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Summary: With respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes’ theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash–Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method. [Copyright &y& Elsevier]

The hydrologic design of nonstationary flood extremes is an emerging field that is essential for water resources management and hydrologic engineering design to cope with changing environment. This paper aims to investigate and compare the capability of four nonstationary hydrologic design strategies, including the expected number of exceedances (ENE), design life level (DLL), equivalent reliability (ER), and average design life level (ADLL), with the last three methods taking into consideration the design life of the project. The confidence intervals of the calculated design floods were also estimated using the nonstationary bootstrap approach. A comparison of these four methods was performed using the annual maximum flood series (AMFS) of the Weihe River basin, Jinghe River basin, and Assunpink Creek basin. The results indicated that ENE, ER and ADLL yielded the same or very similar design values and confidence intervals for both increasing and decreasing trends of AMFS considered. DLL also yields similar design values if the relationship between DLL and ER/ADLL return periods is considered. Both ER and ADLL are recommended for practical use as they have associated design floods with the design life period of projects and yield reasonable design quantiles and confidence intervals. Furthermore, by assuming that the design results using either a stationary or nonstationary hydrologic design strategy should have the same reliability, the ER method enables us to solve the nonstationary hydrologic design problems by adopting the stationary design reliability, thus bridging the gap between stationary and nonstationary design criteria. [ABSTRACT FROM AUTHOR]