tourism entrepreneurs may induce
losses due to intensified foresting activities (Strehl et al., 2019a). For the
measure agricultural water restrictions, farmers affected by the measure would
carry the major burden, but would be compensated by the regional or national
government for loss of production capacity. The local groundwater supply and
natural environment are positively affected, which directly benefits land
owners, local inhabitants and tourists.</p>
      <p id="d1e945">In the case of Cyprus, the CEA shows that the most cost-effective measure is
the maintenance of groundwater recharge systems (in this case check dams),
yielding a 1250 <inline-formula><mml:math id="M9" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> groundwater recharge per euro invested, compared
to treated sewage water for irrigation (32.6 <inline-formula><mml:math id="M10" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> recycled water used
per euro invested), desalination (1.5 <inline-formula><mml:math id="M11" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> desalinated water consumed
per euro invested) and irrigation scheduling technologies (0.90 <inline-formula><mml:math id="M12" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>
water savings per euro invested). For the irrigation sector, the irrigation
scheduling technologies measure had the highest MCA weighting score (13.5)
compared to the treated sewage water option (12.1). For the domestic water
supply sector, groundwater recharge systems received the highest final MCA
score (14.6) compared to the use of water desalination (13.3) (Strehl et al.,
2019a).</p>
      <p id="d1e993">According to the governance needs analysis, the implementation of this
maintenance scheme mainly requires better coordination between the water
authority and the local community councils. Structural, institutional and
political rigidities negatively affect the adoption of irrigation scheduling
technologies in Cyprus. The lack of political will to charge irrigators with
water prices that cover the full costs, i.e. financial, environmental and
resource, does not provide an incentive to invest in water-saving technologies
(van Alphen et al., 2019). Giannakis et al. (2016) suggest that the low
irrigation water price elasticities, the ageing and lower training levels of
farming population, the small farm size, and the low level of farm investments
also impede the uptake of irrigation scheduling technologies.  Support for
farm training schemes, including issues such as water conservation and climate
change adaptation, could improve the skills of the farmers and foster the
adoption of new technologies (Giannakis and Bruggeman, 2015, 2018; van Alphen
et al., 2019).</p>
      <p id="d1e996">It follows from the governance needs analysis that the use of treated sewage
water for irrigation could be implemented relatively easily. However, the
total benefit is small, considering that only 6 <inline-formula><mml:math id="M13" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">%</mml:mi></mml:mrow></mml:math></inline-formula> of the farmers
have access to this source. Also, the long-term effects of possible
contaminants are yet unknown. For desalinisation the key governance challenge
is financial viability. Local households will pay a higher price for the
desalinated water. Yet, as community councils will be responsible for
selecting the source of water, there are concerns regarding the prioritisation
of a cheaper source (van Alphen et al., 2019).</p>
      <p id="d1e1007">All four measures proposed in the Cyprus case study are financed at least in
part by the sectoral groups/communities that benefit directly and/or
indirectly. Irrigation scheduling technologies and the maintenance of
groundwater recharge systems have potential side effects which benefit the
general public as they increase the qualitative and quantitative state of the
groundwater system. Desalination and the use of treated sewage water for
irrigation only benefit specific groups of water users, namely the households
of the downstream communities of Peristerona watershed (desalination) and the
farmers that have access to the treated waste water.  Also, they have notable
negative side effects (impact of emerging contaminants, carbon emissions and
brine discharges) which burden the general public and future generations
(Strehl et al., 2019a)</p>
      <p id="d1e1010">In the first Wupper River basin case (insufficient reservoir storage due to
drought), it was found that the technical infrastructure measures are very
expensive compared to the behavioural measures, also in relation to the level
of risk reduction. The reduction of low water elevation (which effectively
reduces the outflow from the reservoir) is by far the most cost-effective
measure (EUR 0.001 <inline-formula><mml:math id="M14" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>). The water-saving scheme is not expensive, but the effect on the
capacity of the reservoir is relatively low, and so is the cost effectiveness
(EUR 0.194 <inline-formula><mml:math id="M15" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>). Water transportation (EUR 0.040 <inline-formula><mml:math id="M16" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>)
and the horizontal well (EUR 0.054 <inline-formula><mml:math id="M17" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) rank in between. It was
noted, however, that these infrastructural measures may be needed as a risk
buffer in the future, since they provide redundant capacity.</p>
      <p id="d1e1069">With regard to the governance needs, the reduction of low water elevation is
also the easiest measure to implement, with most of the governance needs being
met at the research site. The main concern is the effect on the downstream
