Fecal indicator bacteria (FIB) levels during dry weather from Southern California reference streams

High levels of fecal indicator bacteria (FIB) in surface waters is a common problem in urban areas that often leads to impairment of beneficial uses such as swimming. Once impaired, common management and regulatory solutions include development of total maximum daily loads (TMDLs) and other water quality management plans. A critical element of these plans is establishment of a "reference" level of exceedances against which to assess management goals and TMDL compliance. The goal of this study was to provide information on indicator bacteria contributions from natural streams in undeveloped catchments throughout southern California during dry weather, non-storm conditions. To help establish a regional reference data set, bacteria levels [i.e. Escherichia coli (E. coli), enterococci and total coliforms] were measured from 15 unimpaired streams in 11 southern California watersheds weekly for one full year. Concentrations measured from reference areas were typically between one to two orders of magnitude lower than levels found in developed watersheds. Nearly 82% of the time, samples did not exceed daily and monthly bacterial indicator thresholds. E. coli had the lowest daily percent exceedance (1.5%). A total of 13.7% of enterococci exceeded daily thresholds. Indicator bacteria levels fluctuated seasonally with an average of 79% of both enterococci and total coliforms exceedances occurring during summer months (June to August). Temperature, at all sites, explained about one-half the variation in total coliforms density suggesting that stream temperatures regulated bacterial populations. Accounting for natural background levels will allow for management targets that are more reflective of the contributions from natural sources.     

Integrating probabalistic and targeted compliance monitoring for comprehensive watershed assessment

Environmental monitoring typically falls into one of two broad categories. Targeted designs, utilizing fixed stations, focus on describing and quantifying impacts, tracking trends, and assessing compliance with regulatory guidelines or limits. Probabilistic designs, in contrast, draw sampling stations at random from an area or region, and the stations are used to describe conditions in the region of interest based on a subpopulation of sites. These two design approaches are usually viewed as mutually exclusive, with randomized designs used for broader regional assessments of overall ambient condition and targeted designs for demonstrating regulatory compliance and/or characterizing specific, localized impacts. Combining elements of both approaches into a single design provides benefits not available from either design alone. Embedding targeted monitoring within the framework of a probabilistic design enables data from targeted stations to be viewed in a more accurate regional context and provides a consistent background against which to identify characteristic regional patterns of contamination and impact. We use the San Gabriel River Regional Monitoring Program, recently implemented in southern California, to illustrate the structure of a hybrid design and how it enables data analyses and assessments that provide a more complete picture of conditions in the watershed. For example, the hybrid design showed that approximately 80% of the metals levels at compliance sites were below the 25th percentile of the overall watershed condition as indicated by the probabilistic sampling.     

Space-time statistics for environmental and agricultural related phenomena

This paper presents an overview of space-time statistical procedures to analyse agricultural and environmental related phenomena. It starts with an application on root-rot development in cotton. Dependence modelling in space and time is done with the space-time variogram. Various kriging interpolators are presented for making predictions in space and time. Simulated annealing is used to design an optimal monitoring network for estimation of space-time variograms. In the application no clear indication was found for anisotropy, although strong evidence exists that the disease not only proceeds within rows but also jumps between rows. The optimal sampling scheme showed a spatial clustering of observations at the first and the last monitoring day and less observations at intermediate times.     

Status and trends of fecal indicator bacteria in two urban watersheds.

This paper examines bacterial levels and their causes in two Houston bayous (Texas). Buffalo and Whiteoak bayous are two of the most contaminated water bodies in Texas for indicator bacteria, based on the frequency and magnitude of contact recreation water quality exceedances. Examination of historical data indicates frequent exceedances, although some improvement has been made since the 1970s. Statistical analyses showed some correlation between in-stream fecal coliform concentrations and rainfall and with land use. Differences in fecal coliform concentrations were found between high- and low-flow conditions in Whiteoak Bayou, while reservoir releases confounded this relationship in Buffalo Bayou. Wastewater treatment plant effluent was found to make up two-thirds to three-fourths of the median flow in both bayous. Effluent sampling was conducted at 72 of the approximately 140 wastewater treatment plants (WWTPs) in the watersheds, providing evidence that WWTP effluent could act to maintain low-flow concentrations of fecal coliform in the bayous.     

Channel Evolution Model of Semiarid Stream Response to Urban‐Induced Hydromodification1

Hawley, Robert J., Brian P. Bledsoe, Eric D. Stein, and Brian E. Haines, 2012. Channel Evolution Model of Semiarid Stream Response to Urban‐Induced Hydromodification. Journal of the American Water Resources Association (JAWRA) 48(4): 722‐744. DOI: 10.1111/j.1752‐1688.2012.00645.x Abstract: We present a novel channel evolution model (CEM) that qualitatively describes morphologic responses of semiarid channels to altered hydrologic and sediment regimes associated with urbanization (hydromodification). The CEM is based on southern California data from 83 detailed channel surveys, hundreds of synoptic surveys, and historical analyses of aerial photographs along 14 reaches. Channel evolution sometimes follows the well‐known sequence described by Schumm et al. (Incised Channels: Morphology, Dynamics, and Control, Water Resources Publications, Littleton, Colorado, 1984) for incising, single‐thread channels; however, departures from this sequence are common and include transitions of single thread to braided evolutionary endpoints, as opposed to a return to quasi‐equilibrium single‐thread planform. Thresholds and risk factors associated with observed channel response are also presented. In particular, distance to grade control and network position emerged as key controls on channel response trajectory. The CEM and quantitative extensions provide managers with a framework for understanding channel responses and rehabilitation alternatives, and may be transferable to other semiarid settings. It also offers insights regarding channel susceptibility to hydromodification, highlights key boundary conditions for high‐risk channels, and underscores critical knowledge gaps in predicting the complex, discontinuous response trajectories that are highly prevalent in urbanized watersheds.     

Effects of interpolation and data resolution on methane emission estimates from rice paddies

Rice paddies are an important source of the greenhouse gas methane (CH₄). Global methane emission estimates are highly uncertain and do not account for effects of interpolation or data resolution errors. This paper determines such scaling effects for the influence of soil properties on calculated CH₄ emissions for the island of Java, Indonesia. The effects of different interpolation techniques, variograms and neighbor optimization were tested for soil properties by cross-validation. Interpolated organic carbon values were not significantly different from the original soil samples, in contrast to interpolated soil iron contents. Interpolation of soil properties coupled to a process-based model on CH₄emissions led to a significant change in distribution of calculated CH₄ emissions, i.e., the variance decreased. Effects of data resolution were examined by interpolating soil properties to derive data at different data resolutions and then calculating CH₄ emissions by applying the process-based model at these resolutions. The soil properties did not differ significantly for different data resolutions, in contrast to calculated CH₄ emissions. These scaling effects were caused by the combination of interpolation and a non-linear model. Real scaling effects may even be larger because small-scale variability was not accounted for. Scaling effects, including those caused by small-scale variability, have to be considered to achieve unbiased and less uncertain global CH₄emissions estimates from rice paddies.