The Catskill/Delaware reservoirs supply 90% of New York City's drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.
Enterococci bacteria are used to indicate the presence of human and/or animal fecal materials in surface water. In addition to human influences on the quality of surface water, a cattle grazing is a widespread and persistent ecological stressor in the Western United States. Cattle may affect surface water quality directly by depositing nutrients and bacteria, and indirectly by damaging stream banks or removing vegetation cover, which may lead to increased sediment loads. This study used the State of Oregon surface water data to determine the likelihood of animal pathogen presence using enterococci and analyzed the spatial distribution and relationship of biotic (enterococci) and abiotic (nitrogen and phosphorous) surface water constituents to landscape metrics and others (e.g. human use, percent riparian cover, natural covers, grazing, etc.). We used a grazing potential index (GPI) based on proximity to water, land ownership and forage availability. Mean and variability of GPI, forage availability, stream density and length, and landscape metrics were related to enterococci and many forms of nitrogen and phosphorous in standard and logistic regression models. The GPI did not have a significant role in the models, but forage related variables had significant contribution. Urban land use within stream reach was the main driving factor when exceeding the threshold (> or =35 cfu/100 ml), agriculture was the driving force in elevating enterococci in sites where enterococci concentration was <35 cfu/100 ml. Landscape metrics related to amount of agriculture, wetlands and urban all contributed to increasing nutrients in surface water but at different scales. The probability of having sites with concentrations of enterococci above the threshold was much lower in areas of natural land cover and much higher in areas with higher urban land use within 60 m of stream. A 1% increase in natural land cover was associated with a 12% decrease in the predicted odds of having a site exceeding the threshold. Opposite to natural land cover, a one unit change in each of manmade barren and urban land use led to an increase of the likelihood of exceeding the threshold by 73%, and 11%, respectively. Change in urban land use had a higher influence on the likelihood of a site exceeding the threshold than that of natural land cover.
Forty-six broad-scale landscape metrics derived from commonly used landscape metrics were used to develop potential indicators of total phosphorus (TP) concentration, total ammonia (TA) concentration, and Escherichia coli bacteria count among 244 sub-watersheds of the Upper White River (Ozark Mountains, USA). Indicator models were developed by correlating field-based water quality measurements and contemporaneous remote-sensing-based ecological metrics using partial least squares (PLS) analyses. The TP PLS model resulted in one significant factor explaining 91% of the variability in surface water TP concentrations. Among the 18 contributing landscape model variables for the TP PLS model, the proportions of a sub-watershed that are barren and in human use were key indicators of water chemistry in the associated sub-watersheds. The increased presence and reduced fragmentation of forested areas are negatively correlated with TP concentrations in associated sub-watersheds, particularly within close proximity to rivers and streams. The TA PLS model resulted in one significant factor explaining 93% of the variability in surface water TA concentrations. The eight contributing landscape model variables for the TA PLS model were among the same forest and urban metrics for the TP model, with a similar spatial gradient trend in relationship to distance from streams and rivers within a sub-watershed. The E. coli PLS model resulted in two significant factors explaining 99.7% of the variability in E. coli cell count. The 17 contributing landscape model variables for the E. coli PLS model were similar to the TP and TA models. The integration of model results demonstrates that forest, riparian, and urban attributes of sub-watersheds affect all three models. The results provide watershed managers in the Ozark Mountains with a broad-scale vulnerability prediction tool, focusing on TP, TA, and E. coli, and are being used to prioritize and evaluate monitoring and restoration efforts in the vicinity of the White River, a major tributary to the Mississippi River and Gulf of Mexico.
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