Coğrafi bilgi sistemi ile toprak kaybı ve sediment verimi tahmin modelinin (est) oluşturulması ve Seyhan örkün alt havzasına uygulanması

TEZ4519Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2003.Kaynakça (s. 87-93) var.vıı, 101 s. 30 cm.…Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi Tarafından Desteklenmiştir. Proje No:FBE.2002.D.1

Evaluating the impact of land use uncertainty on the simulated streamfow and sediment yield of the Seyhan River basin using the SWAT model

As a result of the increased availability of spatial information in watershed modeling, several easy to use and widely accessible spatial datasets have been developed. Yet, it is not easy to decide which source of data is better and how data from diferent sources afect model outcomes. In this study, the results of simulating the stream fow and sediment yield from the Seyhan River basin in Turkey using 3 diferent types of land cover datasets through the soil and water assessment tool (SWAT) model are discussed and compared to the observed data. Te 3 land cover datasets used include the coordination of information on the environment dataset (CORINE; CLC2006), the global land cover characterization (GLCC) dataset, and the GlobCover dataset. Streamfow and sediment calibration was done at monthly intervals for the period of 2001–2007 at gauge number 1818 (30 km upstream of the Çatalan dam). Te model simulation of monthly streamfow resulted in good Nash–Sutclife efciency (NSE) values of 0.73, 0.71, and 0.68 for the GLCC, GlobCover, and CORINE datasets, respectively, for the calibration period. Furthermore, the model simulated the monthly sediment yield with satisfactory NSE values of 0.48, 0.51, and 0.46 for the GLCC, GlobCover, and CORINE land cover datasets, respectively. Te results suggest that the sensitivity of the SWAT model to the land cover datasets with diferent spatial resolutions and from diferent time periods was very low in the monthly streamfow and sediment simulations from the Seyhan River basin. Te study concluded that these datasets can be used successfully in the prediction of streamfow and sediment yield.As a result of the increased availability of spatial information in watershed modeling, several easy to use and widely accessible spatial datasets have been developed. Yet, it is not easy to decide which source of data is better and how data from diferent sources afect model outcomes. In this study, the results of simulating the stream fow and sediment yield from the Seyhan River basin in Turkey using 3 diferent types of land cover datasets through the soil and water assessment tool (SWAT) model are discussed and compared to the observed data. Te 3 land cover datasets used include the coordination of information on the environment dataset (CORINE; CLC2006), the global land cover characterization (GLCC) dataset, and the GlobCover dataset. Streamfow and sediment calibration was done at monthly intervals for the period of 2001–2007 at gauge number 1818 (30 km upstream of the Çatalan dam). Te model simulation of monthly streamfow resulted in good Nash–Sutclife efciency (NSE) values of 0.73, 0.71, and 0.68 for the GLCC, GlobCover, and CORINE datasets, respectively, for the calibration period. Furthermore, the model simulated the monthly sediment yield with satisfactory NSE values of 0.48, 0.51, and 0.46 for the GLCC, GlobCover, and CORINE land cover datasets, respectively. Te results suggest that the sensitivity of the SWAT model to the land cover datasets with diferent spatial resolutions and from diferent time periods was very low in the monthly streamfow and sediment simulations from the Seyhan River basin. Te study concluded that these datasets can be used successfully in the prediction of streamfow and sediment yield

Estimation of monthly precipitation based on machine learning methods by using meteorological variables

Aims: The aim of this study is to estimate monthly precipitation by support vector regression and the nearest neighbourhood methods using meteorological variables data of Chabahar station. Methods and Results: Monthly precipitation was modelled by using two support vector regression and the nearest neighbourhood methods based on the two proposed input combinations. Conclusions: The results showed that the support vector regression method using normalized polynomial kernel function has higher accuracy and it has lower estimation error than the nearest neighbour method. Significance and Impact of the Study: Precipitation is one of the most important parts of the water cycle and plays an important role in assessing the climatic characteristics of each region. Modelling of monthly precipitation values for a variety of purposes, such as flood and sediment control, runoff, sediment, irrigation planning, and river basin management, is very important. The modelling of precipitation in each region requires the existence of accurately measured historical data such as humidity, temperature, wind speed, etc. Limitations such as insufficient knowledge of precipitation on spatial and temporal scales as well as the complexity of the relationship between precipitation-related climatic parameters make it impossible to estimate precipitation using conventional inaccurate and unreliable methods

RE-EVALUATION OF TRENDS IN ANNUAL STREAMFLOWS OF TURKISH RIVERS FOR THE PERIOD 1968-2007

The Mann-Kendall rank correlation test was performed to detect trends in this study. The research investigated 3 annual stream-flow variables including annual instantaneous minimum, mean and instantaneous maximum streamflows for a network of 57 Turkish streamflow gauging stations in 25 basins of Turkey, during 1968-2007. The application of trend detection technique to 3 stream-flow variables has resulted in the identification of significant decreasing trends at the 0.05 level, appearing mostly in the basins in western and partly in southeastern Turkey, whereas almost no evidence of significant change was experienced with a general downward direction in the rest of the country. Of the 25 basins, however, only basins with the numbers of 12 and 22 exhibited significant increasing trend for one station each. The number of stations showing a decreasing trend is more than 9-fold that of stations with an upward trend whereas the significant downtrend exceeded the uptrends 49-fold. Besides, almost 2/3 of the decreasing trends (144 times over 159) were found to be statistically significant while approximately 13% of the increasing trends (15 times) exhibited significant trend