Digital twin developments in DTC Hydrology Next: reservoirs and flooding

Climate change is affecting the global water, energy and carbon cycle resulting in more severe hydrometeorological events with more societal impact (e.g. precipitation, floods, droughts). Decision support systems for operational or planning purposes are essential to accurately predict and monitor environmental disasters, and optimally manage water and environmental resources now and in the future. The Digital Twin Component (DTC) Hydrology Next project focuses on solutions for monitoring and simulations and forecasting, it requires high-resolution (1 km, 1 hour-day) satellite Earth Observation (EO) data, fully integrated with advanced and spatially distributed modelling systems. Within this scope,  we aim to improve operational reservoir monitoring to obtain reliable estimates  of surface area, water level and volume (i.e. storage). Secondly, we aim to enhance predictions by data assimilation using wflow_sbm (Imhoff et al., 2020, Eilander et al., 2021, van Verseveld et al., 2024, Imhoff et al., 2024). The focus will be on the Rhine catchment focusing on flooding in co-creation with RWS (Dutch ministry of traffic and waterways) and Zambia focusing on reservoir monitoring and flood management working together with WARMA (Water Resources Management Authority). We also consider including  2D hydraulic flood simulations using SFINCS (Leijnse et al., 2021) driven by outputs from wflow_sbm.

 

Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J. (2021) A hydrography upscaling method for scale invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021.

Imhoff, R. O., van Verseveld, W. J., Osnabrugge, B., and Weerts, A. H. (2020) Scaling Point-Scale (Pedo)transfer Functions to Seamless LargeDomain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River, Water Resour. Res., 56, https://doi.org/10.1029/2019WR026807.

Imhoff, Ruben and Buitink, Joost and van Verseveld, Willem and Weerts, Albrecht, A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm. Environmental Modelling & Software,179,https://doi.org/10.1016/j.envsoft.2024.106099, 2024

Leijnse, T. W. B. et al. (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes. Coastal Engineering, 163, Article 103796. https://doi.org/10.1016/j.coastaleng.2020.103796

van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024.