Due to the amount of monitoring undertaken in the last 10 years, as well as the increased use of real time data acquisition, many large data stets now exist. Trend analysis is used to collect information from large data sets to understand how the process works and enables us to make informed decisions about what to do when faced with similar situations in the future. There is a growing need for programming skills amongst scientist so that they can affectively process large data sets from sensors and historical monitoring. This thesis deals with how sensor technology coupled with statistical analysis can provide an invaluable tool for monitoring water quality and managing this vital natural resource. Statistical analysis of the sensor data was performed to determine the optimum conditions for deployment, future monitoring, and determine what precautions, if any, are to be taken. R statistical programming was chosen as a method of analysis as it is capable of processing large complex time series data sets whilst making analysis easily reproducible by other scientists. This thesis examined trends found in historical data and real time data sets of physical and biological properties related to water quality. The physical properties of water were examined in the Dodder catchment. A network of affordable high frequency ultrasonic water level gauges were deployed throughout the catchment. The historical biological properties of the Grand Canal Basin was also examined. A bacterial level sensor capable of rapid detection was tested in the basin to determine its use for future monitoring. Lastly the real time impact of cattle entering a stream was examined against real time turbidity data to examine trends due to monitored external forces. The impact of the cattle on the stream was examined by looking at the difference between the upstream and downstream sensor data.