How Probability Survey Data Can Help Integrate 305(b) and 303(d)Monitoring and Assessment of State Waters

Environmental Monitoring and Assessment (2005) 103: 41–57DOI: 10.1007/s10661-005-6854-0 c© Springer 2005

HOW PROBABILITY SURVEY DATA CAN HELP INTEGRATE 305(B)AND 303(D) MONITORING AND ASSESSMENT OF STATE WATERS

BARBARA S. BROWN1, NAOMI E. DETENBECK2 and RICHARD ESKIN3

1U.S. Environmental Protection Agency, Office of Research and Development, 27 Tarzwell Drive,Narragansett, Rhode Island, USA; 2U.S. Environmental Protection Agency, Office of Research and

Development, 6201 Congdon Blvd., Duluth, Minnesota, USA; 3Maryland Department of theEnvironment, Technical and Regulatory Services Administration, 2500 Broening Highway,

Baltimore, Maryland, USA(∗author for correspondence, e-mail: [email protected])

Abstract. Section 305(b) of the United States’ Clean Water Act (CWA) requires states to assessthe overall quality of waters in the states, while Section 303(d) requires states to develop a list ofthe specific waters in their state not attaining water quality standards (a.k.a impaired waters). Anintegrated, efficient and cost-effective process is needed to acquire and assess the data needed to meetboth these mandates. A subset of presentations at the 2002 Environmental Monitoring and AssessmentProgram (EMAP) Symposium provided information on how probability data, tools and methods couldbe used by states and other entities to aid in development of their overall assessment of conditionand list of impaired waters. Discussion identified some of the technical and institutional problemsthat hinder the use of EMAP methods and data in the analysis to identify impaired waters as well asdevelopment needs to overcome these problems.

Keywords: Clean Water Act, 305(b), 303(d), EMAP, impaired waters, integrated assessment,monitoring

1. Introduction

1.1. PROBLEM

The United States’ Clean Water Act (CWA) sets as a goal the restoration andmaintenance of the chemical, physical, and biological integrity of the nation’swaters. Section 305(b) of the CWA directs states to assess the overall quality ofthe waters in their jurisdiction, determine whether that quality is changing overtime, identify problem areas and management actions necessary to resolve thoseproblems, and evaluate the effectiveness of CWA programs in maintaining andrestoring water quality to meet the water quality standards adopted by the states.When a state determines that a water does not meet its designated use, it is consideredimpaired. Under Section 303(d) of the CWA, states must develop lists of all impairedwater bodies and prioritize these waters for establishment of total maximum dailyload allocations (TMDLs) designed to restore degraded areas.

The U.S. Government’s right to retain a non-exclusive, royalty free licence in and to any copyright isacknowledged.

42 B.S. BROWN ET AL.

Effective implementation of programs to meet CWA goals has been hindered,however, by the fact that the states use a variety of approaches to identify their im-paired waters. These variations include: 1) differences in water quality standards,including designated uses and criteria which are too broadly stated to be used oper-ationally to identify impaired waters; 2) the types of monitoring used to determinewhether the standards are exceeded, including the strong reliance on physical andchemical measures in place of direct measures of ecological response; and 3) theprocedures used to assess water quality data to make listing decisions, includinglisting of impaired waters based on limited or absent data and an emphasis onadministrative as opposed to performance-based outcomes (NRC, 2001).

Monitoring information is needed to address these CWA implementation con-cerns effectively. A variety of monitoring information is needed to meet the multiplechallenges of effective water quality management, from large-scale, broad nationalassessments of overall condition to small-scale, intensive evaluation to determinewhich particular management action should be taken at a specific site. The ability tocollect the breadth of information needed poses many challenges, including how tostructure and integrate the different monitoring and assessment activities to meet thedifferent information needs of the various water quality management organizations,and develop the most effective and cost-efficient monitoring program overall.

Four sessions conducted as part of the 2002 Environmental Monitoring and As-sessment Program (EMAP) Symposium focused on this specific part of the CWAimplementation challenge, with emphasis on the role probability data can play inhelping meet these needs for overall assessment (305(b)) and listing of impairedwaters (303(d)). Presenters during these sessions identified some available frame-works and tools which could be used to help integrate the monitoring and assessmentneeded to support both Sections 305(b) and 303(d) of the CWA in an efficient andcost-effective manner. Political and scientific impediments to integration were alsoidentified during a separate discussion session. This paper synthesizes the presen-tations and discussions at these sessions.

