Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows

A growing number of investigations on uncertainty quantification for hydrological models have been widely reported in past years. However, limited studies are found on uncertainty assessment in simulating streamflow extremes so far. This article presents an intercomparison of uncertainty assessment of three different well-known hydrological models in simulating extreme streamflows using the approach of generalized likelihood uncertainty estimation (GLUE). Results indicate that: (1) The three modified hydrological models can reproduce daily streamflow series with acceptable accuracy. When the threshold value used to select behavioral parameter sets is 0.7, XAJ model generates the best GLUE estimates in simulating daily flows. However, the percentage of observations contained in the calculated 95 % confidence intervals (P-95CI) is low (<50 %) when simulating the high-flow index (Q10). (2) Decreasing average relative length (ARIL), P-95CI and increasing average asymmetry degree (AAD) are found, when the threshold value increases for both daily-flows and high-flows. However, there is a significant inconsistence between sensitivity of daily-flows and high-flows to various threshold values of the likelihood function. Uncertainty sources from parameter sets, model structure and inputs collectively accounts for above sensitivity. (3) The best hydrological model in simulating daily-flows is not identical under different threshold values. High P-95CIs of GLUE estimate for high-flows (Q10 and Q25) indicate that TOPMODEL generally performs best under different threshold values, while XAJ model produces the smallest ARIL under different threshold values. The results are expected to contribute toward knowledge improvement on uncertainty behaviors in simulating streamflow extremes by a variety of hydrological models.