ecology when the outflow of the reservoir is reduced. The technical
infrastructural measures in this case are much harder to implement. To build
the transfer pipe between the two catchments, water authorities and the
environmental agency should be involved, as well as the property owners
affected by the route. It also requires setting legal standards and assessing
the technical feasibility and environmental impact.  The same goes for
implementing a new abstraction well (horizontal well). An additional barrier
for that measure is the potential change in water quality (harder water) due
to mixing of sources.</p>
      <p id="d1e1072">Reduction of low water elevation
enhances social justice by securing the water supply to the general public,
without increasing the price of water. The main negative side effect is a
reduced flow passing the dam, leading to a decrease in energy production and
potential decline of ecological quality. This also affects the general
public. The substitution with an alternative water source may increase
existing<?pagebreak page2156?> inequalities since it increases the price of water, which
disproportionally impacts low-income groups. The same goes for the water
transport between catchments, with the addition that it negatively impacts the
property owners near the infrastructure. To the extent that these owners
will be compensated by the Wupper Association, the cost will be carried by the
general public. Finally, the pipe/channel route can have a negative impact on
the environment and landscape, which impacts the general public.</p>
      <p id="d1e1076">For the second Wupper River basin case (flood risk due to increased
precipitation) three technical measures were analysed. The retention basin is
the most expensive measure (EUR 88 <inline-formula><mml:math id="M18" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">k</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>), but it also performs best in terms of risk
reduction. Alignment protection (EUR 10 <inline-formula><mml:math id="M19" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">k</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) and protection
measures for property (EUR 3 <inline-formula><mml:math id="M20" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">k</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">yr</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) have a much smaller risk
reduction effect, about 10 to 15 times smaller. Since these results are
calculated for a specific scenario, it cannot be assumed that just
increasing the investment in the latter two protection measures will yield the
same risk reduction as the retention basin.</p>
      <p id="d1e1130">According to the governance analysis, all requirements for implementing the
retention basin are in place. With regard to the technical protection measure
for property, one of the barriers for implementation is convincing the
property owners to take action. Support and funding need to be coordinated
between public services and property owners. Flood protection is considered a
public service instead of a (partially) private responsibility.  When this is
the case, it does not encourage private investment (Geaves and
Penning-Rowsell, 2016). In the case of alignment protection, this is indeed a
matter of public action, where land may be acquired from property owners, but
no investment from their side is necessary.</p>
      <p id="d1e1133">From the social justice analysis, it follows that the retention basin will
benefit people downstream of the basin, while property owners above the
endangered areas have the basin built on or near their properties. The basin
will be financed by the Wupper Association, but property owners may face
decreasing value of their properties because of negative environmental impact
or decreasing aesthetics. This can be mitigated by an appealing design and
environmentally friendly construction of the basin. A positive side effect of
the basin that benefits the broader public is the improvement of water quality
due to a reduction of direct run-off into natural streams.  Protection
measures for property are generally paid for by the property owners, who also
reap the benefits of reduced flood risk. In the case of municipal buildings, the
municipality has the opportunity to embellish public spaces by choosing an
appealing design. The alignment protection will most likely be financed by the
Wupper Association, as the measure benefits the general public. In the case
property owners bear the costs, this will likely lead to increased social
inequalities.</p>
      <p id="d1e1136">In the Sorraia Valley in Portugal, the technical measures involve the
rehabilitation and modernisation of existing irrigation networks, which consist
of a canal, a transport and distribution system, and a secondary irrigation
system. Improving the canal is not the most cost effective in terms of cubic
metres of water saved, but it is in terms of impacted area.  Improving the
transport and distribution system and the secondary irrigation system only
affect a small area and need to be replicated in other areas to reach the
same impact as improving the canal (Strehl et al., 2019a). The Tagus water
resources management model has the potential to be very cost effective, but
this is dependent on the level of use the water authority will promote. This is
also the most important challenge with regards to the governance
needs. Implementing the water resources management model requires a shift from
a top-down management approach to a more network-oriented governance
model. This requires an integrated approach to water resource management and
the participation of a broad range of actors. The rehabilitation and
