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Watershed Attributes (watershed + attribute)
Selected AbstractsANALYZING CORRELATIONS BETWEEN STREAM AND WATERSHED ATTRIBUTES,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2003John Van Sickle ABSTRACT: Bivariate correlation analysis has been widely used to explore relationships between stream and watershed attributes that have all been measured on the same set of watersheds or sampling locations. Researchers routinely test H0: ,= 0 for each correlation in a large table and then go on to discuss only those that are declared "significant." Such test results are inaccurate because no allowance is made for multiple testing, and also because the tests are not mutually independent. This paper reviews the Bonferroni approach to controlling the overall error rate in multiple testing and shows how the approach becomes impractical for large correlation tables. The Hotelling/Williams test is introduced for comparing two dependent correlations that share a variable, and numerical constraints for two such correlations are illustrated. References are also given for testing other hypothesized patterns among dependent correlations, and links to dependent correlation software are provided. The methods are illustrated for watershed and stream variables sampled in 23 small agricultural watersheds of the Willamette Valley, Oregon. [source] Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks,HYDROLOGICAL PROCESSES, Issue 16 2009Anne B. Hoos Abstract Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States,higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow. Published in 2009 by John Wiley & Sons, Ltd. [source] Controls on surface water chemistry in two lake-watersheds in the Adirondack region of New York: differences in nitrogen solute sources and sinksHYDROLOGICAL PROCESSES, Issue 10 2007Mari Ito Abstract The southwestern Adirondack region of New York receives among the highest rates of atmospheric nitrogen (N) deposition in the USA. Atmospheric N deposition to sensitive ecosystems, like the Adirondacks, may increase the acidification of soils through losses of exchangeable nutrient cations, and the acidification of surface waters associated with enhanced mobility of nitrate (NO3,). However, watershed attributes, including surficial terrestrial characteristics, in-lake processing, and geological settings, have been found to complicate the relationships between atmospheric N deposition and N drainage losses. We studied two lake-watersheds in the southwestern Adirondacks, Grass Pond and Constable Pond, which are located in close proximity (,26 km) and receive similarly high N deposition, but have contrasting watershed attributes (e.g. wetland area, geological settings). Since the difference in the influence of N deposition was minimal, we were able to examine both within- and between-watershed influences of land cover, the contribution of glacial till groundwater inputs, and in-lake processes on surface water chemistry with particular emphasis on N solutes and dissolved organic carbon (DOC). Monthly samples at seven inlets and one outlet of each lake were collected from May to October in 1999 and 2000. The concentrations of NO3, were high at the Grass Pond inlets, especially at two inlets, and NO3, was the major N solute at the Grass Pond inlets. The concentrations of likely weathering products (i.e. dissolved Si, Ca2+, Mg2+, Na+) as well as acid neutralizing capacity and pH values, were also particularly high at those two Grass Pond inlets, suggesting a large contribution of groundwater inputs. Dissolved organic N (DON) was the major N solute at the Constable Pond inlets. The higher concentrations of DON and DOC at the Constable Pond inlets were attributed to a large wetland area in the watershed. The DOC/DON ratios were also higher at the Constable Pond inlets, possibly due to a larger proportion of coniferous forest area. Although DON and DOC were strongly related, the stronger relationship of the proportion of wetland area with DOC suggests that additional factors regulate DON. The aggregated representation of watershed physical features (i.e. elevation, watershed area, mean topographic index, hypsometric-analysis index) was not clearly related to the lake N and DOC chemistry. Despite distinctive differences in inlet N chemistry, NO3, and DON concentrations at the outlets of the two lakes were similar. The lower DOC/DON ratios at the lake outlets and at the inlets having upstream ponds suggest the importance of N processing and organic N sources within the lakes. Although an inverse relationship between NO3, and DOC/DON has been suggested to be indicative of a N deposition gradient, the existence of this relationship for sites that receive similar atmospheric N deposition suggest that the relationship between NO3, and the DOC/DON ratio is derived from environmental and physical factors. Our results suggest that, despite similar wet N deposition at the two watershed sites, N solutes entering lakes were strongly affected by hydrology associated with groundwater contribution and the presence of wetlands, whereas N solutes leaving lakes were strongly influenced by in-lake processing. Copyright © 2006 John Wiley & Sons, Ltd. [source] ANALYZING CORRELATIONS BETWEEN STREAM AND WATERSHED ATTRIBUTES,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2003John Van Sickle ABSTRACT: Bivariate correlation analysis has been widely used to explore relationships between stream and watershed attributes that have all been measured on the same set of watersheds or sampling locations. Researchers routinely test H0: ,= 0 for each correlation in a large table and then go on to discuss only those that are declared "significant." Such test results are inaccurate because no allowance is made for multiple testing, and also because the tests are not mutually independent. This paper reviews the Bonferroni approach to controlling the overall error rate in multiple testing and shows how the approach becomes impractical for large correlation tables. The Hotelling/Williams test is introduced for comparing two dependent correlations that share a variable, and numerical constraints for two such correlations are illustrated. References are also given for testing other hypothesized patterns among dependent correlations, and links to dependent correlation software are provided. The methods are illustrated for watershed and stream variables sampled in 23 small agricultural watersheds of the Willamette Valley, Oregon. [source] |