The generalized likelihood uncertainty estimation (GLUE) technique is an innovative uncertainty method that is often employed with environmental simulation models. Over the past years, hydrological literature has seen a large increase in the number of papers dealing with uncertainty. There are now a lot of citations to their original paper which illustrates GLUE tremendous impact. GLUE's popularity can be attributed to its simplicity and its applicability to nonlinear systems, including those for which a unique calibration is not apparent. The GLUE was introduced for use in uncertainty analysis of watershed models has now been extended well beyond rainfall-runoff watershed models. Given the widespread adoption of GLUE analyses for a broad range or problems, it is appropriate that the validity of the approach be examined with care. In this article, we present an overview of the application of GLUE for assessing uncertainty distribution in hydrological models particularly surface and subsurface hydrology and briefly describe algorithms for sampling of the prior parameter in hydrologic simulation models.

Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the Standardized Precipitation Evapotranspiration Indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin.

Selection of the right modeling technique is always a challenging issue because every model can produce only an approximation of the reality it is attempting to illustrate. As a result, model performance in a specific situation is the only criterion that confirms the model's applicability in that particular situation. This study investigated the applicability of the adaptive neuro‐fuzzy inference system (ANFIS) and the autoregressive integrated moving average (ARIMA) models in water‐level modeling. Results showed a definite preference for the ANFIS model against the simple‐ARIMA model, but an updated‐ARIMA model outperformed ANFIS. A mean absolute error of < 1% in each model confirmed the applicability of these models in predicting the water level in the Klang River in Malaysia. On the basis of the obtained prediction accuracy level, the updated‐ARIMA and ANFIS models are introduced as reliable and accurate models for prompt decision‐making, planning, and urgent managing of water resources in crisis.

People in the United States drink almost four times the amount of bottled water than they did 20 years ago, even though tap water supplies in the United States are considered to be among the safest in the world. To understand ‘how do people make a decision on tap or bottled water’, a consumer preferences survey was administered to the Civil and Environmental Engineering students attending a US university. The survey elicited information on participants’ preferences and real life attitude/preferences and included a multi‐criteria pairwise comparison. The pairwise comparison preferences were further analysed by integrating nonparametric bootstrap simulations to determine the underlying uncertainty. The results revealed that although safety issues were deemed most important, participants were also subconsciously aware of other crucial issues related to drinking water. These findings provide useful information for drinking water policy experts and water utilities about consumer perceptions of the relative virtues of tap and bottled water.

Disaster prevention planning is affected in a significant way by a lack of in‐depth understanding of the numerous uncertainties involved with flood delineation and related estimations. Currently, flood inundation extent is represented as a deterministic map without in‐depth consideration of the inherent uncertainties associated with variables such as precipitation, streamflow, topographic representation, modelling parameters and techniques, and geospatial operations. The motivation of this study is to estimate uncertainties in flood inundation mapping based on a non‐parametric bootstrapping method. The uncertainty is addressed through the application of non‐parametric bootstrap sampling to the hydrodynamic modelling software, HEC‐RAS, integrated with Geographic Information System (GIS). This approach was used to simulate different water levels and flow rates corresponding to different return periods from the available database. The study area was the Langat River Basin in Malaysia. The results revealed that the inundated land and infrastructure are subject to a flooding hazard of high‐frequency events and that the flood damage potential is increasing significantly for residential areas and valuable land‐use classes with higher return periods. The proposed methodology, as well as the study outcomes, of this paper could be beneficial to policymakers, water resources managers, insurance companies and other flood‐related stakeholders. Copyright © 2017 John Wiley & Sons, Ltd.