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  <front>
    <journal-meta><journal-id journal-id-type="publisher">HESS</journal-id><journal-title-group>
    <journal-title>Hydrology and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">HESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Hydrol. Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1607-7938</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/hess-27-1493-2023</article-id><title-group><article-title>Patterns and drivers of water quality changes <?xmltex \hack{\break}?> associated with dams in the Tropical Andes</article-title><alt-title>Patterns and drivers of water quality changes</alt-title>
      </title-group><?xmltex \runningtitle{Patterns and drivers of water quality changes}?><?xmltex \runningauthor{R. S. Winton et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2 aff3">
          <name><surname>Winton</surname><given-names>R. Scott</given-names></name>
          <email>scott.winton@gmail.com</email>
        <ext-link>https://orcid.org/0000-0002-9048-9342</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4 aff5">
          <name><surname>López-Casas</surname><given-names>Silvia</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5 aff6 aff7">
          <name><surname>Valencia-Rodríguez</surname><given-names>Daniel</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8999-1757</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Bernal-Forero</surname><given-names>Camilo</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Delgado</surname><given-names>Juliana</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Wehrli</surname><given-names>Bernhard</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-7029-1972</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Jiménez-Segura</surname><given-names>Luz</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Department of Surface Waters, Eawag, Swiss Federal Institution of
Aquatic Science and Technology, <?xmltex \hack{\break}?> Kastanienbaum, Switzerland</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Department of Earth System Science, Stanford Doerr School of Sustainability, <?xmltex \hack{\break}?> Stanford University, Stanford, California, USA</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Wildlife Conservation Society Colombia, Bogotá, Colombia</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Grupo de Ictiología, Instituto de Biología, Universidad de Antioquia, Medellín, Colombia</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Fundacion Horizonte Verde, Cumaral, Colombia</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>Red de Biología Evolutiva, Instituto de Ecología AC, Xalapa, Veracruz, Mexico</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>Autoridad Nacional de Licencias Ambientales, Bogotá, Colombia</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>The Nature Conservancy Colombia, Bogotá, Colombia</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">R. Scott Winton (scott.winton@gmail.com)</corresp></author-notes><pub-date><day>6</day><month>April</month><year>2023</year></pub-date>
      
      <volume>27</volume>
      <issue>7</issue>
      <fpage>1493</fpage><lpage>1505</lpage>
      <history>
        <date date-type="received"><day>31</day><month>May</month><year>2022</year></date>
           <date date-type="rev-request"><day>13</day><month>June</month><year>2022</year></date>
           <date date-type="rev-recd"><day>23</day><month>December</month><year>2022</year></date>
           <date date-type="accepted"><day>31</day><month>January</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://hess.copernicus.org/articles/.html">This article is available from https://hess.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://hess.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://hess.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e198">The Tropical Andes is a biodiversity hotspot facing pressure from
planned and ongoing hydropower development. However, the effects of dams on
the region's river ecosystems, as mediated by physicochemical changes in the water quality, are poorly known. Colombia is unique among its peers in South America with respect to managing central public environmental databases, including surface water quality data sets associated with the environmental monitoring of dams. To assess the relationship between hydropower and Colombian river conditions, we analyze monitoring data associated with 15 dams, focusing on oxygen availability, thermal regimes and sediment losses because these properties are influenced directly by river damming and impose fundamental constraints on the structure of downstream aquatic ecosystems. We find that most Colombian dams (7 of 10) seasonally reduce concentrations of total suspended solids by large percentages (50 %–99 %) through sediment trapping. Most dams (8 of 15) also, via the discharge of warm reservoir surface waters, seasonally increase river temperatures by 2 to 4 <inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C with respect to upstream conditions. A subset of four dams generate downstream hypoxia
(<inline-formula><mml:math id="M2" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 4 mg L<inline-formula><mml:math id="M3" 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 water that is 2 to 5 <inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C colder than inflows, with both processes driven by the turbination and discharge of cold and anoxic hypolimnetic waters during periods of reservoir stratification. Reliance on monitoring data likely leads us to under-detect impacts: many rivers are only sampled once or twice per year, which cannot capture temporal shifts across seasons and days (i.e., in response to hydropeaking). Despite these blind spots, the monitoring data point to some opportunities for planners and hydropower companies to mitigate downstream ecological impacts. These findings highlight the importance of implementing
environmental monitoring schemes associated with hydrologic infrastructure
in developing countries.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Staatssekretariat für Bildung, Forschung und Innovation</funding-source>
<award-id>SMG1924</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <?pagebreak page1494?><p id="d1e247">Freshwater ecosystems and the services they provide to society are
threatened worldwide, especially biodiversity
(He et al., 2019) and fisheries
resources  (Deines et al., 2013; Stone, 2016). One of the
major threats is the construction of dams for hydropower generation, which
is booming, especially in tropical regions  (Zarfl et
al., 2014) where biodiversity is high  (Ailly et al., 2014), as is
fish protein dependence for poor rural populations  (Kirby
et al., 2010). Hydropower development is routinely promoted for its
potential to meet the United Nations Sustainable Development Goals (SDGs), but the
negative side effects of dam construction may create obstacles for achieving
SDGs.</p>
      <p id="d1e250">The benefits of hydropower are well understood – namely the provision of a
renewable source of energy with a potentially low carbon emission intensity
(Almeida et al., 2019), thereby contributing toward SDG 7
(affordable and clean energy) and SDG 13 (climate action). Critics, however,
argue that the external costs of hydropower are frequently overlooked
(Opperman et al., 2015). Dams cause the fragmentation of rivers
(Anderson et al., 2018), disrupt hydrologic regimes critical for
life histories of potamodromous freshwater fish
(de Fex-wolf et al., 2019; Carvajal-Quintero
et al., 2017) and alter the downstream physicochemical condition of river
water  (Winton et al., 2019). These threats to
freshwater ecosystems misalign with SDG 14 (life below water), SDG 2 (zero
hunger) and SDG 1 (no poverty).</p>
      <p id="d1e253">Although retrospective case studies have been able to identify major
ecological changes associated with dam construction, catastrophic ecological
consequences of damming are not inevitable
(Winemiller et al., 2016). Careful siting, design and
operation can potentially mitigate the most costly of externalities
associated with new reservoirs for hydropower  (Moran
et al., 2018; Opperman et al., 2017). With thousands of new projects under
construction or in a planning phase globally
(Opperman et al., 2015, 2017; Zarfl et
al., 2014), the amount of work needed to analyze each case easily outpaces
the available expertise, especially as hydropower planning and impact
assessment spans financial, economic, social, engineering, hydrologic and
ecologic dimensions. Synthetic assessments of existing hydropower portfolios
