STREAM WASTE ASSIMILATIVE CAPACITY ANALYSIS USING REAERATION COEFFICIENTS MEASURED BY TRACER TECHNIQUES1

Atmospheric reaeration is a natural mechanism of oxygen transfer from the atmosphere to a water body. In practice, stream water quality models are developed with reaeration coefficients (K₂) estimated by predictive equations. This leads to uncertainties in modeling analysis because these equations are empirical in nature and may yield greatly different K₂ values for the same stream. Values of K₂ may be adjusted in model calibration, but unfortunately, values of other model parameters are no less easy to identify and require adjustment in model calibration as well. Therefore, validity of a stream model would be enhanced significantly if K₂ could be determined directly and reliably. In this research, values of K₂ in the Canandaiqua Outlet in Central New York have been measured by using a gas tracer method. A successful modeling analysis was conducted using these K₂ values. As a result, effluent limitations of several waste water discharges into the Outlet were established. It was concluded that field measurements of reaeration rate would improve modeling results significantly, and that the gas tracer method can be easily incorporated into intensive water quality surveys normally required for stream modeling.     

DEVELOPMENT OF PREDICTION EQUATIONS FOR A PHREATIC AQUIFER IN RESPONSE TO INFILTRATION: DIMENSIONAL ANALYSIS1

ABSTRACT: The need for a quick means to predict the subsurface flow into a stream caused the authors to take a different approach to the problem. Dimensional analysis was applied to deduce a functional relationship between the dependent and independent variables. This paper lays the ground work for the development of prediction equations for a phreatic aquifer in response to infiltration.     

CLIMATOLOGICAL WATERSHED CALIBRATION1

ABSTRACT: This study tests the hypothesis that climatic data can be used to develop a watershed model so that stream flow changes following forest harvest can be determined. Measured independent variables were precipitation, daily maximum and minimum temperature, and concurrent relative humidity. Computed variables were humidity deficit, saturated vapor pressure, and ambient vapor pressure. These climatic variables were combined to compute a monthly evaporation index. Finally, the evaporation index and monthly precipitation were regressed with measured monthly stream flow and the monthly estimates of stream flow were combined for the hydrologic year. A regression of predicted versus measured annual stream flow had a standard error of 1.5 inches (within 6.1 percent of the measured value). When 10, 15, and 20 years of data were used to develop the regression equations, predicted minus measured stream flow for the last 7 years of record (1972–1978) were within 16.8, 11.5, and 9.7 percent of the measured mean, respectively. Although single watershed calibration can be used in special conditions, the paired watershed approach is expected to remain the preferred method for determining the effects of forest management on the water resource.     

SIMULATION OF SPATIAL AND TEMPORAL CHANGES IN WATER QUALITY WITHIN A HYDROLOGIC UNIT1

Water quality must be considered in the development and planning aspects of water resource management. To accomplish this, the decision-maker needs to have at his disposal a systematized procedure for simulating water quality changes in both time and space. The simulation model should be capable of representing changes in several parameters of water quality as they are influenced by natural and human factors impinging on the hydrologic system. The objective of this work is two-fold. The first goal is to demonstrate the feasibility of developing and utilizing a water quality simulation model in conjunction with a hydrologic simulation model. The model represents water quality changes in both time and space in response to changing atmospheric and hydrologic conditions and time-varying waste discharges at various points in the system. This model has been developed from and verified with actual field data from a prototype system selected for this purpose. The second aim is to set forth procedural guidelines to assist in the development of water quality simulation models as tools for use in the quality-quantity management of a hydrologic unit.     

Linking Biological Integrity and Watershed Models to Assess the Impacts of Historical Land Use and Climate Changes on Stream Health

Land use change and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. Meanwhile, linking these disturbances with hydrology and water quality conditions is challenged due to the lack of high-resolution datasets and the selection of modeling techniques that can adequately deal with the complex and nonlinear relationships of natural systems. This study addresses the above concerns by employing a watershed model to obtain stream flow and water quality data and fill a critical gap in data collection. The data were then used to estimate fish index of biological integrity (IBI) within the Saginaw Bay basin in Michigan. Three methods were used in connecting hydrology and water quality variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The IBI predictive model developed using fuzzy logic showed the best performance with the R ² = 0.48. The variables that identified as most correlated to IBI were average annual flow, average annual organic phosphorus, average seasonal nitrite, average seasonal nitrate, and stream gradient. Next, the predictions were extended to pre-settlement (mid-1800s) land use and climate conditions. Results showed overall significantly higher IBI scores under the pre-settlement land use scenario for the entire watershed. However, at the fish sampling locations, there was no significant difference in IBI. Results also showed that including historical climate data have strong influences on stream flow and water quality measures that interactively affect stream health; therefore, should be considered in developing baseline ecological conditions.     

WATERSHED IMPERVIOUSNESS IMPACTS ON STREAM CHANNEL CONDITION IN SOUTHEASTERN PENNSYLVANIA1

ABSTRACT: Forty‐six independent stream reaches in southeastern Pennsylvania were surveyed to assess the relationships between geomorphic and habitat variables and watershed total impervious area (TIA) and to test the ability of the impervious cover model (ICM) to predict the impervious category based on stream reach variables. Ten variables were analyzed using simple and multivariate statistical techniques including scatter‐plots, Spearman's Rank correlations, principal components analysis (PCA), and discriminant analysis (DA). Graphical analysis suggested differences in the response to TIA between the stream reaches with less than 13 percent TIA and those with greater than 24 percent TIA. Spearman's Rank correlations showed significant relationships for large woody debris and sinuosity when analyzing the entire dataset and for depth diversity and the standard deviation of maximum pool depths when analyzing stream reaches with greater than 24 percent TIA. Classification into the ICM using DA was 49 percent accurate; however, the stream reaches did support the ICM in other ways. These results indicate that stream reach response to urbanization may not be consistent across geographical regions and that local conditions (specifically riparian buffer vegetation) may significantly affect channel response; and the ICM, used in the appropriate context, can aid in the management of stream reaches and watersheds.     

Headwater Influences on Downstream Water Quality

We investigated the influence of riparian and whole watershed land use as a function of stream size on surface water chemistry and assessed regional variation in these relationships. Sixty-eight watersheds in four level III U.S. EPA ecoregions in eastern Kansas were selected as study sites. Riparian land cover and watershed land use were quantified for the entire watershed, and by Strahler order. Multiple regression analyses using riparian land cover classifications as independent variables explained among-site variation in water chemistry parameters, particularly total nitrogen (41%), nitrate (61%), and total phosphorus (63%) concentrations. Whole watershed land use explained slightly less variance, but riparian and whole watershed land use were so tightly correlated that it was difficult to separate their effects. Water chemistry parameters sampled in downstream reaches were most closely correlated with riparian land cover adjacent to the smallest (first-order) streams of watersheds or land use in the entire watershed, with riparian zones immediately upstream of sampling sites offering less explanatory power as stream size increased. Interestingly, headwater effects were evident even at times when these small streams were unlikely to be flowing. Relationships were similar among ecoregions, indicating that land use characteristics were most responsible for water quality variation among watersheds. These findings suggest that nonpoint pollution control strategies should consider the influence of small upland streams and protection of downstream riparian zones alone is not sufficient to protect water quality.     

Classification of Ephemeral,Intermittent, and Perennial Stream Reaches Using a TOPMODEL‐Based Approach

Whether a waterway is temporary or permanent influences regulatory protection guidelines, however, classification can be subjective due to a combination of factors, including time of year, antecedent moisture conditions, and previous experience of the field investigator. Our objective was to develop a standardized protocol using publically available spatial information to classify ephemeral, intermittent, and perennial streams. Our hypothesis was that field observations of flow along the stream channel could be compared to results from a hydrologic model, providing an objective method of how these stream reaches can be identified. Flow‐state sensors were placed at ephemeral, intermittent, and perennial stream reaches from May to December 2011 in the Appalachian coal basin of eastern Kentucky. This observed flow record was then used to calibrate the simulated saturation deficit in each channel reach based on the topographic wetness index used by TOPMODEL. Saturation deficit values were categorized as flow or no‐flow days, and the simulated record of streamflow was compared to the observed record. The hydrologic model was more accurate for simulating flow during the spring and fall seasons. However, the model effectively identified stream reaches as intermittent and perennial in each of the two basins.     

