Spatial data analysis for monitoring and prediction of selected water quality parameters in reservoirs: Porsuk Dam reservoir case

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2014
Yenilmez, Firdes
In the design of a water quality monitoring network, selection of water quality sampling locations is crucial to adequately represent the water quality of the water body when high costs of analyses and field work are taken into account. In this study, a new approach was proposed to identify the representative water quality sampling locations in reservoirs and lakes using geostatistical tools for estimation of spatial distribution of selected water quality parameters. To do so, kernel density estimation (KDE) was coupled with ordinary 2-dimensional kriging (OK) in order to select the representative sampling locations in kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Field data obtained in August 2010 were used to start the process of sampling point elimination while maintaining the spatial correlation structure of DO. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved. Results indicated that lower sampling points resulted in loss of information in regard to spatial correlation structure in DO when more than 30 sampling points were removed from the initial 65. Representativeness of the selected network for specific conductivity (SC) was also checked and confirmed. Furthermore, potential hotspots for DO and SC were also assessed based on landuses in the vicinity of PDR. Then, efficacy of the representative sampling locations selection method was tested against the networks generated by experts. It was shown that the evaluation approach used in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained. In the second part of the study, three-dimensional (3D) kriging of DO with the 81 sampling points was performed using Stanford Geostatistical Modeling Software (SGeMS). Hence, not only the hotspots at the surface of PDR but also in deeper layers were constituted and evaluated in terms of the inlets of pollution sources. Similar hotspots were obtained both for 2D kriging and 3D kriging of DO for the dataset used in this study. Moreover, 3D distributions of DO, SC and temperature were constituted to determine the location of the thermocline layer. It was identified that the traditional approach of collecting samples from mid depths may cause incomplete characterization and evaluation of water quality since thermocline layer may not coincide with mid-depth.

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Citation Formats
F. Yenilmez, “Spatial data analysis for monitoring and prediction of selected water quality parameters in reservoirs: Porsuk Dam reservoir case,” Ph.D. - Doctoral Program, Middle East Technical University, 2014.