have the potential to identify guidelines that could provide a shortcut for
planners, helping them prioritize projects less likely to cause ecological
harm and shelve more problematic proposals. Such assessments have been
conducted at global scales with a focus on specific processes, such as
sediment transport
(Maavara
et al., 2015; Vörösmarty et al., 2003), fragmentation
(Grill et al., 2015), greenhouse gas emissions
(Harrison et al., 2021), water supply  (Opperman
et al., 2017) and water quality  (Winton et al.,
2019).</p>
      <p id="d1e256">Global-scale analyses, however, may have limited applicability to local-scale contextual realities and be less useful for decision-makers and on-site managers. Regional studies may play an important role with respect to providing synthetic understanding from a portfolio of existing hydropower projects while remaining relevant for a specific geography (Flecker et al., 2022; Kummu et al., 2010). In this study, we aim to provide a gap-bridging regional assessment focused on one of the many regional concentrations of hydropower expansion, the Tropical Andes of South America, a hotspot for freshwater biodiversity with 967 known endemic species and 17.5 % of fish species at risk of extinction  (Tognelli et al., 2016). The region is rich in untapped hydropower resources thanks to steep elevational gradients and humid climates. Among the countries representing the region (including Peru, Ecuador and Bolivia), Colombia stands out as an ideal case for study because it maintains a centralized and publicly available data repository for all hydropower monitoring data and hosts roughly 40 % of the region's hydropower dams  (Fig. 1; Table 1; International Commission on Large Dams, 2018).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e262">A map of 22 reservoir-forming hydropower stations in Colombia
regulated by the Autoridad Nacional de Licencias Ambientales (ANLA). The labeled
reservoirs are those analyzed in this study. The topographic base map is
publicly available from the Sistema de Información Ambiental de Colombia,
and the locations of the hydropower projects are provided by Sistema de
Información Geográfica, maintained by Autoridad Nacional de Licencias
Ambientales.</p></caption>
        <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f01.jpg"/>

      </fig>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e274">Summary of the data available (in samples per year) for the assessment of reservoir stratification (no. of profiles), changes in river temperature, dissolved oxygen (DO) and total suspended solids (TSS) caused by dams for all hydropower stations licensed by the Autoridad Nacional de Licencias Ambientales (ANLA) in Colombia. Power plants with data for upstream vs. downstream comparisons are listed first and sorted by surface area.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="9">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:colspec colnum="9" colname="col9" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Elevation</oasis:entry>
         <oasis:entry colname="col3"/>
         <oasis:entry namest="col4" nameend="col5" align="center">Reservoir </oasis:entry>
         <oasis:entry colname="col6">Profiles</oasis:entry>
         <oasis:entry colname="col7">Temp.</oasis:entry>
         <oasis:entry colname="col8">DO</oasis:entry>
         <oasis:entry colname="col9">TSS</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Power plant</oasis:entry>
         <oasis:entry colname="col2">(m a.s.l.)</oasis:entry>
         <oasis:entry colname="col3">Built</oasis:entry>
         <oasis:entry colname="col4">km<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5">km<inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col6">No. (<inline-formula><mml:math id="M9" display="inline"><mml:mi>n</mml:mi></mml:math></inline-formula>) per year</oasis:entry>
         <oasis:entry namest="col7" nameend="col9" align="center">Upstream vs. downstream </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
         <oasis:entry namest="col7" nameend="col9" align="center">pairs per year </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">El Quimbo</oasis:entry>
         <oasis:entry colname="col2">600</oasis:entry>
         <oasis:entry colname="col3">2015</oasis:entry>
         <oasis:entry colname="col4">83.2</oasis:entry>
         <oasis:entry colname="col5">1.82</oasis:entry>
         <oasis:entry colname="col6">5</oasis:entry>
         <oasis:entry colname="col7">5</oasis:entry>
         <oasis:entry colname="col8">5</oasis:entry>
         <oasis:entry colname="col9">5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Urrá</oasis:entry>
         <oasis:entry colname="col2">70</oasis:entry>
         <oasis:entry colname="col3">2000</oasis:entry>
         <oasis:entry colname="col4">74.0</oasis:entry>
         <oasis:entry colname="col5">1.74</oasis:entry>
         <oasis:entry colname="col6">12</oasis:entry>
         <oasis:entry colname="col7">12</oasis:entry>
         <oasis:entry colname="col8">12</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Sogamoso</oasis:entry>
         <oasis:entry colname="col2">175</oasis:entry>
         <oasis:entry colname="col3">2015</oasis:entry>
         <oasis:entry colname="col4">70.0</oasis:entry>
         <oasis:entry colname="col5">4.80</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">4</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Betania</oasis:entry>
         <oasis:entry colname="col2">561</oasis:entry>
         <oasis:entry colname="col3">1984</oasis:entry>
         <oasis:entry colname="col4">68.9</oasis:entry>
         <oasis:entry colname="col5">1.97</oasis:entry>
         <oasis:entry colname="col6">2</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">1</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Guatapé</oasis:entry>
         <oasis:entry colname="col2">983</oasis:entry>
         <oasis:entry colname="col3">1976</oasis:entry>
         <oasis:entry colname="col4">51.4</oasis:entry>
         <oasis:entry colname="col5">1.24</oasis:entry>
         <oasis:entry colname="col6">2</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Prado</oasis:entry>
         <oasis:entry colname="col2">361</oasis:entry>
         <oasis:entry colname="col3">1971</oasis:entry>
         <oasis:entry colname="col4">33.8</oasis:entry>
         <oasis:entry colname="col5">0.97</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">1</oasis:entry>
         <oasis:entry colname="col8">1</oasis:entry>
         <oasis:entry colname="col9">1</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Calima</oasis:entry>
         <oasis:entry colname="col2">1408</oasis:entry>
         <oasis:entry colname="col3">1964</oasis:entry>
         <oasis:entry colname="col4">21.0</oasis:entry>
         <oasis:entry colname="col5">0.53</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">1</oasis:entry>
         <oasis:entry colname="col8">1</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Patángoras (Miel)</oasis:entry>
         <oasis:entry colname="col2">700</oasis:entry>
         <oasis:entry colname="col3">2002</oasis:entry>
         <oasis:entry colname="col4">13.6</oasis:entry>
         <oasis:entry colname="col5">0.57</oasis:entry>
         <oasis:entry colname="col6">6</oasis:entry>
         <oasis:entry colname="col7">6</oasis:entry>
         <oasis:entry colname="col8">6</oasis:entry>
         <oasis:entry colname="col9">6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Chivor</oasis:entry>
         <oasis:entry colname="col2">1258</oasis:entry>
         <oasis:entry colname="col3">1976</oasis:entry>
         <oasis:entry colname="col4">12.0</oasis:entry>
         <oasis:entry colname="col5">0.76</oasis:entry>
         <oasis:entry colname="col6">3</oasis:entry>
         <oasis:entry colname="col7">3</oasis:entry>
         <oasis:entry colname="col8">3</oasis:entry>
         <oasis:entry colname="col9">3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Guavio</oasis:entry>
         <oasis:entry colname="col2">1640</oasis:entry>
         <oasis:entry colname="col3">1989</oasis:entry>
         <oasis:entry colname="col4">13.3</oasis:entry>
         <oasis:entry colname="col5">1.04</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">3</oasis:entry>
         <oasis:entry colname="col8">3</oasis:entry>
         <oasis:entry colname="col9">3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Rio Grande</oasis:entry>
         <oasis:entry colname="col2">2270</oasis:entry>
         <oasis:entry colname="col3">1988</oasis:entry>
         <oasis:entry colname="col4">12.1</oasis:entry>
         <oasis:entry colname="col5">0.20</oasis:entry>
         <oasis:entry colname="col6">1</oasis:entry>
         <oasis:entry colname="col7">1</oasis:entry>
