A monitoring-forecasting tool for advancing surface water quality management in lakes, reservoirs and major rivers

In water resources management, predictive services are essential to support sustainable planning and operations over a range of time scales, from the short term (days) to the medium term (seasons) to the long term (years to decades). Current forecasting tools mainly address water availability (i.e., quantity), with limited practical applications for water quality. Within the framework of the project called “Strumenti di monitoraggio e previsionali sullo stato di QUalità delle Acque Superficiali” (SQUAS; founded by CARITRO Foundation, Italy; website: https://sites.google.com/unitn.it/hydrosquas), we aim to fill this gap, which is particularly relevant in view of the ongoing transformation of water resources due to rapidly changing climatic conditions. More specifically, we aim to 1) increase the accessibility of tools for diagnosing and predicting surface water quality for use by local authorities and managers of surface water resources, such as agricultural consortia, hydroelectric plant operators, municipal companies, and public entities, and 2) improve the ability of these entities to plan and manage water resources efficiently and sustainably. Anchored in a multidisciplinary approach, the project integrates physical-based modelling used to forecast key water quality parameters with satellite remote sensing data for monitoring purposes. As for the modelling component, the project will be based on the widely used air2water and air2stream models for water temperature prediction in lakes and rivers. Central to the project is the revision, improvement and extension of these models by including water quality variables (e.g., turbidity, dissolved oxygen) and by integrating them into a state-of-the-art web-based Geographic Information System (GIS) platform. The web-GIS platform will not only allow to forecast future conditions based on the above models but also allow for real-time monitoring of water quality. Its Python fast-api based interface will provide a user-friendly GUI for the user interaction, using any web browser. The speed of computation of the forecasting models will be ensured by efficient Cython-based functions. The intuitive interface of the web-GIS platform will appeal to a wide range of users, from policy makers and water resource managers to academic researchers, facilitating informed decision-making and sustainable management practices. An interactive presentation of the web-GIS tool will be given during the session.