1.2. FRAMEWORK

Sound data on water quality are becoming increasingly important as numerous law-suits direct nationwide attention to the cleanup of water quality problems throughthe development of TMDLs. Identification of water quality problems is necessarilybased on measurements of the condition of state waters. It is cost-prohibitive tophysically monitor all the waters in the country and therefore most states monitora subset of their waters. Figure 1 displays a proposed tiered process which can beused to integrate and structure the different types of data collection and assessmentneeded to address the CWA requirements. This tiered approach is designed to aidin generating the necessary information needed to address the various managementagencies’assessment needs, but not generate more information than is necessary.This allows optimal use of resources by focusing the least effort on those areas

MONITORING AND ASSESSMENT OF STATE WATERS 43

Figure 1. Integrated monitoring and assessment framework for Clean Water Act implementation.

where the potential (or lack thereof) for unacceptable adverse impact is clear, andexpending the most effort on areas requiring more extensive investigation to de-termine the potential (or lack thereof) for impact. To achieve this objective, theproposed framework is arranged in a series of tiers, or phases of intensity of in-vestigation, targeted toward providing ever greater certainty about the conclusionsreached and reduce errors in listing. The CWA is obviously being implementedcurrently. The proposed framework can therefore also be thought of as a structurefor continuously improving the implementation process, starting now with the ex-isting standards, uses, and classifications of resources currently used, and refiningthese fundamentals based on the additional data which will be collected throughthe process over time.

The first phase of the process starts with an accurate, though broad-scaled,overall assessment of the quality of all waters, then refines that assessment throughscreening procedures and models to target follow-up confirmatory monitoring toareas which have a high probability of impairment or a high degree of uncertaintyabout their status. Intensive monitoring at those sites can then be performed tosupplement the data and assessments already conducted to confirm impairment(or lack thereof), identify cause if necessary, and refine designated uses or tieredstandards based on subclasses, if appropriate, for the next monitoring cycle.


The first necessary component of this process is data which can be used to de-velop an initial unbiased, scientifically valid estimate of the condition of all waters.Although a number of states densely sample their waters, true “censusing” (i.e.,measuring every drop of water) is impossible. Assumptions about what a singlesample “represents” are therefore necessarily part of a state’s sampling design. Formany states’ designs, inference that the data collected at any particular site repre-sents another is based on the professionals’ interpretation of the data in light of theirunderstanding of the system. An alternative, cost-effective approach to achievingan estimate of status is through the use of probability-based survey designs. Prob-ability surveys are used regularly in arenas such as the common opinion poll, andresearch over the last decade has developed methods to apply these designs inaquatic monitoring (Olsen et al., 1999).

A probability sample has some inherent characteristics that distinguish it fromother samples: first, the population being sampled is explicitly described; second,every element in the population has the opportunity to be sampled with knownprobability; and third, the selection is carried out by a process that includes anexplicit random element. A probability sample from an explicitly defined resourcepopulation is a means to certify that the data collected are free from any selec-tion bias, conscious or not. Thus, it is possible to rely on a design-based inferenceprocedure for basic estimates of population descriptors. Probability-based monitor-ing designs identify categories of waters as populations or strata (e.g., streams, orstreams of particular size, or streams of particular size in a particular ecoregion) andprovide the basis for estimating resource extent and condition, for characterizingtrends in extent and condition, and for representing spatial pattern, all with knowncertainty. These techniques can be used to characterize any spatial scale, and atvarious temporal frequencies. The nature of the question or decision being made,and the certainty of conclusion needed, will drive the scale of the target population,with smaller scale, or multiple strata at finer spatial scales, being needed to addresssome questions more than others. The number of samples required by such de-signs is largely dependent on the degree of precision or confidence needed to makemanagement decisions. Based on an analysis of variance of a sample population, aselection of about 50 samples provides an estimate for a given parameter for a givenpopulation (or strata) with approximately ±10% uncertainty. The level of confi-dence needed in the decision may necessitate more strata, and therefore requiremore samples to characterize all strata. However, the tiered approach allows forthat kind of intensification only in those areas of interest rather than over the entirepopulation, thus allowing resources to be allocated only to those areas which needthat type of precision for management decisions. Thus, this method can supportthe requirements of 305(b) of the CWA by providing a cost-effective, and unbiasedestimate of waters meeting (or not meeting) water quality standards.

Missing from the probabilistic statements of overall condition intended for305(b) reporting is the explicit identification of the individual water bodies im-paired, as needed for the requirements for listing under Section 303(d). Often


overlooked is the fact that the survey data used for 305(b) reporting can be usedfor estimating overall condition of aquatic resources and are valid information onthe actual sites sampled. However, by the nature of survey designs, these data donot completely census the entire aquatic resource. In their assessment of the 303(d)process, the NRC (2001) recommended the use of predictive models when data arelimited to develop a preliminary “planning list” prior to actual listing, as shownin Figure 1. Given limited financial resources and limited data, models are neededthat help predict aquatic resource impairment in support of the 303(d) process, thusproviding states with an explicit rationale for targeting follow-up investigationsof aquatic resources in a focused manner. The challenge is one of using informa-tion from regional-scale probabilistic surveys of aquatic ecosystem condition, inconjunction with other information describing watersheds, to predict which waterbodies are impaired.

