Impacts of social indicators on assessing the recovery potential of impaired watersheds.

An analysis was carried out to understand how watersheds' potential for restoration was impacted by social indicators. This study employed the USEPA Recovery Potential Screening tool, a decision support system, to compare 51 watersheds in the state of Mississippi, USA, using ecological, stressor, and social indices, and the recovery potential integrated (RPI) index. An in-depth analysis was performed on four watersheds in the Delta region of Mississippi (Lake Washington, Harris Bayou, Steele Bayou, and Coldwater River), each impaired by sediments and nutrients. Sixteen social indicators were categorized into three subcategories: Socio-Economic, Organizational, and Informational. Watersheds with lower social indices had lower RPI scores. In the particular watersheds studied, the Socio-Economic subcategory was observed to be the most impactful to the overall recovery potential when compared to the other two social subcategories. As a sensitivity analysis, a "what if" simulation was performed to explore alternatives to upgrade a watershed's social index and, consequently, the relative recovery potential of the watershed to a target level. This analysis is useful for understanding how particular social indicators of a community impact the relative potential for recovering a watershed, beyond just the ecological and stressor conditions. It also sheds light on assessing which social indicators can be improved.

Fecal Indicator Bacteria Entrainment from Streambed to Water Column: Transport by Unsteady Flow over a Sand Bed.

Storms cause a substantial increase in the fecal indicator bacteria (FIB) concentrations in stream water as a result of FIB-laden runoff and the release of FIB from stream sediments. Previous work has emphasized the association between FIB and bed sediments finer than sand. The objectives of this work were to elucidate the effect of various velocities on the entrainment of bed-dwelling coliforms in sand-bed streams and to refine methodologies for quantifying sandy streambeds as sources of FIB. Pump-induced hydrographs were created using a stainless steel nonrecirculating flume. Experiments consisted of simulating four storm hydrographs and collecting water samples upstream and downstream of a sand bed at selected intervals. Bed sediment samples were collected before and after each event. The highest concentrations of total coliform and suspended sediments generally occurred in the downstream samples during the rising limb of the hydrographs as a result of entrainment of coliforms and sand from the bed to the water column. There was a first flush effect in the system, as the upper layer of sand was influenced by a rapidly increasing velocity at ∼0.2 m s. Coliforms downstream of the sand bed increased rapidly as velocity exceeded this threshold but then declined even as velocity and discharge continued to increase. This likely reflects the depletion of coliforms as the more densely populated sediment layer was flushed out. There is evidence that streams with sand beds harbor enough FIB that development of total maximum daily loads (TMDLs) should include consideration of them as a source.

A multi-variate methodology for analyzing pre-existing lake water quality data.

Environmental agencies are given the task of monitoring water quality in rivers, lakes, and other bodies of water, for the purpose of comparing the results with regulatory standards. Monitoring follows requirements set by regulations, and data are collected in a systematic way for the intended purpose. Monitoring enables agencies to determine whether water bodies are polluted. Much effort is spent per monitoring event, resulting in hundreds of data points typically used solely for comparison with regulatory standards and then stored for little further use. This paper devises a data analysis methodology that can make use of the pre-existing datasets to extract more useful information on water quality trends, without new sample collection and analysis. In this paper, measured lake water quality data are subjected to statistical analyses including Principal Component Analysis (PCA) to deduce changes in water quality spatially and temporally over several years. It was found that the lake as a whole changed temporally by season, rather than spatially. Storm events caused the greatest shifts in water quality, though the shifts were fairly consistent across sampling stations. This methodology can be applied to similar datasets, especially with the recent emphasis by the U.S. EPA on protection of lakes as water sources. Water quality managers using these techniques may be able to lower their monitoring costs by eliminating redundant water quality parameters found in this analysis.

Ecological control of fecal indicator bacteria in an urban stream.

