The National Hydrological Model for Denmark (DK-model) is a distributed, integrated hydrological model coupling 3D groundwater flow to descriptions of root zone processes, overland flow and river routing, including anthropogenic interference with the hydrological cycle. It covers all of Denmark (~43,000km2) at 500m and 100m grid scale. Its constant development over the last three decades has both been driven by research projects and projects for public authorities. It is being used for various tasks such as water resource assessments, climate change impact assessments, hydrological real-time monitoring and nutrient transport studies. Recently, we endeavored novel ways to calibrate and parameterize the DK-model. The model is placed on the edge between research interest and practical applications, with a demand for adequately representing various aspects of the hydrological cycle across the entirety of the model domain. In combination with its large-scale distributed nature and high computational demand, conventional (groundwater) model optimization techniques are challenged: The complex nature and versatile applications of the DK-model require suitable parametrization schemes and inclusion of diverse calibration and evaluation data, beyond conventional groundwater head observations and streamflow. This also leads to trade-offs between the multiple objective functions. Hence, we moved beyond previously used single solution, gradient-based optimization algorithms. The Pareto Archived Dynamically Dimensioned Search (PADDS) algorithm allows us to use a global parameter optimization, effective even at a few hundred model runs. Another major advantage of PADDS is that it does not require the a-priori weighting of objective function groups – instead, it explores the tradeoffs (pareto front) between the different objective function groups, allowing weighting after gaining knowledge about tradeoffs during the optimization process. Also, all solutions explored during the optimization are stored and remain open to analysis after finished optimization. This not only sheds light on tradeoffs between different objective functions in a unique manner, but also supports understanding of parameter sensitivity and uncertainty in a manner which otherwise is hard to achieve due to computational constraints. Moreover, we included evapotranspiration patterns from satellite products as well as a machine learning based estimate of artificial drain flow as novel spatial data in the model evaluation. This helps us constraining some of the model processes crucial for e.g. nutrient transport, but otherwise poorly constrained by conventional data such as streamflow (305 stations) and groundwater heads (24,000 wells) covering practically the entire model domain. We explored the benefits of this optimization setup applied to the DK-model, advancing not only the calibration process itself, but also our understanding of model process representation and performance.

Nitrate transport from cultivated areas poses a significant risk to water quality of inland and marine water bodies. During subsurface transport nitrate may undergo reduction under anaerobic conditions. However, in temperate climates tile drainage are widely used in agriculture, which provides short-circuits between the root zone and surface water systems, with limited or no nitrate reduction. To identify areas most prone to loss of nitrate to the aquatic environment, it is thus vital to assess and quantify not only the transport out of the root zone, but also the fraction of nitrate being transport by tile drains vs. groundwater transport. The spatio-temporal pattern of tile drainage can be estimated by use of physically-based distributed hydrological models, but their setup and evaluation are generally challenged by limited data on drains with respect to both the tile drain network and in particular with respect to the efficiency of the drains, i.e. the amount of recharging water that is transported via drains. To support water management, the models must cover relevant scales (100 – 1000 km2) posing an additional upscaling modelling challenge. As part of nitrogen usage regulation in Denmark, a national nitrogen model has been developed, which is currently under revision. An important task is to improve the description of drain transport. This is achieved through detailed hydrological modelling of fields with drain flow observations from which drain fractions, i.e. the fraction of precipitation being drained, are calculated for each model grid. A machine learning algorithm (gradient boosted decision tree) is then used to regionalise the drain fraction to the national scale. Results are used in model calibration to improve the spatial and temporal description of drain flow. While the drainage estimates are needed at a fine scale, preferably at grid scale, data to evaluate model accuracy in terms of nitrate transport is not available at this scale. At catchment scale, the seasonal dynamics of observed nitrogen transport in streams provide valuable information on the amount and temporal variation of the contributions from drains. Analyses of the observed time series are used to further constrain nitrate drainage transport at catchments scale. Uncertainty in model results is assed using a stochastic approach calling for numerous model runs. To limit computational time, model simulations are carried using different spatial resolutions (100 and 500 m grids), where the coarser model is run for an entire 30-year period while the finer resolution is only run for a decade. Results from the overlapping simulation period is used for tuning a downscaling of the drain flow in 500 to a 100 m resolution, thereby providing model results of nitrate transport via drainage at a 100 m resolution for the entire 30 years.

