Water quality in reservoirs is often compromised in many regions worldwide by nutrients and trace metals. This demands continuous monitoring; however, analyses of large data sets collected during regular monitoring remain a difficult task. Multivariate techniques offer a fast and robust approach for interpreting complex results. The objective of this study was to check the efficacy of self‐organizing maps (SOMs) as a tool to investigate biogeochemical processes. This tool can also help to illustrate influences of land use patterns on the water quality of reservoirs. Here we use the Itupararanga Reservoir in Brazil as a subtropical example. Vertical profiles were sampled from seven sites in the reservoir in a total of seven campaigns over 24 months. Next to physicochemical parameters in the water column (dissolved oxygen, Eh, pH, and temperature), levels of nutrients (NO₃⁻, NH₄⁺, and PO₄³⁻), transition and trace metals (Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, and Zn), and chlorophyll‐a (Chla) were measured. These variables were correlated with land use using SOM. With this technique samples were classified into 17 distinct groups that showed distinct influences of spatial heterogeneity and seasonality. The analyses helped to reveal a seasonal stratification period, where Fe, Mn, and P were released from sediments. Nutrients and some metal inputs (Al and Fe) were related to agricultural, urban, and grass/pasture areas around the reservoir. Our approach also helped to explain physical and biogeochemical seasonality in the reservoir.

The annual variability of the photosynthetic production (PP) by phytoplankton in the lacustrine zone of the Jurumirim Reservoir (São Paulo, Brazil) was evaluated in a three-year study to identify recurrent patterns and their causes. Variability in PP was measured daily during two periods of the year (the dry and rainy seasons). An analysis of the PP data failed to identify a recurrent pattern, since the PP values showed no correlation with hydrological factors (rainfall, water level and discharge, and washout) nor, apparently, with the water’s nutritional conditions. A principal component analysis revealed that the PP and assimilation ratio were higher when the PO4(3-) and N-NH4+ contents were low and the Z EU/Z MIX ratios were at their highest. Areal primary productivity can be predicted based on the ratio between the maximum volumetric productivity and the coefficient of vertical extinction of light. However, the biomass integrated for Z EU was a poor predictor of areal primary productivity. No correlation was found between water temperature and areal and maximum volumetric productivity. Thus, the three-year PP study indicated that the variability pattern is typically chaotic. As for the short-term measurements, the PP was found to be higher in the dry season than in the rainy, although both seasons showed an areal PP variability of 35 to 40%. This pattern was attributed to the daily variation in the nutritional conditions and the magnitude of light penetrating through the water, combined with the mixing of phytoplanktonic cells. A comment about the relationship between primary production by phytoplankton and fish yield is also briefly discussed here.