Fecal indicator bacteria (FIB) have long been used as a marker of fecal pollution in surface waters subject to point source and non-point source discharges of treated or untreated human waste. In this paper, we set out to determine the source(s) of elevated FIB concentrations in Cucamonga Creek, a concrete-lined urban stream in southern California. Flow in the creek consists primarily of treated and disinfected wastewater effluent, mixed with relatively smaller but variable flow of runoff from the surrounding urban landscape. Dry and wet weather runoff contributes nearly 100% of FIB loading to Cucamonga Creek, while treated wastewater contributes significant loading of nutrients, including dissolved organic carbon (DOC), phosphorus, nitrate, and ammonium. FIB concentrations are strongly positively correlated with DOC concentration in runoff (Spearman's rho >or= 0.66, P

An alternative to specious linearization of environmental models.

The solution of a number of environmental models is incorrectly obtained by linearizing a nonlinear analytical solution. The linearization can yield a model that includes a common variable on both sides of the equal sign (i.e., ratio analysis), which in calibration causes highly inflated goodness-of-fit statistics. These specious practices continue likely because of tradition, i.e., "that is the way it is done". Goodness-of-fit statistics that result from these erroneous practices do not accurately reflect the actual prediction accuracy of the model. Additionally, the linearly calibrated coefficients can be poor estimators of the true coefficients. The goal of this paper is to demonstrate the pitfalls of models based on ratio analyses. Several environmental models are used to demonstrate the erroneous procedure. Monte Carlo simulation is used to show the distribution of the true correlation coefficient and compare it to the distribution that results from the erroneous linearization. Linearization can produce correlation coefficients above 0.9 when the actual correlation is near 0. Nonlinear least squares algorithms can be used to more accurately fit nonlinear data to nonlinear models.

Flow fingerprinting fecal pollution and suspended solids in stormwater runoff from an urban coastal watershed.

Field studies were conducted to characterize the concentration vs streamflow relationships (or "flow fingerprints") of fecal pollution and suspended solids in stormwater runoff from the Santa Ana River watershed, the largest watershed in southern California. The concentrations of fecal indicator bacteria and F+ coliphages (viruses infecting E. coli) exhibit little-to-no dependence on streamflow rates, whereas the concentrations of total suspended solids (TSS) exhibit a very strong (power-law) dependence on streamflow rates. The different flow fingerprints observed for fecal pollutants, on one hand, and TSS, on the other hand, reflect different sources and transport pathways for these stormwater constituents. The flow-independent nature of fecal indicator bacteria and F+ coliphages is consistent with the idea that these contaminants are ubiquitously present on the surface of the urban landscape and rapidly partition into the surface water as the landscape is wetted by rainfall. The flow-dependent nature of TSS, on the other hand, is usually ascribed to the shear-induced erosion of channel bed sediments and/or the expansion of drainage area contributing to runoff. The apparent ubiquity of fecal indicator bacteria and F+ coliphages, together with the very high storm-loading rates of fecal indicator bacteria and the low detection frequency of human adenovirus and human enterovirus, suggest that fecal pollution in stormwater runoff from the Santa Ana River watershed is primarily of nonhuman waste origin.

Coastal water quality impact of stormwater runoff from an urban watershed in southern California.

Field studies were conducted to assess the coastal water quality impact of stormwater runoff from the Santa Ana River, which drains a large urban watershed located in southern California. Stormwater runoff from the river leads to very poor surf zone water quality, with fecal indicator bacteria concentrations exceeding California ocean bathing water standards by up to 500%. However, cross-shore currents (e.g., rip cells) dilute contaminated surf zone water with cleaner water from offshore, such that surf zone contamination is generally confined to < 5 km around the river outlet. Offshore of the surf zone, stormwater runoff ejected from the mouth of the river spreads out over a very large area, in some cases exceeding 100 km2 on the basis of satellite observations. Fecal indicator bacteria concentrations in these large stormwater plumes generally do not exceed California ocean bathing water standards, even in cases where offshore samples test positive for human pathogenic viruses (human adenoviruses and enteroviruses) and fecal indicator viruses (F+ coliphage). Multiple lines of evidence indicate that bacteria and viruses in the offshore stormwater plumes are either associated with relatively small particles (< 53 microm) or not particle-associated. Collectively, these results demonstrate that stormwater runoff from the Santa Ana River negatively impacts coastal water quality, both in the surf zone and offshore. However, the extent of this impact, and its human health significance, is influenced by numerous factors, including prevailing ocean currents, within-plume processing of particles and pathogens, and the timing, magnitude, and nature of runoff discharged from river outlets over the course of a storm.