The increasing demand for stream restoration projects in the Netherlands, driven by legislation, prompted interest in more integrated approaches. TAUW (Technische Adviesbureau van Unie Waterschappen) and WSAM (Waterboard Aa and Maas) collaborated on stream restoration measures for the Lage Raam stream in the Raamvallei, initially using a 1-dimensional model. However, questions arose regarding the suitability of a 2D model for this project.

This research explored the applicability and potential of a 2D hydrological model made in Tygron to provide new insights and outputs for this stream restoration project, including inundation maps, water level fluctuations, and evaluating designed restoration measures. Simultaneously, the study assessed Tygron's applicability for large water systems in the Netherlands by evaluating its underlying settings and parameters.

In the first part of this research, the study demarcated stream restoration measures for the Lage Raam, focusing on redesigning the stream to enhance nature-friendly banks. The Tygron water module was introduced, emphasizing critical simulation setup adjustments such as the rainfall overlay and simulation settings investigated in the initial testcase study. The settings investigated in the testcase were: 'Water level to shorelines', 'Waterline reconstruction', 'Angle stabilizers for partly flooded cells', 'Manning value', 'Grid cell size', and 'Grid/stream placement'.

Results from part 1 indicated that among the six settings tested, only three significantly influenced water level simulations in channels. Variations in Manning values demonstrated a pronounced effect on water height accuracy, with lower values correlating with better simulation outcomes in the testcase. The influence of Manning values was more pronounced in narrower streams, where shallower water depth worsened inaccuracies in the model's backwater effect. Notably, Grid cell size and Grid/stream placement were crucial for achieving accurate outcomes. The optimal grid cell size was found to be 1 by 1 meter or of higher resolution. Additionally, aligning streams parallel to grid cells generally improved results, although the influence of grid placement diminished with increased grid cell count per channel.

The second part introduced the study area, the 'Raamvallei', for case studies 2 and 3, outlining designs for cross sections with swamp areas as restoration measures. Case study 2 validated the Tygron model using measured data from the Raamvallei obtained from WSAM and rain events, testing its suitability and model setup for water systems. Case study 3 implemented TAUW's restoration design to evaluate Tygron's effectiveness of these measures.

The results in part 2 showed that evaluation in a larger watershed scenario (Raamvallei) underscored the model's robustness when configured for extensive water systems. Grid cell size sensitivity analysis highlighted the optimal range (1m x 1m or smaller), lower resolutions causing water loss in the Lage Raam water system, underscoring the resolution’s impact on modeling outcomes. Achieving accurate connectivity between primary, secondary, and tertiary waterways was crucial, requiring iterative adjustments including culvert generation and hydraulic structure calibration. The third case study highlighted challenges in data retrieval and storage due to Tygron's limitations in exporting detailed simulation data over time. However, it also demonstrated Tygron's capability in simulating level fluctuations and flow rates, despite challenges in data analysis.

In conclusion, Tygron was capable of using the explicit Saint-Venant scheme to calculate 2D shallow water equations where it accurately simulated a complex large water system in the Netherlands. Additionally, it could be used for projects such as the Lage Raam to provide insights into stream restoration designs. However, for a model to be successfully used and have results that could be easily understood, some settings were important to look at and some changes in data collection were needed. Future research should encompass diverse test cases to validate Tygron's performance across various scenarios and compare it with other 2D hydrological models for broader applicability insights.

Based on the study's findings, several recommendations were proposed to enhance Tygron's utility in hydrological modeling. These included exploring new data storage approaches to handle extensive datasets more efficiently, optimizing the use of limit areas to simplify model complexity without compromising simulation accuracy, and improving connectivity tools like the culvert generator for seamless integration with external data sources.

This report provides a flood risk assessment of the Isiolo River Basin, in collaboration with Nelen & Schuurmans (3Di, FIS Tool) and Zephyr Consulting (SLAMDAM). This flood risk assessment includes a study of the current flood risk management in Kenya, and in the Isiolo River Basin in particular, because the need for proper flood management is urgent: various climate studies predict an increase in rainfall and an increase in flood risk as a result of the effects of climate change.

Current flood risk management is inadequate. Kenya has defined 21 flood-prone areas whereof one of them is Isiolo Town. Isiolo Town is located in the ENN basin which is, relatively, the most prone to the effects of climate change compared to the other basins. Furthermore, the ENN basin currently has the highest poverty rate and avoidance of further enlargement in poverty rate is important, so there is a need to mitigate flood risks. Since Isiolo Town is located in the Isiolo River Basin, this basin has been chosen for an in-depth study.

The Isiolo River Basin is an Arid Semi-Arid Land region which is often prone to flash floods. Isiolo Town is a flat area located downstream of mountainous area, the rain which falls upstream flows fast downstream and converges into town, often resulting in inundation. Many hazards, both natural and others, are increasing the flood risk in the basin and specifically Isiolo Town.

This flood risk demands flood risk mitigation measures. One possible measure is the SLAMDAM. The SLAMDAM is a movable water-filled flood-barrier. One dam has a length of 5 meters and a height of 1 meter and the dams can be connected to a desired length. The water stored in the dam can be used afterwards for irrigation or other uses.
To recommend effective areas to implement the SLAMDAM, 3Di and the FIS Tool are used. 3Di is a hydrodynamic model and it creates flood maps for different rain events. These flood maps are used as input for the FIS Tool. The FIS Tool calculates the benefits for deploying the SLAMDAM at a certain location for a particular length. The locations which result in the highest benefit are recommended to deploy the SLAMDAM in case of particular rain events. However, a site visit is required to see whether the modelled situation aligns with the real-life situation and to see whether boundary conditions are met.
The SLAMDAM is also compared to other flood risk mitigation measures. Some were analysed using the FIS Tool, whereas others are evaluated based on five self-formulated ranking criteria. These criteria form the base of a scoring matrix where each relevant mitigation measure is scored on.

The performed research has shown the SLAMDAM to rank the best compared to other mitigation measures, both when using the scoring matrix and when using the FIS Tool. However, it is highly recommended to use the SLAMDAM in combination with a Flood Early Warning System. In this way the community downstream can be warned in time to deploy the SLAMDAM. The FIS Tool is found to be especially valuable in finding proper locations for deployment and the dam can be stored close to these locations, enabling fast deployment of the dam in case of need.