The paper analyzes the national DK-model hydrological information and prediction (HIP) system and HIP portal viewed as a ‘digital twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can make room for plug-in submodels with real-time boundary conditions made available from an HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges were addressed as part of the Joint Governmental Digitalization Strategy 2016–2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terabytes of hydrological model data to the portal, with robust calibration methods and hybrid machine learning (ML) being key parts of the deliverables. This paper discusses the challenges and potentials of further developing the HIP digital twin with ‘plug-in digital twins’ for local river basins, including feedback to the national level.

Denmark's complete reliance on groundwater for water supply presents a unique case study in management of natural resources, urban planning, and water resilience in the face of climate change. This paper examines the groundwater management strategies in Denmark in general, focusing on Denmark's four largest cities—Copenhagen, Aarhus, Odense, and Aalborg— each facing distinct challenges due to their demographic, geographical, hydrogeological, and economic characteristics. Through analysis of these cities' approaches to groundwater management, this research contributes to the global discourse on sustainable urban water supply systems. As coastal groundwater cities (CGC), these urban areas must navigate the complexities of sustaining growing populations, mitigating climate change impacts, and coastal processes while ensuring the long-term viability of their groundwater resources. Copenhagen and Aalborg, built atop semi-confined fractured and locally karstic carbonate rocks, highlights the specific challenges associated with karstic groundwater systems, while, Aarhus, and Odense built on glaciofluvial aquifers faces different issues. The different groundwater challenges in these cities underscores the importance of integrating urban development with water resource management and environmental sustainability, offering valuable insights and lessons learned for other regions facing similar challenges. This study, thus not only sheds light on Denmark's groundwater management practices, but also emphasizes the need for innovative solutions to ensure the resilience of urban water supply systems in a changing climate and increasing pressures of emerging organic contaminants and elevated concentrations of geogenic elements induced by water abstraction and fluctuating water tables. Advanced Danish monitoring and modelling tools applied to support decision-making and innovation within the water sector are continuously developed and improved to support resilient and sustainable management of the available water resources.

Groundwater constitutes 99% of the Earth’s liquid freshwater and is crucial for human health, economic development, and ecosystem sustainability. This study assesses groundwater sustainability in Denmark by employing a comprehensive hydrological model and a set of ensemble indicators. The paper describes the methodology and the results based on nine selected indicators. Three indicators focus on recharge capture and aquifer sustainability, one focuses on groundwater level and wetland capture, two focus on baseflow and drainage flow capture, and three focus on eco flow capture. Our findings highlight that while overall exploitable groundwater resources are estimated at 1.1 billion m³/year, significant regional disparities exist, with certain areas, notably Zealand, facing over-exploitation rates exceeding 250% of sustainable limits. The indicators developed not only provide a framework for assessing current groundwater resource limits, but also serve as a basis for future monitoring and adaptive management strategies. This research underscores the need for stakeholder engagement and integrated approaches to ensure the sustainability of groundwater resources in the face of growing anthropogenic pressures and climate change. Our work contributes to the ongoing discourse on sustainable water management and offers a robust methodology for assessing groundwater sustainability.

The paper analyses the national DK-model Hydrological Information and Prediction (HIP) system and HIP portal viewed as a ‘Digital Twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can give room for plug-in sub-models with real-time boundary conditions made available from a HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges have been addressed as part of the Joint Governmental Digitalization Strategy 2016-2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terra-byte of hydrological model data to the portal with robust calibration methods and hybrid Machine Learning (ML) being key parts of the deliverables. The paper discusses the challenges and potentials of further developing the HIP Digital Twin with ‘plug-in Digital Twins’ for local river basins including feedback to the national level.

The number of studies on submarine groundwater discharge (SGD) and the evidence of its significance in biogeochemical cycling and potential impacts on the chemical and ecological status of coastal waters is increasing globally. Here, we briefly present SGD studies from the Baltic Sea identified along the coastlines of Denmark, Finland, Germany, Poland, Sweden and Russia in the southwestern, southern and north–northeastern parts of the Baltic Sea. We introduce a digital SGD map viewer and information platform enabling easy overview and access to information on identified SGD sites in the coastal areas of the Baltic Sea. SGDs potentially transport critical pollutants from urban and agricultural areas on land to the marine environment. The pollutants include nutrients, dissolved organic and inorganic carbon, metals, pharmaceuticals, and other emerging contaminants, potentially harming marine ecosystems and biodiversity and possibly contributing to the poor chemical or ecological status of coastal waters, affecting human and environmental health. We focus on case studies from Finland, Germany, Poland and Denmark that include the results and interpretations from the applied geochemical, geophysical and geological methods, as well as bionic autonomous underwater vehicles (AUVs) for locating, investigating, modelling and visualizing SGD sites in 2D and 3D. The potential Pan-European or even global SGD information platform established within the European Geological Data Infrastructure (EGDI) enables the easy combination and comparison of map layers such as seabed sediment types and coastal habitats. The EGDI map viewer provides easy access to information from SGD studies and may serve as an entry point to relevant information on SGDs, including contents of pollutants, for the scientific community and policy-makers. The information potentially includes the results of model simulations, data from near real-time sensors at permanently installed monitoring stations and surveys in time and space conducted by AUVs. The presented digital SGD information platform is particularly pertinent to the UN Sustainable Development Goal (SDG) No. 14, which focuses on the conservation and sustainable use of oceans and marine resources.

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.