Studies On Watershed Hydrological Modeling And Forecasting

Watershed hydrological forecasting is one of the most important issues in the field ofhydrology. It plays a vital role in water resources management and sustainable utilization.As a result of unique geographical position and the climatic conditions of China, spatialand temporal distribution of water resources is extremely uneven in our country. Also dueto the impact of human activity and global warming, temporal and spatial distributioncharacteristics and evolution of current water resource is undergoing profound change.Especially the construction of large water control projects is increasing the complex ofwatershed hydrological characteristics in recent years. This puts forward highrequirements on hydrological modeling and forecasting.In this thesis, the main attention is focused on watershed hydrological modeling andforecasting. This research can provide essential scientific support for optimal use of waterresources, and is of great theoretical significance and practical value for reducing floodlosses and achieving safe and efficient sustainable use of water resources. The main workand innovation of this thesis can be summarized as follows:(1) The lumped conceptual hydrological models cannot reflect the uneven distributionof watershed underlying surface and elements of the watershed water cycle. To overcomethis drawback, a novel distributed hydrological model named XAJGrid model is proposedin this research. XAJGrid model is an extension of classic lumped Xinanjiang model. Thisnovel model divides the watershed into several grids. The horizontal and verticalrelationship between each grid and its adjacent grids are described by physical equations.The XAJGrid model can fully take account of temporal and spatial variation of climateconditions and underlying surface.(2) Two typical system theoretic models are constructed based on Artificial NeuralNetwork and Support Vector Machine. The models’ feasibility is demonstrated throughseveral case studies. And the results show that both models can be capable of modelingthe dynamic characteristics of streamflow processes. Moreover, the Support VectorMachine model seems to be with better performance. Meanwhile, this model has strongnon-linear fitting capability and easy to implement. It can be seen as a valuable alternativeto Artificial Neural Network model.(3) A novel multi-objective algorithm named culture shuffled complex differentialevolution (MOCSCDE) algorithm is proposed to make model calibration based on multi-objective functions. In MOCSCDE algorithm, the culture evolution is taken as theevolving framework and the SCE-UA algorithm is employed as the core evolutionalgorithm for the population space. This evolution strategy can make use of the knowledgeobtained along with the evolution process to guide the algorithm toward the optimizationdirection. Meanwhile, the differential evolution algorithm is employed as a substitute ofthe simplex search operator in SCE-UA to enhance the efficiency of algorithm.(4) Traditional hydrological model calibration is always done with single objectivefunction. It cannot properly measure all characteristics of the hydrological system. Tocircumvent this problem, the proposed MOCSCDE algorithm is applied to optimize theparameters of hydrological models under the multi-objective optimization framework.This mechanism can significantly avoid the homogenization phenomenon existing intraditional hydrological model calibration. Besides, the MOCSCDE algorithm is adoptedto optimize the model structure and model parameters of a theoretic model based onSupport Vector Machine. This form of optimization can effectively improve the predictaccuracy by automatically choose the best model structure according differenthydrological characteristics.(5) The SCEM-UA algorithm is employed to analyze the parameter and predictiveuncertainty of lumped and distributed models. Simultaneously, the uncertainty of atheoretic model based on Artificial Neural Network is also discussed. The results showthat overparameterisation is very likely to be existed in this type of models. Furthermore,based on the results of uncertainty assessment, a method is proposed to reduce the modeluncertainty. It provides a new way of analyzing and reducing the uncertainty of theoreticmodels.Several relevant research results have been applied to National Science and TechnologySupport Program, National Basic Research Program of China (973Program) and SpecialResearch Foundation for the Public Welfare Industry of the Ministry of Science andTechnology. This can provide important theoretical basis and data support for theseprojects.