Nitrogen Leaching Estimation System version 5 (NLES5) is an empirical model extensively used for estimating annual nitrate leaching from the root zone. The model is based on leaching data obtained by multiplying the measured nitrate concentration below the root zone depth by the percolation calculated using a hydrological model, which together provides estimates of annual nitrate leaching from the root zone. However, this approach has some limitations, including redundancy and unclear error propagation in the relationship between nitrate concentration and percolation without considering seasonal variability. This study presents an approach to estimate the monthly distribution of nitrate concentration based on measurements of soil water samples taken with suction cells installed below the root zone. Our workflow includes screening algorithms to identify the most relevant predictors, testing the predictive performance, reducing the number of predictions for practical implementation, and evaluating the impact on the final nitrate leaching calculations. The workflow was applied to the suction cup measurement dataset in the NLES5 support database of field experiments. The results show that the regression tree-based Extreme Gradient Boosting algorithm effectively estimates monthly variations in nitrate concentrations without relying on percolation data, by using time, management, soil, and weather covariates such as month, spring mineral fertilization, main crop, winter crop, clay content, mean monthly temperature, and accumulated precipitation in the harvest year. A cross-validated error of 34% was achieved for nitrate concentration, and a correlation of 0.8 with nitrate leaching calculated from observed concentrations demonstrates a consistent description of the seasonal distribution of nitrate concentrations below the root zone.

Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10 m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree). Our variable of interest is drain, as artificial drainage of agricultural land is widespread in areas with high groundwater tables. Drainage has significant effects on the hydrological cycle, and impacts groundwater recharge, streamflow partitioning and nutrient transport. Drain flow is controlled by small-scale variations in topography, geology and groundwater depth, which presents challenges to its estimation at large scale. Drain fraction is the average ratio between drain flow and precipitation. The metamodel combines covariates based on topography, land use and geology with simulated drain fraction from 45 field-scalephysically-based hydrological models of Danish drain catchments. The 45 models were jointly calibrated against timeseries of drain flow observations. The metamodel was used to upscale predictions of drain fractions for the entirety of Danish agricultural land. This involved considerable extrapolation beyond the 45 drain catchments used for training, calling for an assessment of spatial transferability. To map transferability of the model, and distinguish areas where metamodel results are reliable or not, we used the concept of area of applicability (AOA). The AOA is determined from the similarity of covariate space covered by the training data compared to each prediction point, assuming a correlation between model performance and covariate similarity. AOA mapping showed 71 % of Denmark's agricultural land falling within the AOA of the metamodel. The study presents a stepwise methodology to obtain national-scale results using a ML model trained on local-scale numeric models and an evaluation of its spatial transferability, highly relevant for decision-support purposes.

Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development, and performance benchmarking. CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets have been developed in several countries and regions around the world, providing valuable data sources and test beds for hydrological analysis and new frontiers in data-driven hydrological modeling. Regarding the lack of samples from lowland, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments covering all of Denmark from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. For 304 of those catchments, streamflow observations are provided, whereas simulated streamflow is provided for all 3330 catchments. The dataset contains time series spanning 30 years (1989–2019) with a daily time step, and the data will be updated once new observations and model simulations become available. The dense and full spatial coverage for all 3330 catchments, instead of only gauged catchments, together with the addition of various simulation data from a distributed, process-based model, enhances the applicability of such CAMELS data, for example, for the development of data-driven and hybrid physically informed modeling frameworks or other cases where consistent full spatial coverage is required. We also provide quantities related to the human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024).

