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Development and application of a parsimonious statistical model to predict tile flow in minerogenic soils

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Subsurface drainage systems are a dominant flow and nitrate transport pathway from fields to surface waters. Existing methods to estimate tile flow are either expensive, complex or need diverse data types. In this study, a parsimonious statistical model was derived and validated for use to obtain estimates of annual tile flow at field scale (1–10 ha) and catchment scale (200–1400 ha) in tile-drained minerogenic soils. The model was developed from tile flow and precipitation data from 38 drainage stations distributed all over Denmark. Firstly, a significant linear relationship between tile flow and precipitation was derived (R2 =0.57; P < 0.0001). The threshold value in the linear regression model (543 mm) can be viewed as a value from where tile flow is initiated, and the slope value (0.48) indicates that when the threshold value is reached, precipitation is partitioned into recharge (52%) and tile flow (48%). Secondly, the developed model was evaluated at catchment scale in 14 smaller Danish catchments and in more detail in a 15th catchment (Lillebæk). The evaluation showed that the model was more accurate and precise at catchment scale than at field scale. This difference might appear because the natural spatial variability in tile flow generation among fields within a catchment is averaged when evaluating the mean of multiple fields at catchment scale. Thirdly, the capability of the model to estimate transport of nitrate-N from tile drains was evaluated in Lillebæk at catchment scale, and here the model performed satisfactorily against the measured transport of nitrate-N (NSE=0.72 and PBIAS=−20%). We suggest that the model can be used to aid agricultural water management.

TidsskriftAgricultural Water Management
StatusUdgivet - maj 2023

Bibliografisk note

Funding Information:
The authors would like to thank Bo V. Iversen from Department of Agroecology at Aarhus University in Denmark for access to hydrological data. We would also like to acknowledge technical reviews by two anonymous reviewers. This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Publisher Copyright:
© 2023 The Authors

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