By combining survey monitoring data with widely available, spatially ubiquitousdata such as land cover, an effective screening tool to predict aquatic impairment inspecific, unsampled locations becomes available. Output of other commonly usedpredictive models (e.g., atmospheric deposition models) may also be of use in char-acterizing stresses on aquatic resources. These predictive screening tools provideinformation on where to direct additional monitoring, and support identification ofthe chemical or physical parameters that would suggest the underlying problems aswell as the likely causes of these problems. The data generated by probability sur-veys, when used in conjunction with landcover and other watershed data, addressthe following CWA needs:

1. Estimates of overall condition. Estimates of the percentage of a given aquaticresource that violate standards can be developed (e.g., “20% of stream mileshave dissolved oxygen concentrations of less than 3 mg/l”). Depending on thedesign, the extent of the resource or the number of individual water bodies canalso be determined (e.g., “500 stream miles [or 200 lakes] have dissolved oxygenconcentrations of less than 3 mg/l”). These types of statements can be combinedto form the basis of the CWA 305(b) report. They answer the question of “Howmuch (of the population of water bodies) meets a certain criterion?”.

2. Screening level assessments of the locations and likely causes of impairment.Probability survey data can also be analyzed in a different manner. Monitoreddata characterizing a stressor and an effect can be combined to develop empir-ical relationships. Such analyses can be shown as a graph of the relationshipbetween, for example, water temperature and fish indices of biotic integrity(IBI). Probability survey measures can also be combined with remotely sensedlandscape data to develop similar empirical relationships. For example, a re-lationship could be developed between percent of land in agriculture on steepslopes and sediment concentrations in receiving streams. These relationships canthen be used in empirical models to identify characteristics of watersheds thatmight be most susceptible to adverse impacts and predict those non-monitored


areas that have the highest probability of impairment. These data answer thequestion of “Where is the criterion likely to be violated?”. Use of such modelsto predict where impairment is most likely would help a state to prioritize useof its limited monitoring resources to conduct intensified monitoring to con-firm impairment and gather more detailed information to evaluate alternativemanagement actions. As additional data are collected over the years, both fromprobability sampling and more site-specific monitoring, these empirical modelscan be further validated or refined to reflect changes in relationships which mayevolve from effective management actions (such as non-point source controlactions), or from introduction of new stressors from different land use practices.

3. Trends. Once management actions are taken, it is important to determine whetherthe actions are having the desired effect. A probability survey design can alsobe used to assess the trends in condition of a watershed or other managementunit over time – for example, changes in the total number of impaired streammiles. Assuming a number of samples sufficient to characterize the scale ofinterest, analysis of the data generated from an unbiased survey design allowsthe manager to gain an accurate picture of what is changing throughout the area,rather than simply adjacent to where the management action was implemented,as is often the case with monitoring of the efficacy of non-point source controls.

2. Tools to Prioritize Follow-up Monitoring: Session Summaries

The tools to implement the portion of the framework described in Section 1 arewell developed and have been demonstrated in several regional assessments andin numerous cases at the state level. Many of these demonstrations were presentedin other sessions at the 2002 EMAP Symposium which are described in relatedpapers in this volume. Tools and examples using probability data to help implementthe next step, prioritization of follow-up monitoring, need further development.Some of the options, prototype examples, and remaining issues presented as part ofthe 2002 EMAP Symposium are discussed later.

2.1. SUSCEPTIBLE SUBPOPULATIONS: WATERSHED CHARACTERIZATION

One method to prioritize follow-up monitoring is to target those water bodies whichwould be most susceptible to degradation due to physical and biological charac-teristics of the watershed. Some of the technical issues which must be addressedto support this identification include consistent delineation of watersheds to en-sure consistent analysis, development of suitable methods and indices to determinewatershed degradation objectively, and development of sampling designs whichoptimize the ability to analyze the resulting data as described above.

Watershed delineation is important in the identification of impaired water bodiesand watersheds in both the survey design and analysis phases of state monitoring


programs. In addition, development of empirical relationships between watershedindicators of pollutant sources or causes of impairment, and biological, chemical,or physical (habitat) response endpoints, will be facilitated through development ofautomated watershed delineation methods (NRC, 2001). The US Geological Survey(USGS) and the National Resource Conservation Service (NRCS) are jointly devel-oping the National Watershed Boundary Database (NWBD) (Leigleiter, 2001) incooperation with state partners (http://www.ftw.nrcs.usda.gov/huc data.html). Thisdelineation of watersheds and subwatersheds at the 10- and 12-digit HydrologicCataloging Unit (HUC) level (Seaber et al., 1987) will support development ofwatershed-based survey designs (Leigleiter, 2001; Rea, 2001). For basin HUCs,hydrologic units generally constitute the true contributing area (for the purposes ofthis discussion, “watershed”) to the downstream-most point in the HUC, as com-pared to interbasin HUCs, which require aggregation of upstream hydrologic unitsto encompass the entire watershed. Exceptions exist for coastal settings in which asingle hydrologic unit may be a collection of parallel watersheds. Although somestates have developed their own geographic information systems (GIS) coveragesof watershed boundaries at finer scales, the development of a consistent seamlessnationwide database, the NWBD, will facilitate consistent monitoring, assessment,and diagnosis of interstate waters. Different approaches for automated watersheddelineation are available, each with advantages and disadvantages (Berelson et al.,2001). Development of the Elevation Derivatives for North America (EDNA) GISdataset by USGS and EPA in conjunction with the NWBD will increase the ef-ficiency, topographic accuracy and compatibility with the National HydrographyDataset (NHD) of watershed boundary delineations from any point along a streamnetwork (Franken et al., 2001). The EDNA dataset, under development by theUSGS Earth Resources Observing System (EROS) Data Center, will consist of hy-drologically corrected digital elevation models (DEMs) with hydrologic derivatives,including assignment of hierarchical Pfafstetter codes allowing efficient identifi-cation of subcatchments upstream and downstream of any point along a drainagenetwork (Verdin and Verdin, 1999).