Redox conditions play a crucial role in determining the fate of many contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. Geospatial machine learning has previously successfully modelled large-scale redox conditions. This study is the first to consolidate the complementary information provided by sediment color and water chemistry to enhance our understanding of redox conditions in Denmark. In the first step, the depth to the first redox interface is modelled using sediment color from 27,042 boreholes. In the second step, the depth of the first redox interface is compared against water chemistry data at 22,198 wells to classify redox complexity. The absence of nitrate containing water below the first redox interface is referred to as continuous redox conditions. In contrast, discontinuous redox conditions are identified by the presence of nitrate below the first redox interface. Both models are built using 20 covariate maps, encompassing diverse hydrologically relevant information. The first redox interface is modelled with a mean error of 0.0 m and a root-mean-squared error of 8.0 m. The redox complexity model attains an accuracy of 69.8 %. Results indicate a mean depth to the first redox interface of 8.6 m and a standard deviation of 6.5 m. 60 % of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated in clay rich glacial landscapes. Both maps, i.e., first redox interface and redox complexity are largely driven by the water table and hydrogeology. The developed maps contribute to our understanding of subsurface redox processes, supporting national-scale land-use and water management.

Abstract. Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development and performance benchmarking. CAMELS (Catchment Attributes and Meteorological time series for Large Samples) datasets have been developed in several countries and regions around the world, providing valuable data sources and testbeds for hydrological analysis and new frontiers in data-driven hydrological modelling. Regarding the lack of samples from low-land, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both, gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. The dataset is enhanced with streamflow observations in 304 of those catchments. The spatially dense and full spatial coverage, supplying data for 3330 catchments, instead of only gauged catchments, together with the addition of simulation data from a distributed, process-based model enhance the applicability of such CAMELS data. This is especially relevant for the development of data-driven and hybrid physical informed modelling frameworks. We also provide quantities related to human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024).

Riparian lowlands are important for protecting aquatic ecosystems threatened by contamination. Their ability to attenuate and reduce nutrient rich water provides a useful ecosystem service. Previous field studies have shown that the hydrological conditions within a lowland can affect their reduction capabilities dependent on the dominant flow pathways, i.e., surface runoff, groundwater discharge, and drain flow. For example, the likelihood of nutrient reduction within a riparian lowland dominated by surface runoff is low, conversely if groundwater discharge dominates the likelihood is higher. Hence, knowledge of the flow pathways can be used to provide a qualitative estimate of the reduction capacity in riparian lowlands, information that is vital in assessing catchment scale processes. The objective of this study is to establish a relationship between the dominant riparian flow pathways and lowland features, such as slope and hydrogeology/geology. Previous work had shown the ability of a downscaled high-resolution numerical model to replicate observed annual flow patterns within a riparian lowland. This downscaled numerical model was used to provide quantitative information regarding the riparian flow partitioning. To diversify the dataset, the riparian lowland was segmented to provide flow information at different scales, and topographic and hydraulic properties within the model were perturbed to capture the range of topographic and geological characteristics present at large scale. These data were then used to train a random forest (RF) model, where the target variable was the fraction of overland flow. Applying the RF model to a 12,785.5 km2 large region in Denmark provided a prediction in line with our understanding of the area. This approach can prove useful in enhancing existing nitrate management tools by incorporating variability in lowland nitrate reduction capacity.

