Next Article in Journal
Effect of Radio-Frequency Treatment on the Changes of Dissolved Organic Matter in Rainwater
Previous Article in Journal
Spatiotemporal Organic Carbon Distribution in the Capo Peloro Lagoon (Sicily, Italy) in Relation to Environmentally Sustainable Approaches
Article

Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

1
Department of Hydrology, Geological Survey of Denmark and Greenland, DK-1350 Copenhagen, Denmark
2
Department of Water Resources, Ramboll Denmark, DK-2300 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Academic Editor: Aizhong Ye
Water 2022, 14(1), 110; https://doi.org/10.3390/w14010110
Received: 9 November 2021 / Revised: 10 December 2021 / Accepted: 29 December 2021 / Published: 5 January 2022
(This article belongs to the Section Water, Agriculture and Aquaculture)
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 km2) 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. View Full-Text
Keywords: streamflow signatures; hydrological models; agriculture; artificial drain; model optimization; regional scale streamflow signatures; hydrological models; agriculture; artificial drain; model optimization; regional scale
Show Figures

Figure 1

MDPI and ACS Style

Schneider, R.; Stisen, S.; Højberg, A.L. Hunting for Information in Streamflow Signatures to Improve Modelled Drainage. Water 2022, 14, 110. https://doi.org/10.3390/w14010110

AMA Style

Schneider R, Stisen S, Højberg AL. Hunting for Information in Streamflow Signatures to Improve Modelled Drainage. Water. 2022; 14(1):110. https://doi.org/10.3390/w14010110

Chicago/Turabian Style

Schneider, Raphael, Simon Stisen, and Anker L. Højberg. 2022. "Hunting for Information in Streamflow Signatures to Improve Modelled Drainage" Water 14, no. 1: 110. https://doi.org/10.3390/w14010110

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop