Arazi kullanımı ve havza özelliklerinin Türkiye’deki sığ göllerin trofik durumuna etkisi.

Anthropogenic (human) factors can dramatically increase the nutrient concentrations of the shallow lakes which cause to cultural eutrophication. The most apparent effect of cultural eutrophication is excessive plant population and dense algal blooms that reduce the water quality. The artificial inputs of nutrients come from surface runoff such as excessive fertilize use in the agriculture, untreated wastewater effluents from the urban area trigger the eutrophication. These human activities linked with degradation of water quality lead to also dramatic consequences for drinking water sources, fisheries, and recreational water bodies. The aim of this study was to evaluate land use and landscape characteristics influence on the trophic status of shallow lakes through rendering the statistical relations. Study area covers 38 shallow lakes from north to south in the west part of the Turkey. Catchment variables were produced by using geographic information system (GIS). Trophic status of the lakes was described by principal component analysis (PCA), using total nitrogen (TN), total phosphorous (TP), Chlorophyll a (Chl-a) and Secchi depth. In the analysis PCA result regarded as PC1 which determined the trophic state of the lake. Almost all studied lakes were eutrophic in terms of nutrient concentration, however two deep lakes (Lake Abant and Lake Büyük) were oligotrophic showed up outliers. Statistical analysis for PC1 versus catchment variables were done without these two lakes. Firstly, catchment variables effect on lake trophic status was analyzed with 36 shallow lakes. Contrasting with the other studies, there was no significant relation in the simple linear regressions with the land use and nutrient concentrations of the lakes. There were only 3 catchment characteristics as temperature (14.41%), latitude (9.41%) and longitude (6.25%) had significant relation with the PC1. Multiple regression analysis was applied to show PC1 versus cumulative effect of catchment characteristics. There was still unexpected result that significant features for PC1 are ‘slope, wetland, latitude and temperature’, respectively. There was weak relationship between these variables and PC1 which explained 26.75% of the variance in PC1. Secondly, analysis of catchment influence on PC1 was repeated with 30 lakes. Six lakes had high TN, despite their entirely forested catchment area were evaluated as outlier. The result has been changed from the first analysis that PC1 versus the significant catchment variables in the simple linear regression are catchment area (20.07%), forest and semi natural areas (19.67%), latitude (12.98%), agricultural area (12.84%) and temperature (11.04%). Multiple regression analysis was done for PC1 versus all catchment variables using 30 lake data. There was strong relationship between catchment characteristics and PC1 that PC1 explained trophic status of the lakes as 60.13%. The significant variables in the multiple regression analysis are ‘slope, catchment area, latitude, wetlands, lake area and precipitation’ respectively. PC1 had negative relation between the slope, latitude, wetland and lake area, while PC1 had positive relation between catchment area and precipitation.M.S. - Master of Scienc