Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Commonly used methods to predict streamflow at ungauged watersheds implicitly predict streamflow magnitude and temporal sequence concurrently. An alternative approach that has not been fully explored is the conceptualization of streamflow as a composite of two separable components of magnitude and sequence, where each component is estimated separately and then combined. Magnitude is modeled using the flow duration curve (FDC), whereas sequence is modeled by transferring streamflow sequence of gauged watershed(s). This study tests the applicability of the approach on watersheds ranging in size from about 25‐7,226 km² in Southeastern Coastal Plain (U.S.) with substantial surface storage of wetlands. A 19‐point regionalized FDC is developed to estimate streamflow magnitude using the three most selected variables (drainage area, hydrologic soil index, and maximum 24‐h precipitation with a recurrence interval of 100 years) by a greedy‐heuristic search process. The results of validation on four watersheds (Trent River, North Carolina: 02092500; Satilla River, Georgia: 02226500; Black River, South Carolina: 02136000; and Coosawhatchie River, South Carolina: 02176500) yielded Nash‐Sutcliffe efficiency values of 0.86‐0.98 for the predicted magnitude and 0.09‐0.84 for the predicted daily streamflow over a simulation period of 1960‐2010. The prediction accuracy of the method on two headwater watersheds at Santee Experimental Forest in coastal South Carolina was weak, but comparable to simulations by MIKE‐SHE.     

The recovery of schools from earthquake effects: Lessons from Mexico City

Much work has been done to prepare schools and institutions for the immediate effects of earthquakes and other disasters. This paper looks at the preparation needed for medium to long term recovery from the physical and psychological effects of earthquakes. Colegio Madrid, a school of 3,000 students that suffered major damage in the September 1985 Mexico City earthquake serves as the focus of this study. The experiences and actions of faculty, staff, students and parents at this school provide lessons for not only schools, but all institutions and communities which may face the prospect of reconstructing themselves following a disaster.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

A measure of sustainability of Brazilian agribusiness using directional distance functions and data envelopment analysis

The social and environmental impacts caused by the modernization of Brazilian agribusiness have evoked a growing interest in the search of balance between social-economics development and the adequate use of natural resources, driving the country to sustainable development. Therefore, the main aim of this article is to estimate a set of indicators of relative efficiency in the Brazilian agricultural sector, which satisfy the concept of Pareto optimality, potentiates simultaneously both economic, ecological dimensions and social functions. To reach this purpose, the method of directional distance functions and data envelopment analysis was applied. These indicators confirm, in different ways, the hypothesis that it is possible to perform consistent productive strategies with the maximization of social welfare, despite the apparent antagonism among these three dimensions. In addition, it is shown that efficient Brazilian states tend to combine the three dimensions in different ways. Hence, it can be concluded that several equilibrium taken sustainable can be achieved through different actions on poverty and environmental impact reduction without necessarily generating productive inefficiencies. This result can be considered of prominent importance for sustainable development in Brazil and can also serve as a reference in the definition of goals of the plan ‘Brazil without Misery’ and international commitments to reduce Greenhouse Gas – GHG – in Brazil, especially for the 17 inefficient Brazilian states.     

Implications of non-equilibrium transport in heterogeneous reactive barrier systems: evidence from laboratory denitrification experiments

Organic substrates in reactive barrier systems are often heterogeneous material mixtures with relatively large contrasts in hydraulic conductivity and porosity over short distances. These short-range variations in material properties imply that preferential flow paths and diffusion between regions of higher and lower hydraulic conductivity may be important for treatment efficiency. This paper presents the results of a laboratory column experiment where denitrification is investigated using a heterogeneous reactive substrate (sawdust mixed with sewage sludge). Displacement experiments with a non-reactive solute at three different flow rates are used to estimate transport parameters using a dual porosity non-equilibrium model. Parameter estimation from breakthrough curves produced relatively consistent values for the fraction of the porosity consisting of mobile water (β) and the mass transfer coefficient (α), with average values of 0.27 and 0.42 d(-1), respectively. The column system removes >95% of the influent nitrate at low and medium flow, but only 50-75% of the influent nitrate at high flow, suggesting that denitrification kinetics and diffusive mass transfer rates are limiting the degree of treatment at lower hydraulic residence times. Reactive barrier systems containing dual porosity media must therefore consider mass transfer times in their design; this is often most easily accommodated by adjusting flowpath length.     

EPA SITE Demonstration of the BioTrol Soil Washing Process

A pilot-scale soil washing process, patented by BioTrol, Inc., was demonstrated on soil contaminated by wood treating waste, primarily pentachlorophenol (PCP) and creosote-derived polynuclear aromatic hydrocarbons (PAHs). Although soil washing was the main object of this demonstration, the treatment train that was evaluated included two other BioTrol technologies for treatment of waste streams from the soil washer. The three technologies were: ? The BioTrol Soil Washer (BSW)—a volume reduction process, which uses water to separate contaminated soil fractions from the bulk of the soil.

? The BioTrol Aqueous Treatment System (BATS)—a biological water treatment process.

? The Slurry Bioreactor (SBR)—a BioTrol biological slurry treatment process conducted in an EIMCO BIOLIFT^tm reactor.

The sandy soil at the site, consisting of less than 10 percent of fines, was well suited for treatment by soil washing. The soil washer was evaluated in two tests on soil samples containing 130 ppm and 680 ppm of PCP, respectively.

The BSW successfully separated the feed soil (dry weight basis) into 83 percent of washed soil, 10 percent of woody residues, and 7 percent of fines. The washed soil retained about 10 percent of the feed soil contamination while 90 percent of the feed soil contamination was contained within the woody residues, fines, and process water.

The soil washer achieved up to 89 percent removal of PCP and 88 percent of total PAHs, based on the difference between their levels in the as-is (wet) feed soil and the washed soil. PCP concentrations of 14 ppm and 87ppm in the washed soil were achieved from PCP concentrations of 130 ppm and 680ppm in the feed soil. Concentrations of total PAHs were reduced from 247 ppm to 42 ppm and 404 ppm to 48 ppm, respectively, in the two tests.

The BATS degraded up to 94 percent of PCP in the process water from soil washing. PAH removal could not be determined due to low influent concentrations.

The SBR achieved over 90 percent removals of PCP and 70 to 90 percent removals of PAHs from the slurry of contaminated fines from soil washing. However, steady state operation was not achieved during the single test and the results were variable.

Cost of a commercial-scale soil washing, assuming use of all three technologies, was estimated to be $168 per ton of soil treated. Incineration of woody material accounts for 76 percent of the cost.