QDA-System: A Cloud-Based System for Monitoring Water Quality in Brazilian Hydroelectric Reservoirs from Space

Abstract

This article presents the QDA-System (Sistema Qualidade da Água, from Portuguese), a system developed to monitor the quality of surface waters in Brazilian hydroelectric reservoirs using satellite images and cloud computing services. The development requirements of the QDA-System considered its use for operational monitoring purposes, with all processing steps automated, and a user-friendly interface to access and query the data generated automatically by the system. A pilot application of the QDA-System was customized and implemented for monitoring the Foz do Chapecó hydroelectric reservoir located in southern Brazil. For the pilot application, the QDA-System was customized to estimate nine water quality parameters,: five were estimated directly from Sentinel-2 multispectral images and four were estimated indirectly. We expect that in the near future the QDA-System can be replicated to monitor other Brazilian reservoirs, bringing benefits and cost reduction related to water quality monitoring, not only for the sector of hydroelectric generation but for other sectors that also need similar monitoring, such as sanitation and aquaculture production.

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

“…In the ideal future, the acquisition frequency and accuracy of satellite images will be the same as that of water sample data, so as to reduce the time difference between different satellite images. At the same time, more matching data can be obtained by adding monitoring points or stations at different locations, as was performed by Curtarelli et al who arranged them in the reservoir near the dam, in the middle of the reservoir, at the tail of the reservoir, and near the tributary [57]. In addition, the impact on the water quality of inland reservoirs can also be studied in terms of hydrological changes such as water volume and reservoir depth [58], so as to more comprehensively judge the current status and future trends of reservoir water quality.…”

Temporal Dynamics of the Hydropower Water Reservoirs of the Tocantins–Araguaia Basin, Brazil, Based on Remote Sensing and Hydrometeorological Station Datasets

“…Recent studies have relied on field and remote sensing datasets to monitor different aspects of water reservoirs, such as the water quality and reservoir metrics (e.g., flooded area, water level, and volume) [11][12][13][14][15][16][17][18]. Some of these studies intended to monitor large areas and/or long time series [19][20][21][22][23][24], yet very few of them integrated multiple parameters, including weather variations and land use and land cover (LULC) changes, into the analysis of the spatiotemporal dynamics of water reservoirs.…”