A database system for querying of river networks: facilitating monitoring and prediction applications

Abstract

The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimisation of water resources management. Global challenges such as climate change, intensive agriculture and urbanisation put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data, for example on the location of the measurement, and upstream and downstream catchment characteristics, are required. In this paper, we present a data management system to support flow-path-related functionality for decision making and prediction modelling. Adding meta-datasets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks. HIGHLIGHTS Line geometries; Queryable topology; Flow-path detection; Discharge prediction; Data driven hydrological applications;

Keywords

• data driven modelling
• iot
• recursive querying
• relational databases
• river monitoring
• water management