Long-term environmental time series of continuously collected data in hydroelectric reservoirs in Brazil

Long-term environmental time series of continuously collected data are fundamental to identify and classify pulses and determine their role in aquatic systems. This paper presents a web based archive for limnological and meteorological data collected by integrated system for environmental monitoring (SIMA). The environmental parameters that are measured by SIMA are: chlorophyll-a (µg/L), water surface temperature (ºC), water column temperature by a thermistor string (ºC), turbidity (NTU), pH, dissolved oxygen concentration (mg/L), electric conductivity (µS/cm), wind speed (m/s) and direction (º), relative humidity (%), short wave radiation (W/m**2), barometric pressure (hPa). The data are collected in preprogrammed time interval (1 hour) and are transmitted by satellite in quasi-real time for any user in a range of 2500 km from the acquisition point. So far 11 hydroelectric reservoirs being monitored using the SIMA buoy. A basic statistics (mean and standard deviation) for some parameters and an example of time series were displayed. The main observed problem are divided into sensors and satellite. The sensors problems is due to the environmental characteristics of each water body. In acid waters the sensors of water quality rapidly degrade, and the collected data are invalid. Another problem is the infestation of periphyton in the sensor. SIMA buoy makes the parameters readings every hour, or 24 readings per day. However, not always received all readings because the system requires satellites passing over the buoy antenna to complete the transfer and due to the satellite constellation position, some locations inland are not met as often as necessary to complete all transmissions. This is the more often causes for lack in the time series.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.