Recent reports show that deforestation in Latin-America has been severe over the last decades. Especially Brazil is subject to an alarming rate of forest loss, which will remain a factor in the coming decades. In addition to deforestation, there is an increasing amount of hazards like floods and mudslides. These hazards result in major damage to human life and nature. This research analyzes the relation between deforestation and river discharge for observed data, a simple conceptual model and complex model WFLOW for 30 catchments subject to deforestation in Brazil. These three studies use satellite data provided by \cite{GEE} to determine annual cumulative deforestation per catchment. In the study with observed data the relation between deforestation and data from CAMELS-BR \citep{CAMELS2020} is researched by analyzing annual values of two discharge driven parameters: Runoff coefficient and recession coefficient. The second study consists of a simple self-made conceptual model. In this second study, the relation between recession coefficient $\alpha$ and annual deforestation is analyzed by calibrating the model per year for parameter $\alpha$. Lastly, a study is conducted on the performance of WFLOW in catchments subject to deforestation. During this study, several input parameters are changed to observe the response of hydrological processes in WFLOW.

The study with observed data shows both increasing and decreasing trends in coefficients for several degrees of deforestation. Literature shows that landcover type after deforestation is a major factor in the interpretation of these results. However, a lack of quality annual landcover data prevents better research in the non-masked impact of deforestation on discharge. The results of the simple model study show no significant relation between deforestation and recession coefficient $\alpha$. The simplicity of this self-made conceptual model is the weak and strong point of this sub-study. The simplicity makes the results less suitable for analyzing the exact impact of deforestation on discharge, but it is useful for observing general signals and is easily scalable to different catchments. The simulated discharge by WFLOW show a steep overestimation of discharge during peak flow in comparison to observed discharge by CAMELS-BR. Therefore it is not possible to analyze how WFLOW reacts to deforestation. Instead, an in-depth analysis on the cause of this poor performance is conducted by analyzing timeseries for hydrological factors like unsaturated zone depth. The results of this analysis indicate the overestimation of discharge is caused by a lack of outflow from soil layers. In addition, the difference between the Budyko framework of different data sets used in this research, show that uncertainty in quality of input data is a plausible factor on the output of WFLOW.

In conclusion it is observed that deforestation does not necessarily lead to higher runoff coefficients and recession coefficients for measured data in this study area. In addition, the conclusion of the simple model study is that no significant relation between deforestation and recession coefficient $\alpha$ is observed for this conceptual model. Finally, the performance of WFLOW is considered too poor to analyze the impact of deforestation on discharge. The root of this poor performance is considered to be a combination between lack of groundwater modelling and uncertainty on the quality of input-data.