Monitoring System of the Mar Menor Coastal Lagoon (Spain) and Its Watershed Basin Using the Integration of Massive Heterogeneous Data

Abstract

The tool created aims at the environmental monitoring of the Mar Menor coastal lagoon (Spain) and the monitoring of the land use of its watershed. It integrates heterogeneous data sources ranging from ecological data obtained from a multiparametric oceanographic sonde to agro-meteorological data from IMIDA’s network of stations or hydrological data from the SAIH network as multispectral satellite images from Sentinel and Landsat space missions. The system is based on free and open source software and has been designed to guarantee maximum levels of flexibility and scalability and minimum coupling so that the incorporation of new components does not affect the existing ones. The platform is designed to handle a data volume of more than 12 million records, experiencing exponential growth in the last six months. The tool allows the transformation of a large volume of data into information, offering them through microservices with optimal response times. As practical applications, the platform created allows us to know the ecological state of the Mar Menor with a very high level of detail, both at biophysical and nutrient levels, being able to detect periods of oxygen deficit and delimit the affected area. In addition, it facilitates the detailed monitoring of the cultivated areas of the watershed, detecting the agricultural use and crop cycles at the plot level. It also makes it possible to calculate the amount of water precipitated on the watershed and to monitor the runoff produced and the amount of water entering the Mar Menor in extreme events. The information is offered in different ways depending on the user profile, offering a very high level of detail for research or data analysis profiles, concrete and direct information to support decision-making for users with managerial profiles and validated and concise information for citizens. It is an integrated and distributed system that will provide data and services for the Mar Menor Observatory.

Deep Learning-Based Time Series Forecasting Models Evaluation for the Forecast of Chlorophyll a and Dissolved Oxygen in the Mar Menor

“…The methodology used in this work is finally transformed into a process integrated into the ML Model Generator module of the Mar Menor Observatory Monitoring System [6]. Each time new CTD data are uploaded, normally on a weekly frequency, the process is run to generate a series of models to obtain a forecast of the state of dissolved oxygen and chlorophyll a in the Mar Menor.…”

“…Once homogenized at the temporal frequency level, the data are provided to advanced neural networks specialized in time series forecasts: firstly, to obtain the most reliable forecast of future dissolved oxygen and chlorophyll a values, and secondly, to understand the strengths and weaknesses of each of these neural networks to improve them. Finally, this mechanism is transformed into a process integrated into the ML Model Generator module of the OMM's Mar Menor lagoon monitoring system [6].…”

Understanding Nutrient Loads from Catchment and Eutrophication in a Salt Lagoon: The Mar Menor Case

“…This fact produces a large amount of available phosphorus in the ecosystem during the following days after rainfall events, so there is a very high risk of algal blooms, and consequently, of mortality. Sentinel Satellite images of the MM [40] show an increase in the chlorophyll content 3-10 days after small rainfall events and how it requires around 1 month to recover the initial status [37].…”

Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows

“…The proliferation of the invasive algae Caulerpa Prolifera, which now covers a significant portion of the Mar Menor’s seabed, is a major concern due to its potential environmental impacts. The continuous monitoring of Caulerpa Prolifera’s distribution, density, and expansion is crucial for assessing its effects on local biodiversity, water quality, and aquatic ecosystems, as well as identifying areas where its growth is most aggressive, allowing for the timely implementation of preventive or corrective measures [ 3 ]. Advanced technologies, such as remote sensing systems, sonars, echo sounders, underwater sensors, and underwater robots, are essential for the accurate and detailed monitoring of underwater aquatic vegetation in aquatic ecosystems [ 4 , 5 ].…”