         <oasis:entry colname="col8">1</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Porce II</oasis:entry>
         <oasis:entry colname="col2">850</oasis:entry>
         <oasis:entry colname="col3">2001</oasis:entry>
         <oasis:entry colname="col4">8.9</oasis:entry>
         <oasis:entry colname="col5">0.14</oasis:entry>
         <oasis:entry colname="col6">2</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">2</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Porce III</oasis:entry>
         <oasis:entry colname="col2">700</oasis:entry>
         <oasis:entry colname="col3">2011</oasis:entry>
         <oasis:entry colname="col4">4.7</oasis:entry>
         <oasis:entry colname="col5">0.17</oasis:entry>
         <oasis:entry colname="col6">12</oasis:entry>
         <oasis:entry colname="col7">12</oasis:entry>
         <oasis:entry colname="col8">12</oasis:entry>
         <oasis:entry colname="col9">12</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Playas<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">983</oasis:entry>
         <oasis:entry colname="col3">1986</oasis:entry>
         <oasis:entry colname="col4">4.4</oasis:entry>
         <oasis:entry colname="col5">–</oasis:entry>
         <oasis:entry colname="col6">2</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Punchiná</oasis:entry>
         <oasis:entry colname="col2">775</oasis:entry>
         <oasis:entry colname="col3">1982</oasis:entry>
         <oasis:entry colname="col4">3.4</oasis:entry>
         <oasis:entry colname="col5">0.07</oasis:entry>
         <oasis:entry colname="col6">3</oasis:entry>
         <oasis:entry colname="col7">2</oasis:entry>
         <oasis:entry colname="col8">2</oasis:entry>
         <oasis:entry colname="col9">2</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Ituango</oasis:entry>
         <oasis:entry colname="col2">300</oasis:entry>
         <oasis:entry colname="col3">2018</oasis:entry>
         <oasis:entry colname="col4">38.1</oasis:entry>
         <oasis:entry colname="col5">1.63</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Salvajina</oasis:entry>
         <oasis:entry colname="col2">1100</oasis:entry>
         <oasis:entry colname="col3">1985</oasis:entry>
         <oasis:entry colname="col4">22.1</oasis:entry>
         <oasis:entry colname="col5">0.76</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">San Lorenzo<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mi mathvariant="normal">a</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">b</mml:mi></mml:mrow></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">1247</oasis:entry>
         <oasis:entry colname="col3">1988</oasis:entry>
         <oasis:entry colname="col4">10.7</oasis:entry>
         <oasis:entry colname="col5">–</oasis:entry>
         <oasis:entry colname="col6">3</oasis:entry>
         <oasis:entry colname="col7">n/a</oasis:entry>
         <oasis:entry colname="col8">n/a</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Miraflores<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">2062</oasis:entry>
         <oasis:entry colname="col3">1965</oasis:entry>
         <oasis:entry colname="col4">8.0</oasis:entry>
         <oasis:entry colname="col5">–</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">El Paraíso<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">2564</oasis:entry>
         <oasis:entry colname="col3">1950</oasis:entry>
         <oasis:entry colname="col4">6.2</oasis:entry>
         <oasis:entry colname="col5">–</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Anchicayá</oasis:entry>
         <oasis:entry colname="col2">655</oasis:entry>
         <oasis:entry colname="col3">1952</oasis:entry>
         <oasis:entry colname="col4">1.4</oasis:entry>
         <oasis:entry colname="col5">0.05</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">San Francisco</oasis:entry>
         <oasis:entry colname="col2">1300</oasis:entry>
         <oasis:entry colname="col3">1969</oasis:entry>
         <oasis:entry colname="col4">0.8</oasis:entry>
         <oasis:entry colname="col5">0.01</oasis:entry>
         <oasis:entry colname="col6">0</oasis:entry>
         <oasis:entry colname="col7">0</oasis:entry>
         <oasis:entry colname="col8">0</oasis:entry>
         <oasis:entry colname="col9">0</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p id="d1e277"><inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula> Reservoir volume not known. <inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">b</mml:mi></mml:msup></mml:math></inline-formula> Turbinated discharge goes directly into Playas Reservoir, so there is no downstream river for the comparison of inflow conditions. n/a stands for not applicable.</p></table-wrap-foot><?xmltex \gdef\@currentlabel{1}?></table-wrap>

      <p id="d1e1144">We limit our focus to alterations of physicochemical
parameters: temperature, dissolved oxygen and total suspended solids. These
parameters are included in most aquatic environmental monitoring programs
and define the critical oxic living conditions for aquatic fauna and the
dynamic reconstruction of riparian habitat. Thermal shocks or regime changes
are known to disrupt the life cycles of tropical biota
(Caissie, 2006; Ward and Stanford, 1982),
with cold-water discharges from dams frequently cited as negatively
impacting fish
(King
et al., 1998; Todd et al., 2005; Preece and<?pagebreak page1495?> Jones, 2002; van Vliet et al.,
2013; Cooper et al., 2019). Changes to the availability of dissolved oxygen
impose physiological constraints on the potential metabolic activity of
aquatic animals   (Ekau et al., 2010),
with all but the most adapted of macroscopic fauna unable to tolerate
hypoxia  (Agostinho et al., 2021;
Chapman et al., 2002; Kramer and McClure, 1982). Disruptions to sediment
transport rob floodplains and deltas of a critical lifeline
(Giosan et al., 2014; Constantine et al., 2014) and
can even restructure fish communities  (Granzotti et al.,
2018).</p>
      <p id="d1e1147">Our goal is to systemically assess Colombian dams with respect to their impacts on temperature, dissolved oxygen and total suspended sediments via the analysis of available monitoring data. As it is the mixing behavior of reservoirs that largely governs thermal and oxygen dynamics surrounding dams, we assess the Colombian dam portfolio for evidence of thermal stratification. Regarding the aforementioned parameters, we compare downstream monitoring stations to those upstream, which serve as a reference condition for the state of the river. We ask the following research questions:
<?xmltex \hack{\newpage}?>
<list list-type="bullet"><list-item>
      <p id="d1e1154">Do Colombian reservoirs stratify?</p></list-item><list-item>
      <p id="d1e1158">Does the stratification lead to
alteration of river thermal regimes?</p></list-item><list-item>
      <p id="d1e1162">Does stratification lead to hypoxia in
downstream waters?</p></list-item><list-item>
      <p id="d1e1166">How effectively do dams trap sediment and reduce the
concentration of suspended matter?</p></list-item></list>
The answers to these questions have
important implications for planning the future of hydropower development in
Colombia and throughout the Tropical Andes biodiversity hotspot. We assess
which characteristics of dams appear to be associated with the most
problematic outcomes and provide recommendations to decision-makers and
regulators regarding how to minimize harm moving forward.</p><?xmltex \hack{\newpage}?>
</sec>
<?pagebreak page1496?><sec id="Ch1.S2">
  <label>2</label><title>Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Colombian dams and monitoring data</title>
      <p id="d1e1186">Colombia's Autoridad Nacional de Licencias Ambientales (ANLA) maintains
environmental monitoring databases for all of Colombia's hydropower projects
generating <inline-formula><mml:math id="M14" display="inline"><mml:mi mathvariant="italic">&gt;</mml:mi></mml:math></inline-formula> 100 MW (26 power stations). Hydropower installations are distributed throughout the country's three branches of the Andes, but they are concentrated in the Magdalena–Cauca Basin, which is home to roughly 70 % of the total national population of 50 million people. Colombian hydropower installations span a wide range of elevations – from 70 m in the foothills to more than 2200 m in the high Andes, with a correspondingly high range of mean ambient temperatures. Because they are distributed across the many slopes of the Colombian Andes including within relatively dry inter-Andean valleys, they experience highly divergent precipitation regimes, including monomodal and bimodal rainfall patterns. The western slope of the western Andes is one of the Earth's wettest regions, with some municipalities reporting annual rainfall of more than 10 000 mm yr<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>; in contrast, the Sogamoso Reservoir near Bucaramanga receives roughly 1/10 this amount of annual rainfall. The climate settings of Colombian dams are enormously diverse. Of the hydropower projects under ANLA jurisdiction, 22 are associated with a dam and reservoir. The remaining four are micro-hydro systems that divert a portion of river discharge into turbine intakes but do not completely impound their associated rivers; thus, we exclude them from analysis.</p>
