Understanding and quantifying total organic nitrogen (TON) concentrations in streams and their spatial variation is essential for accurately assessing their importance for total nitrogen (TN) loadings to coastal waters and the possible sources of TON in the landscape. Total organic nitrogen constitutes almost 20% of the TN riverine loadings to Danish coastal waters. We used environmental monitoring data from 390 stations across Denmark to calculate indirectly measured annual average TON concentrations using a wide range of predictor variables. We then trained a machine learning model to predict spatially distributed average annual TON concentrations in Danish streams, achieving a mean error of 0 mg L⁻¹ and a root‐mean‐squared error of 0.20 mg L⁻¹. The mean annual predicted (measured) TON concentrations in Danish streams were 0.84 (0.70) mg L⁻¹, with a standard deviation of 0.36 (0.31) mg L⁻¹. The model is primarily driven by mean elevation and the percentages of agricultural land, tile‐drained areas, lakes and carbon‐enriched soils in the catchment. The developed model contributes to our understanding of the spatial variation in annual TON concentrations in streams at a national scale, supporting our understanding of processes driving nitrogen cycling.

Modeling of nitrate transport and retention in agricultural land use areas provides useful information to support water quality assessment and management. The accuracy and precision of model simulations are highly dependent on model input factors for which the appropriate values are generally difficult to determine and from which various uncertainties are induced into the modeling procedure. In this study, we applied a Distance-based Generalized Sensitivity Analysis (DGSA) to a high-resolution (25 × 25 m) nitrate transport and retention model for a tile-drained agricultural catchment (4.4 km²) to investigate the extent to which model input factors affect the spatially distributed nitrate retention. The input factors included the nitrate leaching from the root zone, the partitioning of nitrate into tile drainage and groundwater flux, the groundwater flux out of the catchment, the hydrogeological properties, and the denitrification rates in groundwater. The DGSA results were examined in both spatially lumped and distributed perspective. We found that the partitioning of nitrate into tile drainage and groundwater flux was the most important factor for modeling nitrate retention while the hydrogeological properties were secondary but also important. Conversely, the nitrate leaching from the root zone and denitrification rates in groundwater were noninfluential. By increasing the resolution of the DGSA analysis from catchment to model pixel, we found that input factors noninfluential on catchment scale were influential on pixel scale in discrete areas, and, as a general take-home-message, input factors influential on nitrate retention in at least 25 % of the model pixels were sensitive on catchment scale as well. Improved understanding of sensitivity of modelling nitrate retention may help the modelers and water managers to decide which input factors to prioritize in the modelling and data collection to improve the accuracy and precision of the model responses.

High total phosphorus (TP) concentrations in freshwater such as streams, rivers, and lakes remain a global issue, causing eutrophication and poor ecological conditions. We extracted annual flow‐weighted TP concentration data ( N = 3144) from 207 monitored Danish headwater streams (catchment area < 50 km ² ) during the period 1990–2019 as the basis for developing a machine learning (ML) model for diffuse phosphorus in streams, using 22 potential predictor variables. We tested 70 different algorithms, applying an AI platform and a random split strategy with a holdout dataset (20%), a validation dataset (16%), and four training datasets (4 × 16%) in a fivefold cross‐validation procedure across a total of 207 stream stations. The ML algorithm “eXtreme Gradient Boosted trees regressor: XGBoost” was identified as the best‐performing model, based on 13 predictor variables, with a relatively high explanatory power for the training dataset ( R ²  = 0.68), the cross‐validation dataset ( R ²  = 0.71), and the holdout dataset ( R ²  = 0.66). The most important catchment characteristics of the 13 predictor variables were paved area, tile drained area, clay content in subsoil, farmed area, and TP loss from bank erosion. An external test of model transferability on a dataset, using an additional 142 stream stations ( N = 1261), revealed a somewhat lower predictive power of the final model ( R ²  = 0.41). The final model was applied to simulate annual diffuse flow‐weighted TP concentrations for 3200 unique headwater catchments (ca. 15 km ² ) and this analysis now supports the calculation of annual TP loadings to surface waters from otherwise ungauged coast‐near areas in Denmark.

Study regionVejrumbro, a shallow drained riparian peatland in the Nørreå River valley, central Jutland, Denmark.Study focusRiparian peatlands exhibit strong spatial and temporal heterogeneity. Consequently, controls on water table levels are not constant. We investigated how dominant controls shift through time and space using a high-resolution (5 m x 5 m) physically based model and a distance-based generalized sensitivity analysis.

Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. The use of physics-based water flow models to understand tile drained systems is common. These models are complex, with large parameter sets and require high computational effort. The primary goal of this study was to examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions. The specific aim of our study was to assess the potential of ML tools for predicting drain flow time series in multiple catchments subject to a range of climatic and landscape conditions. The investigation is based on unique data containing time series of daily drain flow in multiple field scale drain sites in Denmark. The data include: climate (precipitation, potential evapotranspiration, temperature); geological properties (clay fraction, first sand layer thickness, first clay layer thickness); and topographical indexes (curvature, Topographical wetness indexes, Topographical position index, elevation). Both static and dynamic variables are used in the prediction of drain flows. The ML algorithm extreme gradient boosting (XGBoost) and convolutional neural network (CNN) were examined, and the results were compared with a physics-based distributed model (MIKE-SHE). The results show that XGBoost performs similarly to the physics-based MIKE-SHE models, and both outperform CNN. Both ML models required significantly less effort to build, train, and run than MIKE-SHE. In addition, the ML models support efficient feature importance analysis. This showed that climatic variables were important for CNN models and XGBoost. The results support the use of ML models for hydrologic applications with sufficient data for training. Further, the insights offered by the feature importance analysis may support further data collection and developments of physics-based models when existing data are insufficient to support ML approaches.