The frequency, intensity and duration of cyanobacterial harmful algal blooms are expected to increase with climate change. Here we tested the null hypothesis that successive severe drought years would not differ in the magnitude, community composition and controlling factors for Microcystis blooms during 2014 and 2015, the third and fourth most severe drought years on record in the San Francisco Estuary, California, USA. Identical sets of physical, chemical and biological data were collected every 2 wk at 10 stations between August and November for each year. Primary producer biomass, abundance, biovolume, community composition and toxin production were quantified. Contrary to expectation, the surface and subsurface Microcystis bloom in 2014 was at least an order of magnitude greater than in 2015, the drier and warmer year. In addition, the 2015 drought had a greater percentage of other cyanobacteria (non-Microcystis) and eukaryotic phytoplankton than 2014. Median water quality conditions were similar between years, but correlations among physical, chemical and biological variables often differed in magnitude and direction. PRIMER DISTLM (BEST) analysis identified water temperature, the landward extent of saltwater intrusion and outflow as variables that accounted for the most variation in Microcystis surface biovolume (R 2 = 0.48) or subsurface abundance (R 2 = 0.45). We conclude that the magnitude of Microcystis blooms may not always increase with drought severity or prolonged drought, and are dependent on within-year spatial and temporal variation.

Various models based on Budyko framework, widely applied to quantify the impacts of climate change and land use/cover change (LUCC) on runoff, assumed a fixed partition used to distinguish the impacts. Several articles have applied a weighting factor describing arbitrary partitions for developing a total differential Budyko (TDB) model and a complementary Budyko (CB) model. This study introduces the weighting factor into a decomposition Budyko (DB) model and applies these three models to analyze runoff variation due to the impacts in the upper-midstream Heihe River basin. The Pettitt test is first applied to determine a change point of a time series expanded by the runoff coefficient. The cause for the change point is analyzed. Transition matrix is adopted to investigate factors of LUCC. Results suggest the consistency of the CB, TDB, and present DB models in estimating runoff variation due to the impacts. The existing DB model excluding the weighting factor overestimates the impact of climate change on runoff and underestimates the LUCC impact as compared with the present DB model. With two extreme values of the weighting factor, runoff decrease induced by LUCC falls in the range of 65.20%–66.42% predicted by the CB model, 65.01%–66.57% by the TDB model, and 64.83%–66.85% by the present DB model. The transition matrixes indicate the major factors of LUCC are climate warming in the upstream of the study area and cropping in the midstream. Our work provides researchers with a better understanding of runoff variation due to climate change and LUCC.

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