Tile drains constitute a shortcut from agricultural fields to surface water systems, significantly altering the transport pathways and fate of nitrate during transport. A correct representation of tile drainage flow is thus crucial for estimating nitrate load at the catchment scale and to identify optimal locations for N-mitigation measures. Drainage is a local process, controlled by local properties and drain configurations, which are rarely known for individual fields, making drainage flow and transport a challenging task in catchment scale models. This study tests the potential for improving drainage flow dynamics at catchment scale, by utilising local drainage flow measurements in a spatial calibration scheme. A distributed hydrological model, MIKE SHE, for the agricultural-dominated Norsminde catchment (145 km2) in Denmark, was calibrated using spatially distributed surrogate parameters (pilot points) to represent heterogeneity in the soil (top 3 m) and the deeper geology below 3 m. The model was calibrated using hydraulic heads, stream discharge, and measured drainage flow from eight drain catchments. Drain measurements were very important in guiding the calibration of top 3 m and subsurface pilot points located in the drainage fields, showing that drain flow hold information on both local (shallow) and regional (deeper) flow patterns. Contrarily, pilot points located outside the drained fields were mainly sensitive to the hydraulic head measurements and the summer water balance of the stream discharge on a catchment scale. Consequently, incorporation of the drain data improved local performance, but did not improve the parameterization and drain description of the entire catchment. Exploitation of the drain flow information is thus difficult beyond the drain catchments, and other approaches are needed to extrapolate and exploit the local data.

<p>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.</p> <p>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.</p>

Study region: The study uses 78 groundwater head time series across 10 European countries with various geological and hydrological settings.Study focus: The estimation of groundwater recharge using time series analysis and lumped modelling based on groundwater head time series is a low-cost and practical method. However, lumped recharge estimation models based on groundwater level variations are uncertain, and successful applications are known to depend on both climate and hydrogeological setting. Here, we assess the suitability of three different models to estimate recharge (Metran -Transfer Function-Noise model, AquiMod -groundwater level driven hydrological model, and GARD & PRIME;ENIA -lumped catchment model). New hydrological insights: Results showed that while all three models generally did well during the modelling of groundwater heads, the resulting recharge estimations from the models were different. The analysis showed that the transfer-noise modelling of groundwater heads with recharge and evapotranspiration in Metran is not generally applicable for recharge estimation. The addition of physical information in AquiMod improved the recharge estimations, but the reliability was still limited without control of the water balance due to non-uniqueness. By adding discharge information to the modelling, GARD & PRIME;ENIA can provide more reliable recharge values. Thus, recharge estimation from groundwater head time series without water balance information must be considered uncertain with low precision, but applicability can be improved when including knowledge of the local system.

In agricultural fields, tile drains represent potential pathways for the migration of solutes, such as nitrates, in groundwater and surface water bodies. Tile drain flow is controlled by the temporal and spatial dynamics of the shallow groundwater table, which results from complex interactions between climate, topography and soil heterogeneity. Studies on the effect of topsoil heterogeneity on shallow water and drainage dynamics by fully 3D surface water and groundwater flow modeling are limited. The objective of our study is to demonstrate the use of depth specific electrical conductivity (EC) estimates to improve hydrological simulations in a tile drained field. The model was applied to a field site in Denmark where times series of drainage discharge and water table elevations are available. Clay-rich soil zones were identified in a tile-drained field using depth specific electrical conductivity estimates generated by the inversion of apparent electrical conductivity data measured using an electromagnetic induction instrument. One model that included the low-permeability clayey zones in the soil layers down to a depth of 1.2 m was compared to a simpler model that assumed homogeneous soil layers. Both models simulate drainage discharge that compares well to the observations. However, including the clayey zones improves the simulation of hydraulic heads, and water table fluctuations, and generates flooded areas that are more representative of those observed during the wet seasons. Our results suggest that the simulation of water table fluctuations can be improved when the soil heterogeneity determined from depth specific EC estimates is included in integrated hydrological models. A better representation of the subsurface flow dynamics will also improve subsequent simulations of the transport and fate of agrochemical substances leaching from fields such as nitrate, which may deteriorate the quality of groundwater and surface water bodies.

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.

