Abstract. Temperate regions across Europe, such as Denmark, are projected to be subjected to substantial changes in the hydrological cycle due to climate change. Changes in climate can materialize as general changes in long term means, extremes, or in seasonal patterns, e.g., dampening or intensification of the seasonal contrasts. Changes in seasonal patterns can affect the hydrological cycle in various ways, due to the interlinkage between hydrological compartments. To detect, track and quantify the impact of changes in climate and seasonal patterns, integrated hydrological modelling is needed. This makes Denmark an ideal test case due to the established integrated and physically based National Hydrological Model of Denmark (DK-model). Utilizing climate projections from 17 RCP8.5 climate models, downscaled and bias-corrected for Denmark, we calculate climate change impacts on both overall values and seasonality for the variables soil moisture, streamflow, shallow and deeper groundwater to the end of the century. Moreover, standardized hydrological drought indices are calculated for the same variables. Climate change projections point towards a future with higher annual precipitation, mainly due to wetter winters, while climatic water balance deficits increase during summer; thus, intensifying the seasonal contrast. The increased contrast is reappearing in the fast-responding hydrological variable, soil moisture; while streamflow and shallow groundwater clearly reproduce increase during the wetter winter, the summer signal differs. The deep groundwater systems experience higher future groundwater heads across the entire year. Common for all variables is a larger seasonality, defined as contrast between intra-annual low and highs. Notably, the ensemble of hydrological projections is more in agreement regarding the seasonal contrast than on the direction of absolute change, with results agreeing for 85 % to 99 % of the area of Denmark on increased seasonality, whereas only agreeing for 50 % to 98 % on the absolute direction of that change. The drought indices exhibit a similar seasonal change, with more droughts during summer and more wet anomalies during winter for soil moisture, while summer droughts for streamflow and shallow groundwater partially are buffered by wetter winters and the related recharge increase. In summary, the results indicate that despite considerable increases in precipitation, projected climate change for Denmark is expected to enhance hydrological seasonality instead of producing a uniform transition to a wetter regime, potentially impacting the climate adaptation and mitigation effort, agricultural yields, and water supply.

Catchment modelling has undergone tremendous developments during the past decades. In the 1970s, the focus was on simulation of catchment runoff with process descriptions and data inputs being lumped to the catchment scale. Later developments included spatially distributed models allowing data inputs and hydrological processes to be simulated at model grid scale, i.e. much finer than catchment scale. These models were able to explicitly simulate various processes such as soil moisture, evapotranspiration, groundwater and surface runoff. With the advancements in remote sensing technology and availability of high-resolution data, increased attention has in recent years been given to enhancing the capability of catchment models to reproduce spatial patterns and in this way improve our understanding of hydrological processes and the physical realism of catchment models. This development process has involved a wide spectrum of different aspects in the modelling process, reaching from an improved understanding of uncertainties in data, model parameters and model structures to new protocols for good modelling practices in water management. Recognizing the important role of biodiversity and social aspects, hydrologists are now extending the scope of their models to capture the interactions between water, biota and human social systems.

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.

Post audits of hydrological or groundwater models are the last part of the modelling protocol, where the original model predictions are tested using new data obtained after a certain period. The evaluation of model predictions and associated predictive uncertainty was performed by comparing an original hydrological model, a model with post audited geology, and a model with post audited geology and calibrated against new types of observation data. The post audit showed original model predictions close to what was observed (in terms of abstracted volumes necessary to lower a shallow groundwater table). In contrast to the robust original model predictions, the original model underestimated the predictive uncertainty compared to the assessments of uncertainty using the new and updated post audit model. To ensure a robust model evaluation, we propose a four-step post audit protocol, including (1) testing the validity of the original model predictions with new data, (2) estimating the predictive uncertainty of the original model, (3) producing a new post audit model(s) based on revising the conceptual model and calibration, and (4) assessing the predictive uncertainty of the new post audit models. The work presented here was motivated by the lack of studies that, after a certain time, have re-evaluated model predictions (post audit) with new data.

