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This project has worked to develop an innovative methodology that will allow public and private bodies to address the problem of harmful blooms of algae and cyanobacteria (those that, when carrying out oxygenic photosynthesis, consume all the oxygen in a body of water, causing fish and other aquatic animals to have difficulty breathing) in continental water bodies, increasingly common. To develop this methodology, the use of remote sensing and remote sensors has been combined, based on IoT (Internet of Things) and Big Data technologies. This has allowed the development of statistical models to analyse the vulnerability of environments and warn of the appearance of outcrops.

According to the entity, it is expected that the results achieved will have a socioeconomic impact on the affected areas, as well as that it will be useful to identify vulnerable areas both nationally and internationally. In addition, the direct participation of citizens in the project will influence the awareness of the population about the impacts of climate change and the sustainable use of water resources.

The main objective of the project has been to model the problem of algae and cyanobacteria blooms in two climatic zones of the Iberian Peninsula: As Conchas area (Ourense) and Albufera Park (Valencia).

The specific objectives were as follows:

• Determine the climatic and geographical variables related to the history of outcrops in each environment.
• To develop the univariate statistical model of vulnerability to outcrop episodes.
• Create a network of sensors that allow the collection of physicochemical data remotely and in real time.
• To generate a multivariate statistical model for early warning of upwellings.
• To give visibility to the scientific results obtained during the research and to involve society in the tool generated.
• Have a methodology for replicability or adaptability of the project to other geographical and climatic areas.

• Data analysis using a geographic information system (GIS) of the history of blooms in the two study areas.
• Realization of the univariate statistical model of vulnerability of the studied environments to the presence of algae and cyanobacteria blooms, based on telemetry and GIS techniques.
• Implementation of a network of sensors for the measurement of physicochemical parameters of two bodies of water in the study areas. The aim has been to develop a system capable of estimating or predicting the state of a body of water in which the presence of cyanobacteria has been previously verified by means of a laboratory study.
• Realization of the multivariate statistical model of early warning based on physicochemical parameters acquired by the sensors placed in situ, and evaluation of the implementation of the sensors in each environment.
• Temporary study of the quality of water bodies by remote sensing and information collection.
• Actions for the transfer of scientific knowledge: participation in Science Week and 4 congresses, writing of 4 articles published in high-impact international journals, publication of 17 reviews on websites and publication of an informative brochure.
• Actions for citizen participation, which include the development of a mobile app for information and citizen participation and the installation of two information panels (one in each study area).
• Creation of a manual to replicate and adapt the methodology developed in different environments, which has been sent to 10 key agents.

Climate change and pollution of aquatic ecosystems have intensified harmful blooms of algae and cyanobacteria. One of the great challenges today is to effectively monitor their rapid growth, extension and distribution in continental water bodies.

In this sense, the CianoMOD project has addressed this challenge in two different environments: the As Conchas reservoir (Ourense) and the Albufera lagoon in Valencia. The solution developed consists of the implementation of an autonomous system that allows monitoring this phenomenon in real time. Firstly, an exhaustive cartographic and quality analysis of the data available by the managing bodies has been carried out, with the aim of proposing a replicable solution adjusted to the specific needs of each environment. This has made it possible to analyse the vulnerability and make future projections based on the available data.

Subsequently, a monitoring system has been implemented based on networks of wireless sensors deployed in situ and the capture of satellite images, whose data can be viewed free of charge through a web and mobile application. In addition, after the collection of sufficient massive data (Big Data) and statistical modelling, the regressive variables that explain the appearance of the outcrops and that manage to predict them in a time window of 72 hours have been obtained.

Finally, the compilation in a methodological manual of all the actions developed will allow the project to be replicated in other bodies of water affected by this problem, thus offering water managers a useful tool to be in continuous contact with the environment in real time.