The influence of suburban land use on habitat and biotic integrity of coastal Rhode Island streams

Watershed land use in suburban areas can affect stream biota through degradation of instream habitat, water quality, and riparian vegetation. By monitoring stream biotic communities in various geographic regions, we can better understand and conserve our watershed ecosystems. The objective of this study was to examine the relationship between watershed land use and the integrity of benthic invertebrate communities in eight streams that were assessed over a 3-year period (2001-2003). Sites were selected from coastal Rhode Island watersheds along a residential land-use gradient (4-59%). Using the rapid bioassessment protocol, we collected biological, physicochemical, habitat, and nutrient data from wadeable stream reaches and compared metrics of structure and integrity. Principal component analyses showed significant negative correlation of indicators for stream physicochemical, habitat, and instream biodiversity with increasing residential land use (RLU) in the watershed. The physicochemical variables that were most responsive to percent RLU were conductivity, instream habitat, nitrate, and dissolved inorganic nitrogen (DIN). The positive correlation of DIN with percent RLU indicated an anthropogenic source of pollution affecting the streams. The biotic composition of the streams shifted from sensitive to insensitive taxa as percent RLU increased; the most responsive biological variables were percent Ephemeroptera, percent Scrapers, percent Insects, and the Hilsenhoff biotic index. These data show the importance of land management and conservation at the watershed scale to sustaining the biotic integrity of coastal stream ecosystems.     

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.     

Statistically Extracted Fundamental Watershed Variables for Estimating the Loads of Total Nitrogen in Small Streams

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km²) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.     

Relations of Habitat-Specific Algal Assemblages to Land Use and Water Chemistry in the Willamette Basin, Oregon

Benthic algal assemblages, water chemistry, and habitat were characterized at 25 stream sites in the Willamette Basin, Oregon, during low flow in 1994. Seventy-three algal samples yielded 420 taxa — mostly diatoms, blue-green algae, and green algae. Algal assemblages from depositional samples were strongly dominated by diatoms (76% mean relative abundance), whereas erosional samples were dominated by blue-green algae (68% mean relative abundance).Canonical correspondence analysis (CCA) of semiquantitative and qualitative (presence/absence) data sets identified four environmental variables (maximum specific conductance, % open canopy, pH, and drainage area) that were significant in describing patterns of algal taxa among sites. Based on CCA, four groups of sites were identified: streams in forested basins that supported oligotrophic taxa, such as Diatoma mesodon;small streams in agricultural and urban basins that contained a variety of eutrophic and nitrogen-heterotrophic algal taxa; larger rivers draining areas of mixed land use that supported planktonic, eutrophic, and nitrogen-heterotrophic algal taxa; and streams with severely degraded or absent riparian vegetation (> 75% open canopy) that were dominated by other planktonic, eutrophic, and nitrogen-heterotrophic algal taxa. Patterns in water chemistry were consistent with the algal autecological interpretations and clearly demonstrated relationships between land use, water quality, and algal distribution patterns.     

Development of a reference coastal wetland set in Southern New England (USA)

Various measures of plants, soils, and invertebrates were described for a reference set of tidal coastal wetlands in Southern New England in order to provide a framework for assessing the condition of other similar wetlands in the region. The condition of the ten coastal wetlands with similar hydrology and geomorphology were ranked from least altered to highly altered using a combination of statistical methods and best professional judgment. Variables of plants, soils, and invertebrates were examined separately using principal component analysis to reduce the multidimensional variables to principal component scores. The first principal component scores of each set of variables (i.e., plants, soil, invertebrates) significantly (p?<?0.05) correlated with both residential land use and watershed nitrogen (N) loads. Using cumulative frequency diagrams, the first principal component scores of each plant, soil, and invertebrate data set were plotted, and natural breaks and best professional judgment were used to rank the first principal component scores among the sites. We weighted all three ranked components equally and calculated an overall salt marsh condition index by summing the three ranks and then transforming the index to a 0–1 scale. The overall salt marsh condition index for the reference coastal wetland set significantly correlated with the residential land use (R?=???0.87, p?=?0.001) and watershed N loads (R?=???0.86, p?=?0.001). Overall, condition deteriorated in salt marshes and their associated discharge streams when subjected to increasing watershed residential land use and N loads.     