The region studied is the 137.000 km2 North China Plain (NCP). A region with high population density and a major agricultural production leading to unsustainable exploitation of groundwater resources. Previous modeling studies in the region have utilized simplified representations of model boundary fluxes, both regarding lateral inflows from surface and groundwater and related to water demands and consumptions. The current study focusses on developing a hydrological modeling framework, with better spatial descriptions of major water balance components regarding water demands, model boundary conditions of surface and groundwater inflows and evaporative losses due to irrigation. Compared to previous efforts, the modeling framework utilizes a novel multi-objective parameter optimization strategy combined with an ensemble modeling approach to illuminate optimization trade-offs and impacts of parameter uncertainty. Groundwater storage declines are estimated to be in the order of 25–55 mm/y for the period 2000–2013. The impacts of water management strategies are explored using the model ensemble and show that this decline can be counterbalanced by approximately 15–20 mm/y by substantial reductions in irrigation (20%) or implementation of planned inter-basin water transfers. Managed aquifer recharge in the form of infiltrating excess river peak flows, can only reduce groundwater storage declines to a limited degree. However, at the local to regional scale storage decline reductions from MAR are in the same order of magnitude as other extensive water management strategies.

ContextAgricultural activities constitute the most significant source of nitrate pollution, posing a threat to water quality and ecosystem services. The Nitrates Directive is an integral feature of the Water Framework Directive, which seeks to reduce nitrate pollution from agricultural sources. Directive compliance has proven to be problematic for every Member State in fulfilling their respective implementation duties.ObjectivesThe research focuses on the nitrate management discourse within agricultural landscapes of Poland and provides a governance capacity framework to understand how social factors shape local implementation performance. The case study examines how the social factors of social capital and street-level bureaucrats constrain or enable stakeholder agency within agricultural landscapes.MethodsThe empirical investigation utilizes a multi-method assessment, including a survey categorizing social capital levels among 31 Polish farmers, interviews with nine stakeholders, and a literature review.ResultsThe findings demonstrate how differentiated social capital levels are a result of complex social dynamics within the nitrate management discourse. Achieving policy objectives rests on stakeholder interactions in their capacity to navigate myriad changes and translate policy messages into practical actions. Due to low social capital levels exhibited by farmers and limited agency of street-level bureaucrats, overall capacity for effective nitrogen management in Polish agricultural landscapes is constrained.ConclusionsOverall, the study contributes new insights in identifying how social factors affect the ability of Member States to fulfill implementation obligations. Further, the study discusses the influence of social factor interplay upon actor agency and subsequent policy relevance amidst changing agri-environmental landscapes.

Abstract Mike Abbott was an outsider to hydrological science, who nevertheless fundamentally advanced hydrological modelling by introducing knowledge from computational hydraulics and later hydroinformatics. His main contribution was the development of the concept of physically based distributed modelling and the European Hydrological System - Système Hydrologique Européen ‘SHE’, in a programme, which he initiated and led during the period 1975-1986, by forging a strong collaboration between public and private European research institutes and companies. The development of the SHE was a pioneering effort resulting in a quantum leap in hydrological modelling at its time of development. Technologically, the SHE remained for the next 1-2 decades the most advanced hydrological modelling system and it is still in use today, around 40 years after its birth. The SHE modelling philosophy was both challenged and imitated during these years. This chapter describes the obstacles to and the achievements of the SHE development, along with the first application studies and the debates generated by the scientific challenges to the SHE concept. The chapter also describes how Mike Abbott's ideas on encapsulation of knowledge in the so-called fourth-generation software systems inspired the development of user-friendly software packages, enabling professionals with domain understanding but without computer knowledge to use models. Further, the chapter describes Mike Abbott's vision of using intelligent hydroinformatics software systems to empower stakeholders and in this way democratise the decision-making processes and ensure social justice in the water sector, and briefly discusses why this vision, in contrast to the very successful ideas related to the SHE and the fourth-generation systems, had limited impact so far. Finally, the chapter discusses the strengths and weaknesses of Mike Abbott's contributions to hydrological modelling seen from today's state of the art and the impacts his contributions continue to have on hydrological modelling today.

