Abstract: An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models) coupled with three hydrological models (HM) is presented for a snowmelt-dominated basin in Canada. The present study aims to compare the performance and the reliability of different types of SM-HM combinations at simulating snowmelt flows over the 1961–2000 historical period. The multi-model approach also allows evaluating the uncertainties associated with the structure of the SM-HM ensemble to better predict river flows in Nordic environments. The 20-year calibration shows a satisfactory performance of the ensemble of 21 SM-HM combinations at simulating daily discharges and snow water equivalents (SWEs), with low streamflow volume biases. The validation of the ensemble of 21 SM-HM combinations is conducted over a 20-year period. Performances are similar to the calibration in simulating the daily discharges and SWEs, again with low model biases for streamflow. The spring-snowmelt-generated peak flow is captured only in timing by the ensemble of 21 SM-HM combinations. The results of specific hydrologic indicators show that the uncertainty related to the choice of the given HM in the SM-HM combinations cannot be neglected in a more quantitative manner in simulating snowmelt flows. The selection of the SM plays a larger role than the choice of the SM approach (degree-day versus mixed degree-day/energy balance) in simulating spring flows. Overall, the snow models provide a low degree of uncertainty to the total uncertainty in hydrological modeling for snow hydrology studies.
Abstract: An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.
Abstract: Northern Vietnam is a region prone to heavy flash flooding events. These often have devastating effects on the environment, cause economic damage and, in the worst case scenario, cost human lives. As their frequency and severity are likely to increase in the future, procedures have to be established to cope with this threat. As the prediction of potential flash floods represents one crucial element in this circumstance, we will present an approach that combines the two models KINEROS2 and HEC-RAS in order to accurately predict their occurrence. We used a documented event on 23 June 2011 in the Nam Khat and the larger adjacent Nam Kim watershed to calibrate the coupled model approach. Afterward, we evaluated the performance of the coupled models in predicting flow velocity (FV), water levels (WL), discharge (Q) and streamflow power (P) during the 3–5 days following the event, using two different precipitation datasets from the global spectral model (GSM) and the high resolution model (HRM). Our results show that the estimated Q and WL closely matched observed data with a Nash–Sutcliffe simulation efficiency coefficient (NSE) of around 0.93 and a coefficient of determination (R2) at above 0.96. The resulting analyses reveal strong relationships between river geometry and FV, WL and P. Although there were some minor errors in forecast results, the model-predicted Q and WL corresponded well to the gauged data.
Abstract: Uniform storms are generally applied in most of the research on sewer systems. This is for modeling simplicity. However, in the real world, these conditions may not be applicable. It is very important to consider the migration behavior of storms not only in the design of combined sewers, but also in controlling them. Therefore, this research was carried out to improve Rathnayake and Tanyimboh’s optimal control algorithm for migrating storms. Promising results were found from the model improvement. Feasible solutions were obtained from the multi-objective optimization and, in addition, the role of on-line storage tanks was well placed.
Abstract: A statistical model to predict the probability and magnitude of floods in non-stationary conditions is presented. The model uses a time-dependent and/or covariate-dependent generalized extreme value (GEV) distribution to fit the annual maximal (AM) discharge, and it is applied to five gauging stations in the Ouémé River Basin in Benin Republic, West Africa. Different combinations of the model parameters, which vary with respect to time and/or climate covariates, were explored with the stationary model based on three criteria of goodness of fit. The non-stationary model more adequately explains a substantial amount of variation in the data. The GEV-1 model, which incorporates a linear trend in its location parameter, surpasses the other models. Non-stationary return levels for different return periods have been proposed for the study area. This case study tested the hypothesis of stationarity in estimating flood events in the basin and it demonstrated the strong need to account for changes over time when performing flood frequency analyses.
Abstract: Impacts of urban land use can include increased stormwater runoff temperature (Tw) leading to receiving water quality impairment. There is therefore a need to target and mitigate sources of thermal pollution in urban areas. However, complex relationships between urban development, stormwater runoff and stream water heating processes are poorly understood. A nested-scale experimental watershed study design was used to investigate stormwater runoff temperature impacts to receiving waters in a representative mixed-use urbanizing watershed of the central US. Daily maximum Tw exceeded 35.0 °C (threshold for potential mortality of warm-water biota) at an urban monitoring site for a total of five days during the study period (2011–2013). Sudden increases of more than 1.0 °C within a 15 min time interval of Tw following summer thunderstorms were significantly correlated (CI = 95%; p < 0.01) to cumulative percent urban land use (r2 = 0.98; n = 29). Differences in mean Tw between monitoring sites were significantly correlated (CI = 95%; p = 0.02) to urban land use practices, stream distance and increasing discharge. The effects of the 2012 Midwest USA drought and land use on Tw were also observed with maximum Tw 4.0 °C higher at an urban monitoring site relative to a rural site for 10.5 h. The current work provides quantitative evidence of acute increases in Tw related to urban land use. Results better inform land managers wishing to create management strategies designed to preserve suitable thermal stream habitats in urbanizing watersheds.