Disaster Risk Mitigation & Climate Adaptation

Deltares, together with its partners KNMI, Tecnalia and 510 Red Cross, are developping a digital twin that will support disaster risk reduction and climate adaptation. A generic, globally-applicable modelling framework using open-source model components is being applied. Where needed, individual components are replaced or refined further using local models, thus, applying a “global to local” approach.

A total of five case study areas have been selected:

• Basque Country
• United Kingdom
• Philippines
• La Reunion
• Dutch Caribbean Islands (BES Islands)

The diversity in environmental conditions and user needs across the case studies helps in demonstrating the applicability of the service. For each case study area, an end-user is identified, who will provide iterative feedback to determine case-specific requirements.

The overall modelling framework follows the same structure for all case study areas, see figure 1.2. The modelling chain exists of locally implemented models and global datasets for simulating the various physical processes. In general, the modelling chain exists of open-source model components from Deltares that can still be replaced by existing user-owned models. The modelling chain has been set up, with as final result flood inundation (water depth levels) translated into flood impact maps. The model implementations are similar for all regions. However, depending on the relevant flood drivers some model components may not be included in all workflows. The description of the type of models used for each physical process is described in the following sections.

For the Tier-1 dataset ERA5 was used as meteorological forcing and for each region together with the end-users an historic event was selected that resulted in severe flooding in the region. For the Tier-2 dataset climate and forecast datastreams from DestinE will be integrated. In addition, where available higher resolution, ensemble meteorological and hydrological forecast or hind-cast products will be used for uncertainty evaluation.

Modelling of the individual flood drivers

Tide and Storm Surge

Sea levels comprise effects from astronomical tides, storm surges, wind waves and mean sea level variations, with each component operating on different spatial and temporal scales. Using the in-house Global Tide and Surge Model (GTSM see Figure 1‑3), which makes use of the Delft3D Flexible Mesh(FM) software, tide and storm surges are modelled using depth-averaged hydrodynamic models (2D). A flexible mesh refers to the unstructured nature of the grids used for modelling, allowing for spatially-varying resolutions and higher computational efficiency. As GTSM has a uniquely higher resolution along coastal areas (1-25-2.5km), this makes the software suitable for simulating extreme water levels in events such as tropical cyclones. The GTSM model is extensively validated (Muis et al., 2020; Dullart et al., 2020) and calibrated (Wang et al., 2021; Wang et al., 2022), demonstrating good performance for tide and storm surge simulations.

Waves

Wave model descriptions

HurryWave – Deep-water wave generation model developed by Deltares to simulate the generation and propagation of sea-swell waves by wind forcing. A third-generation spectral wave model similar to SWAN or WAVEWATCH-III, HurryWave is, on average, an order of magnitude faster than the SWAN model due to its very slightly reduced physics, combined with simpler wave propagation schemes.

SNAPWAVE – Another model developed by Deltares, SNAPWAVE is used to estimate nearshore wave setup within the SFINCS (Super-Fast Inundation of CoastS, described in later sections) model.

BEWARE (Bayesian Estimator of Wave Attack in Reef Environments) – A large synthetic database originally constructed based on approximately 100,000 1-Dimensional XBeach simulations, forced with a range of hydrodynamic conditions (Pearson et al., 2017). XBeach is an open-source model for simulating wave propagation, long waves, mean flow, sediment transport and morphological changes in the nearshore environment.

Van Ormondt (2021) leverages empirical formulations for sandy coastlines to approximate nearshore infragravity wave conditions. The empirical formulations were derived using 1-Dimensional XBeach-NH+ model runs for sandy profiles (XBeach Non-hydrostatic).

Hydrology

Hydrological processes play a pivotal role in the Earth’s water cycle, influencing the distribution and movement of water across the landscape. These processes encompass a wide range of phenomena, such as precipitation (rainfall), evaporation, infiltration, runoff and groundwater flow. Understanding and quantifying these complex interactions is crucial for effective flood forecasting. Mathematical models can be deployed to simulate these hydrological processes, enabling us to predict and prepare in advance during extreme events. Hydrological models can have a wide variety of complexity, from simple conceptual models to multi-dimensional gridded models.

Within this contract, the Wflow_sbm model will be used to predict discharge volumes and water levels. Wflow_sbm is a free and open-source distributed hydrological model that maximises the use of global and publicly available datasets. Models can be set-up in an automated fashion for river basins around the globe by using the Wflow plug-in within the HydroMT python package, which is also developed by Deltares. Wflow_sbm incorporates the processes included in Figure 1-4.

Flood inundation

Extreme events such as tropical cyclones may result in a flooding phenomenon where the impact of flooding may be amplified in certain coastal areas due to the simultaneous occurrence of multiple factors. These factors can include increased river discharge and increased coastal water levels due to tide, storm surge and wave setup. Having an Early Warning System (EWS) and a multi-hazard risk analysis in-place could help reduce these impacts by providing timely information for disaster preparedness. However, compound flooding is also challenging to predict due to large computational effort needed in short periods of time when considering the implications for early warning. Current modelling approaches have their own benefits and disadvantages, such as the ‘bathtub’ approach, which is extremely fast but too simplistic, and numerical models that achieve accuracy but lose out in speed, such as Delft3D or XBeach. As a compromise between these approaches, models that have reduced physical complexity can be deployed instead, resulting in much quicker computations due to the reduction in the number of equations to solve for.

SFINCS (Super-Fast Inundation of CoastS) is one example of a reduced-physics model that is capable of simulating compound flooding with high computational efficiency, balanced with adequate accuracy (Figure 1‑5). Its efficiency makes it suitable for forecasting compound flood events in operational forecasting systems, and includes fluvial, pluvial, tidal, wind- and wave-driven processes (Figure 1‑6).

Flood impact

Floods can wreak devastating damages on buildings, services, mobility and may even lead to loss of lives. Modelling floods and potential damages can provide actionable information to decision makers, allowing them to prioritise or plan appropriate interventions on various timescales, such as between a few days (disaster preparedness) to decades in advance (climate adaptation). Delft-FIAT is a fast, open-source Python-based tool developed by Deltares to rapidly assess direct economic impacts to buildings, utilities and transport networks from user-input flood maps (Figure 1‑7). Delft-FIAT is made in a flexible manner, allowing a user to define their own damage functions, include other hazards, or even generate different damage classes.