Nitrate pollution and eutrophication are of increasing concern in agriculturally dominated regions, and with projected future climate changes, these issues are expected to worsen for both surface and groundwater. Changes in land use and management have the potential to mitigate some of these concerns. However, to what extent these changes will interact is unknown, and are associated with significant uncertainty. Here, we estimate nitrate fluxes and contributions of major uncertainty sources (variance decomposition analysis) affecting nitrate leaching from the root zone and river load from groundwater sources for an agricultural catchment in Denmark under future changes (2080-2099) in climate (four climate models) and land use (four land use scenarios). To investigate the uncertainty from impact model choice, two different agro-hydrological models (SWAT and DAISY-MIKE SHE) both traditionally used for nitrate impact assessments are used for projecting these effects. On average, nitrate leaching from the root zone increased by 55%-123% due to different climate models, while the impact of land use scenarios showed changes between -9% and 88%, with similar projections for river loads, while the worst-case combination of the three factors yielded a fivefold increase in nitrate transport. Thus, in the future, major land use changes will be necessary to mitigate nitrate pollution likely in combination with other measures such as advanced management and farming technologies and differentiated regulation. The two agro-hydrological models showed substantially different reaction patterns and magnitude of nitrate fluxes, and while the largest uncertainty source was the land use scenarios for both models, DAISY-MIKE SHE was to a higher degree affected by climate model choice. The dominating uncertainty source was found to be the agro-hydrological model; however, both uncertainties related to land use scenario and climate model were important, thus highlighting the need to include all influential factors in future nitrate flux impact studies.

Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

Hydrological process knowledge has advanced significantly during the past six decades. During the same period catchment models have undergone major developments including simple black box models, lumped conceptual models, hydrological response unit models, spatially distributed process-based models and, recently, the emergence of machine learning hybrid models. This development has been enabled by improved understanding of hydrological processes together with ever increasing computer power and improved availability and accessibility of data. During the first couple of decades, a key assumption motivating the development towards increasing complexity of model codes was that more detailed process description would lead to more accurate model simulations and enable prediction of impacts from human activities that previous models were not able to provide. Subsequently, scientific tests showed that this is very often not the case, leading towards a recognition of the importance of careful model evaluation accounting for key uncertainties in data, model parameters and model conceptual understanding. We have reviewed 54 model studies from the past 60 years and characterized them with respect to model type, spatial discretization and model evaluation techniques. This showed clear development trends and different strategies for enhancing hydrological process knowledge in models. In addition, we present a case study, where we use two models for the same catchment. The models are identical except for the spatial discretization of 100 m and 500 m, respectively. The two models have an apparent equal performance measured against standard calibration metrics, but nevertheless show large differences when considering detailed process information such as partitioning of streamflow components and water table depth patterns, that was not considered during the model calibration process. The paper discusses perspectives for enhancing hydrological process knowledge in future catchment modelling concluding that the emergence of big data is likely to become a major game changer.

This paper presents the first study assessing the climate change impact on groundwater levels of the Zagreb alluvial aquifer in Croatia by coupling climate projections under RCP8.5 for the period 2040-2070 with local scale groundwater flow modelling. As a novelty in groundwater modelling of climate change impacts, this study utilizes an ensemble of five climate models and two land surface models for providing projections of relevant boundary conditions to the groundwater model. The groundwater model is used for predicting climate driven changes in groundwater levels in the aquifer system influenced by both changes in local groundwater recharge and changes in river-flow. Both boundary conditions are obtained from the land surface models. In addition, the uncertainty contributions to groundwater levels from both climate models and land surface models were quantified using variance decomposition. The results revealed that the spatial pattern of changes in groundwater levels can be related to different processes influencing groundwater dynamics. In areas dominated by a strong groundwater-surface water interaction, close to the Sava river, the multi-model ensemble mean change in groundwater levels is negligible for low and average water levels. However, for high water levels, an increase was identified. In contrast, a decline in groundwater levels prevails in all areas where aquifer recharge is largely driven by rainfall infiltration. For the variance decomposition analysis, climate models were identified as the main source of uncertainty for groundwater levels. However, the uncertainty contribution of the choice of land surface model to the impact simulations was found to be more important in the area affected by a strong groundwater-surface water interaction and for periods of high groundwater levels. Also, specific local issues related to low and high groundwater levels that are associated with extreme hydrological regimes were found to exacerbate under climate projections.