PREDICTION OF STREAM TEMPERATURE IN FORESTED WATERSHEDS1

ABSTRACT: Removal of streamside vegetation changes the energy balance of a stream, and hence its temperature. A common approach to mitigating the effects of logging on stream temperature is to require establishment of buffer zones along stream corridors. A simple energy balance model is described for prediction of stream temperature in forested headwater watersheds that allows evaluation of the performance of such measures. The model is designed for application to “worst case” or maximum annual stream temperature, under low flow conditions with maximum annual solar radiation and air temperature. Low flows are estimated via a regional regression equation with independent variables readily accessible from GIS databases. Testing of the energy balance model was performed using field data for mostly forested basins on both the west and east slopes of the Cascade Mountains, and was then evaluated using the regional equations for low flow and observed maximum reach temperatures in three different east slope Cascades catchments. A series of sensitivity analyses showed that increasing the buffer width beyond 30 meters did not significantly decrease stream temperatures, and that other vegetation parameters such as leaf area index, average tree height, and to a lesser extent streamside vegetation buffer width, more strongly affected maximum stream temperatures.     

Assessing Ecological Integrity of Ozark Rivers to Determine Suitability for Protective Status

Preservation of extraordinary natural resources, protection of water quality, and restoration of impaired waters require a strategy to identify and protect least-disturbed streams and rivers. We applied two objective, quantitative methods to determine stream ecological integrity of headwater reaches of 10 Ozark rivers, 5 with Wild and Scenic River federal protective status. Thirty-four variables representing macroinvertebrate and fish assemblage characteristics, in-stream habitat, riparian vegetation, water quality, and watershed attributes were quantified for each river and analyzed using two multivariate approaches. The first approach, cluster and discriminant analyses, identified two groups of river with only one variable (% forested watershed) reliably distinguishing groups. Our second approach employed ordinal scaling to compare variables for each river to conceptually ideal conditions that were developed as a composite of optimal attributes among the 10 rivers. The composite distance of each river from ideal was then calculated using a unidimensional ranking technique. Two rivers without Wild and Scenic River designation ranked highest relative to ideal (highest ecological integrity), and two others, also without designation, ranked most distant from ideal (lowest ecological integrity). Fish density, number of intolerant fish species, and invertebrate density were influential biotic variables for scaling. Contributing physical variables included riparian forest cover, water nitrate concentration, water turbidity, percentage of forested watershed, percentage of private land ownership, and road density. These methods provide a framework for refinement and application in other regions to facilitate the process of establishing least-disturbed reference conditions and identifying rivers for protection and restoration.     

Modeling the Effects of Land Use Change on the Water Temperature in Unregulated Urban Streams

Streams, in their natural state, are typically diverse and biologically productive environments. Streams subject to urbanization often experience degradation brought about by the cumulative effects of flow alteration, unsanitary discharge and channelization. One of the water quality parameters affected by urbanization is stream temperature. This study offers a model for predicting the impact of land use change on the temperature of non-regulated streams during extreme events. A stream temperature model was created by considering the gains and losses of thermal energy resulting from radiation, convection, conduction, evaporation and advection. A sensitivity analysis showed that out of 14 variables, shade/transmissivity of riparian vegetation, groundwater discharge, and stream width had the greatest influence on stream temperature. These same three variables are highly influenced by land use. Individual component models were developed to predict how urbanization changes stream width and baseflow discharge. Using 3-D computer modeling, a model was also developed to illustrate the effects of altering the extent and composition of riparian vegetation on streams with different orientations. By modeling these three variables as a function of urbanization, the results became inputs into the stream temperature model. The critical urban stream temperature model (CrUSTe), an aggregation of these four models, allows the prediction of stream temperature change as a result of amount, type and location of urbanization within a watershed. It has the potential to become a valuable tool for environmental managers.     

