Extensive usage of vital freshwater is highly undesirable in the current era of climate change and population growth. However, the 6 millionm3/y of brackish seepage that occurs in the deep Horstermeer Polder, located in between the cities of Amsterdam and Hilversumin the Netherlands, is mitigated by using up to 207 millionm3/y of freshwater. This is very unsustainable, especially since the
pressure on the freshwater reserves in the Netherlands is rising. Therefore, this research focuses on evaluating a new mitigation measure of the brackish
upconing below the polder: the extraction of brackish water. In March 2021 AD, a pilot well has been installed in the polder, and its effects were assessed in this research. To enable the evaluation, an extensive analysis of all available measurements of the geology, hydraulic head, and chloride distribution in the area was performed, and a 3D regional variable-density groundwater model using MODFLOW 6 was built. The model result has a reasonable good agreement with these measurements, since at 70% of the measurement locations, the simulated hydraulic head deviates less than 30 cm, and at 80% of them, the simulated chloride concentration deviates less than 200 mg/l. Moreover, compared to previous research, the representation of the brackish upconing was significantly improved. The effects of the pilot well were calculated using five different operational settings. The results indicated that a shallow well, starting below -50 m NAP, reduces the chloride load in the polder more than a deep well (-100 m NAP). However, even in the best scenario, i.e. an active shallow well with a constant pumping rate of 100 m3/h, the reduction of the chloride load is limited to 5.5%. In perspective, these results suggested that at least 24 of these wells are needed to lower the chloride concentration in the discharge water out of the polder to 250 mg/l. On the contrary, the shallow wells cause a considerable average drawdown of 4 cm at surface level in a radius of 50 meters around the well, which might result in damage to wooden pile foundations in the northeast of the polder.

Due to the predicted impacts of climate change on the frequency of storm events, water managers are challenged to improve and adapt the current urban infrastructure. Cities need to be able to deal with the adverse effects due to more frequent and heavier rainfall. This implies the need for sustainable urban drainage systems (SUDS) that can deal with these challenges. SUDS can be seen as designs that can improve both, the quantitative as well as qualitative characteristics of stormwater. The study area in the south of Amsterdam will be equipped with a SUDS, namely a SediSubstrator L by Fränkische Rohrwerke. The objective is to remove particles from stormwater discharge to prevent clogging of the infiltration facility (AquaBASE) installed in sequence. Additionally, it can remove harmful pollutants which are adsorbed to the fine particles.
This research aims to investigate the characteristics of the sediments in stormwater in theory and develop an appropriate sampling strategy to monitor the relevant parameters in the field. The long-term objective hereby is to determine the removal efficiency of the SediSubstrator L using a finite amount of parameters. To do so, a threefold approach composed by a detailed literature review, stormwater sediment sampling as well as a model simulation was used.
Based on the identified parameters to be assessed, a specific monitoring and sampling setup is proposed to determine the removal efficiency of stormwater sediments in the study area. Along with the efficiency, the volume of sediments that are caught by the facility can be approximated. The latter needs to be removed once the entire storage volume of the sedimentation facility is filled. It is recommended that Waternet uses measured stormwater data to improve the model, while monitoring the efficiency under field conditions. Once the results of the model and the actual field measurements match, the model can be used to simulate the sediment removal for long time series. In this way the cleaning intervals can be predicted, while avoiding expensive long-term measurements to do so.