The uncertainty of climate model projections is recognized as being large. This represents a challenge for decision makers as the simulation spread of a climate model ensemble can be large, and there might even be disagreement on the direction of the climate change signal among the members of the ensemble. This study quantifies changes in the hydrological projection uncertainty due to different approaches used to select a climate model ensemble. The study assesses 16 Euro-CORDEX Regional Climate Models (RCMs) that drive three different conceptualizations of the MIKE-SHE hydrological model for the Ahlergaarde catchment in western Denmark. The skills of the raw and bias-corrected RCMs to simulate historical precipitation are evaluated using sets of nine, six, and three metrics assessing means and extremes in a series of steps, and results in reduction of projection uncertainties. After each step, the overall lowest-performing model is removed from the ensemble and the standard deviation is estimated, only considering the members of the new ensemble. This is performed for nine steps. The uncertainty of raw RCM outputs is reduced the most for river discharge (5 th , 50 th and 95 th percentiles) when using the set of three metrics, which only assess precipitation means and one 'moderate' extreme metrics. In contrast, the uncertainty of bias-corrected RCMs is reduced the most when using all nine metrics, which evaluate means, 'moderate' extremes and high extremes. Similar results are obtained for groundwater head (GWH). For the last step of the method, the initial standard deviation of the raw outputs decreases up to 38% for GWH and 37% for river discharge. The corresponding decreases when evaluating the bias-corrected outputs are 63% and 42%. For the bias corrected outputs, the approach proposed here reduces the projected hydrological uncertainty and provides a stronger change signal for most of the months. This analysis provides an insight on how different approaches used to select a climate model ensemble affect the uncertainty of the hydrological projections and, in this case, reduce the uncertainty of the future projections.

Study region: This study is developed in three catchments located in Denmark, France and Spain, covering different climate and physical conditions in Europe. Study focus: The simulation skill of hydrological models under contrasting climate conditions is evaluated using a Differential Split Sample Test (DSST). In each catchment, three different hydrological models are given a weight based on their simulation skill according to their robustness considering the DSST results for traditional and purpose-specific metrics. Four weighting approaches are used, each including a different set of evaluation metrics. The weights are applied to obtain reliable future projections of annual mean river discharge and purpose-specific metrics. New hydrological insights: Projections are found to be sensitive to model weightings in cases where the models show significantly different skills in the DSST. However, when the skills of the models are similar, there is no significant change when applying different weighting schemes. Nevertheless, the methodology proposed here increases the reliability of the purpose-for-fit hydrological projections in a climate change context.

Nitrate reduction maps have been used routinely in northern Europe for calculating the efficiency of remediation measures and the impact of climate change on nitrate leaching. These maps are, therefore, valuable tools for policy analysis and mitigation targeting. Nitrate reduction maps are normally based on output from complex hydrological models and, once generated, are largely assumed constant in time. However, the distribution, magnitude, and efficiency of nitrate reduction cannot necessarily be considered stationary during changing climate and land use as flow paths, nitrate release timing, and their interaction may shift. This study investigates the potential improvement of using transient nitrate reduction maps, compared to a constant nitrate reduction map that is assumed during land use and climate change, both for nitrate loads and the spatial variation in reduction. For this purpose, a crop and soil model (DAISY) was set up to provide nitrate input to a distributed hydrological model (MIKE SHE) for an agricultural catchment in Funen, Denmark. Nitrate reduction maps based on an observed dataset of land use and climate were generated and compared to nitrate reduction maps generated for all combinations of four potential land use change scenarios and four future climate model projections. Nitrate reduction maps were found to be more sensitive to changes in climate, leading to a reduction map change of up to 10 %, while land use changes effects were minor. The study, however, also showed that the reduction maps are products of a range of complex interactions between water fluxes, nitrate use, and timing. What is also important to note is that the choices made for future scenarios, model setup, and assumptions may affect the resulting span in the reduction capability. To account for this uncertainty, multiple approaches, assumptions, and models could be applied for the same area. However, as these models are very time consuming, this is not always a feasible approach in practice. An uncertainty of the order of 10 % on the reduction map may have major impacts on practical water management. It is, therefore, important to acknowledge if such errors are deemed acceptable in relation to the purpose and context of specific water management situations.

