A5022446248- Hydrology and Watershed Management Studies
- Water resources management and optimization
- Groundwater flow and contamination studies
- Flood Risk Assessment and Management
- Reservoir Engineering and Simulation Methods
- Bayesian Modeling and Causal Inference
- Groundwater and Isotope Geochemistry
- Hydrological Forecasting Using AI
- Disaster Management and Resilience
- Environmental Monitoring and Data Management
- Social and Educational Sciences
- Groundwater and Watershed Analysis
- Hydraulic Fracturing and Reservoir Analysis
- Complex Systems and Decision Making
- Soil and Water Nutrient Dynamics
- Fish Ecology and Management Studies
- demographic modeling and climate adaptation
- Environmental Science and Water Management
- Climate variability and models
- Hydrology and Drought Analysis
- Water Systems and Optimization
- Integrated Water Resources Management
- Urban Stormwater Management Solutions
- Community Development and Social Impact
- Sustainability and Climate Change Governance
Geological Survey of Denmark and Greenland
2014-2024
Geocenter Denmark
2012
Norsk Hydro (Norway)
2008
Abstract. Machine learning provides great potential for modelling hydrological variables at a spatial resolution beyond the capabilities of physically based modelling. This study features an application random forests (RF) to model depth shallow water table, wintertime minimum event, 50 m over 15 000 km2 domain in Denmark. In Denmark, groundwater poses severe risks with respect groundwater-induced flood events, affecting both urban and agricultural areas. The risk is especially critical...
Detailed knowledge of the uppermost water table representing shallow groundwater system is critical in order to address societal challenges that relate mitigation and adaptation climate change enhancing resilience general. Machine learning (ML) allows for high resolution modeling depth beyond capabilities conventional numerical physically-based hydrological models with respect spatial overall accuracy. For this, in-situ well proxy observations are used as training data combination...
Zorrilla, P., G. Carmona, Á. De la Hera, C. Varela-Ortega, P. Martínez-Santos, J. Bromley and H. Jorgen Henriksen 2009. Evaluation of bayesian networks as a tool for participatory water resources management: application to the upper guadiana basin in spain. Ecology Society 15(3): 12. https://doi.org/10.5751/ES-03278-150312
Abstract. Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This critical because significant precipitation biases make the calibration exercise pointless, especially when supposedly physically-based models are play. study addresses general importance of appropriate through a detailed exercise. An existing method addressing solid and liquid applied, both as national mean monthly factors based on...
The paper analyzes the national DK-model hydrological information and prediction (HIP) system HIP portal viewed as a ‘digital twin’ how introduction of real-time dynamic updating simulations can make room for plug-in submodels with boundary conditions made available from an portal. possible feedback to risk knowledge base during extreme events (flooding drought) is also discussed. Under climate change conditions, Denmark likely experience more rain in winter, evapotranspiration summer,...
Institutional arrangements at work in the governance of natural hazard risks Denmark, Finland, Iceland, Norway and Sweden are reviewed analyzed against territorial conceptual framework. The review analysis based on information gathered through literature, an expert online survey, interviews a one half-day workshop. Nordic countries share certain characteristics, such as welfare state legacy, promotion transparency, inclination to bottom-up polycentric approaches. In this context, it is no...
Abstract. There is an urgent demand for assessments of climate change impacts on the hydrological cycle at high spatial resolutions. In particular, shallow groundwater levels, which can lead to both flooding and drought, have major implications agriculture, adaptation, urban planning. Predicting such typically performed using physically based models (HMs). However, are computationally expensive, especially This study Danish national model, set up as a distributed, integrated...