modernisation of the irrigation networks pose no specific governance
challenges, apart from acquiring funding for the investments.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2" specific-use="star"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e1142">Assessment of the applied analyses.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="60mm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="60mm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">BINGO analysis</oasis:entry>
         <oasis:entry colname="col2">Benefit</oasis:entry>
         <oasis:entry colname="col3">Limitation</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Governance analysis</oasis:entry>
         <oasis:entry colname="col2">Provides systematic overview of requirements and whether they are met; takes into account broad range of factors, not only finances and technical capability.</oasis:entry>
         <oasis:entry colname="col3">Method itself does not provide standards in whether requirements are sufficiently met; <?xmltex \hack{\hfill\break}?>relies on self-reporting by researchers and stakeholders.</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Socio-economic analysis</oasis:entry>
         <oasis:entry colname="col2">Helps to structure decision-relevant information about adaptation alternatives, focusing on measurable outcomes of each option; <?xmltex \hack{\hfill\break}?>applied science offers straightforward methods to quantify or at least rank relevant indicators affecting costs and benefits from a socio economic point of view; <?xmltex \hack{\hfill\break}?>methods for a socio-economic analysis are flexible to integrate the scenario based thinking of climate change projections.</oasis:entry>
         <oasis:entry colname="col3">Limitations arise with data availability; <?xmltex \hack{\hfill\break}?>in cases with very broad decision-relevant socio-economic indicators to cover, (un)reliable input data for a quantitative analysis effects the robustness of conclusions drawn from the analysis.</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Social justice analysis</oasis:entry>
         <oasis:entry colname="col2">Helps to focus not only on plain outcomes of adaptation, but also on distributional effects among society; broadens the scope of the analysis, eventually leading to identify additional co-benefits or unbalanced burdens for stakeholders of climate change adaptation measures, allowing a better informed decision.</oasis:entry>
         <oasis:entry colname="col3">Information acquisition for a social justice analysis relies on qualitative input, e.g. by interviews and pre-structured questionnaires as conducted in the BINGO-project; <?xmltex \hack{\hfill\break}?>time and financial resources and available interview partners may limit the scope of the analysis.</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e1221">From the social justice analysis, it follows that the rehabilitation and
modernisation of irrigation networks mainly benefit the farmers, who also pay
for the measures. To alleviate the financial burden, they can apply for
funding. The assured agricultural sustainability in the region benefits a
broader public as well. The water resource management model helps to better
plan and manage water resources in the Tagus river basin, which benefits the
general public. The costs are borne by the water authorities and then
allocated to all water users through a tax or a fee.</p>
</sec>
<sec id="Ch1.S7" sec-type="conclusions">
  <label>7</label><title>Conclusions</title>
      <p id="d1e1232">The application of the BINGO approach has been successful in generating
decision-relevant outcomes for developing adaptation strategies at the
research sites. The governance analysis allowed to stakeholders to identify
gaps in the governance needs to implement measures and to prepare steps to
fill those gaps. The outcomes of the socio-economic analysis allowed
stakeholders to prioritise measures by their cost effectiveness, cost–benefit
ratio or performance on a broader range of criteria. Sometimes this yielded
surprising results, such as the high cost effectiveness of check dams
maintenance in the case of Cyprus. Finally, the social justice analysis can
help stakeholders choose a proper financing mechanism that fits the desired
principle (solidarity, egalitarian, deontological) and gives a first
indication of how positive and negative impacts are distributed over different
groups. Although the research sites were very different, in both their
challenges and their socio-economic and institutional context, the
approach presented in this paper yielded useful results in all cases.  This
supports the transferability of the approach to other cases in Europe.</p>
      <p id="d1e1235">However, we can identify specific benefits and limitations for each of the
analyses (Table 2). The main benefit of the governance analysis is that
it provides a systematic overview<?pagebreak page2157?> of the requirements for implementing a
certain measure, with attention to a broad range of building blocks for
adequate governance. This is not limited to technical and economic aspects, but
also includes cultural, communicative and legal aspects. A limitation in the
way that the method was applied is that it does not provide specific
thresholds for the required level of these indicators, other than reported by
the researchers and stakeholders involved.</p>
      <p id="d1e1238">The socio-economic analysis contributed in structuring decision-relevant
information on adaptation measures focusing on potential outcomes of each
measure. The methods applied help to quantify and/or rank indicators affecting
costs and benefits of the selected measures, from a socio-economic point of
view. Moreover, the methods can be integrated in a broader scenario-based