A methodology to quantify the stratigraphic uncertainty of the aquifer bottom is used in assessing the impact of sheet piles on the water table in the vicinity of a motorway. The method includes uncertainties on model parameters and depth of sheet piles. While parameter uncertainty dominates when simulating the existing groundwater conditions, the stratigraphic uncertainty dominates when predicting the impacts of the motorway sheet piles. The uncertainty of groundwater head predictions is a key element in risk assessments for infrastructure projects, where groundwater affects construction costs. While geological structural uncertainty often is a dominating source of uncertainty in large-scale water-resource studies, stratigraphic uncertainty (defined as the uncertainty of the location of geological boundaries between units/facies) becomes more important for small-scale infrastructure projects. The methodologies used so far for handling stratigraphic uncertainty typically use a somewhat simplistic approach basing the uncertainties of the geological surfaces on the kriging variance. The proposed methodology has two novel elements. Firstly, the uncertainties in the geological interpretation of the borehole data are explicitly included. Secondly, conditional sequential Gaussian simulation (sGs) is used to quantify the uncertainties. The advantage of sGs over standard kriging-based approaches is that sGs allows descriptions of small-scale variations that are crucial in some contexts. The methodology was tested on a case site in Denmark where a new motorway has been constructed below the water table and with sheet-pile walls designed to penetrate the aquifer down to 1 m above the aquifer bottom.

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.

The most significant source of nitrate pollution in the European Union (EU) is attributed to agricultural activities, which threaten drinking water, marine, and freshwater resources. The Nitrates Directive is a key feature of the Water Framework Directive (WFD), which seeks to reduce nitrate pollution from agricultural sources. Yet, weak compliance by Member States (MS) diminishes the legitimacy of the EU environmental acquis and undermines efforts to achieve environmental objectives. This study examines the nitrate management discourse in Poland to identify influencing factors that impact governance capacity and overall compliance performance. The empirical investigation is based on nine stakeholder interviews, three written correspondences, and a literature review that collectively comprise an evaluation study. A comparison in governance approaches between Poland and Denmark provides a calibration in assessing performance respective to another MS. The findings categorize both Poland and Denmark as "laggard" in WFD compliance. This case contributes new insights in identifying 6 enabling and 13 constraining factors affecting the ability of MS to fulfill their implementation duties. The findings demonstrate that divergent stakeholder views based on historical and cultural norms require a differentiated approach tailored to domestic conditions for effective fulfillment of the objectives set forth in EU environmental legislation.

Global Climate Models (GCMs) are the main tools used to assess the impacts of climate change. Due to their coarse resolution, with cells of c. 100 km × 100 km, GCMs are dynamically downscaled using Regional Climate Models (RCMs) that better incorporate the local physical features and simulate the climate of a smaller region, e.g. a country. However, RCMs tend to have systematic biases when compared with local observations, such as deviations from day-to-day measurements, and from the mean and extreme events. As a result, confidence in the model projections decreases. One way to address this is to correct the RCM output using statistical methods that relate the simulations with the observations, producing bias-corrected (BC) projections. Here, we present the first assessment of a previously published method to bias-correct 21 RCM projections of daily temperature and precipitation for Denmark. We assess the projected changes and sources of uncertainty. The study provides an initial assessment of the bias correction procedure applied to this set of model outputs to adjust projections of annual temperature, precipitation and potential evapotranspiration (PET). This method is expected to provide a foundation for further analysis of climate change impacts in Denmark.