At present, there are over 34,000 impaired waters and over 58,000 associated impairments officially listed in the U.S. Nutrients and sediment are two of the most common pollutants included in the list. States are required to identify and list those waters within their boundaries that are not meeting standards, to prioritize them, and to develop Total Maximum Daily Loads (TMDLs) for the pollutants of concern. Models are used to support development of TMDLs, typically to estimate source loading inputs, evaluate receiving water quality, and determine source load allocations so that receiving water quality standards are met. Numerous models are available today, and selection of the most suitable model for a specific TMDL project can be daunting. This article presents a critical review of models simulating sediment and nutrients in watersheds and receiving waters that have potential for use with TMDL development and implementation. The water quality models discussed, especially those with sediment and/or nutrient components, include loading models (GWLF and PLOAD), receiving water models (AQUATOX, BATHTUB, CE-QUAL-W2, QUAL2E, and QUAL2K), and watershed models having both loading and receiving components (AGNPS, AnnAGNPS, CASC2D/GSSHA, DWSM, HSPF, KINEROS2, LSPC, MIKE SHE, and SWAT). Additional models mentioned include another receiving water quality model (WASP), watershed models (ANSWERS storm event, ANSWERS continuous, PRMS storm event, SWMM, and WEPP), and BMP models (APEX, REMM, and VFSMOD). Model sources, structures, and procedures for simulating hydrology, sediment, and nutrients are briefly described for the reviewed models along with an assessment of their strengths, limitations, robustness, and potentials for using in sediment and/or nutrient TMDLs. Applications of AGNPS, APEX, BATHTUB, CE-QUAL-W2, GWLF, and SWAT in TMDL developments are presented. Applications of some of the other models (DWSM, GSSHA, and KINEROS2) relevant to TMDL studies are also presented. The models proved to be useful; however, they require a learning process. Simple models are easy to use but have limitations; comprehensive models are labor and data intensive but offer extensive analysis tools. Finally, recommendations are offered for advancing the sediment and nutrient modeling technologies as applied to TMDL development and implementation. Advances could be made towards: making the best use of existing models, enhancing the existing models, combining strengths of existing models, developing new models or supplemental components with physically based robust routines, numerous field applications, sensitivity analyses, full documentation, and rigorous education and training.

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.

Two total maximum daily load (TMDL) studies were performed for Linville Creek in Rockingham County, Virginia, to address bacterial and benthic impairments. The TMDL program is an integrated watershed management approach required by the Clean Water Act. This paper describes the procedures used by the Center for TMDL and Watershed Studies at Virginia Tech to develop the Linville Creek TMDLs and discusses the key lessons learned from and the ramifications of the procedures used in these and other similar TMDL studies. The bacterial impairment TMDL was developed using the Hydrological Simulation Program-Fortran (HSPF). Fecal coliform loads were estimated through an intensive source characterization process. The benthic impairment TMDL was developed using the Generalized Watershed Loading Function (GWLF) model and the reference watershed approach. The bacterial TMDL allocation scenario requires a 100% reduction in cattle manure direct-deposits to the stream, a 96% reduction in nonpoint-source loadings to the land surface, and a 95% reduction in wildlife direct-deposits to the stream. Sediment was identified as the primary benthic stressor. The TMDL allocation scenario for the benthic impairment requires an overall reduction of 12.3% of the existing sediment loads. Despite the many drawbacks associated with using watershed-scale models like HSPF and GWLF to develop TMDLs, the detailed watershed and pollutant-source characterization required to use these and similar models creates information that stakeholders need to select appropriate corrective measures to address the cause of the water quality impairment when implementing the TMDL.

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