The Maurikse Wetering is a tributary of the Line river in the centre of the Netherlands. As the catchment is situated between and in close proximity to the larger Nederrijn and Waal rivers, intercatchment groundwater flow (IGF; groundwater flow crossing topographic divides) might have a significant influence on the water balance of the Maurikse Wetering catchment. As the IGF cannot be measured directly and due to the complex nature of the IGF, the IGF is considered to be one of the hardest fluxes to quantify in conceptual hydrological modelling. The objective of this thesis is to express the IGF as a function of easy-to-measure variables. The most common method used to estimate the net IGF to a catchment in conceptual hydrology is to equate the IGF to the missing water in the water balance of the catchment. A different approach is analyzed in this thesis. To quantify the IGF, the Maurikse Wetering catchment and the surrounding area is modelled in the groundwater model MORIA. The direction of the groundwater flow shows that the groundwater flow in the region is heavily influenced by the Nederrijn and Waal rivers. A multiple linear regression (MLR) analysis was performed to study the influence of variables affecting the water balance of the Maurikse Wetering, e.g. the water level in the Nederrijn, the groundwater level in the Maurikse Wetering catchment and the precipitation, on the IGF. The resulting relations reliably model the IGF. The fact that multiple easy-to-measure factors have a relation to the IGF, shows that there are alternative methods to equating the IGF to the missing water in the water balance. This provides a basis for the usage of IGF relations in predictive modelling. To analyse the effect the IGF has on conceptual hydrological models, the catchment is modelled in WALRUS. The model is calibrated, modelled and validated both without IGF data and with the IGF as modelled by the derived relations. Including the IGF in WALRUS shows an improvement in modelling the variation in groundwater level over smaller time steps compared to the WALRUS model without IGF, the latter only showing a seasonal change in groundwater level. During validation, the model with the IGF relation retains a higher efficiency in modelling the average groundwater level in the Maurikse Wetering than the model without an IGF flux. The results with and without the incorporation of IGF into the model show that the IGF can contribute to significant improvements for conceptual hydrological models.

The Phetchaburi River in Phetchaburi province, Thailand, has a watershed with many different water resource projects. The surrounding farms rely on the Phetchaburi River for irrigation water and the drinking water companies rely on it as a source of water. However, the Phetchaburi basin has problems with yearly floods, salt intrusion and pollution. Water monitoring stations in the region are scarce. A new telemetering system has been put in place, but due to the cost of these stations they are few in number. This project presents a showcase for a cheap and robust water monitoring system in terms of both quantity through water level data and quality through various water quality parameters using apps on an android or iPhone device to gather and analyse the data. The app from Mobile Water Management (MWM) is used to measure the water level through reading a photo of a staff gauge. The Akvo app uses various methods like electronic devices and reading strips via a photo to measure several water quality parameters. It was proven that construction of the staff gauges needed for the MWM app is cheap and does not require highly skilled workers. The resulting data is reliable, if the app is handled by someone trained in handling the app, and/or the data that is created is checked by a trained person. The fact that the pictures taken by the app are uploaded to the database makes for easy verification of the data. This makes verification of telemetric data possible, which as it turns out is not always reliable when compared to the MWM data. The Akvo app has a similar advantage in the sense that verification of the data at a later moment is not only possible, but also easy. This eliminates several human errors in the data collection process and effectively increases the data quality. Right now, several RID officers are needed to collect this data. Using the Akvo app, the required manpower can be lowered. Data analysis shows that the Phetchaburi River has significant levels fecal contamination (E. coli) and issues with low oxygen concentrations at certain moments. For this reason, it is not recommended to use as recreational, fishing or irrigation water. The boundary between salt and freshwater is constantly changing depending on weather conditions and can cause serious problems for local farmers. When constructing the staff gauge there are multiple possible human errors that need to be avoided in order for the MWM app to work correctly. This mainly has to do with the placement of the staff gauge sticker, keeping it straight and unobstructed and also directed towards the user. It turned out that several of the Akvo strips are not working correctly. Other than that, taking data from many parameters can also be time consuming. We recommend that the RID looks into this method of data collection further, both as a cheap and easy way to expand their water monitoring network, and in the case of the MWM app to verify the effectiveness of the telemetering systems.