      <p id="d1e1208">As part of the environmental licensing process, the companies that operate
the hydropower projects are required (by ANLA) to monitor the environmental
impacts of their operations and submit annual reports and monitoring data
sets. The companies (which may be public or private) contract environmental
consulting firms to collect field samples and analyze them in their
laboratories. The data are assimilated into a central georeferenced database
maintained by ANLA, which screens the data for quality. In a legal sense,
these data are public, and ANLA works to guarantee access; however, to date, plans to build an online portal to facilitate direct data acquisition
have not yet been implemented. Currently, data are available to the public via direct request to ANLA. For this study, we requested and were granted access to systematized hydropower monitoring data from the years 2017 and 2018, which were the most recent years of fully quality-controlled data (Table 1). ANLA is working to incorporate historical data into its database; however, as our primary goal was to examine changes across the portfolio of hydropower systems, rather than to examine the evolution of dammed rivers over time, we did not request data from previous years. Data are stored in .gdb files, which require graphical information systems software (i.e., Esri ArcMap) to read them. We screened all data for statistical outliers and for chemical and physical plausibility and did not find the need to discard any data from our focal parameters.</p>
      <p id="d1e1211">Data collection frequency (summarized in Table 1) is highly heterogeneous
across sites, with two dams (Urrá and Porce III) being measured monthly,
whereas most dams provide just one to three data points per year. Sampling
frequency is generally higher for more recently constructed reservoirs,
reflecting an update to regulations in 1993. Older reservoirs (pre-2000) are
normally monitored only once or twice per year. Each dam's monitoring
approach is uniquely tailored to its geographic circumstances, but it generally includes surface water sampling from one or more stations upstream of the reservoir and downstream of the dam as well as samples from various parts of the reservoir itself, often including depth profiles. Adding to the data heterogeneity is a lack of standardization, leaving key fields missing in some entries. For example, Colombia's largest dam by volume, Sogamoso Reservoir, has been extensively profiled, but the depth and time information has been omitted, limiting interpretability.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Analytical approach</title>
      <p id="d1e1222">As stratification is a fundamental driver of thermal and oxygen dynamics
within and downstream of hydropower reservoirs (Winton et al., 2019), we first qualitatively assessed depth profiles (where available) for stratification strength. Because the profile data availability is quite variable, including the number of depth points sampled and seasonal coverage, we focus on deepwater oxygen levels (below the thermocline) and the magnitude of the oxygen concentration difference between surface and deep waters to sort each reservoir into coarse categories of strongly stratifying, weakly stratifying or non-stratifying. We classified reservoirs as strongly stratifying if they showed oxygen concentrations of <inline-formula><mml:math id="M16" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 2 mg O<inline-formula><mml:math id="M17" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> L<inline-formula><mml:math id="M18" 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> at depth and a difference between the surface of at least 3 mg O<inline-formula><mml:math id="M19" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> L<inline-formula><mml:math id="M20" 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>. We classified all other reservoirs as weakly stratifying, as all showed differences in temperature and oxygen between the surface and deep waters of at least 2 <inline-formula><mml:math id="M21" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and 1 mg O<inline-formula><mml:math id="M22" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> L<inline-formula><mml:math id="M23" 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> (for a summary, see Table S1 in the Supplement). Secondarily we use the densiometric Froude number  (Parker et al., 1975), which compares the inertial force of reservoir water, based on mean flow-through velocity, with the gravitational force tending to maintain densiometric stability (Orlob, 1983; Deas and Lowney, 2000). The Froude number can be approximated using the following simplified formula including reservoir length (<inline-formula><mml:math id="M24" display="inline"><mml:mi>L</mml:mi></mml:math></inline-formula>), depth (<inline-formula><mml:math id="M25" display="inline"><mml:mi>D</mml:mi></mml:math></inline-formula>), discharge (<inline-formula><mml:math id="M26" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula>) and volume (<inline-formula><mml:math id="M27" display="inline"><mml:mi>V</mml:mi></mml:math></inline-formula>): <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mi>F</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 320(<inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>/</mml:mo><mml:mi>D</mml:mi></mml:mrow></mml:math></inline-formula>)(<inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:mi>Q</mml:mi><mml:mo>/</mml:mo><mml:mi>V</mml:mi></mml:mrow></mml:math></inline-formula>) (Parker et al., 1975). This formula has been applied as a metric for stratification behavior (Ledec and Quintero, 2003).</p>
      <p id="d1e1368">To understand dam impacts, we rely on contemporaneous paired measurements of
upstream vs. downstream conditions, and we interpret the differences to be
attributable to dam effects, which is a widely used approach  (Fovet et
al., 2020). Colombian regulations stipulate that industrial or<?pagebreak page1497?> commercial
activities should not alter water temperatures by more than 5 <inline-formula><mml:math id="M31" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C,
which is a temperature difference that has been associated with acute responses from tropical biota, such as fish mortality from warm water (Cooper et al., 2019) or disruption to fish reproductive cycles from cold water (King et al., 1998). A more conservative thermal change threshold of <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M33" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C may be warranted given that the community composition of macroinvertebrates is highly sensitive to subtle changes in the thermal regime (Eady et al., 2013; Preece and Jones, 2002) and assessments of global climate change effects on fish delimit a 2 <inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C threshold for impacts (van Vliet et al., 2013). Furthermore, as we are limited (by data availability) to just a few random comparisons for most sites for Colombia, we are unlikely to be capturing the most extreme moments of thermal impact; thus, the precautionary principle would dictate a stricter approach. Therefore, we consider a change of <inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> or <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M37" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C to be evidence of warm-water or cold-water pollution, respectively. For changes in dissolved oxygen, we found that a loss of <inline-formula><mml:math id="M38" display="inline"><mml:mi mathvariant="italic">&gt;</mml:mi></mml:math></inline-formula> 2 mg L<inline-formula><mml:math id="M39" 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> imposed by dams always corresponded to a downstream concentration of <inline-formula><mml:math id="M40" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 5 mg L<inline-formula><mml:math id="M41" 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>, which
is the regulatory minimum concentration for cold freshwaters in Colombia.