Once consistent watershed boundaries are available, populations of watershedsand subsets of watershed classes can be used in a survey design. A variety ofprobability survey design alternatives exists for watershed-based stream and rivermonitoring programs. Examples of the elements of a target population could include(1) all watersheds associated with every location on stream/river linear networks,(2) all hydrologic units at a specified field level for HUCs, and (3) all watershedsassociated with streams defined at confluence. The first definition yields an infinitepopulation of watersheds, while the latter two define finite populations. The earlysurvey designs developed by EMAP (Lesser and Overton, 1993) did not easilysupport watershed-based sampling because of the complicating factors inherent inwatershed-based designs: 1) development of estimates for particular subpopula-tions (watersheds or classes of watersheds) often requires an unequal distributionof sampling effort; 2) diagnostic modeling of cause–effect relationships requires


that the full range of explanatory variables be covered by the sampling effort; and 3)in areas where watersheds and subwatersheds have not been delineated a priori thesample frame can not be defined. However, techniques have since been developedto adapt EMAP random tesselation survey (RTS) designs to deal with these issues(Olsen et al., 1999). For example, distribution of sampling effort among strata oralong predetermined gradients can be accomplished through either unequal prob-ability weighting or further stratification. Two-stage survey designs can be usedwhere it is not possible to define the full sampling frame or suite of parameters apriori (see Detenbeck et al., this issue, for detailed examples).

The Mid-Atlantic Coastal Plain streams study (Ator et al., 2001) provides anexample of watershed characterization using a stratified survey design with unequalweighting to meet multiple objectives: 1) assessment of the regional conditionof coastal non-tidal first-order streams on the Mid-Atlantic Coastal Plain; and 2)derivation of empirical relationships between potential causative factors (e.g., landuse) within each of seven hydrogeologic units of differing sensitivities. The Mid-Atlantic Coastal Plain has been mapped into hydrogeologic units, ranging from theCoastal Lowlands region with fine organic soils and potentially low susceptibilityto groundwater pollution by nitrate and pesticides, to the Middle Coastal Plainregion with coarse sand and gravel soils and a high potential for groundwaterpollution. The survey design incorporated the hydrogeologic units as strata. Withineach strata, all watersheds were defined as upstream of confluences of first-orderstreams (1:100,000 scale) and characterized for level of development (non-naturalcover). The probabilities for sample selection were weighted unequally to distributethe samples evenly across the development classes. (Ator et al., 2001).

The West Virginia REMAP wadeable streams study design demonstrates anothersurvey design approach to characterize watersheds (Cincotta et al., 2000). For thisstudy, 12-digit HUCs were used as the target population, with sampling sites se-lected at the base of the associated watersheds. Watershed sensitivity classes werepre-defined based on land-use (level of agriculture, impervious surface area, or min-ing activity) and natural features affecting the flashiness of the hydrologic regime(e.g., watershed storage as the fraction of lake and wetland area within a watershed)(Figure 2). Sample elements were selected from within each strata, with unequalprobability weighting applied to maximize selection of hydrologically independentunits (i.e., not upstream or downstream of one another) within a sample strata.

2.2. EXTRAPOLATION TO UNSAMPLED AREAS: LANDSCAPE MODELS

Identifying characteristics of watersheds and/or water bodies which make themmore susceptible to impairment from particular stressors is one method to helpmanagers target their limited monitoring resources. Models which extrapolate thepopulation data collected at particular locations to predict conditions at other spe-cific but unsampled locations are another tool. Aquatic probability sampling de-signs allow development of population-wide statements of condition. The same


Figure 2. Watershed sensitivity classes assigned to selected West Virginia watersheds based on landcover and storage characteristics.

data, however, can be used with other data to develop screening models, whichcan be used to target areas for the more intensive monitoring needed to evaluatespecific management actions. Some of the gaps which must be addressed to developthese screening models include the need to identify those landscape characteristicswhich correlate with metrics of impairment, and the need to develop techniques tocharacterize areas which have few sampling sites.

