[Dynamic Changes of Dissolved Organic Matter Derived from Algal Decomposition and the Environmental Effects in Eutrophic Lakes].

The global occurrences of lake eutrophication have led to algal bloom and the subsequent algal decomposition, releasing high amounts of algae-derived dissolved organic matter (DOM) into the lake water. Algae-derived DOM could regulate the quantity and composition of DOM in lake water and further impact the biogeochemical cycles of multiple elements. In this study, the dynamic changes in the quantity and quality of DOM during algal decomposition under different eutrophic scenarios (e.g., from oligotrophication to severe eutrophication) were monitored, and the corresponding environmental effects (e.g., microbial responses and greenhouse gas emissions) caused by algal decomposition were further explored. The results showed that algal decomposition significantly increased the DOM levels, bioavailability, and intensities of fluorescent components in the water. The total DOM levels gradually decreased, whereas the average molecular weight increased along the decomposition process. Furthermore, unsaturated hydrocarbon and aliphatic compounds were preferentially utilized by microorganisms during algal decomposition, and some refractory molecules (e.g., lignin, condensed hydrocarbons, and tannin with high O/C values) were synchronously generated, as evidenced by the results from ultra-high-resolution mass spectrometry. The dominant bacterial species during algal decomposition shifted from Proteobacteria (46%) to Bacteroidetes (42%). In addition, algae addition resulted in 1.2-5 times the emissions of CO2 and CH4 from water, and the emission rates could be well predicted by the optical index of a254 in water. This study provides comprehensive perspectives for understanding the environmental behaviors of aquatic DOM and further paves the ways for the mitigation of lake eutrophication.

Uncertainty in watershed response predictions induced by spatial variability of precipitation.

Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the "true" rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km(2) with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.