Gene Yagow

USE OF BIOLOGICAL INDICATORS IN TMDL ASSESSMENT AND IMPLEMENTATION

Most states in the U.S. have a general water quality standard intended to protect water from all potential pollutants not specifically named or identified in other standards. Biological indicators are used, in part, to assess the level of water quality with respect to this general standard. Under EPA’s Total Maximum Daily Load (TMDL) program, impaired waters based on a biological assessment require an additional step compared with non-biological TMDLs. In non-biological TMDLs, the “pollutant” is typically the parameter being monitored, with a direct link to the impairment. In biological TMDLs, cause and effect must first be established between one or more pollutants and the impacted biological community. This article presents examples of approaches taken in different states to monitor and assess the biological health of our streams based on varying combinations of algal, macroinvertebrate, and fish communities. While fish are the ultimate integrator of lower ecological organisms, their occurrence and abundance has been greatly manipulated by humankind. Periphytic algae are perhaps the fastest responding biological population and can be used for some pollutant-specific diagnoses, but most states lack the expertise required for detailed taxonomic classification. Macroinvertebrates, the most commonly monitored biological community, are abundant in most streams, but most metrics are not diagnostic of specific stressors. Within the TMDL framework, issues are discussed related to setting TMDL targets, linking biological impairments with pollutants, and defining biological target endpoints. Although surrogate measures are often used for setting TMDL target loads, biological recovery is measured against biological endpoints. The use of biological indicators for assessment and development of biological TMDLs can be improved through modeling procedures that better define cause-and-effect relationships, through a better understanding of the limits of restoration, and through a more unified national policy that focuses on restoration.

Applications of Microbial Source Tracking in the TMDL Process

The US Environmental Protection Agency’s Total Maximum Daily Load (TMDL) program is frequently cited as a primary driver in the development of microbial source tracking (MST) techniques. As MST techniques continue to mature, it is prudent to identify those areas where further MST-related research is most likely to contribute to the efficient development and implementation of bacterial TMDLs. The objectives of this chapter are to review the basic phases in the TMDL process, to describe current applications of MST within these stages, to identify research needed to increase MST application, and to discuss opportunities for the expanded use of MST data within the TMDL process.

TMDL Modeling of Fecal Coliform Bacteria with HSPF

Fecal coliform TMDLs were developed for nine watersheds in Virginia using the HSPF model. The primary HSPF algorithms used to simulate FC loading and fate in the models are described in detail. Parameter values are summarized for all HSPF parameters related to FC simulation, as well as source data used external to the model for developing input loads from the various individual FC sources. Although there are many areas of uncertainty in modeling fecal coliform, a scientific approach was used in the evaluation of sources, the representation of the sources, and the evaluation of parameters used to simulate fecal coliform fate and transport with the HSPF model for nine sub-watersheds. The similarity of source reductions called for in each of the nine TMDLs support recommendations for the regional application of key results from TMDL studies to watersheds with similar sources and for the use of adaptive implementation as presented in a recent National Research Council report to Congress assessing the scientific basis of TMDLs (NRC, 2001).

Use of the GWLF Model for Statewide NPS Pollution Assessment in Virginia

A procedure was developed for statewide NPS pollution assessment in Virginia on a watershed basis using the GWLF model. The model was calibrated to output from the Chesapeake Bay Program’s Chesapeake Bay Watershed Model (CBWM) for an area covering approximately half of the state to provide consistency with watershed priorities identified through CBWM modeling. Statewide regions were defined based on common watershed attributes, so that calibration adjustments made within the Bay drainage area of these regions would have a basis for application to uncalibrated areas of the state. Regional calibration adjustment factors were calculated for various parameters and then applied to a new set of GWLF parameter values that were re-evaluated for the statewide NPS assessment model runs using watershed-specific rainfall, landuse, and other data.

Developing Sediment Load Thresholds Protective of Aquatic Life

Abstract. A method was developed for setting target Total Maximum Daily Load (TMDL) sediment loads in non-tidal watersheds of Virginia that directly relates biological conditions with sediment load levels. The new method is based on a modification of methodology developed by the state of Maryland which simulated sediment loads using Chesapeake Bay Watershed Model procedures. The modified method is proposed for use in several case study watersheds in Virginia using the GWLF model. The biological condition is represented by the average Virginia Stream Condition Index (VSCI), while sediment is represented as the existing sediment load normalized by the corresponding load under an all-forest condition. The existing sediment load in any given watershed divided by the corresponding sediment load simulated under an all-forest condition, results in an all-forest load multiplier, AllForX. When AllForX is regressed against VSCI for a number of healthy watersheds surrounding a particular impaired watershed, the developed relationship can be used to quantify the value of AllForX for the biological health threshold (VSCI = 60) used to assess aquatic life use impairments in Virginia. The TMDL is then calculated as the value of AllForX at the VSCI threshold times the all-forest sediment load of the impaired watershed. Since a number of watersheds are used to set the regression, a confidence interval around the threshold can also be quantified and used to calculate the margin of safety in the TMDL equation. The relationship between AllForX and the biological condition is validated with plots and regressions between AllForX and various independent sediment-related habitat metrics.

