Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning
<p>Transmission losses are the loss in the flow volume of a river as water moves downstream. These losses provide crucial ecosystem services, particularly in ephemeral and intermittent river systems. Transmission losses can be quantified at many scales using different measurement techniques. O...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/27/703/2023/hess-27-703-2023.pdf |
| _version_ | 1811168236243255296 |
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| author | A. Di Ciacca S. Wilson J. Kang T. Wöhling T. Wöhling |
| author_facet | A. Di Ciacca S. Wilson J. Kang T. Wöhling T. Wöhling |
| author_sort | A. Di Ciacca |
| collection | DOAJ |
| description | <p>Transmission losses are the loss in the flow volume of a river as water
moves downstream. These losses provide crucial ecosystem services,
particularly in ephemeral and intermittent river systems. Transmission
losses can be quantified at many scales using different measurement
techniques. One of the most common methods is differential gauging of
river flow at two locations. An alternative method for non-perennial
rivers is to replace the downstream gauging location by visual
assessments of the wetted river length on satellite images. The
transmission losses are then calculated as the flow gauged at the
upstream location divided by the wetted river length. We used this
approach to estimate the transmission losses in the Selwyn River
(Canterbury, New Zealand) using 147 satellite images collected between
March 2020 and May 2021. The location of the river drying front was
verified in the field on six occasions and seven differential gauging
campaigns were conducted to ground-truth the losses estimated from the
satellite images. The transmission loss point data obtained using the
wetted river lengths and differential gauging campaigns were used to
train an ensemble of random forest models to predict the continuous
hourly time series of transmission losses and their uncertainties. Our
results show that the Selwyn River transmission losses ranged between 0.25 and 0.65 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><msup><mi mathvariant="normal">m</mi><mn mathvariant="normal">3</mn></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">s</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">km</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="0ed1234798f0f79e3dcead838e525bad"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-703-2023-ie00001.svg" width="60pt" height="13pt" src="hess-27-703-2023-ie00001.png"/></svg:svg></span></span> during most of the 1-year study period. However, shortly after a flood peak the losses could reach up to
1.5 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><msup><mi mathvariant="normal">m</mi><mn mathvariant="normal">3</mn></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">s</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">km</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="9a30f2c0c32de2ead91fba3c2b0f119a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-703-2023-ie00002.svg" width="60pt" height="13pt" src="hess-27-703-2023-ie00002.png"/></svg:svg></span></span>. These results enabled us to improve our
understanding of the Selwyn River groundwater–surface water interactions and provide valuable data to support water management. We
argue that our framework can easily be adapted to other ephemeral rivers
and to longer time series.</p> |
| first_indexed | 2024-04-10T16:22:46Z |
| format | Article |
| id | doaj.art-b4ef5fa40ac44dd1b281f5ee166b7d6d |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-04-10T16:22:46Z |
| publishDate | 2023-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-b4ef5fa40ac44dd1b281f5ee166b7d6d2023-02-09T10:51:14ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382023-02-012770372210.5194/hess-27-703-2023Deriving transmission losses in ephemeral rivers using satellite imagery and machine learningA. Di Ciacca0S. Wilson1J. Kang2T. Wöhling3T. Wöhling4Environmental Research, Lincoln Agritech Ltd, Lincoln, New ZealandEnvironmental Research, Lincoln Agritech Ltd, Lincoln, New ZealandNational Institute of Water and Atmospheric Research (NIWA), Christchurch, New ZealandEnvironmental Research, Lincoln Agritech Ltd, Lincoln, New ZealandChair of Hydrology, Technische Universität Dresden, Dresden, Germany<p>Transmission losses are the loss in the flow volume of a river as water moves downstream. These losses provide crucial ecosystem services, particularly in ephemeral and intermittent river systems. Transmission losses can be quantified at many scales using different measurement techniques. One of the most common methods is differential gauging of river flow at two locations. An alternative method for non-perennial rivers is to replace the downstream gauging location by visual assessments of the wetted river length on satellite images. The transmission losses are then calculated as the flow gauged at the upstream location divided by the wetted river length. We used this approach to estimate the transmission losses in the Selwyn River (Canterbury, New Zealand) using 147 satellite images collected between March 2020 and May 2021. The location of the river drying front was verified in the field on six occasions and seven differential gauging campaigns were conducted to ground-truth the losses estimated from the satellite images. The transmission loss point data obtained using the wetted river lengths and differential gauging campaigns were used to train an ensemble of random forest models to predict the continuous hourly time series of transmission losses and their uncertainties. Our results show that the Selwyn River transmission losses ranged between 0.25 and 0.65 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><msup><mi mathvariant="normal">m</mi><mn mathvariant="normal">3</mn></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">s</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">km</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="0ed1234798f0f79e3dcead838e525bad"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-703-2023-ie00001.svg" width="60pt" height="13pt" src="hess-27-703-2023-ie00001.png"/></svg:svg></span></span> during most of the 1-year study period. However, shortly after a flood peak the losses could reach up to 1.5 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><msup><mi mathvariant="normal">m</mi><mn mathvariant="normal">3</mn></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">s</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">km</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="9a30f2c0c32de2ead91fba3c2b0f119a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hess-27-703-2023-ie00002.svg" width="60pt" height="13pt" src="hess-27-703-2023-ie00002.png"/></svg:svg></span></span>. These results enabled us to improve our understanding of the Selwyn River groundwater–surface water interactions and provide valuable data to support water management. We argue that our framework can easily be adapted to other ephemeral rivers and to longer time series.</p>https://hess.copernicus.org/articles/27/703/2023/hess-27-703-2023.pdf |
| spellingShingle | A. Di Ciacca S. Wilson J. Kang T. Wöhling T. Wöhling Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning Hydrology and Earth System Sciences |
| title | Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| title_full | Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| title_fullStr | Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| title_full_unstemmed | Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| title_short | Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| title_sort | deriving transmission losses in ephemeral rivers using satellite imagery and machine learning |
| url | https://hess.copernicus.org/articles/27/703/2023/hess-27-703-2023.pdf |
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