Tile drains are important water flow paths in agricultural catchments and must be included in hydrological models. However, their locations are rarely known and the explicit incorporation of tile drains in hydrological models requires refined meshes around the drains, which can significantly increase computational times. Although seepage nodes have been used to represent tile drains with satisfactory performance, they have never been applied to represent all tile drainage systems in a catchment. The goal of this study is to compare different conceptual models for tile drains and soil heterogeneity for the numerical simulation of tile drainage in an agricultural catchment in Denmark. The first conceptual model for tile drains uses seepage nodes to represent only the main collector drains in the catchment and the second model uses seepage nodes distributed over all the agricultural areas, without considering the specific locations of tile drains. A third conceptual model, labelled the Benchmark Model, represent all tile drains at their known locations with seepage nodes and a fourth conceptual model implicitly represents tile drains as a high-permeability layer. The four models performed satisfactorily to simulate the observed outlet stream discharge and could be recommended almost interchangeably. The simulation of the water table depth was very satisfactory compared to modeling studies with similar mesh resolution (~50 m). Results indicated that the three models using seepage nodes i) simulated similar monthly discharges and cumulative discharge volumes for most of the studied tile-drained areas, and ii) simulated surface water flow in tile-drained fields without runoff or ponding water. The shorter simulation times (around 35% faster) of the model representing the main drains and the distributed seepage node model suggest that they are suitable for model calibration, compared to the Benchmark Model. Whenever the location of tile drains is unavailable, using seepage node to represent drains in agricultural areas may satisfactorily simulate catchment-scale stream and drainage discharges. Four alternative soil models were developed to evaluate the effect of soil heterogeneity on the simulations. Our results suggest that at smaller scales (drainage area) soil heterogeneity is more relevant than the drainage conceptualization to improve model results. However, at the subcatchment scale, the opposite was observed and, at the catchment scale, both criteria had a comparable effect.

In Denmark, about half of the agricultural land is artificially drained. These drainage systems have a significant effect on the hydrological system. Knowledge about the spatio-temporal distribution of drain flow is crucial to understand aspects such as groundwater recharge, streamflow partitioning and nutrient transport. Still, quantification of drain flow at regional and large scale remains a major challenge: Data on the distribution of the installed subsurface drainage system are scarce, as are measurements of drain flow. Large-scale simulations of drain with physically-based hydrological models are challenged by scale, as drain flow is controlled by small-scale variations in groundwater depth often beyond the model resolution. Purely data-driven models can struggle representing the complex controls behind drain flow. Here, we suggest a metamodel approach to obtain a more accurate estimate of generated drain flow at high spatial resolution of 10 m, combining physically-based with data-driven models. Our variable of interest is drain fraction, defined as the ratio between drain flow and recharge per grid cell, which is an indicator for flow partitioning between drain and recharge to deeper groundwater. First, we setup distributed, integrated groundwater models at 10 m grid resolution for 28 Danish field-scale drain catchments with observations of drain flow timeseries. A joint calibration of these field-scale models against observed drain flow resulted in an average KGE of above 0.5. Subsequently, the simulated drain fractions from the field-scale models were used to train a decision tree machine learning algorithm. This metamodel uses various mappable covariates (topography and geology-related) available at high resolution for all of Denmark. The metamodel then is used to predict drain fractions, within its limits of applicability, across relevant areas of Denmark with significant drain flow outside of the field-scale models. Eventually, the predicted drain fractions are intended to inform national, large-scale physically based hydrological models: An improved representation of drain can, for example, make those models more fit to improve national targeted nitrate regulation.

Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.

Sustainable evolution of groundwater quantity and quality is essential for sustainable development and protection of society and nature, globally, as acknowledged in the UN sustainable development goals and the European Green Deal. Too much? – too little? – and/or too polluted? are important questions to pose and answer in a changing climate with increasing pressures on water resources, severe loss of biodiversity, and a projected increase in extreme events resulting in an increasing risk of floods, droughts, landslides and land subsidence. Easy access to digital and FAIR (Findable, Accessible, Interoperable and reusable) data on groundwater quantity and quality is imperative for informed decision making and efficient climate change mitigation and adaptation to which sustainable groundwater management will contribute. Here we briefly present selected highlights and digital data products from the four GeoERA groundwater projects developed for and made available on the digital subsurface information platform of the European geological survey organizations. The ambition is to develop the digital information platform, EGDI (the European Geological Data Infrastructure) as the leading information platform for sustainable and integrated management of subsurface resources in Europe and one of the leading platforms, globally.