Oxygen availability imposes a fundamental constraint on many aquatic species
(Coble, 1982; Spoor, 1990; Ekau et al., 2010); therefore, we assess impact along a change threshold of 2 mg L<inline-formula><mml:math id="M42" 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> to distinguish between “minor” and “severe” oxygen loss. For sediment trapping, although the literature is clear about the potential consequences of dam-induced sediment loss from rivers, choosing an appropriate threshold demarcating what constitutes a severe loss is highly subjective, and there are no regulatory guidelines for total suspended solids. We report the gradient of responses, noting how many dams trapped <inline-formula><mml:math id="M43" display="inline"><mml:mi mathvariant="italic">&gt;</mml:mi></mml:math></inline-formula> 50 % and <inline-formula><mml:math id="M44" display="inline"><mml:mi mathvariant="italic">&gt;</mml:mi></mml:math></inline-formula> 99 % of inbound suspended sediments.</p>
      <p id="d1e1503">We note that, in some cases, dams can be oriented in a cascade whereby the
outflow from one reservoir rapidly (or immediately) enters the reservoir for
the next power station. In such a configuration, the inflowing water does not
necessarily represent a neutral reference condition, as it has likely already been altered by the previous dam. This may bias us to underestimate
the potential of lower-chain dams to alter water quality; however, without access
to data on pre-dam river conditions, there is no alternative metric for
reference conditions beyond upstream waters. As only 1 year of recent
data is available for most projects, we focus our analyses on just the most
recent year (2017 or 2018) under the assumption that covering an annual
climate cycle is more important for answering our research questions than studying
variation between years.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Stratification</title>
      <p id="d1e1522">Our analyses of reservoir mixing behavior indicate that most, if not all,
Colombian reservoirs stratify strongly. Of the 22 reservoirs evaluated, 12
had available depth profile information; of these 12 reservoirs, 8 exhibited anoxia (dissolved oxygen (DO) concentration <inline-formula><mml:math id="M45" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 1.5 mg O<inline-formula><mml:math id="M46" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> L<inline-formula><mml:math id="M47" 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>) in deep waters, indicating that they stratify sufficiently to prevent consistent reoxygenation from the surface (Table S1). The remaining four reservoirs lack evidence of acutely hypoxic deep water; however, with just two or three depth profiles per year, it is possible that this limited sampling did not coincide with periods of stronger stratification, which can develop rapidly in tropical lakes (Lewis, 1996). From the few reservoirs that have been profiled several times per year, it appears that Colombian reservoirs are rarely, if ever, well mixed (Figs. S1, S2 and S3 in the Supplement). This result is supported by our calculations of the densiometric Froude number (<italic>Fr</italic>) (Parker et al., 1975; Orlob, 1983), which indicate that, of the 12 dams with discharge data available (necessary for calculating <italic>Fr</italic>), all reservoirs except for Guatapé fall into the strongly stratifying domain of <italic>Fr</italic> <inline-formula><mml:math id="M48" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 0.3, regardless of whether mean depth or maximum depth is used for the calculation (Fig. 2). In Colombia, large reservoirs tend to stratify strongly, and this limnological reality creates the potential for thermal and biogeochemical disruptions to downstream aquatic ecosystems.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e1572">Colombian reservoirs sorted by the densiometric Froude number (<italic>Fr</italic>), which is a function of reservoir depth, length, volume and discharge (Parker et al., 1975). The vertical lines at <italic>Fr</italic> <inline-formula><mml:math id="M49" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.3 and <italic>Fr</italic> <inline-formula><mml:math id="M50" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 1.0 indicate the expected boundaries between strongly stratifying, weakly stratifying and non-stratifying waterbodies (Orlob, 1983). Small dots represent <italic>Fr</italic> if the maximum depth (height of dam wall) is used instead of the mean depth, as recommended by Ledec and Quintero (2003). Underlying data were sourced from Autoridad Nacional de Licencias Ambientales and the International Commission on Large Dams  (International Commission on Large Dams, 2018) (<uri>https://www.icold-cigb.org/</uri>, last access: 17 November 2020). Five reservoirs from Table 1 are excluded because of missing discharge data.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f02.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Thermal regime change</title>
      <p id="d1e1619">Consistent with the expectation that stratifying reservoirs will alter
thermal regimes, we find that 9 of the 12 (75 %) Colombian reservoirs
assessed generated temperature anomalies in the river of at least
2 <inline-formula><mml:math id="M51" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (Fig. 3). Four reservoirs create cold-water pollution of at
least <inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M53" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C downstream, and seven reservoirs create warm-water pollution of at least <inline-formula><mml:math id="M54" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2 <inline-formula><mml:math id="M55" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C downstream. Two reservoirs – Urrá and El Quimbo – generate both cold- and warm-water pollution at different times of the year, illustrating the importance of temporal dynamics. Seasonal climate cycles that govern stratification, fluctuations in inflowing discharge associated with droughts/floods and dam operation (i.e., hydropeaking) may all influence the direction and magnitude of downstream thermal effects over timescales of months to minutes. Our ability to more broadly assess thermal changes is severely limited by the lack of frequent (seasonal or monthly) and detailed (with depth profiles) monitoring for most projects.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e1668">The temperature differences between the upstream and downstream river
surfaces of 15 hydropower dams in Colombia. Each point represents one pair
of contemporaneous measurements from the most recent year of available data
(either 2018 or 2017).</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f03.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1679">Differences in dissolved oxygen between the upstream inflowing river
surface and the river surface downstream of 15 hydropower dams in Colombia. Each
point represents one pair of contemporaneous measurements from the most
recent year of available data (either 2018 or 2017).</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f04.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Hypoxia</title>
      <?pagebreak page1498?><p id="d1e1696">We find that minor loss of oxygen (change in DO <inline-formula><mml:math id="M56" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 2 mg L<inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>) in waters downstream of Colombian dams was common (evident in 9 of 15 reservoirs), but severe oxygen loss relative to upstream (change in DO <inline-formula><mml:math id="M58" display="inline"><mml:mi mathvariant="italic">&gt;</mml:mi></mml:math></inline-formula> 2 mg L<inline-formula><mml:math id="M59" 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>) was
associated with only 4 reservoirs (Fig. 4). These hydropower schemes
