Oleg Makhnin

Geostatistical Mapping of Mountain Precipitation Incorporating Autosearched Effects of Terrain and Climatic Characteristics

Hydrologic and ecologic studies in mountainous terrain are sensitive to the temporal and spatial distribution of precipitation. In this study a geostatistical model, Auto-Searched Orographic and Atmospheric Effects Detrended Kriging (ASOADeK), is introduced to map mountain precipitation using only precipitation gauge data. The ASOADeK model considers both precipitation spatial covariance and orographic and atmospheric effects in estimating precipitation distribution. The model employs gauge data and a multivariate linear regression approach to autosearch regional and local climatic settings (i.e., infer the spatial gradient in atmospheric moisture distribution and the effective moisture flux direction), and local orographic effects (the effective terrain elevation and aspect). The observed gauge precipitation data are then spatially detrended by the autosearched regression surface. The spatially detrended gauge data are further interpolated by ordinary kriging to generate a residual precipitation surface. The precipitation map is then constructed by adding the regression surface to the kriged residual surface. The ASOADeK model was applied to map monthly precipitation for a mountainous area in semiarid northern New Mexico. The effective moisture flux directions and spatial moisture trends identified by the optimal multiple linear regressions, using only gauge data, agree with the regional climate setting. When compared to a common precipitation mapping product [Precipitation-elevation Regression on Independent Slopes Model (PRISM)], the ASOADeK summer precipitation maps of the study area agree well with the PRISM estimates, and with higher spatial resolution. The ASOADeK winter maps improve upon PRISM estimates. ASOADeK gives better estimates than precipitation kriging and precipitation-elevation cokriging because it considers orographic and atmospheric effects more completely.

Mapping Mean Monthly Temperatures over a Coastal Hilly Area Incorporating Terrain Aspect Effects

AbstractEfforts in the past two decades on air temperature mapping based on sparse monitoring networks reveal that algorithms based on multiple linear regressions with geographical and topographical parameters perform promisingly. In this study, a multiple-regression model, previously for precipitation characterization using autosearched orographic and atmospheric effects (PCASOA), is applied to analyze spatial distribution of mean monthly daily maximum and minimum temperatures (at 33 stations) in Adelaide and the Mount Lofty Ranges (9000 km2), a coastal hilly area in South Australia. Terrain aspect (or slope orientation) is transformed and explicitly incorporated in the model, together with some other topographic variables. Overall, PCASOA captures 91% and 70% observed spatial variability for mean monthly maximum (Tmax) and minimum (Tmin) temperature, respectively. The regression also infers some physical processes influencing the air temperature distribution. The results indicate horizontal gradients of...

Stochastic precipitation generation based on a multivariate autoregression model.

Abstract The problem of stochastic precipitation generation has long been of interest. A good generator should produce time series with statistical properties to match those of the real precipitation. Here, a multivariate autoregression model designed to capture the covariance and lag-1 cross-covariance structure of the precipitation measurements is presented. A truncated and power-transformed normal distribution is used to simultaneously model both occurrences and amounts of daily precipitation. The methodology is illustrated using daily rain gauge datasets for three areas in the continental United States.

Metagenome phylogenetic profiling of microbial community evolution in a tetrachloroethene-contaminated aquifer responding to enhanced reductive dechlorination protocols

Chlorinated solvent contamination of potable water supplies is a serious problem worldwide. Biostimulation protocols can successfully remediate chlorinated solvent contamination through enhanced reductive dechlorination pathways, however the process is poorly understood and sometimes stalls creating a more serious problem. Whole metagenome techniques have the potential to reveal details of microbial community changes induced by biostimulation. Here we compare the metagenome of a tetrachloroethene contaminated Environmental Protection Agency Superfund Site before and after the application of biostimulation protocols. Environmental DNA was extracted from uncultured microbes that were harvested by on-site filtration of groundwater one month prior to and five months after the injection of emulsified vegetable oil, nutrients, and hydrogen gas bioamendments. Pair-end libraries were prepared for high-throughput DNA sequencing and 90 basepairs from both ends of randomly fragmented 400 basepair DNA fragments were sequenced. Over 31 millions reads were annotated with Metagenome Rapid Annotation using Subsystem Technology representing 32 prokaryotic phyla, 869 genera, and 3,181 species. A 3.6 log2 fold increase in biomass as measured by DNA yield per mL water was measured, but there was a 9% decrease in the number of genera detected post-remediation. We apply Bayesian statistical methods to assign false discovery rates to fold-change abundance data and use Zipf’s power law to filter genera with low read counts. Plotting the log-rank against the log-fold-change facilitates the visualization of the changes in the community in response to the enhanced reductive dechlorination protocol. Members of the Archaea domain increased 4.7 log2 fold, dominated by methanogens. Prior to remediation, classes Alphaproteobacteria and Betaproteobacteria dominated the community but exhibit significant decreases five months after biostimulation. Geobacter and Sulfurospirillum replace “Sideroxydans” and Burkholderia as the most abundant genera. As a result of biostimulation, Deltaproteobacteria and Epsilonproteobacteria capable of dehalogenation, iron and sulfate reduction, and sulfur oxidation increase. Matches to thermophilic, haloalkane respiring archaea is evidence for additional species involved in biodegradation of chlorinated solvents. Additionally, potentially pathogenic bacteria increase, indicating that there may be unintended consequences of bioremediation.