Improving drain flow simulations in a national hydrologic model with machine learning estimates of drain fraction

Agricultural areas are often artificially drained, especially in temperate and flat landscapes. This also applies to Denmark, where approximately half of the agricultural area is artificially drained, mostly with tile drains. The generated drain flow has significant impacts on various aspects of the hydrologic cycle such as groundwater recharge, flow paths and transport times. Consequently, drain flow is a major control on the transport of nutrients such as nitrogen. Yet, detailed knowledge of spatial and temporal variability of drain flow is inadequate due to insufficient observations of drain flow, lacking knowledge of drain infrastructure and issues of scale and hydrogeologic heterogeneity.

The objective was to improve the simulation of both the spatial and temporal variability of drain flow in a large-scale hydrological model used to map nitrate transport. This model is a physically-based, distributed groundwater-surface water model of all of Denmark. It is a major challenge to simulate drain flow distribution in space and time with the national model due to its coarse horizontal resolution (500m or 100m), and the lack of drain flow observations at relevant scale. Hence, to achieve the objective, we gathered existing field-scale drain flow observations from all over Denmark. For these drain catchments, fine-scale (10m) physically based hydrological models were setup and calibrated against the drain flow observations. After successful calibration, the resulting simulated distributions of drain fraction (drain flow relative to precipitation) were regionalized to applicable areas across all of Denmark. The regionalization was performed using decision tree machine learning algorithms, and a set of topographic and geologic covariates available nationally at fine resolution. An analysis of spatial transferability of the machine learning algorithm allowed to limit predictions to applicable areas. Finally, these estimates of drain fraction are used in the calibration of the large-scale national hydrologic model, amongst other objective functions such as streamflow and groundwater heads.