Meteo-Hydrological Sensors within the Lake Maggiore Catchment: System Establishment, Functioning and Data Validation

Abstract

Climate change and human activities have a strong impact on lakes and their catchments, so to understand ongoing processes it is fundamental to monitor environmental variables with a spatially well-distributed and high frequency network and efficiently share data. An effective sharing and interoperability of environmental information between technician and end-user fosters an in-depth knowledge of the territory and its critical environmental issues. In this paper, we present the approaches and the results obtained during the PITAGORA project (Interoperable Technological Platform for Acquisition, Management and Organization of Environmental data, related to the lake basin). PITAGORA was aimed at developing both instruments and data management, including pre-processing and quality control of raw data to ensure that data are findable, accessible, interoperable, and reusable (FAIR principles). The main results show that the developed instrumentation is low-cost, easily implementable and reliable, and can be applied to the measurement of diverse environmental parameters such as meteorological, hydrological, physico-chemical, and geological. The flexibility of the solutions proposed make our system adaptable to different monitoring purposes, research, management, and civil protection. The real time access to environmental information can improve management of a territory and ecosystems, safety of the population, and sustainable socio-economic development.

Size-Pattern and Larval-Length–Mass Relationships for the Most Common Chironomid Taxa in the Deep Subalpine Lake Maggiore

“…According to some studies, the increase in the flow regime resulted in reduced biomass and faunal changes, such as a decrease in the biomass of Stictochironomus pictulus [80]. Due to the continuous water demand for human purposes, the low summer precipitation, and the scarce snowfall in the last few years [81], the dominance of this species is quite evident in summer-autumn, as a result of extreme water-level fluctuations. However, the alfa-and beta diversity components and the Shannon diversity index of the benthic chironomid assemblages also seemed to respond significantly to within-lake environmental heterogeneity in a large and shallow lake [82].…”

High-frequency monitoring through in-situ fluorometric sensors: A supporting tool to long-term ecological research on lakes

“…The monitoring covered several physical, chemical, and biological variables, and these longterm data series allowed us to evaluate the lake trophic evolution i.e., oligotrophication, as well as its response to climate drivers (Tanentzap et al, 2020;Rogora et al, 2021). However, until recently, only discrete data, based on regular field campaigns, have been collected, except for meteo-hydrological data for which high-frequency monitoring stations have been established in the lake watershed (Ciampittiello et al, 2021).…”

CEBA: A Data Lake for Data Sharing and Environmental Monitoring

“…Managing the data heterogeneity and interoperability are still common challenges [ 7 ]. For example, Ciampittiello et al [ 8 ] identify that the specific needs of research in the environmental field are among others the collection of high-frequency data that are not readily available in commercial instruments, a data processing software and interoperability. As it is the case in many domains, they also indicate that the sensors used are both commercially produced and built by the individual research groups, according to their different specific needs [ 9 ], generating even more heterogeneity.…”

“…At the same time, Internet of Things (IoT) has become a common technology for large deployment with multiple applications [ 19 ] such as smart health care [ 20 ], environment monitoring [ 8 , 21 , 22 , 23 ], smart cities [ 24 ], industrial control [ 10 , 25 ] and agriculture [ 26 , 27 , 28 ]. The number of connected devices is estimated at the horizon of 2022 at 42.5 billion and at the horizon of 2025 at 75.5 billion with a data volume created by IoT connections projected to reach a massive total of 79.4 zettabytes ( (accessed on 1 December 2021)).…”

“…From the end-user point of view, the IoT seems to make real-time uses possible. Ciampittiello et al [ 8 ] explain, real-time processing requires continual input, constant processing, and steady output of data. For some applications, the near real-time is sufficient, and has become an hot-topic with the widespread adoption of IoT (see for example the applications to smart agriculture [ 26 , 31 ]).…”