Effects of geomorphology, habitat, and spatial location on fish assemblages in a watershed in Ohio, USA

In this paper, we evaluate relationships between in-stream habitat, water chemistry, spatial distribution within a predominantly agricultural Midwestern watershed and geomorphic features and fish assemblage attributes and abundances. Our specific objectives were to: (1) identify and quantify key environmental variables at reach and system wide (watershed) scales; and (2) evaluate the relative influence of those environmental factors in structuring and explaining fish assemblage attributes at reach scales to help prioritize stream monitoring efforts and better incorporate all factors that influence aquatic biology in watershed management programs. The original combined data set consisted of 31 variables measured at 32 sites, which was reduced to 9 variables through correlation and linear regression analysis: stream order, percent wooded riparian zone, drainage area, in-stream cover quality, substrate quality, gradient, cross-sectional area, width of the flood prone area, and average substrate size. Canonical correspondence analysis (CCA) and variance partitioning were used to relate environmental variables to fish species abundance and assemblage attributes. Fish assemblages and abundances were explained best by stream size, gradient, substrate size and quality, and percent wooded riparian zone. Further data are needed to investigate why water chemistry variables had insignificant relationships with IBI scores. Results suggest that more quantifiable variables and consideration of spatial location of a stream reach within a watershed system should be standard data incorporated into stream monitoring programs to identify impairments that, while biologically limiting, are not fully captured or elucidated using current bioassessment methods.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.     

Spatial Patterns of Water Quality in the Cuiabá River Basin, Central Brazil

This study evaluates the spatial patterns of land occupation and their relationship to water quality in the Cuiabá River watershed, one of the main affluents of the Pantanal floodplain. The impact of farming and other land occupation forms were studied using a three year time series. Monitoring included 15 parameters at 21 stations with a total of 1266 different samples. Ten stations along the Cuiabá River were ordinated by Principal Component Analysis (PCA). For an exploratory analysis in the spatial domain, sub-basins of the Cuiabá watershed were classified according to mean concentrations of selected water quality parameters. Supervised classification of digital Landsat ETM imagery and standard GIS techniques were applied to parameterize land use and occupation according to a watershed scale. Redundancy Analysis (RDA) was then used to evaluate impacts of environmental and socio-economic factors on water quality.A Cuiabá headwater station only shows slightly elevated total coliform counts and concentrations of nutrients in the river after it passes regions of extensive cattle farming. After the confluence with the Manso River, nutrient and COD concentrations increase significantly, receiving loads from sub-basins under intensive agricultural land use, with mean annual concentrations up to 1.74 mg/L of total nitrogen (Kjedahl). Sub-watersheds with intensive fishing culture activities were shown to have significant impact on nitrogen concentrations, reaching mean concentrations of 2.66 mg/L of total nitrogen in the affluents. Most serious biological and chemical water pollution can be observed at stream outlets in the urban agglomeration of Cuiabá/Várzea Grande. Affluent pollution is reflected in the water quality of the Cuiabá River: subsequent monitoring stations in the urban area are ordinated on a gradient of increasing degradation of chemical and biological water quality. The auto-depuration capacity of the Cuiabá River is intact, but elevated concentrations of Phosphorous and Chemical Oxygen Demand can be observed as far away as the Pantanal floodplain, about 120 km downstream from the urban agglomeration.     

Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992–2008

This study aims to assess the relative importance of natural and anthropogenic variables on the change of the red-crowned crane habitat in the Yellow River Nature Reserve, East China using multitempopral remote sensing and geographic information system. Satellite images were used to detect the change in potential crane habitat, from which suitable crane habitat was determined by excluding fragmented habitat. In this study, a principal component analysis (PCA) with seven variables (channel flow, rainfall, temperature, sediment discharge, number of oil wells, total length of roads, and area of settlements) and linear regression analyses of potential and suitable habitat against the retained principal components were applied to explore the influences of natural and anthropogenic factors on the change of the red-crowned crane habitat. The experimental results indicate that suitable habitat decreased by 5,935 ha despite an increase of 1,409 ha in potential habitat from 1992 to 2008. The area of crane habitat changed caused by natural drivers such as progressive succession, retrogressive succession, and physical fragmentation is almost the same as that caused by anthropogenic forces such as land use change and behavioral fragmentation. The PCA and regression analyses revealed that natural factors (e.g., channel flow, rainfall, temperature, and sediment discharge) play an important role in the crane potential habitat change and human disturbances (e.g., oil wells, roads, and settlements) jointly explain 51.8 % of the variations in suitable habitat area, higher than 48.2 % contributed by natural factors. Thus, it is vital to reduce anthropogenic influences within the reserve in order to reverse the decline in the suitable crane habitat.     

A Fish-Based Index of Biotic Integrity to Assess Intermittent Headwater Streams in Wisconsin, USA

I developed a fish-based index of biotic integrity (IBI) to assess environmental quality in intermittent headwater streams in Wisconsin, USA. Backpack electrofishing and habitat surveys were conducted four times on 102 small (watershed area 1.7–41.5 km²), cool or warmwater (maximum daily mean water temperature ≥22 C), headwater streams in spring and late summer/fall 2000 and 2001. Despite seasonal and annual changes in stream flow and habitat volume, there were few significant temporal trends in fish attributes. Analysis of 36 least-impacted streams indicated that fish were too scarce to calculate an IBI at stations with watershed areas less than 4 km² or at stations with watershed areas from 4–10 km² if stream gradient exceeded 10 m/km (1% slope). For streams with sufficient fish, potential fish attributes (metrics) were not related to watershed size or gradient. Seven metrics distinguished among streams with low, agricultural, and urban human impacts: numbers of native, minnow (Cyprinidae), headwater-specialist, and intolerant (to environmental degradation) species; catches of all fish excluding species tolerant of environmental degradation and of brook stickleback (Culaea inconstans) per 100 m stream length; and percentage of total individuals with deformities, eroded fins, lesions, or tumors. These metrics were used in the final IBI, which ranged from 0 (worst) to 100 (best). The IBI accurately assessed the environmental quality of 16 randomly chosen streams not used in index development. Temporal variation in IBI scores in the absence of changes in environmental quality was not related to season, year, or type of human impact and was similar in magnitude to variation reported for other IBI's.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

Development of a Provisional Physical Habitat Index for Maryland Freshwater Streams

Provisional physical habitat indices were developed and validatedfor Maryland Coastal and Non-Coastal Plain streams using variables (commonly called metrics) that best discriminated reference and degraded conditions based on biological, chemicaland land use data from the 1994–97 Maryland Biological Stream Survey (MBSS). These habitat indices contained variables that described structural, hydrological, vegetative and aesthetic components of stream habitat. Variables with the best discriminatory power for Coastal Plain streams were: instream habitat, velocity/depth diversity, pool/glide/eddy quality, embeddedness, maximum depth and aesthetic rating. Physical habitat variables with the best discriminatory power for Non-Coastal Plain sites were: instream habitat, velocity/depth diversity, riffle/run quality, embeddedness, number of rootwads and aesthetic rating. The overall classification efficiency forindex validation was 76% for both indices pooled over both strata. Scaled physical habitat index values (0–100) for bothstrata identified nearly twice as many good sites (31%) as very poor sites (16%). More than half the Maryland sites werein the poor to fair range (53%).     

