A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> SWAT Modelling <s> Abstract Two Monte Carlo-based approaches for assessing parameter uncertainty in complex hydrologic models are considered. The first, known as importance sampling, has been implemented in the generalised likelihood uncertainty estimation (GLUE) framework of Beven and Binley. The second, known as the Metropolis algorithm, differs from importance sampling in that it uses a random walk that adapts to the true probability distribution describing parameter uncertainty. Three case studies are used to investigate and illustrate these Monte Carlo approaches. The first considers a simple water balance model for which exact results are known. It is shown that importance sampling is inferior to Metropolis sampling. Unless a large number of random samples are drawn, importance sampling can produce seriously misleading results. The second and third case studies consider more complex catchment models to illustrate the insights the Metropolis algorithm can offer. They demonstrate assessment of parameter uncertainty in the presence of bimodality, evaluation of the significance of split-sample tests, use of prior information and the assessment of confidence limits on hydrologic responses not used in calibration. When compared with the capabilities of traditional inference based on first-order approximation, the Metropolis algorithm provides a quantum advance in our capability to deal with parameter uncertainty in hydrologic models. <s> BIB001 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> SWAT Modelling <s> Abstract An uncertainty assessment method for evaluating models, the Sources of UNcertainty GLobal Assessment using Split SamplES (SUNGLASSES), is presented, which assesses predictive uncertainty that is not captured by parameter or other input uncertainties. The method uses the split sample approach to generate a quantitative estimate of the fit-for-purpose of the model, thus focusing on the purpose for which the model is used. It operates by comparing the output to be used for decision making to its observed counterpart and the associated uncertainty. The described method is applied on a Soil Water Assessment Tool (SWAT) model of Honey Creek, a tributary of the Sandusky catchment in Ohio, USA. Water flow and sediment loads are analysed. In this case study the uncertainty estimated by the proposed method is much larger than the typically estimated parameter uncertainty. <s> BIB002 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> SWAT Modelling <s> In this paper, the issue of nonlinear sensitivity analysis for dimensionality reduction in hydrologic model calibration is discussed, and a novel method to quantify the sensitivity of each parameter that considers the nonlinear relationship in the model is presented. The method is based on computing the absolute variation of the nonlinear function represented by the model in its parameter space. The paper discusses the theoretical background of the method and presents the algorithm. The algorithm employs neural network as a pseudo simulator to reduce the computational burden of the analysis. The proposed approach of sensitivity analysis is illustrated through a case study on a physically based distributed hydrologic model. The results indicate that the method is able to rank the parameters effectively, and the ranking can be interpreted in the context of the physical processes being considered by the model. <s> BIB003 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> SWAT Modelling <s> Abstract Deterministic watershed models are frequently used for agricultural non-point source (NPS) pollution simulations. However, parameter uncertainty should be analyzed before the modeling results are used to make decisions regarding watershed NPS pollution control programs. In this study, the Soil and Water Assessment Tool (SWAT) was used to simulate the total phosphorus (TP) loads caused by NPS pollution in the upper Daning River Watershed in China's Three Gorges Reservoir Area. The Generalized Likelihood Uncertainty Estimation (GLUE) methodology was used to analyze the parameter uncertainty in SWAT modeling. The impacts of three subjective options of GLUE, the parameter ranges, the level of confidence, and the threshold value of the likelihood measure, on the parameter uncertainty analysis results were analyzed. Specifically, we investigated if there was a combination of these factors that was most appropriate for expression of the uncertainty assessment results. The results indicated that the “observed data” may not always lie within the confidence intervals of GLUE, so the confidence interval was not sufficient to represent the uncertainty for the specific requirements of this study. Therefore we suggest there should be alternative measures to express the parameter uncertainty of GLUE. <s> BIB004
Watershed hydrology and river water quality models are important tools for watershed management for both operational and research programs (Quilb'e & Rousseau, 2007; BIB002 BIB003 . However, due to spatial variability in the processes, many of the physical models are highly complex and generally characterized by a multitude of parameters  ). The SWAT model parameters can be divided into the conceptual group and the physical group BIB004 . A typical hydrological model generally consists of a large number of equations describing the hydrological processes and a small number of parameters describing the watershed properties. Without a realistic assessment of parameter uncertainty, it is difficult to have confidence in tasks, such as evaluating prediction limits on future hydrological responses and assessing the value of regional relationships between model parameters and watershed characteristics BIB001 .