Real-time PCR quantification and diversity analysis of the functional genes aprA and dsrA of sulfate-reducing prokaryotes in marine sediments of the Peru continental margin and the Black Sea

Sulfate-reducing prokaryotes (SRP) are ubiquitous and quantitatively important members in many ecosystems, especially in marine sediments. However their abundance and diversity in subsurface marine sediments is poorly understood. In this study, the abundance and diversity of the functional genes for the enzymes adenosine 5'-phosphosulfate reductase (aprA) and dissimilatory sulfite reductase (dsrA) of SRP in marine sediments of the Peru continental margin and the Black Sea were analyzed, including samples from the deep biosphere (ODP site 1227). For aprA quantification a Q-PCR assay was designed and evaluated. Depth profiles of the aprA and dsrA copy numbers were almost equal for all sites. Gene copy numbers decreased concomitantly with depth from around 10(8)/g sediment close to the sediment surface to less than 10(5)/g sediment at 5 mbsf. The 16S rRNA gene copy numbers of total bacteria were much higher than those of the functional genes at all sediment depths and used to calculate the proportion of SRP to the total Bacteria. The aprA and dsrA copy numbers comprised in average 0.5-1% of the 16S rRNA gene copy numbers of total bacteria in the sediments up to a depth of ca. 40 mbsf. In the zone without detectable sulfate in the pore water from about 40-121 mbsf (Peru margin ODP site 1227), only dsrA (but not aprA) was detected with copy numbers of less than 10(4)/g sediment, comprising ca. 14% of the 16S rRNA gene copy numbers of total bacteria. In this zone, sulfate might be provided for SRP by anaerobic sulfide oxidation. Clone libraries of aprA showed that all isolated sequences originate from SRP showing a close relationship to aprA of characterized species or form a new cluster with only distant relation to aprA of isolated SRP. For dsrA a high diversity was detected, even up to 121 m sediment depth in the deep biosphere.

A space-time hybrid hourly rainfall model for derived flood frequency analysis

For derived flood frequency analysis based on hydrological modelling long continuous precipitation time series with high temporal resolution are needed. Often, the observation network with recording rainfall gauges is poor, especially regarding the limited length of the available rainfall time series. Stochastic precipitation synthesis is a good alternative either to extend or to regionalise rainfall series to provide adequate input for long-term rainfall-runoff modelling with subsequent estimation of design floods. Here, a new two step procedure for stochastic synthesis of continuous hourly space-time rainfall is proposed and tested for the extension of short observed precipitation time series. First, a single-site alternating renewal model is presented to simulate independent hourly precipitation time series for several locations. The alternating renewal model describes wet spell durations, dry spell durations and wet spell intensities using univariate frequency distributions separately for two seasons. The dependence between wet spell intensity and duration is accounted for by 2-copulas. For disaggregation of the wet spells into hourly intensities a predefined profile is used. In the second step a multi-site resampling procedure is applied on the synthetic point rainfall event series to reproduce the spatial dependence structure of rainfall. Resampling is carried out successively on all synthetic event series using simulated annealing with an objective function considering three bivariate spatial rainfall characteristics. In a case study synthetic precipitation is generated for some locations with short observation records in two mesoscale catchments of the Bode river basin located in northern Germany. The synthetic rainfall data are then applied for derived flood frequency analysis using the hydrological model HEC-HMS. The results show good performance in reproducing average and extreme rainfall characteristics as well as in reproducing observed flood frequencies. The presented model has the potential to be used for ungauged locations through regionalisation of the model parameters.