Land use impacts on river health of Uma Oya,Sri Lanka: implications of spatial scales

Human actions on landscapes are a principal threat to the ecological integrity of river ecosystems worldwide. Tropical landscapes have been poorly investigated in terms of the impact of catchment land cover alteration on water quality and biotic indices in comparison to temperate landscapes. Effects of land cover in the catchment at two spatial scales (catchment and site) on stream physical habitat quality, water quality, macroinvertebrate indices and community composition were evaluated for Uma Oya catchment in the upper Mahaweli watershed, Sri Lanka. The relationship between spatial arrangement of land cover in the catchment and water quality, macroinvertebrate indices and community composition was examined using univariate and multivariate approaches. Results indicate that chemical water quality variables such as conductivity and total dissolved solids are mostly governed by the land cover at broader spatial scales such as catchment scale. Shannon diversity index was also affected by catchment scale forest cover. In stream habitat features, nutrients such as N-NO₃ ^?, macroinvertebrate family richness, %shredders and macroinvertebrate community assemblages were predominantly influenced by the extent of land cover at 200 m site scale suggesting that local riparian forest cover is important in structuring macroinvertebrate communities. Thus, this study emphasizes the importance of services provided by forest cover at catchment and site scale in enhancing resilience of stream ecosystems to natural forces and human actions. Findings suggest that land cover disturbance effects on stream ecosystem health could be predicted when appropriate spatial arrangement of land cover is considered and has widespread application in the management of tropical river catchments.     

Chemometric application in identifying sources of organic contaminants in Langat river basin

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state (n?=?1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes (n?≤?75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.     

An assessment of the impacts of timber plantations on water quality and biodiversity values of Marbellup Brook, Western Australia

Despite the fact that the establishment and maintenance of blue gum plantations can potentially result in the removal of riparian vegetation, the presence of increased levels of sediments, pesticides, and nutrients, and consequently, the loss of in-stream biodiversity, few studies exist that have looked at the impacts of timber plantations on in-stream biota. The goals of this study were thus to determine water quality, riparian condition, and in-stream biodiversity values of local streams draining blue gum plantations in the Marbellup Brook catchment in Western Australia and to compare these values with those of streams associated with other land uses. Selected water quality and habitat variables and in-stream macroinvertebrate biodiversity were measured in 2006 and 2007 at 28 sites falling into five broad categories based on the predominant land use within 200 m of each study reach. Overall, the results indicated that ??blue gum plantation?? sites often had better water quality, riparian condition, and biodiversity values than ??pasture unfenced,?? and sometimes ??pasture fenced?? sites, but water quality and biodiversity values at these sites were not as good as those associated with ??remnant?? native vegetation sites. The location of the blue gum plantation sites along the disturbance gradient investigated was attributed to both present management and past land uses in the subcatchments investigated. As this study was conducted at a time when blue gum plantations were in an on-growing phase, it was recommended that future research on the impact of blue gum plantations on waterways in southwestern Australia should include an investigation of the impacts of timber clear-cutting and extraction. Longer-term cumulative and downstream effects of blue gum plantations on local waterways also need to be investigated.     

On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin,Indiana, USA

Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.     

Nonpoint Source Pollution Assessment of Wujiang RiverWatershed in Guizhou Province,SW China

The amount of pollution from nonpoint sources flowing in the streams of the Wujiang River watershed in Guizhou Province, SW China, is estimated by a geographic information system (GIS)-based method using rainfall, surface runoff and land use data. A grid of cells of 100 m in size is laid over the landscape. For each cell, mean annual surface runoff is estimated from rainfall and percent land use, and expected pollutant concentration is estimated from land use. The product of surface runoff and concentration gives expected pollutant loading from that cell. These loadings are accumulated going downstream to give the expected annual pollutant loadings in streams and rivers. By dividing these accumulated loadings by the similarly accumulated mean annual surface runoff, the expected pollutant concentration from nonpoint sources is determined for each location in a stream or river. Observed pollutant concentrations in the watershed are averaged at each sample point and compared to the expected concentrations at the same locations determined from the grid cell model. In general, annual nonpoint source nutrient loadings in the Wujiang River watershed are seen to be predominantly from the agricultural and meadow areas. The total annual loadings through the outlet of the watershed are 40,309 and 2,607 tons for total nitrogen (TN) and total phosphorus (TP), respectively.