As a preliminary step in creating an integrated assessment of landscape andsurface water condition in the western US, Stoddard and colleagues (personal com-munication) have begun modeling linkages between watershed level metrics (e.g.,land use/landcover, road density, etc.) and ecological stressors in streams (e.g., nu-trients and sediments). Using data from the National Land Cover Database and theEMAP Oregon Pilot conducted in 1997–1998, multiple regression models were de-veloped for the western mountains, and western xeric and Willamette Valley ecore-gions. These models related key landscape metrics, such as riparian urban landuse,riparian cropland, riparian shrubland, and percent forest in the watershed, to pre-dict stream total phosphorus concentrations at the scale of the state. The resultingmap, shown in Figure 3, predicts phosphorus concentrations in various areas.The original EMAP Oregon Pilot allowed an assessment of the percent of streammiles above and below particular concentrations of phosphorus. Such an assessment


Figure 3. Predicted phosphorus concentrations in Oregon streams and rivers based on a model asso-ciating land use as measured by the National Land Cover Database and phosphorus measured usinga random survey design.

would determine the condition of a state’s waters relative to a parameter value suchas a water quality criterion, as is typically required in CWA 305(b) reporting. Be-cause the data are collected via a probability survey, they can also be used to predictthe number of stream miles that would be expected to be listed (through the 303(d)process) for excess phosphorus concentrations if all the state’s waters were sampled.The landscape model would allow for a screening assessment of where impairedwaters are mostly likely located.

Jones et al. (1997) developed a number of landscape metrics which can beevaluated for their explanatory power to evaluate surface water conditions. Usinglogistic regression models, Smith et al. (2001) showed that a selected number ofthese metrics correlated with fecal coliform water quality standard exceedances


measured in South Carolina (albeit, in this case, with a judgmental as opposed toprobabilistic sampling design). Jones et al. (2001), using multiple linear regressionmodels, were able to predict nutrient and sediment loading to unsampled streams inthe Mid-Atlantic region as a function of a few key landscape metrics, such as percentagricultural and forested land, nitrate deposition, and number of road crossings.Wickham et al. (2001) developed a nutrient-export risk model based on literature-derived nutrient export coefficients which expressed the differences in export dueto land cover. This model was used to estimate the probability of nitrogen andphosphorus exceeding a threshold using both current landcover distributions andfuture landcover distributions derived from urbanization scenarios. Those estimateswith greatest change were used to predict the areas of highest vulnerability.

Preston et al. (1998) applied the SPARROW model (SPAtially-Referenced Re-gressions On Watershed attributes) to relate water quality in the Chesapeake Bay tosources of nutrients in the watershed and to factors that affect the transport of nutri-ents to the Bay. The SPARROW method uses regression equations relating measuredtransport rates in streams to descriptors of pollution sources and land-surface andstream channel characteristics. Water quality measurements were obtained fromUSGS National Stream Quality Monitoring Network monitoring stations locatedin a subset of the stream reaches. Water quality predictors in the model were devel-oped as a function of both reach and land surface attributes and included quantitiesdescribing contaminant sources (point and non-point) as well as factors associatedwith rates of material transport through the watershed (such as soil permeabilityand stream velocity). The initial application of the model to assess nutrient loadingeffects to the Chesapeake Bay demonstrated the importance of instream loss thatoccurs as nitrogen is transported to the Bay. It was found that most of the areas thathad relatively high local loading were less important to overall loading to the bay;the highest delivered yields to the Bay were from areas that drain directly to largestreams or from areas of high local loading in close proximity to the Bay.

Landscape assessment methodologies such as those outlined above showpromise as tools to screen large areas for water conditions relative to nutrients,sediments, and pathogens. Another challenge, however, is that monitoring pro-grams are frequently asked to provide estimates for subpopulations that have fewsampling sites. This is known as the small area estimation problem in survey sam-pling (Platek, 1987). One approach to this problem is based on using hierarchicalBayes prediction models. Statistical approaches to parameter estimation and hy-pothesis testing which use prior known distributions of parameters are known asBayesian methods. The Mid-Atlantic Integrated Assessment (MAIA) stream sur-vey from 1993 to 1998 acquired data on water quality, benthic macroinvertebrate,and fish indicators at over 600 sites. Olsen and colleagues (personal communica-tion) have used these data to construct a model relating watershed variables to anindicator of stream condition. A hierarchical Bayes model was specified that corre-lated watershed predictor variables with the proportion of stream length in commonbetween two watersheds and the Euclidean distance between the two sites defining


the watersheds. The model is being used to predict stream condition at the sites se-lected in a very large probability sample of streams (approximately 200 sites withineach HUC). For each of these sites, watersheds are automatically delineated andwatershed variables determined. Predictions of the stream condition indicator fromthe hierarchical Bayes model are then developed in place of measured indicatorsin the survey design estimation process for each HUC.

2.3. FRAMEWORK IMPLEMENTATION: CASE STUDIES

Some state and regional management agencies are working to implement assess-ment strategies using survey data consistent with the integrated framework inFigure 1. Such initial efforts provide both examples for other agencies to follow, aswell as highlight gaps needing further development work.

