Quality Monitoring in Small Rivers

Outline
Team
  • Expected start date:
    2015•10•01
    Expected end date:
    2019•09•30
    Institute:
    UNU-FLORES
    Project Status:
    Ongoing
    Project Type:
    Research
    Project Manager :
    Hiroshan Hettiarachchi

     

    Water quality monitoring is considered as a critical component for effective water resources management. Current riverine monitoring systems often encounter problems related to water quality data collection and data assessment. Existing monitoring systems use fixed stations for water sampling, which can only provide a “snap-shot” of the complex water quality processes. In fact, water quality varies rapidly both in time and space. There have been many studies on temporal-spatial distribution of water quality in rivers. However, most of these studies use discrete data from synoptic monitoring stations. The resolution of these measurements is usually on the scale of kilometres and thus, processes between stations remain undetected. To address this gap, the proposed study uses continuous longitudinal water quality data for extensive data assessment. The samples are collected using a moving boat specially designed with multiple sensors. The study sites are Freiberger Mulde and Tollense Rivers in Germany, representative of both mountainous and estuarine conditions respectively. Twelve water quality parameters will be measured on-site. Fine-scale data are analysed using multivariate statistical techniques and uncertainty analysis to identify the spatio-temporal variation of water quality and precision of boat-based sampling respectively. From the analysis, the priority parameters responsible for water quality variation in two rivers will be determined and improvements on current monitoring strategies including sampling frequencies and locations will be proposed. This study illustrates a new sampling technique for river water quality monitoring and application of multivariate statistical technique for analysis and interpretation of spatial-continuous data and thus aims to contribute to a new approach to sustainable water resources management.