Study On City-region Stormwater Simulation And Data Assimilation

The current urban storm water models are mostly applied in the early planning stages of projects. In order to apply urban storm water models into urban flood prevention and drainage system management, it is necessary to establish an urban storm water simulation and prediction system which is capable of reflecting actually the operation of the drainage system and predicting the hydrographic condition during a period of time in the future. Therefore, the thesis will start with an analysis of the characteristics of runoff in urban areas to build an appropriate hydrology-hydrodynamics coupling model, and develop a real-time system that is applicable to storm water simulation, correction and prediction by means of parameter calibration and data assimilation. The research works are as follows:(1) Drawing up a hydrology-hydrodynamics coupling model that couples the hydrology calculation module and the hydrodynamic calculation module together in a matrix, and using multiple measures to improve the calculation stability, accuracy and efficiency, which include:1) processing the alternation of drying and wetting with base flow method and narrow slit method;2) dealing with nonlinear internal boundaries such as weirs, sluices, etc. using iterative computation method;3) furnishing the nodes with storage area to obtain a diagonally dominant matrix;4) combining iterative computation method with matrix indicator method to solve equations;5) proposing a method where the coefficient matrix is fixed and only the right side of equations is variable in order to improve the computing efficiency;6) putting forward a method of calculating small quantities of second order and above of the linearized continuity equations and momentum equations.(2) For the research on bed-fixed river roughness inversion combined with prior knowledge, two inversion models are developed based on prior knowledge of roughness:the first is a model of roughness parameters within the smoothest space distribution, and the second is a model of the estimated values of roughness parameters with the least modification. It is demonstrated by numerical simulations that:1) the inversion models are less affected by the selection of initial values;2) reasonable results can be obtained from such models even if there is not much available observation information, and the results tend to be close to the true values along with the increase of observation information;3) the models are of high noise immunity, i.e. numerical disturbance caused by the errors of observation information can be effectively avoided by controlling the weight of the roughness space distribution item or the roughness modification item.(3) For the research on dynamic identification of roughness parameters, some data assimilation methods are developed based on extended kalman filtering algorithm. It is shown by numerical simulations that:combining with smoothly modification of roughness, the extended kalman filtering algorithm where the roughness parameters and flow variables are seen as system state variables can avoid distortion of roughness parameters, and it can improve the efficiency of data assimilation and the stability of calculation as well.(4) For the research on data assimilation of flow variables such as water level, discharge, etc., three data assimilation methods are developed including extended kalman filtering algorithm, ensemble kalman filtering algorithm and generalized inversion method. It is demonstrated by numerical simulations that:1) the extended kalman filtering algorithm has a good performance, and can be used to data assimilation of roughness parameters and flow variables at the same time;2) the ensemble kalman filtering algorithm features wide applicability and simple computing process;3) the theory and programming of the generalized inversion method are simple, and the serious damage on the previous water balance caused by too much modification of flow variables can be avoided.(5) On the basis of the current research as stated above, suggestions are proposed on how to remedy the deficiencies of the current widely-used urban storm water management model SWMM, and a framework of an urban storm water simulation and prediction system is established.