(Urrá, El Quimbo, Sogamoso and Prado) are the same reservoirs that exhibited cold-water pollution (Fig. 3) – a coincidence that supports our assumption that both effects are driven by thermal stratification  (Hutchinson and Loffler, 1956; Lewis, 1987). As with temperature, we observe high seasonal variation in upstream–downstream oxygen dynamics, which, again, supports the notion that hypoxic effects are sensitive to the seasonality of climate, stratification and dam operations.</p>
</sec>
<sec id="Ch1.S3.SS4">
  <label>3.4</label><title>Sediment trapping</title>
      <p id="d1e1743">Loss of total suspended sediments (TSS) associated with Colombian dams is
pervasive; however, for several reservoirs, loss of TSS is observed to be extreme (Fig. 5). A total of 6 out of the 10
reservoirs that we assessed showed TSS decreases of more than 50 %, and two
dams – El Quimbo and Sogamoso – logged losses of more than 99 %. Some
reservoirs exhibited an increase in TSS downstream of the dam relative to
upstream conditions, which may reflect local erosion processes or activities, while
other reservoirs exhibited upstream sediment losses. For example, the Porce
River has been dammed by several dams in a cascade system and, as a result,
carries a relatively low sediment load; however, the river reach immediately
below Porce III dam is turbid because of local illegal mining activity. Beyond
such local artifacts, turbid inflow and clear outflow are a typical modality
of Colombian dams, especially during rainy periods.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e1748">Proportional change in mass of total suspended solids (TSS) in
river water downstream of 10 Colombian hydropower dams relative to upstream
values. Negative values indicate a loss of TSS, whereas positive values indicate an
increase in TSS. Each point represents one pair of contemporaneous
measurements from the most recent year of available data (either 2018 or
2017).</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f05.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Discussion</title>
<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>Drivers</title>
      <p id="d1e1773">In contrast to sediment loss and warm-water discharge, which appear to be
ubiquitous, cold-water pollution and hypoxic effects appear to both afflict
the same subset of four Colombian reservoirs (Urrá, El Quimbo, Prado and
Sogamoso). Examining the characteristics of these reservoirs reveals some
important commonalities: all are very deep (at least 70 m) and have long
mean residence times, sufficient for anoxia to develop in the hypolimnion
(as is evident at Urrá,<?pagebreak page1499?> which has depth profile data; Fig. S1). As
all of the power plants have fixed depth intakes to run their turbines, there is
no opportunity for operators to avoid discharging cold and hypoxic water
when the thermocline/oxycline is shallower than the depth of intakes. In the
case of El Quimbo, hydropower authorities inject liquid oxygen into
discharge waters to meet the minimum dissolved oxygen requirement of 4 mg L<inline-formula><mml:math id="M60" 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>, which is an expensive measure that they hope will be phased out by 2023 as
oxygen demand in the reservoir lessens, as typically happens as reservoirs
age. For Urrá, authorities have found a policy solution, passing a rule
relaxing the minimum oxygen requirement to 2 mg L<inline-formula><mml:math id="M61" 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> for the first 5 km
downstream of the dam, which is good for hydroelectric generators but does
not reduce impacts on aquatic biodiversity and its associated ecosystems
services. Both Prado and Sogamoso are out of compliance with the 4 mg L<inline-formula><mml:math id="M62" 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> threshold, and authorities are evaluating management options
(unpublished documents from Autoridad Nacional de Licencias Ambientales).</p>
      <p id="d1e1812">The development of hypoxia in lakes and reservoirs can be exacerbated by
long hydraulic residence times and elevated oxygen demand from organic
matter inputs. Colombian dam data illustrate the importance of these
reservoir and catchment characteristics for determining the risk of hypoxia
developing in downstream rivers. Patángoras (Miel), in some important respects, appears
to be like El Quimbo – they have similar depths, hydrologic residence times and fixed-depth intakes – but, in contrast to El Quimbo, Miel shows no sign of downstream hypoxia. Miel also relies on a reoxygenation system – an air bubbler in its oscillation cavern – but this is a much less intensive
and less costly intervention than El Quimbo's need for liquid oxygen injection. Part of Miel's behavior may be attributable to the fact that its inflowing water is near saturation and has a very low biochemical oxygen
demand (BOD; Table S2), meaning that severe hypoxia only appears rarely and
typically well below 50 m depth (Fig. S2). This is likely a function of the
well-preserved state of the Miel catchment, which includes robust
riparian buffers that buffer the reservoir against extreme hydrologic
fluctuations. In contrast, Porce II, which lies downstream of the Medellín metropolis (population of 4 million people), receives inflows that are already hypoxic (<inline-formula><mml:math id="M63" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 5 mg L<inline-formula><mml:math id="M64" 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 have an elevated BOD (Table S2). Therefore, anoxia downstream of Porce II is not surprising. Further downstream, the Porce III Reservoir receives river water with substantial oxygen deficits, but its short residence time of just over 8 d
prevents the dam from discharging water with significantly less oxygen than
its inflows. Although the evidence from Colombian dams is anecdotal, it is
consistent with the conventional logic that reservoirs loaded with high
levels of organic matter from inflows (or left behind by inundated terrestrial ecosystems during reservoir filling) and with long residence
times have greater potential to develop hypolimnetic anoxia for discharge
downstream.</p>
      <p id="d1e1834">Dam design features, rather than environmental factors such as oxygen
demand, offer an alternative explanation for why some dams avoid discharging
hypoxic water downstream. A tower system with multiple intakes spanning 46
vertical meters in the Chivor Reservoir allows for discharged waters to be
sourced from different depths as water levels fluctuate seasonally. Although
we see no evidence of hypolimnetic hypoxia in this reservoir (Table S2),
its selective withdrawal system could ensure oxic surface waters are passed
downstream, as has been proposed as a solution for tailwater hypoxia in other
tropical dams (Kunz et al., 2013). As previously stated, an air blower in the oscillation cavern at
Miel dam provides reaeration of
seasonally hypoxic (2 to 5 mg L<inline-formula><mml:math id="M65" 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>) turbinated waters, such that
discharged waters typically maintain DO concentrations of at least 6 mg L<inline-formula><mml:math id="M66" 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> (Table S2). A low BOD of inflows, as described
above, may be an important mitigating factor, but evidence of the
effectiveness of an engineered reoxygenation system for avoiding downstream
hypoxia cannot be ignored.</p>
</sec>
<sec id="Ch1.S4.SS2">
  <label>4.2</label><title>Implications for river ecology</title>
      <?pagebreak page1500?><p id="d1e1869">Many tropical freshwater aquatic species are highly susceptible to thermal
regime changes (Olden and Naiman, 2010),
and a temperature change of a few degrees (Fig. 3) may very well cause
disruptions to the life cycles of sensitive species
(King
et al., 1998; Clarkson and Childs, 2000). In the Andes, surface-releasing
reservoirs that discharge warm waters might hypothetically favor lowland
