Abstract
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.
Highlights
Publisher’s Note: MDPI stays neutralAround the world, agricultural land is commonly artificially drained to prevent flooding and increase crop yield
The Random Forest (RF) regressor was trained on two thirds of the 73 sub-catchments of the Storå and Odense Å models, Figure 2, while the remaining sub-catchments were used for test
Based on the model simulations, it was found that the drain fraction could be well described by the hydrograph signatures, with a mean absolute error on the predicted drain fraction of 0.126 for the test dataset
Summary
Agricultural land is commonly artificially drained to prevent flooding and increase crop yield. About 66% of Denmark’s land area is used for agriculture, of which about half is assumed to be artificially drained, mainly by tile drains [1]. Tile drain provides a short-cut from the field to surface water bodies, bypassing transport in the deep aquifers. This is crucial to nitrate transport, as nitrate can be reduced only under anaerobic conditions, which primarily are found in the deeper groundwater systems [7,8,9]. Insight into the amount and dynamics of drain flow is essential to understand and quantify transport of nitrate or other substances
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