Phased TMDLs in Mining Areas of Virginia

Current EPA guidance recommends that the phased TMDL approach be used in situations “where limited existing data are used to develop a TMDL and the State believes that the use of additional data or data based on better analytical techniques would likely increase the accuracy of the TMDL load calculation and merit development of a second phase TMDL”. This paper discusses the data uncertainties leading to the use of a phased TMDL in a specific coal mining watershed in southwestern Virginia. The TMDL addressed a benthic impairment that was linked through use of a stressor analysis with excessive sediment (TSS) and total dissolved solids (TDS).

PEMSEA Partnership for Watershed Management in East Asia

The Partnership in Environmental Management for the Seas of East Asia (PEMSEA) has been working to promote implementation of site-specific river basin and coastal management programs in Manila Bay and Jakarta Bay, among others, through “twinning arrangements” with U.S. personnel involved with pollution reduction in the Chesapeake Bay. PEMSEA partnered with the University of Maryland’s Center for Environmental Science (UMCES) and Virginia Tech’s Center for Watershed Studies to develop a training workshop for participants from the Philippines, Indonesia, the People’s Republic of China, and the Republic of Korea. The workshop initially focused on the participants who provided examples of good environmental management practices and challenges in their individual countries. Following this, the training team led participants through a step-by-step process of developing a Total Maximum Daily Load (TMDL), which is an integral part of many watershed management efforts in the U.S. The TMDL portion of the workshop included facilitator- led discussions that explored how the process might apply to watershed planning in East Asia for smaller watersheds. The workshop culminated in the development and presentation of rudimentary TMDLs by two teams of participants. One team addressed the Meycauayan-Marilao-Obando watersheds in Manila and the other addressed the Ciliwung River watershed in Jakarta.

Alternative Procedures for Setting Reference TDS Endpoints in TMDLs

Total dissolved solids (TDS) is an aggregate measurement of chemical salts dissolved in water. Elevated levels of TDS are typically associated with mining operations. State water quality standards are often used, both to trigger the assessment of water quality impairments in a stream, and to serve as the target total maximum daily load (TMDL) endpoint. In the case of biological impairments, where the causative pollutant(s) are identified through an analysis of all available data, pollutant(s) may be identified for which there are no state standards. This is the case with TDS in Virginia where there is neither a state water quality standard for TDS, nor NPDES mining permit limits. As a result, an alternative procedure must be used to identify an acceptable level of TDS in the stream. Three alternative procedures were explored during the development of a TMDL to address a biological impairment in Bull Creek in southwestern Virginia. These procedures included the use of two different models (GWLF and HSPF) and two different types of endpoints (concentrations and loads). The first two procedures used a “reference watershed approach”, where the average annual TDS load from a non-impaired, comparable reference watershed was used to set the load-based endpoint. The first procedure simulated loads simulated with the GWLF model for both the Bull Creek and an area-adjusted reference watershed (Upper Dismal Creek), while the second procedure used the HSPF model. The third procedure used available in-stream TDS monitoring data from a comparable, non-impaired reference watershed to set a concentration endpoint equal to the 90th percentile of the monitoring data (369 mg/L). For this third procedure, HSPF was used to simulate daily TDS concentrations in Bull Creek. Pros and cons of each procedure will be discussed, together with implications of the required reductions and the relative strength of justifications.

Use of Biological Indicators in TMDL Assessment and Implementation

Most states have a general water quality standard intended to protect water quality from all potential pollutants not specifically named or identified in other standards. Biological indicators are used, in part, to assess the level of water quality with respect to this general standard. Under EPA’s Total Maximum Daily Load (TMDL) program, impaired waters based on a biological assessment require an additional step compared with non-biological TMDLs. In non-biological TMDLs, the “pollutant” is typically the parameter being monitored, with a direct link to the impairment. In biological TMDLs, cause and effect must first be established between one or more pollutants and the impacted biological community. This condensed paper, focused on biological indicators, is one of a series developed by members of the CSREES Southern Regional Project S1004 to present an overview of the modeling and assessment tools currently used to develop TMDLs. Examples are presented of approaches taken in different states to assess the biological health of streams based on varying combinations of the macroinvertebrate, fish, and algal biological communities, together with an assessment of the strengths and limitations of each approach. Within the TMDL framework, issues are discussed related to using stressor analysis to link biological impairments with pollutants, to setting TMDL endpoints, and to linking BMP implementation with biological recovery. Our analysis also identifies programmatic needs and recommendations for future research.