Spatial-Scale Effects on Relative Importance of Physical Habitat Predictors of Stream Health

A common theme in recent landscape studies is the comparison of riparian and watershed land use as predictors of stream health. The objective of this study was to compare the performance of reach-scale habitat and remotely assessed watershed-scale habitat as predictors of stream health over varying spatial extents. Stream health was measured with scores on a fish index of biotic integrity (IBI) using data from 95 stream reaches in the Eastern Corn Belt Plain (ECBP) ecoregion of Indiana. Watersheds hierarchically nested within the ecoregion were used to regroup sampling locations to represent varying spatial extents. Reach habitat was represented by metrics of a qualitative habitat evaluation index, whereas watershed variables were represented by riparian forest, geomorphology, and hydrologic indices. The importance of reach- versus watershed-scale variables was measured by multiple regression model adjusted-R² and best subset comparisons in the general linear statistical framework. Watershed models had adjusted-R² ranging from 0.25 to 0.93 and reach models had adjusted-R² ranging from 0.09 to 0.86. Better-fitting models were associated with smaller spatial extents. Watershed models explained about 15% more variation in IBI scores than reach models on average. Variety of surficial geology contributed to decline in model predictive power. Results should be interpreted bearing in mind that reach habitat was qualitatively measured and only fish assemblages were used to measure stream health. Riparian forest and length-slope (LS) factor were the most important watershed-scale variables and mostly positively correlated with IBI scores, whereas substrate and riffle-pool quality were the important reach-scale variables in the ECBP.     

STREAM ASSESSMENTS USING BIOTIC INDICES: RESPONSES TO PHYSICOCHEMICAL VARIABLES1

ABSTRACT: Responses of the Wyoming Stream Integrity Index (WSII), a regionally calibrated multimetric index, were investigated in relation to background elevational changes in water quality and habitat conditions versus accelerated anthropogenic degradation at the watershed scale. Assessments were conducted for three rivers in southeast Wyoming: the Little Medicine Bow River, the Medicine Bow River, and Rock Creek. Pearson correlation coefficients and regression models related “core metrics” and index scores to elevational gradients of physicochemical variables. Velocity, substrate, and weighted habitat values were positively correlated to index scores, while suspended solids was negatively correlated. The exclusive dependence of index scores on physical variables specifies the type of environmental gradients the WSII is most robust in detecting. The individual “core metrics” Plecoptera taxa, Trichoptera taxa, percent Trichoptera without Hydropsychidae, and percent noninsects appeared most sensitive to physical changes and were thus driving associations between index scores and physical variables. Despite strong correlations with physical variables, anomalies existed where habitat conditions were good, unknown stressors existed, or gradients were naturally occurring despite “Poor” index scores (i.e., degraded stream conditions). Such findings illustrate the influence of regional variability on biotic indices and the importance of identifying sufficient reference and impaired stream reaches used to develop and calibrate multimetric indices relying on reference conditions.     

利用二维模型求解太湖水质CODMn的研究

在二维黎曼近似解模型的基础上建立了太湖水质预测模型，并运用该模型对太湖的水质指标CODMn了模拟。模拟的结果跟太湖各监测站的测量值相接近，表明该模型能较好的运用于太湖的水质预测。     

Relationships between landscape pattern, wetland characteristics, and water quality in agricultural catchments

Water quality in streams is dependent on landscape metrics at catchment and wetland scales. A study was undertaken to evaluate the correlation between landscape metrics, namely patch density and area, shape, heterogeneity, aggregation, connectivity, land-use ratio, and water quality variables (salinity, nutrients, sediments, alkalinity, other potential pollutants and pH) in the agricultural areas of a semiarid Mediterranean region dominated by irrigated farmlands (NE Spain). The study also aims to develop wetland construction criteria in agricultural catchments. The percentage of arable land and landscape homogeneity (low value of Simpson index) are significantly correlated with salinity (r(2) = 0.72) and NO(3)-N variables (r(2) = 0.49) at catchment scale. The number of stock farms was correlated (Spearman's corr. = 0.60; p < 0.01) with TP concentration in stream water. The relative abundance of wetlands and the aggregation of its patches influence salinity variables at wetland scale (r(2) = 0.59 for Na(+) and K(+) concentrations). The number and aggregation of wetland patches are closely correlated to the landscape complexity of catchments, measured as patch density (r(2) = 0.69), patch size (r(2) = 0.53), and landscape heterogeneity (r(2) = 0.62). These results suggest that more effective results in water quality improvement would be achieved if we acted at both catchment and wetland scales, especially reducing landscape homogeneity and creating numerous wetlands scattered throughout the catchment. A set of guidelines for planners and decision makers is provided for future agricultural developments or to improve existing ones.     

