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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.

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Abstract. 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.