Transfer function noise (TFN) modelling is a form of time series analysis which regularly uses the recharge as a stress to explain the groundwater table fluctuations. Often the recharge flux is estimated as a linear combination of the precipitation and the (potential) evaporation. However, this is a simplification of the actual hydrological processes in the unsaturated zone. This is tried to be overcome by implementing a nonlinear recharge model in TFN time series models. Additionally, TFN models can use different impulse response functions, where some of them account for dispersion and retardation due to the unsaturated zone.

In this report the performance of a linear and nonlinear recharge model, inside the TFN model, are tested against synthetic time series of the groundwater table. These time series for the groundwater table are created with the unsaturated/saturated zone model HYDRUS-1D. With HYDRUS-1D, thirty-five synthetic time series are created for five different soil types and seven different unsaturated zone thicknesses (up to 5 m). The three most commonly used response functions, exponential, gamma and four-parameters are also tested for these thirty-five time series.

The results show that TFN models using the nonlinear recharge model are almost always better in estimating the groundwater table time series than the linear recharge model. This is confirmed in both the calibration and validation period. The common disadvantage of the linear recharge model, undershooting the groundwater table in (dry) summers, is not observed for the nonlinear recharge model. This can improve the forecasting abilities of TFN models during droughts.

Additionally, the nonlinear recharge model gives a more realistic representation of the fluxes in the root zone. This is confirmed goodness-of-fit parameters when comparing of the recharge flux and evaporation reduction calculated by HYDRUS-1D and the nonlinear recharge model. Especially when using the exponential response function, the recharge flux can be estimated quite well by the nonlinear recharge model. However, the nonlinear recharge model is currently not able to estimate groundwater uptake (upwards recharge) while it is observed in the HYDRUS-1D simulations.

The linear model does perform decently for shallow groundwater tables down to a depth of 150 cm since that is where large groundwater fluctuations and more days with groundwater uptake (upward recharge) are observed. The use of the gamma and four-parameter response functions significantly improves the performance of the linear recharge model. This can be explained by the compensation of these response functions for dispersion and retardation in the root zone. Nevertheless, when performing groundwater table time series analysis on synthetic time series created with HYDRUS-1D, the nonlinear recharge model is preferred to simulate the groundwater table.

The goal of this bachelor thesis is to compare different datasets of precipitation from the Royal Netherlands Meteorological Institute for the purpose of modelling municipal solid waste landfills. It is essential to develop after-care methods for landfills so the future generations do not have to cope with the burden of the emission potential of the contaminants. Due to the complex and inhomogeneous nature of the landfill systems modelling is an essential part of understanding the process and predicting the behaviour of the emissions in the future. To model the mass balance an estimate of the precipitation is needed which can be retrieved from two datasets; rain gauges and the precipitation radar. The precipitation radar dataset has a higher resolution and might provide another, and maybe better, estimate for the modelling of the landfills. To see whether this is the case first a comparison for the daily scale is made, second a statistical analysis is performed to determine the difference in distributions between the datasets and third the datasets are compared as a result of the model of the landfills. The results of these comparisons and test show that the radar precipitation data gives a more accurate estimation on a daily basis but the trend in rainfall between the radar precipitation and the automatic rain gauge system is similar. The thesis concludes that the input of the radar dataset in the model creates a better model of the landfill on both a daily basis as on the long-term.