species over the cooler-water species that we would normally expect to be
present at a given altitude. Several potamodromous fish in Colombia's
Magdalena River, such as bocachico (<italic>Prochilodus magdalenae</italic>) and pimelodids (<italic>Pseudoplatystoma magdaleniatum</italic> and <italic>Pimelodus yuma</italic>) migrate seasonally
from lowlands to up to 1200 or 500 m elevation, respectively, to spawn,
historically transiting river reaches that have been dammed in recent
decades (López-casas et al., 2014, 2016). Therefore, these species may avoid spawning
in rivers altered by upstream dams, effectively reducing the available
reproductive habitat (López-Casas, 2015). Alternatively, if the
fish do spawn, thermal changes may disrupt the timing of embryo development;
this can be lethal, as has been documented in Colombian fish farms
(Harvey and Hoar, 1980) and the Mekong River of tropical
Southeast Asia  (Li et al., 2021). Researchers have found
that hydropower generation is associated with changes in the production of a
hormone driving oocyte maturation in <italic>P. magdalenae</italic>, thus disrupting its spawning cycle
in dammed rivers  (de Fex-wolf et al., 2019). For Andean fish
species, the ranges of thermal tolerance are poorly known; therefore, more research
would be needed to test such a hypothesis.</p>
      <p id="d1e1884">Hypoxic conditions in the tailwaters of dams impose even more dramatic
ecological constraints than temperature. Dissolved oxygen concentrations
from below 3.5 to 5 mg L<inline-formula><mml:math id="M67" 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> trigger escape behavior in most macroscopic
organisms (Spoor, 1990), and monitoring data reveal
concentrations of less than 5 mg L<inline-formula><mml:math id="M68" 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> below seven Colombian hydropower
dams. Because the data come from sparse grab samples, it is not clear how
persistent hypoxia is in these river reaches, but, at minimum, they indicate
a loss of viable habitat for hypoxia-sensitive species for at least some
parts of the year. The fish communities downstream of Colombia's Porce III
dam – one of the projects with hypoxic tailwater – have shifted, with a loss
of some native species and replacement by invasive exotic species
(Valencia-Rodríguez et al., 2022). The
authors of this study attribute these shifts to habitat fragmentation, but
anoxia/hypoxia could be an important factor, as many other studies have
commented that the multiple changes and stressors imposed by dams are
difficult to disentangle   (van
Puijenbroek et al., 2021; Young et al., 1976). A review from Brazil
indicates that poor oxygen management associated with dams is likely to be a
major driver of fish mortality in South American rivers, accounting for
roughly 40 % of fish kills covered by the media (Agostinho et al.,
2021). As with temperature, a limited understanding of species-specific
fish tolerance thresholds makes it impossible to fully grasp the impact
of hypoxia/anoxia on fish communities.</p>
      <p id="d1e1911">Sediment trapping in dams, which can reach 99 % efficiency in some Colombian
cases, exerts ecological impacts at different spatial scales. Unnaturally
clear river water below dams may favor a different pool of top predators
(those adapted for visual hunting), which may displace species adapted to
turbid conditions, setting off trophic cascades rippling down the food
chain. This effect has been documented at a large dam in Brazil
(Granzotti et al., 2018), but it has not yet been reported in the
Tropical Andes. If the sediment load is not replenished through additional
erosion in excess of deposition downstream of the dam, the
floodplain and delta ecosystems, which depend on riverine sediment delivery,
will ultimately be starved, disrupting the cycles of nutrient retention and transport
(Kondolf et al., 2014). A modeling exercise
estimates that up to 40 % of
sediments in the heavily dammed Magdalena River basin are currently being trapped behind dams and that this figure could
increase to up to 68 %, threatening the ecological functioning of the
Colombia's Mompós Depression, one of South America's largest wetland
complexes  (Angarita et al., 2018).
Scientists have sounded the alarm that, globally, sediment trapping at dams
in concert with sea level rise may lead to a massive loss of coastal deltaic
wetlands  (Giosan et
al., 2014; Dunn et al., 2019). Colombian river sediments support mangroves
on the Pacific and Caribbean coasts, although the extent to which dams may be
impacting floodplain lakes, coastal mangroves and deltaic processes within
Colombia's coastal zones is unclear.</p>
      <p id="d1e1914">It is a challenge to pinpoint the exact mechanism by which dams alter their
associated aquatic ecosystems, as they impose so many changes, spanning
physical, hydrological, chemical and biological dimensions, simultaneously
(Young et al., 1976). Many studies of dammed river ecology focus
on hydromorphological changes in habitat structure or availability, loss of
connectivity, and alterations to flow regimes  (Bratrich et
al., 2004; García et al., 2011). In this study, we focus on oxygen,
temperature and suspended sediments and find that these are just as
plausible mechanistic pathways for reductions in habitat quality and availability, thereby driving shifts in ecological communities in dam-adjacent
ecosystems. Environmental assessments of dams should take care not to ignore
the changes dams impose on the physicochemical condition of downstream
waters nor to underestimate temperature, oxygen and suspended sediments as
modes of ecological change.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><?xmltex \currentcnt{6}?><?xmltex \def\figurename{Figure}?><label>Figure 6</label><caption><p id="d1e1920">Monthly monitoring data from the Urrá Reservoir in Cordoba,
Colombia, in 2018 showing the upstream, reservoir and downstream <bold>(a)</bold> dissolved oxygen concentration and <bold>(b)</bold> temperature; the
upstream–downstream difference is also shown for the two abovementioned parameters. Data were sourced from Autoridad Nacional de Licencias Ambientales, and data accessibility is described in Sect. 2.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/27/1493/2023/hess-27-1493-2023-f06.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS3">
  <label>4.3</label><title>Implications for management</title>
<sec id="Ch1.S4.SS3.SSS1">
  <label>4.3.1</label><title>Regulators</title>
      <p id="d1e1950">The frequency of problematic changes to river temperature, oxygen and
sediment associated with dams suggests that regulators should consider
risk assessments specific to these parameters and their associated
ecological side effects in environmental impact assessments of new dam
projects. It may even be worth assessing some older existing dams for which
impacts have been underappreciated because of sparse monitoring
requirements. Our analyses of monitoring data show that measurements
frequent enough to capture seasonal variability are highly valuable. Just
six Colombian reservoirs had three or more sampling dates per year, making
it difficult to assess the effects of seasonality on physicochemical
properties for most Colombian hydropower projects. For reservoirs with few
measurements, we may be missing important seasons, and it is probable that
hypoxia downstream of Colombian dams is more prevalent than what happens to
be<?pagebreak page1501?> captured by these sparse observational “snapshots”. From sites with
more frequent measurements, it is possible to glean a deeper understanding
of the interactions between reservoir limnology and downstream conditions
relative to the associated upstream reference. For example, the monthly monitoring scheme
at Urrá Reservoir allowed us to observe that downstream oxygen
concentrations were always depleted relative to the upstream values, even though the
reservoir hypolimnion was only hypoxic for part of the year (Fig. 6a).