In 1995, the Maryland Department of Natural Resources initiated a statewideprobability-based program designed to evaluate the status of aquatic resources inMaryland’s first- through fourth-order non-tidal streams. The Maryland Biologi-cal Stream Survey (MBSS) incorporates fish, benthic macroinvertebrate, physicalhabitat, and water chemistry data to provide a comprehensive assessment of thestate’s streams and to determine the cumulative effects of acid deposition and otheranthropogenic stresses. Data from the first round of the survey (1995–1999) havebeen used to develop both a fish and a benthic macroinvertebrate IBI (Striblinget al., 1998, Roth et al., 1998). The Maryland Department of the Environment iscurrently using these indices to develop an interim framework for the application ofbiocriteria to the state’s water quality inventory (305(b) report) and list of impairedwaters (303(d) list). Probabilistic sampling of biological communities provides acost-efficient way to meet the regulatory requirement to assess all of a state’s wa-ters. It typically does not provide the specificity and geographic resolution thatmost states desire in making their decision on impaired waters (303(d)) because ofthe difficult question of whether the single samples are representative of biologicalcommunity condition of the entire water body. States faced with streams or streamsegments with single samples that indicate significant impairment need to decideif that information is adequate or not to make a reasonable decision. Maryland haschosen to use the smallest watersheds for which it has consistent data as the “assess-ment units” for the program. Average size of these approximately 1000 watershedsis about 11 miles2. Default confidence intervals were calculated based on replicatesampling across the state. If the upper bound of the 90% confidence interval appliedto single sample results was below an acceptable score, Maryland considered thestream sufficiently degraded that a confident decision could be made. The impairedwater body was listed at the 11 miles2 watershed level. If the confidence intervalaround the value of the single sample in the watershed straddled the acceptablescore, the analysis was considered “inconclusive” and the watershed was targetedfor follow-up investigation. In this way, Maryland applied sound data and meth-ods to make listing decisions while avoiding listing thousands of individual stream


segments. In an example of adaptive management, the MBSS has modified its sec-ond round survey sampling design and methods to provide finer scale data for thebiocriteria framework and facilitate integration with county and local programs.These modifications will provide detail to evaluate aquatic resources at the stateand sub-watershed levels more accurately.

As shown in Figure 1, integrated monitoring and assessment makes an over-all assessment of the quality of all waters, then refines that assessment to tar-get follow-up confirmatory monitoring to areas which have a high probabilityof impairment. A similar approach was used to focus monitoring of New York/New Jersey (NY/NJ) Harbor over the period 1990–2000. The Environmental Mon-itoring and Assessment Program (EMAP) 1990–1993 Virginian Province effort wasa regional scale study (Paul et al., 1999). That study indicated widespread benthicimpairments in the NY/NJ Harbor area. This finding generated the 1993 Regional-EMAP (REMAP) effort which collected additional samples using an intensifiedsurvey design within the NY/NJ Harbor to identify benthic contamination in thatarea. The Contamination Assessment and Reduction Project (CARP) in New YorkHarbor, initiated in 1996, is evaluating monitoring and other data from multiplesources to address the issue of sediment contamination relevant to dredging, with afocus on identifying sources of that contamination. The overall, phased assessmentused probability-based survey information, combined with other data, analyses andmodels, to address specific environmental management needs at multiple scales.It demonstrates a critical linkage between existing programs and new probability-based designs and the ability to use most, if not all, available data.

3. Remaining Needs: Discussion

The integrated monitoring sessions presented tools and applications which arecurrently under development and show promise in helping managers implement theframework shown in Figure 1. It is readily apparent, however, that many technicalgaps remain in providing the methods and information needed to provide a completefoundation for water quality management. This section summarizes the concernsexpressed by the participants in the session and is not intended to be a comprehensivereview and assessment of the current state of implementation, much of which hasbeen captured in previous work (e.g., ITFM, 1995; NRC, 2001; Yoder and Rankin,1998).

3.1. MAJOR CHALLENGES IN MEETING CWA NEEDS

Participants of the facilitated discussion session, including scientists and State andFederal managers, identified challenges to meeting the CWA in four categories:guidelines and regulations, data, insufficient scientific understanding, and technicaltransfer of methodologies. It was recognized that many of the challenges, such as


development of appropriate standards, are complex and will require more thansimply efficient and cost effective monitoring and assessment programs to address.

Guidelines and regulations. The current framework of guidance and standardsdates from the early 1970s, a time when the primary emphasis was controllingchemical pollution from point sources. Most of today’s problems are complex andcaused by multiple factors, including non-chemical stressors such as habitat alter-ation and nutrient enrichment (NRC, 1997). The current framework of designateduses and chemical and biological criteria are inadequate to support water qualitymanagement for these issues. For example, many pollutants of current concernhaving no numeric criteria; it is also difficult to interpret the current narrative bi-ological criteria, especially in light of general use designations. In addition, therehas been a lack of consistency over time in the rules to list and delist waters forimpairment, with little hard criteria for the listing decision. This has resulted insituations where it now takes more effort and data to delist a water body than itoriginally took to place it on the initial list of impaired waters.