N‐loads from subsurface, drains, and groundwater‐fed surface (bypass) flows via two riparian zones (crop field and wetland) to a second order stream were investigated by sampling of shallow and deep groundwater on both sides and monthly measurements of bypass flows from springs, drains, and stream including water quality (nitrate). A push‐pull test in the crop field gave a first‐order denitrification rate (0.23 day –1 ). Reactive transport modeling evaluated observations of water chemistry and denitrification processes in the groundwater below the crop field showing that nitrate was completely removed by denitrification with pyrite in the aquifer (model rates of 0.6–2.5 mmol NO 3 L −1 yr −1 ). A drain in the crop field routed ∼10% (bypass) of the regional groundwater inflow to the stream. Buffer efficiency was high at 90%. The wetland on the other side of the stream hosts several locations of nitrate‐rich bypass flow from springs, predominantly through a non‐maintained drainage system of drainpipes and ditches with bypass flow accounting for 59% of the regional flow input. Nitrate was completely removed in groundwater by denitrification with dissolved organic matter in shallow groundwater. The regional inflow and N load to the wetland is amongst the highest recorded and data shows that the N load to the stream is very high. The buffer efficiency ranged from 45% to 83% depending on if all bypass flows contributed to the stream or only the two with visible outflow. A conceptual model for nitrate removal efficiency as a function of Damköhler number and percent bypass flow is proposed.

Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over-abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases. The reduction in baseflow and alteration of the streamflow regime can potentially have an adverse effect on groundwater-dependent ecosystems. A spatially distributed, coupled groundwater-surface water model can simulate the impacts of groundwater abstraction on aquatic ecosystems. A constrained optimization algorithm and a simulation model in combination can provide an objective tool for the water practitioner to evaluate the interplay between economic benefits of groundwater abstractions and requirements to environmental flow. In this study, a holistic catchment-scale groundwater abstraction optimization framework has been developed that allows for a spatially explicit optimization of groundwater abstraction, while fulfilling a predefined maximum allowed reduction of streamflow (baseflow [Q95] or median flow [Q50]) as constraint criteria for 1484 stream locations across the catchment. A balanced K-Means clustering method was implemented to reduce the computational burden of the optimization. The model parameters and observation uncertainties calculated based on Bayesian linear theory allow for a risk assessment on the optimized groundwater abstraction values. The results from different optimization scenarios indicated that using the linear programming optimization algorithm in conjunction with integrated models provides valuable information for guiding the water practitioners in designing an effective groundwater abstraction plan with the consideration of environmental flow criteria important for the ecological status of the entire system.

The resolution of nation scale hydrological models is often considered a limitation with respect to providing useful management information on local scale issues. Use of these coarse scale models to inform both the structure and parameterisation of local scale models could potentially enhance their utility and in some cases provide initial estimates to local scale water resource issues such as nitrate transport. This study explores whether a finer resolution model, whose conceptualization and parameterisation are based upon a nation scale model, can replicate local scale water balance observations and in this way expand the utility of large-scale models. We use the Danish National Water Resources model (DK-model) as a template to model a small 5 km2 subcatchment in Denmark and compare the flows at four transect locations to those observed in a previous field investigation. The resolution of the DK-model is 500 m, and this model was refined to create four additional models of the subcatchment with 100 m, 50 m, 20 m, and 10 m resolutions. Results suggest that none of the refined models could accurately capture the daily overland and groundwater flow conditions. However, for the finer resolution models (10 m and 20 m), the annual overland and groundwater flows agreed well with the observed flows.