Growing concerns have been given to parameter uncertainty in hydrological modeling because of the associated effects on water resource management. In this study two uncertainty analysis methods, the sequential uncertainty fitting algorithm (SUFI-2) and the generalized likelihood uncertainty estimation (GLUE), were employed to analyze the uncertainty of surface flow and sediment yield modeling results by the soil and water assessment tool (SWAT) model. The two methods were also compared in terms of the capability in quantifying parameter and prediction uncertainties and the computational efficiency. The results showed that the SUFI-2 method was capable of examining the uncertainty by using the Latin hypercube sampling scheme. It also demonstrated the advantages in analyzing the effects of uncertainties for distributed hydrological models with complex structure and high computational demands. The GLUE method is specialized in reflecting parameter correlations and uncertainties associated with parameters and predictants.

Hidrološka simulacija u meteorološki neispitanim područjima oduvijek je težak problem u proučavanju atmosferskih i hidroloških odnosa; to je također jedan od važnih faktora koji ograničavaju razvoj modela i spoznaju o izvoru...

The toxic chemical and heavy metals within wastewater can cause serious adverse impacts on human health. Health risk assessment (HRA) is an effective tool for supporting decision-making and corrective actions in water quality management. HRA can also help people understand the water quality and quantify the adverse effects of pollutants on human health. Due to the imprecision of data, measurement error and limited available information, uncertainty is inevitable in the HRA process. The purpose of this study is to integrate statistical and probabilistic methods to deal with censored and limited numbers of input data to improve the reliability of the non-cancer HRA of dermal contact exposure to contaminated river water by considering uncertainty. A case study in the Kelligrews River in St. John’s, Canada, was conducted to demonstrate the feasibility and capacity of the proposed approach. Five heavy metals were selected to evaluate the risk level, including arsenic, molybdenum, zinc, uranium and manganese. The results showed that the probability of the total hazard index of dermal exposure exceeding 1 is very low, and there is no obvious evidence of risk in the study area.

The snowmelt process is becoming more complex in the context of global warming, and the current existing studies are not effective in using the short-term prediction model to drive the distributed hydrological model to predict snowmelt...

Applications of biosurfactant in enhanced aquifer remediation processes have been widely studied. In order to clarify the significant parameters and analyze their interactions, and thus be able to describe the processes in an efficient and robust approach, as well as evaluate the performance of biosurfactant, this research performed a parameterization study for modeling biosurfactant-enhanced aquifer remediation (BSEAR) processes based on flow cell experiments. Lab synthetized surfactant solution was deployed together with soil flushing using water to remove the diesel contaminants from the soil sample. By using the developed hybrid stochastic and design of experiment (DOE)–aided parameterization method, it was revealed that the interactions between the distribution coefficient and Henry’s constant were significant in general for modeling the removal of benzene, toluene, ethylbenzene, and xylene (BTEX). In particular, the interaction between the distribution coefficient and the first 12-h loading ratio is significant in modeling the removal of ethylbenzene. It was also found that enhanced mobility and solubility increase of contaminants were achieved after applying the surfactant solution. After parameterization, $R^2$ values showed good consistency, which indicated the effectiveness in modeling BSEAR processes.