Hunting for Information in Streamflow Signatures to Improve Modelled Drainage
About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the tran...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/1/110 |
| _version_ | 1797497002047569920 |
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| author | Raphael Schneider Simon Stisen Anker Lajer Højberg |
| author_facet | Raphael Schneider Simon Stisen Anker Lajer Højberg |
| author_sort | Raphael Schneider |
| collection | DOAJ |
| description | About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km<sup>2</sup>) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization. |
| first_indexed | 2024-03-10T03:11:13Z |
| format | Article |
| id | doaj.art-2ac8efaec439477281616eebf620ec6d |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:11:13Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-2ac8efaec439477281616eebf620ec6d2023-11-23T12:33:01ZengMDPI AGWater2073-44412022-01-0114111010.3390/w14010110Hunting for Information in Streamflow Signatures to Improve Modelled DrainageRaphael Schneider0Simon Stisen1Anker Lajer Højberg2Department of Hydrology, Geological Survey of Denmark and Greenland, DK-1350 Copenhagen, DenmarkDepartment of Hydrology, Geological Survey of Denmark and Greenland, DK-1350 Copenhagen, DenmarkDepartment of Water Resources, Ramboll Denmark, DK-2300 Copenhagen, DenmarkAbout half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km<sup>2</sup>) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.https://www.mdpi.com/2073-4441/14/1/110streamflow signatureshydrological modelsagricultureartificial drainmodel optimizationregional scale |
| spellingShingle | Raphael Schneider Simon Stisen Anker Lajer Højberg Hunting for Information in Streamflow Signatures to Improve Modelled Drainage Water streamflow signatures hydrological models agriculture artificial drain model optimization regional scale |
| title | Hunting for Information in Streamflow Signatures to Improve Modelled Drainage |
| title_full | Hunting for Information in Streamflow Signatures to Improve Modelled Drainage |
| title_fullStr | Hunting for Information in Streamflow Signatures to Improve Modelled Drainage |
| title_full_unstemmed | Hunting for Information in Streamflow Signatures to Improve Modelled Drainage |
| title_short | Hunting for Information in Streamflow Signatures to Improve Modelled Drainage |
| title_sort | hunting for information in streamflow signatures to improve modelled drainage |
| topic | streamflow signatures hydrological models agriculture artificial drain model optimization regional scale |
| url | https://www.mdpi.com/2073-4441/14/1/110 |
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