approach to assessing adaptation strategies. Limitations of the method
primarily deal with the availability of data, which has a strong effect on the
validity and reliability of the conclusions drawn from the analysis.</p>
      <p id="d1e1241">Finally, the social justice analysis gives a broader perspective than the
plain focus on the outcomes of adaptation and also considers the
distributional effect on different groups in society. This may result in the
identification of unbalanced burdens or co-benefits, which leads to better
informed decisions and helps to realise climate justice. However, in the way
the method was applied, the acquisition of meaningful social justice
information and derived interpretations relevant for decision makers highly
relies on the interview partners. They need to have a specific knowledge of
the local adaptation measures/options planned and the socio-economic
environment.</p>
</sec>

      
      </body>
    <back><notes notes-type="codedataavailability"><title>Code and data availability</title>

      <p id="d1e1248">Model files and data are not provided
due to the confidentiality of the data and models. Notwithstanding, in
agreement with the other project stakeholders, the authors of this paper
will try to address specific requests for scientific purposes.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e1251">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/nhess-21-2145-2021-supplement" xlink:title="pdf">https://doi.org/10.5194/nhess-21-2145-2021-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1260">HJvA, CS, FV, EI, AP, SG and EB developed the
methodology. All authors were involved in the research at the research
sites. HJvA, CS, FV, EI, AP and SG prepared the paper with contributions from
all authors.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1266">The authors declare that they have no conflict
of interest.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e1272">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>

      <?pagebreak page2158?><p id="d1e1279">This article is part of the special issue “Integrated assessment of climate change impacts at selected European research sites – from climate and hydrological hazards to risk analysis and measures”. It is not associated with a conference.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1285">The authors wish to thank all partners and
stakeholders of BINGO for their input and feedback. We specifically thank
Christos Zoumides, Hakan Djuma, Marinos Eliades (The
Cyprus Institute); Ayis Iacovides, Marios Mouskoundis (IACO); Maria Rafaela
de Saldanha Gonçalves Matos, Ana Estela Barbosa, Maria João Freitas,
Teresa Viseu (LNEC); Alberto Freitas (DGADR); Ana Luís (EPAL); Eduardo
Martinez-Gomariz (Cetaqua); Beniamino Russo (Aquatec); Rita Andrade (SPI);
Thorsten Luckner, Paula Lorza (Wupperverband); Suzanne Buil-van den Bos
(Provincie Gelderland); Jan Hogendoorn, Jolijn van Engelenburg (Vitens);
Juliane Koti, Andreas Hein, Leni Handelsmann (IWW); Pedro Brito (DGADR);
Robert Mittelstädt (Hydrotec); Marit Aase, Magnar Sekse (Municipality of
Bergen); Ashenafi Seifu Grange, Tone Merete Muthanna (NTNU); Adriana
Hulsmann; and Nicolien van Aalderen. (KWR)</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e1290">This research has been supported by the BINGO
European H2020 project (grant no. 641739).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1296">This paper was edited by Adriana Bruggeman and reviewed by two anonymous referees.</p>
  </notes><ref-list>
    <title>References</title>

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    <!--<article-title-html>Selecting and analysing climate change adaptation measures at six research sites across Europe</article-title-html>
<abstract-html><p>As Europe is faced with increasing droughts and extreme precipitation,
countries are taking measures to adapt to these changes. It is challenging,
however, to navigate through the wide range of possible measures, taking into
account the efficacy, economic impact and social justice aspects of these
measures, as well as the governance requirements for implementing them. This
article presents the approach of selecting and analysing adaptation measures
to increasing extreme weather events caused by ongoing climate change that was
developed and applied in the H2020 project BINGO (Bringing Innovation to
Ongoing Water Management). The purpose of this project is (a) to develop an
integrated participatory approach for selecting and evaluating adaptation
measures, (b) to apply and evaluate the approach across six case-study river
basins across Europe, and (c) to support decision-making towards adaptation
capturing the diversity, the different circumstances and challenges river
basins face across Europe. It combines three analyses: governance,
socio-economic and social justice The governance analysis focuses on the
requirements associated with the measures and the extent to which these
requirements are met at the research sites. The socio-economic impact focuses
on the efficacy of the measures in reducing the risks and the broad range of
tools available to compare the measures on their societal impact. Finally, a
tentative social justice analysis focuses on the distributive impacts of the
adaptation measures. In the summary of results, we give an overview of the
outcome of the different analyses. In the conclusion, we briefly assess the
main pros and cons of the different analyses that were conducted. The main
conclusion is that although the research sites were very different in both the
challenges and the institutional context, the approach presented here yielded
decision-relevant outcomes.</p></abstract-html>
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