Study On Parameter Optimization And Uncertainty Analysis For Conceptual Hydrological Model Based On MCMC Method

The conceptual hydrological model not only plays an important role in research area in relation to hydrological cycle but also plays an increasing role in other areas related with hydrological cycle. Decision-making for major issues with respect to hydrology and water resources, environment, ecology, as well as flood control and drought prevention cannot be made without the support of hydrological model. Simulation accuracy of conceptual hydrological model is highly related with model structure and model parameters, therefore, appropriate selection of model structure and model parameters should be highlighted to ensure the performance of conceptual hydrological models for the specific research areas. A systematic research on assessment of model structure and parameter optimization based on Markov chain Monte Carlo method is carried out, focusing on uncertainty analysis of model structure, parameter and model output. The main contents and research results are as follows:(1) The applicability of nine rainfall-runoff models selected from RRMT has been tested on three catchments in the Yangtze River basin (Min River, Jialing River and Wu River Basin), using the SCE-UA optimization algorithm and three performance criteria which measure different aspects of the system behavior. A comprehensive assessment of model structures is given with regard to model complexity and model performance. The model structure with the highest ranking score is proposed as potentially the most suitable for rainfall-runoff simulation in the Min River, Jialing River and Wu River Basin.(2) The Metropolis-annealing scheme in the SCEM-UA algorithm is replaced with a new strategy of adaptive Metropolis sampling, herein forming a new SCEAM-UA algorithm. The newly developed SCEAM-UA algorithm adaptively updates the covariance strategy and acceptance rate during sampling. The case studies show that SCEAM-UA algorithm counters the problem of premature convergence, and the search performance is better than the traditional SCEM-UA algorithm. In addition, sensitivity analysis of model parameters are carried out based on the sampling from SCEAM-UA algorithm for the Min River, Jialing River and Wu River Basin, which can lay good foundation for uncertainty analysis of model parameter. (3) SCE-UA, SCEM-UA, SCEAM-UA, DE-MC and DREAM are compared in aspects of search efficiency, solution quality and stability, the results indicate that DREAM and SCEAM-UA algorithm have better performance than DE-MC and SCEM-UA algorithm. Assessment index system of selecting parameter optimization methods for complex hydrological models is established on the basis of intercomparison on different algorithms, from which appropriate selection of parameter optimization method can therefore benefit. In addition, the optimization module of RRMT is further developed, embedded with SCEM-UA, the newly developed SCEAM-UA, DE-AM, DREAM and MODREAM, further enhancing the function of RRMT optimization module.(4) DREAM algorithm is applied in the CMD-3PAR parameter optimization, which is shown to be capable of inferring the posterior distribution of model parameters and solving the parameter optimization problem and uncertainty analysis for complex hydrological models. Moreover, a new Multi-objective DREAM algorithm with emphasis on overall consideration of water balance, characteristic of hydrographs and peak flow, is proposed based on the concept of modified fitness assignment and external archive strategy, and is illustrated with case studies of parameter optimization of CMD-3PAR hydrological model for the Min River, Jialing River and Wu River Basin. The results show that MODREAM is capable of generating a lot of non-dominated solutions with wide and uniform distribution for decision-makers and is better than the single objective DREAM in achieving accurate simulation of hydrographs and seeking the real hydrological characteristics of the River Basin.In summary, traditional optimization method combined with MCMC algorithm can readily cope with the complex nonlinear parameter optimization problems of hydrological model. Moreover, it is capable of inferring the posterior distribution of model parameters, bringing a broad prospect for the uncertainty analysis of model parameter.