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Hacettepe Üniversitesi (2019)

Statistical analyses of meteorological spatial data : A case of Çatalan dam lake basin

NAMKHAI Otgonbayar

Titre : Statistical analyses of meteorological spatial data : A case of Çatalan dam lake basin

Meteorolojik mekansal verilerin istatistiksel yöntemler ile analizi : Çatalan baraj gölü havzası örneği

Auteur : NAMKHAI Otgonbayar

Université de soutenance : Hacettepe Üniversitesi

Grade : Master Thesis 2019

Résumé
In hydrology, which is a sub-discipline of earth sciences, spatial analysis methods are needed to determine the spatial arial distribution (x, y, z, t) of the hydrometeorological data as point (x, t) time series data. Spearman’s rho correlation test is used to determine the relationship between nonparametric hydrometeorological point time series. Principal Component Analysis / Factor Analysis is used to determine the similarity or proximity of multivariate point data and to reduce the number of variables. Besides, Mann-Kendall, Sen Slope and Innovative Şen Trend Analysis methods are applied for determining the trends of these data over time. The spatial analysis methods used for estimating spatial data from point data are Data Management / Raster, Spatial Analyst and Geostatistical Analyst, which are sub-tools of ESRI ArcGIS package software. Spatial Statistical Analyst tool is implemented to perform geostatistical analysis according to the research aims. In this study ; the meteorological data such as precipitation, temperature, wind speed, relative humidity, actual pressure and drought index, from 18 meteorological stations between year of 1971-2018 are used. It has been determined that the study area is divided by 3 different climatic zones by applying PCA/FA analyses. It is found in the study that, generally the first half of the total 48 years in terms of precipitation as found arid season (decreasing trend) and the rest was found wet season (increasing trend) by applying Mann-Kendall Trend Analyse and Cumulative Deviation Method techniques. Also, in terms of temperature, in the first half of the observation-years it is found the cool/cold season (decreasing trend) and the rest was found warm/hot season (increasing trend) by applying same techniques. In this study, the method for predicting spatial data is found to be IDW method with the minimum error comparing to the alternatives. The spatial arial actual evapotranspiration of the study area was estimated from the raster data obtained by this method. Thus, a multivariate spatial regression equation was estimated. The coefficient of determination was calculated as R2=0.9958. In addition, spatial maps of all independent variables and estimated data were created.

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