Data. When guidance is available, sufficient data to assess the condition of allstate waters, as well as to make listing and delisting decisions, are lacking. Dataare lacking in terms of both spatial extent and in number of parameters measured.There also appears to be concern that the quality of data that are available may bedeficient due to limited attention to quality assurance and quality control proceduresin acquiring the information.

Insufficient scientific understanding or information. Participants particularlyemphasized the challenge in interpreting biological data, as in many cases there isonly general understanding of biotic response to particular stressors or combina-tions of stressors. More information on stressor–response relationships was deemedcritical in order to develop appropriate management responses once biological im-pairment has been identified.

Technology transfer. Finally, current guidance (USEPA, 2003) is now stronglyemphasizing inclusion of probability survey monitoring in state comprehensivemonitoring strategies. However, those states which have not participated in EMAPinitiatives are at a disadvantage in implementing probability survey monitoring dueto lack of training and experience in the methods. States need assistance in devel-oping appropriate monitoring designs and selecting sites such that their programsincorporate probability sampling and at the same time provide all the informationthat a state needs in order to meet its water quality management requirements. Inaddition, increased access is needed to data analysis tools that allow processing ofthe raw data, calculation of landscape metrics and production of 305(b) reports.

3.2. FUTURE FRAMEWORK IMPLEMENTATION DEVELOPMENT NEEDS

Needs to implement the integrated monitoring and assessment framework fell intotwo categories: policy and technical.


Policy needs. Policy needs included a re-evaluation of the concept behind 303(d)listing. This relates to the issue identified above on the discrepancy between thecurrent point source, chemo-centric focus of the water quality management struc-ture, and a management approach which would evolve from holistic watershedmanagement plans. It was also pointed out that consistent assessment guidelinesare needed on listing. These guidelines should emphasize what is required (i.e.,minimum standards of performance or outcome), not how the assessment is to bedone.

Technical needs. Most of the technical needs dealt with approaches to optimizea monitoring program to provide information for multiple management needs in acost-effective manner. These needs included: methods to identify classifying vari-ables of susceptibility for pre-stratification; knowledge of the appropriate scales atwhich to sample for particular impacts or source characteristics in different settings;methods to nest and aggregate these scales with appropriate intensifications, eitherspatially or temporally; the minimum suite of parameters and number of samplesto meet data quality objectives for certainty/uncertainty in the results; and bettermethods to estimate conditions from small sample sizes.

4. Conclusion

The EMAP has historically focused on developing the methods to monitor forenvironmental condition in consistent ways across large geographic areas. Thesemethods are being increasingly used to answer questions associated with under-standing status and trends in the environment, particularly to answer the require-ments of CW Act Section 305(b). With success in this arena comes the recognitionthat other needs remain to be tackled, in particular, providing environmental in-formation in an integrated, cost-effective manner to address multiple water qualitymanagement objectives at multiple scales. This portion of the 2002 EMAP Sym-posium presented a framework for addressing at least some of these objectives,and demonstrated some methods and tools which may have utility in implement-ing the framework. Further work obviously remains to implement this frameworkeffectively.

Acknowledgements

We thank Margarete Heber, Susan Holdsworth, Lyle Cowles, John Paul, Jerry Pesch,and Walt Galloway for valuable discussions over several years which influencedthe development of Figure 1. Several of these individuals continue to refine andimplement this framework. We thank Ed Rankin, Susan Norton, John Paul andthe journal reviewers for their critical review of this manuscript. This report is


contribution AED-02-081 of the Atlantic Ecology Division. Approval for publi-cation does not signify that the contents necessarily reflect the views and policiesof the U.S. Environmental Protection Agency, nor does mention of trade names orcommercial products constitute endorsement or recommendation.

References

Ator, S.W., Denver, J.M. and Pitchford, A.M.: 2001, ‘Developing landscape-indicator models forpesticides and nutrients in streams of the Mid-Atlantic Coastal Plain’, FS 0157-00, U.S, GeologicalSurvey, 4 p.

Berelson, W.L., Caffrey, P.A. and Hamerlinck J.D.: 2001, ‘Creating Hydrologic Unit Boundaries forWyoming: A Semi-automated Approach’, Paper 707 in Proceedings, 2001 ESRI InternationalUser’s Conference, San Diego, CA.

Cincotta, D.: 2000, ‘A small watershed characterization, classification and assessment for WestVirginia utilizing EMAP design and tools’, Proposal submitted to the USEPA ORD NHEERL,Mid Continent Ecology Division, Duluth, MN.

Franken, S.K., Tyler, D.J. and Verdin, K.L.: 2001, ‘Development of a National Seamless Databaseof Topography and Hydrologic Derivatives’, Paper 730 in Proceedings, 2001 ESRI InternationalUser’s Conference, San Diego, CA.

