This study aims to develop an improved time series model to overcome difficulties in modeling monthly short term stream flows. The periodic, serial dependent and independent components of the classical time series models are improved separately by information transfer from a surrounding long term gauging station to the considered flow section having short term records. Eventually, an improved model preserving the mathematical model structure of the classical time series model, while improving general and monthly statistics of the monthly stream flows, is derived by using the improved components instead of the short term model components in the time series modeling. The correlative relationships between the current short term and surrounding long term stations are used to improve periodic and serial dependent behaviors of monthly flows. Independent components (residuals) are improved via the parameters defining their theoretical probability distribution. The improved model approach is tested by using 50 year records of Göksu-Himmetli (1801) and Göksu-Gökdere (1805) flow monitoring stations located on the Ceyhan river basin, in south of Turkey. After 50 year records of the station 1801 are separated into five 10 year sub series, their improved and classical time series models are computed and compared with the real long-term (50 year) time series model of this station to reveal efficiencies of the improved models for each subseries (sub terms with 10 year observation). The comparisons are realized based on the model components, model estimates and general/monthly statistics of model estimates. Finally, some evaluations are made on the results compared to the regression method classically applied in the literature.
A variety of flood models and commercial flood simulation software are provided in the literature, with different accuracies and precisions changing from coarse to fine, depending on model structure and detailed descriptions of basin and hydrologic properties. These models generally focus on river processes, taking overland processes as inputs of 1D or 2D hydrodynamic or hydrologic river flow models. Due to the discrete structure of overland flow and unknown-dynamic boundary conditions, such classical approaches are not cable of fast and reliable spatio–temporal estimations for overland flows, and require detailed and well-organized spatial data that cannot be immediately obtained during an emergency. A spatially-distributed Geographical Information Systems (GIS) based flood model is developed in this study to simulate overland floods, using cellular automata principles. GIS raster cells are considered hydrologic homogeneous areas throughout which hydrologic properties remain constant. Hydrodynamic flow principles, conservations of mass, momentum and energy are applied at pixel level to simulate floodwaters. The proposed GIS model is capable of directly manipulating spatio–temporal pixel level data (e.g., topography, precipitation, infiltration, surface roughness etc.) for modeling of rainfall-induced overland floods; therefore, it can provide fast, temporal and spatial flood depth estimations as well as maximum flood depths and times of concentration for all pixels throughout a study area. The model is quite simple and easy to apply via easily creatable GIS input layers, and is thus very convenient for preliminary engineering applications that need quick and fast response. Its main advantage is that it does not need a predefined flood boundary and boundary conditions. This advantage is especially valuable for coastal plains where delineation of a basin is generally too difficult. Floodwaters of Cyclone Nargis/Myanmar were simulated to test the model. Sensitivity analyses were applied to evaluate the effects of the model parameters (i.e., surface roughness and infiltration rates) on simulation results. The study shows that the proposed GIS model can be readily applied for the fast and inexpensive modeling of rainfall caused floods in areas where flood boundaries and boundary conditions cannot be clearly identified.
Abstract:Five alternative regionalization approaches in two broad categories, named function-free and functional approaches, have been proposed to predict periodic behaviours in the basic parameters of monthly stream flows throughout homogeneous regions defined. Function-free and functional approaches rely on the standardized (or normalized) forms of raw and fitted values of the monthly periodic parameters considered. Homogeneous regions are identified based on these standardized/normalized parameters by means of the hierarchical clustering analysis. The proposed models are tested for two major river basins in south Turkey. It is concluded that the proposed regional models are very effective to estimate periodic behaviour of monthly flows. The functional approaches are quite plausible, and the function-free approach needs much more parameters. Both types of regionalization approaches can be reliably used to get regional monthly flow estimates for the flow sections where monthly records are not available or too short.
Bodrum September 22-23, 2015 Flood Disaster Floods cause significant damages in urban and rural areas. Bodrum city is of high importance for the tourism of Turkey. It suffers unavoidably from floods. Short duration severe storms incited by impervious surfaces cause immense floods, paralyzing city life as well as the tourism of the country. Engineering studies are hampered by intensive and improper urbanization, expensive expropriation costs and the difficulties in sharing responsibilities among governmental organizations. The flood of 22-23 September 2015 hit the city center of Bodrum, affected many structures and endangered human life. Some city roads were closed, some drainage lines were damaged and tourism was interrupted. The flood of Bodrum on September 22-23, 2015 was investigated on the scale of the affected basins. The causes and consequences of the flood were elucidated by field examinations. The drainage systems of upper, middle and lower basins were evaluated regarding their effects on the impact of the flood. Based on the rainfall records, runoff estimations were obtained using the SCS abstraction methodology. Flood hydrographs were derived by means of dimensionless hydrographs. Obtained results were used for the evaluation of the flood, and for suggesting solutions.
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