Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components
<p>Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics, and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast- and slow-flow components constitut...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/26/5431/2022/hess-26-5431-2022.pdf |
| _version_ | 1811256645669355520 |
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| author | T. Olarinoye T. Gleeson A. Hartmann A. Hartmann |
| author_facet | T. Olarinoye T. Gleeson A. Hartmann A. Hartmann |
| author_sort | T. Olarinoye |
| collection | DOAJ |
| description | <p>Analysis of karst spring recession hydrographs is essential for determining
hydraulic parameters, geometric characteristics, and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast- and slow-flow components constituting a karst spring recession hydrograph typically involve manual and subjective
procedures. This subjectivity introduces a bias that exists, while manual procedures can introduce errors into the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective, and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin's karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameter optimization approaches into Mangin's model to increase the degree of freedom, thereby allowing for more parameter interaction. The modified recession extraction and parameter optimization approaches were tested on three karst springs under different climate conditions. Our results showed that the modified extraction methods are capable of distinguishing different recession components and derived parameters that reasonably represent the analyzed karst systems. We recorded an average Kling–Gupta efficiency KGE <span class="inline-formula">></span> 0.85 among all recession events simulated by the recession parameters derived from all combinations of recession extraction methods and parameter optimization approaches. While there are variabilities among parameters estimated by different combinations of extraction methods, optimization approaches, and seasons, we found much higher variability among individual recession events. We provided suggestions to reduce the uncertainty among individual recession events and raised questions about how to improve confidence in the system's attributes derived from recession parameters.</p> |
| first_indexed | 2024-04-12T17:44:15Z |
| format | Article |
| id | doaj.art-7aa403c2a9fc417c934092452fb332c7 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-04-12T17:44:15Z |
| publishDate | 2022-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-7aa403c2a9fc417c934092452fb332c72022-12-22T03:22:43ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382022-11-01265431544710.5194/hess-26-5431-2022Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow componentsT. Olarinoye0T. Gleeson1A. Hartmann2A. Hartmann3Chair of Hydrological Modeling and Water Resource, University of Freiburg, Freiburg, GermanyDepartment of Civil Engineering, University of Victoria, Victoria, BC, CanadaChair of Hydrological Modeling and Water Resource, University of Freiburg, Freiburg, GermanyChair of Groundwater Systems, Technical University Dresden, Dresden, Germany<p>Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics, and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast- and slow-flow components constituting a karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces a bias that exists, while manual procedures can introduce errors into the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective, and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin's karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameter optimization approaches into Mangin's model to increase the degree of freedom, thereby allowing for more parameter interaction. The modified recession extraction and parameter optimization approaches were tested on three karst springs under different climate conditions. Our results showed that the modified extraction methods are capable of distinguishing different recession components and derived parameters that reasonably represent the analyzed karst systems. We recorded an average Kling–Gupta efficiency KGE <span class="inline-formula">></span> 0.85 among all recession events simulated by the recession parameters derived from all combinations of recession extraction methods and parameter optimization approaches. While there are variabilities among parameters estimated by different combinations of extraction methods, optimization approaches, and seasons, we found much higher variability among individual recession events. We provided suggestions to reduce the uncertainty among individual recession events and raised questions about how to improve confidence in the system's attributes derived from recession parameters.</p>https://hess.copernicus.org/articles/26/5431/2022/hess-26-5431-2022.pdf |
| spellingShingle | T. Olarinoye T. Gleeson A. Hartmann A. Hartmann Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components Hydrology and Earth System Sciences |
| title | Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components |
| title_full | Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components |
| title_fullStr | Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components |
| title_full_unstemmed | Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components |
| title_short | Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components |
| title_sort | karst spring recession and classification efficient automated methods for both fast and slow flow components |
| url | https://hess.copernicus.org/articles/26/5431/2022/hess-26-5431-2022.pdf |
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