ON THE MISMATCH BETWEEN DATA AND MODELS OF HYDROLOGIC AND WATER RESOURCE SYSTEMS1

ABSTRACT: Many difficulties exist in the matching of models with data. This paper identifies elements of this problem and discusses considerations involved in model evaluation. The well known multivariate linear regression model is used to illustrate the distinctions between accuracy and precision and between estimation and prediction (because the model is commonly misused.) No amount of additional data will improve the accuracy of a poor model. A high R², while indicative of a good matching between the observed data and model estimates, is a poor criterion for judging adequacy of the model to make good predictions of future events. Model evaluation also includes the problem of introducing secondary data and proxy variables into a model. Secondary data frequently enter, for example, the mass, energy and water budget equations because of difficulties in measuring the primary variables. Proxy variables arise because of a desire to collapse a vector of incomparable values, say, of water quality into a single number. Review of the above issues indicates that model evaluation is a multi-criterion problem, often imbedded in a larger framework where models are intended to meet multiple objectives. The mismatch of models and data has increasing legal and social consequences.     

What’s a Stream Without Water? Disproportionality in Headwater Regions Impacting Water Quality

Headwater streams are critical components of the stream network, yet landowner perceptions, attitudes, and property management behaviors surrounding these intermittent and ephemeral streams are not well understood. Our research uses the concept of watershed disproportionality, where coupled social-biophysical conditions bear a disproportionate responsibility for harmful water quality outcomes, to analyze the potential influence of riparian landowner perceptions and attitudes on water quality in headwater regions. We combine social science survey data, aerial imagery, and an analysis of spatial point processes to assess the relationship between riparian landowner perceptions and attitudes in relation to stream flow regularity. Stream flow regularity directly and positively shapes landowners’ water quality concerns, and also positively influences landowners’ attitudes of stream importance—a key determinant of water quality concern as identified in a path analysis. Similarly, riparian landowners who do not notice or perceive a stream on their property are likely located in headwater regions. Our findings indicate that landowners of headwater streams, which are critical areas for watershed-scale water quality, are less likely to manage for water quality than landowners with perennial streams in an obvious, natural channel. We discuss the relationships between streamflow and how landowners develop understandings of their stream, and relate this to the broader water quality implications of headwater stream mismanagement.     

IMPROVING REGIONAL-MODEL ESTIMATES OF URBAN-RUNOFF QUALITY USING LOCAL DATA1

ABSTRACT: Urban water-quality managers need load estimates of storm-runoff pollutants to design effective remedial programs. Estimates are commonly made using published models calibrated to large regions of the country. This paper presents statistical methods, termed model-adjustment procedures (MAPs), which use a combination of local data and published regional models to improve estimates of urban-runoff quality. Each MAP is a form of regression analysis that uses a local data base as a calibration data set to adjust the regional model, in effect increasing the size of the local data base without additional, expensive data collection. The adjusted regional model can then be used to estimate storm-runoff quality at unmonitored sites and storms in the locality. The four MAPs presented in this study are (1) single-factor regression against the regional model prediction, P_u; (2) least-squares regression against P_u; (3) least-squares regression against P_u and additional local variables; and (4) weighted combination of P_u and a local-regression prediction. Identification of the statistically most valid method among these four depends upon characteristics of the local data base. A MAP-selection scheme based on statistical analysis of the calibration data set is presented and tested.     

A Spatially Explicit Model for Mapping Headwater Streams

Headwater streams are the primary sources of water in a drainage network and serve as a critical hydrologic link between the surrounding landscape and larger, downstream surface waters. Many states, including North Carolina, regulate activity in and near headwater streams for the protection of water quality and aquatic resources. A fundamental tool for regulatory management is an accurate representation of streams on a map. Limited resources preclude field mapping every headwater stream and its origin across a large region. It is more practical to develop a model for headwater streams based on a sample of field data that can then be extrapolated to a larger area of interest. The North Carolina Division of Water Quality has developed a cost‐effective method for modeling and mapping the location, length, and flow classification (intermittent and perennial) of headwater streams. We used a multiple logistic regression approach that combined field data and terrain derivatives for watersheds located in the Triassic Basins ecoregion. Field data were collected using a standard methodology for identifying headwater streams and origins. Terrain derivatives were generated from digital elevation models interpolated from bare‐earth Light Detection and Range data. Model accuracies greater than 80% were achieved in classifying stream presence and absence, stream length and perennial stream length, but were not as consistent in predicting intermittent stream length.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.