Intergovernmental Task Force on Monitoring Water Quality (ITFM): 1995, ‘The strategy for improvingwater quality monitoring in the United States’, Interagency Advisory Committee on Water Data,Washington, DC.

Jones, K.B., Ritters, K.H., Wickham, J.D., Tankersley, R.D., O’Neill, R.V., Chaloud, D.J., Smith,E.R. and Neale, A.C.: 1997, ‘An ecological assessment of the United States Mid Atlantic region:A landscape atlas, EPA/700/R-97/130’, U.S. Environmental Protection Agency, Office of Researchand Development, Washington, DC.

Jones, K.B., Neale, A.C., Nash, M.S., Van Remortel, R.D., Wickham, J.D., Ritters, K.H., and O’Neill,R.V.: 2001, ‘Predicting nutrient and sediment loadings to streams from landscape metrics: Amultiple watershed study from the United States Mid-Atlantic region’, Landsc. Ecol. 16, 301–312.

Legleiter, K.J.: 2001, ‘Interagency Development of National Watershed and Subwatershed HydrologicUnits’, Paper 492 in Proceedings, 2001 ESRI International User’s Conference, San Diego, CA.

Lesser, V.M. and Overton, W.S.: 1993, ‘EMAP status estimation: Statistical procedures andalgorithms’, EPA/620/R-94/008, U.S. Environmental Protection Agency, Office of Research andDevelopment, Corvallis, OR.

National Research Council: 1997, Building a Foundation for Sound Environmental Decisions,National Academy Press, Washington, DC.

National Research Council: 2001, Assessing the TMDL Approach to Water Quality Management,National Academy Press, Washington, DC.

Olsen, A.R., Sedransk, J., Edwards, D., Gotway, C.A., Liggett, W., Rathbun, S.L., Reckhow, K.H.and Young, L. J.: 1999, ‘Statistical issues for monitoring ecological and natural resources in theUnited States’, Environ. Monitor. Assess. 54, 1–45.

Paul, J.F., Gentile, J.H., Scott, K.J., Schimmel, S.C., Campbell, D.E. and Latimer, R.W.: 1999, ‘EMAP-Virginian Province Four-Year Assessment (1990–93)’, EPA/620/R-99/004, U.S. EnvironmentalProtection Agency, Office of Research and Development, Narragansett, RI.

Platek, R., Rao, J.N.K., Sarndal, C.E. and Singh, M.P. (eds): 1987, Small Area Statistics, Wiley,New York.


Preston, S.D., Smith, R.A., Schwartz, G.E., Alexander, R.B. and Brakebill, J.W.: 1998, ‘SpatiallyReferenced Regression Modeling of Nutrient Loading in the Chesapeake Bay Watershed’, inProceedings of the First Federal Hydrologic Modeling Conference, Las Vegas, NV.

Rea, A.: 2001, ‘Status Report on the National Watershed Boundaries Dataset’, Paper 661 in Proceed-ings, 2001 ESRI International User’s Conference, San Diego, CA.

Roth, N.E., Southerland, M.T., Chaillou, J.C., Klauda, R.J., Kazyak, P.F., Stranko, S.A., Weisberg,S.B., Hall, Jr., L.W. and Morgan II, R.P.: 1998, ‘Maryland biological stream survey: Developmentof a fish index of biotic integrity’, Environ. Monitor. Assess. 51, 89–106.

Seaber, P.R., Kapinos, F.P., and Knapp, G.L.: 1987, Hydrologic Unit Maps, U.S. Geological SurveyWater Supply Paper 2294, 63 pp.

Smith, J.H., Wickham, J.D., Norton, D., Wade, T.G. and Jones, K.B.: 2001, ‘Utilization of landscapeindicators to model potential pathogen impaired waters’, J. Am. Water Resources Assoc. 37(4),805–814.

Stribling, J.B., Jessup, B.K., White, J.S., Boward, D., and Hurd, M.: 1998, ‘Development of a benthicindex of biological integrity for Maryland streams’, CBWP-EA-98-3, Maryland Department ofNatural Resources, Annapolis, MD.

U.S. Environmental Protection Agency: 2003, ‘Elements of a State Water Monitoring and AssessmentProgram’, EPA 841-B-03-003, Office of Wetlands, Oceans and Watersheds, Washington, DC.

Verdin, K.L. and Verdin, J.P.: 1999, ‘A topological system for delineation and codification of theEarth’s river basins’, J. Hydrol. 218, 1–12.

Wickham, J.D., O’Neill, R.V., Ritters, K.H., Smith, E.R., Wade, T.G. and Jones, K.B.: 2001, ‘Geo-graphic targeting of increases in nutrient export due to future urbanization’, Ecol. Appl. 12(1),93–106.

Yoder, C.O. and Rankin, E.T. 1998, ‘The role of biological indicators in a state water quality man-agement process’, Environ. Monitor. Assess. 51(1–2), 61–88.

Documents

How Probability Survey Data Can Help Integrate 305(b) and 303(d)Monitoring and Assessment of State Waters