Monthly monitoring at Urrá also gives a much more complete picture of
thermal outcomes. It is evident that the Urrá Reservoir remains
thermally stratified for 12 months of the year; moreover, while upstream river
temperatures fluctuated some 6 <inline-formula><mml:math id="M69" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C seasonally, downstream
temperatures were more homogenous and varied by less than 3 <inline-formula><mml:math id="M70" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C throughout
the year (Fig. 6b). For 8 of the 12 months of the year, Urrá exerted only
minor (<inline-formula><mml:math id="M71" display="inline"><mml:mi mathvariant="italic">&lt;</mml:mi></mml:math></inline-formula> 2 <inline-formula><mml:math id="M72" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) thermal effects on downstream waters. Thus, if
we were to randomly select 3 months of data for Urrá, there would be
a 42 % chance of not detecting one of the more extreme outcomes. Hence, for
reservoirs with one (e.g., Betania) or a few measurements per year (e.g., Porce II), we are undoubtedly missing most of the range of their potential
thermal impacts. It is likely that downstream thermal regime changes are
even more prevalent than our analysis of available data seems to reveal.
Because Urrá also employs many monitoring stations extending downstream
of the dam, reoxygenation and warming can be tracked longitudinally as the
river flows. This makes it possible to assess, for example, the length of the
river reach for which dissolved oxygen is below a certain target threshold.
It would be much easier to detect and understand impacts if more hydropower
dams had such high-frequency and spatially rich monitoring programs.</p>
      <p id="d1e1987">Of course, increasing monitoring effort will increase costs; therefore, regulators
will have to decide whether the utility of higher-frequency and higher-quality information justifies the expense. In addition, monitoring is not
practical for detecting fluctuations in water conditions that occur over
timescales of weeks, days or hours. Automated sensors placed strategically
below reservoirs may be a cost-effective method for generating
high-frequency measurements of dissolved oxygen, temperature, turbidity and
other parameters. Although sensors<?pagebreak page1502?> cannot replace manual grab samples and
laboratory analyses, which cover a much broader suite of parameters, they do
provide a window into sub-daily fluctuations in water properties and may be
effective for some classes of solutes
(Pesántez et al.,
2021). Hourly measurements can give insights into how hydropower operations
(i.e., hydropeaking) interact with water conditions  (Calamita et
al., 2021), or they can capture the effects of episodic events such as a reservoir
drawdown for maintenance. Remote monitoring via satellite is another
potential low-cost solution. Remote sensing would not work for
dissolved oxygen, but it may be viable for tracking changes in temperature and
chlorophyll <inline-formula><mml:math id="M73" display="inline"><mml:mi>a</mml:mi></mml:math></inline-formula> and has been effectively applied for monitoring big shifts
in river turbidity  (Rudorff et al.,
2018). We caution that satellite data alone, even in the most ideal of
scenarios, still require ground-truthing; hence, they cannot completely replace
ground-based monitoring.</p>
      <p id="d1e1997">We note that our analyses are only possible because Colombia has a
centralized and public repository for environmental monitoring data – a
system uncommon in the tropics and which puts it ahead of its peers.
Neighboring countries along the Tropical Andes – Venezuela, Ecuador, Peru,
Bolivia – all lack a national-scale public data repository for hydropower
monitoring data, making systematic assessment of national portfolios
difficult. Colombia's database, mandated and curated by ANLA, probably
provides the best opportunity to understand river responses to hydropower
for the Tropical Andes region.</p>
</sec>
<sec id="Ch1.S4.SS3.SSS2">
  <label>4.3.2</label><title>Planners</title>
      <p id="d1e2008">For siting and designing future hydropower dams in the Tropical Andes,
Colombia provides important lessons. First, loss of river sediments may be
unavoidable, but the impact could be minimized by targeting catchments with
low sediment loads or implementing sediment bypass systems where feasible.
Second, downstream warm-water effects are probably unavoidable unless intake
systems can mix surficial and deep waters to match upstream temperature.
Finally, cold, anoxic discharges may be avoidable through siting/design
choices. High-elevation reservoirs (e.g., Chivor and Guavio) and those in
well-preserved catchments with low BOD (Miel) will be less prone to developing anoxia. Short residence times may prevent reservoirs from discharging hypoxic waters downstream (Porce III). Multiple intakes spanning a range of depths (Chivor) may help avoid downstream hypoxia. Failure to design hydropower schemes for effective oxygen management may later necessitate costly interventions, such as liquid oxygen injection (El Quimbo), or exemptions from environmental protection policies with considerable impacts (Urrá).</p>
      <p id="d1e2011">The energy sectors across the Tropical Andes would be wise to
consider the ecological successes and failures of Colombia's existing
hydropower portfolio as they decide which of the many hundreds of potential
hydropower projects to prioritize. Our findings on the pervasiveness of
challenges with respect to sediment loss, thermal regime change and hypoxia could be
incorporated in existing basin-scale planning frameworks that already use
multiple indices to avoid and minimize the anticipated environmental and social
impacts of hydropower expansion  (Opperman et al., 2015,
2017). The environmental impacts that we document in Colombia may also be useful
for evaluating specific hydropower project plans. Due diligence during dam
planning will not only help industry comply with environmental regulations
but will also potentially optimize the net value that hydropower can deliver to
society by avoiding and minimizing external environmental costs.</p>
</sec>
</sec>
</sec>

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

      <p id="d1e2021">R. Scott Winton wrote R scripts to visualize the monitoring data .csv files extracted from the ANLA .gdb files by Daniel Valencia-Rodríguez. The R scripts and the .csv files are available upon request from the
corresponding author (R. Scott Winton).</p>
  </notes><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e2027">The data used in this work are public and may
be requested directly from ANLA (which has an open data policy): <uri>https://datosabiertos-anla.hub.arcgis.com/</uri> (ANLA, 2023). The .csv files analyzed in this paper may be requested directly from the corresponding author (R. Scott Winton).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e2033">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/hess-27-1493-2023-supplement" xlink:title="pdf">https://doi.org/10.5194/hess-27-1493-2023-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e2042">RSW, SLC, JD and LJS conceptualized the experiments. DVR and CBF curated the
data. RSW analyzed the data. RSW, BW and LJS secured funding. All authors
participated in the investigation. BW and LJS administered the
project. RSW wrote R scripts to analyze and visualize the data. LJS and BW
provided supervision. RSW wrote the original draft. All authors participated
in the editing and review of the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e2048">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="d1e2054">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2060">This work was supported by a grant from the Leading House for the Latin
American Region via the Centro Latino Americano-Suizo of the University of St. Gallen (CLS HSG), mandated by the Swiss State Secretariat for Education,
Research and Innovation (SERI). The authors acknowledge additional in-kind support from the University of Antioquia and The Nature Conservancy. We thank the Autoridad Nacional de Licencias Ambientales<?pagebreak page1503?> for making their data publicly available and Silvia Vanegas Pinzón for facilitating access. The authors are also grateful to Juan Sebastian Hernandez Suarez for helpful suggestions that improved the manuscript.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e2065">This research has been supported by the Staatssekretariat für Bildung, Forschung und Innovation (grant no. SMG1924).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e2071">This paper was edited by Wouter Buytaert and reviewed by three anonymous referees.</p>
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