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.

Compilation of quality controlled nutrient profiles from the Mediterranean Sea

In the last decades, a striking amount of hydrographic data, covering the most part of Mediterranean basin, have been generated by the efforts made to characterize the oceanography and ecology of the basin. On the other side, the improvement in technologies, and the consequent perfecting of sampling and analytical techniques, provided data even more reliable than in the past. Nutrient data enter fully in this context, but suffer of the fact of having been produced by a large number of uncoordinated research programs and of being often deficient in quality control, with data bases lacking of intercalibration. In this study we present a computational procedure based on robust statistical parameters and on the physical dynamic properties of the Mediterranean sea and its morphological characteristics, to partially overcome the above limits in the existing data sets. Through a data pre filtering based on the outlier analysis, and thanks to the subsequent shape analysis, the procedure identifies the inconsistent data and for each basin area identifies a characteristic set of shapes (vertical profiles). Rejecting all the profiles that do not follow any of the spotted shapes, the procedure identifies all the reliable profiles and allows us to obtain a data set that can be considered more internally consistent than the existing ones.