Van-Thanh-Van Nguyen

A comparative study of regression based methods in regional flood frequency analysis

Abstract Reliable estimates of flow statistics are needed for water resources management and flood forecasting purposes. However, the location of gaging station seldom coincides with the site of interest, or the available record becomes too short to make meaningful statistical inferences. Thus regional regression models, such as power-form model of Thomas and Benson (Thomas, D.M., Benson, M.A., 1970. Generalization of streamflow characteristics from drainage-basin characteristics, US Geological Survey, Water Supply Paper, 1975), which relate regional physiographic characteristics to streamflow statistics are developed to estimate the streamflow statistics where data are needed but not available. Depending upon the postulated model type and nature of the data, there are several ways of estimating model parameters. This paper compares the performances of nine methods of estimating parameters of the power-form model that expresses flood quantile as a function of basin area. The performance of each method is assessed based upon its quantile prediction ability from an ungaged site in the region. A jacknife procedure is used to simulate the ungaged site condition in the region. Based upon a case study using the hydrologic and physiographic data from Quebec, Canada, nonlinear models outperformed the log-linearized (or linear) models. Despite the differences in the parameter estimation techniques and suitability for different data type, the quantile prediction abilities of all of the linear models were not different from each other. Most of the linear models had higher bias and higher root mean squared error and they under-predicted floods from large basins.

Impacts of climate change on rainfall extremes and urban drainage systems: a review

A review is made of current methods for assessing future changes in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic-induced climate change. The review concludes that in spite of significant advances there are still many limitations in our understanding of how to describe precipitation patterns in a changing climate in order to design and operate urban drainage infrastructure. Climate change may well be the driver that ensures that changes in urban drainage paradigms are identified and suitable solutions implemented. Design and optimization of urban drainage infrastructure considering climate change impacts and co-optimizing these with other objectives will become ever more important to keep our cities habitable into the future.

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems provides a state-of-the-art overview of existing methodologies and relevant results related to the assessment of the climate change impacts on urban rainfall extremes as well as on urban hydrology and hydraulics. This overview focuses mainly on several difficulties and limitations regarding the current methods and discusses various issues and challenges facing the research community in dealing with the climate change impact assessment and adaptation for urban drainage infrastructure design and management. ISBN: 9781780401256 (Print) ISBN: 9781780401263 (eBook)

Optimal rainfall temporal patterns for urban drainage design in the context of climate change

The main objective of the present study is to propose a method for estimating an optimal temporal storm pattern for urban drainage design in southern Quebec (Canada) in the context of climate change. Following a systematic evaluation of the performance of eight popular design storm models for different typical urban basins, it was found that the Canadian Atmospheric Environment Service (AES) storm pattern and the Desbordes model (with a peak intensity duration of 30 min) were the most accurate for estimating runoff peak flows while the Watt model gave the best estimation of runoff volumes. Based on these analyses, an optimal storm pattern was derived for southern Quebec region. The proposed storm pattern was found to be the most suitable for urban drainage design in southern Quebec since it could provide accurate estimation of both runoff peak flow and volume. Finally, a spatial-temporal downscaling method, based on a combination of the spatial statistical downscaling SDSM technique and the temporal scaling General Extreme Value distribution, was used to assess the climate change impacts on the proposed optimal design storm pattern and the resulting runoff properties.

Uncertainty modeling of facultative aerated lagoon systems

Abstract A stochastic differential equation (SDE) model is proposed to predict output BOD variations of lagoon systems. Assuming that complete mixing and first-order kinetics of biodegradation occur, a mass balance for BOD is written for each lagoon. A random disturbance term that accounts for input noise, is added to the deterministic differential equation that describes the process. The lower (first and second) moments are derived to obtain expectation and variance of the effluent BOD concentration. Assuming steady-state, model calibration and verification are performed on the lagoon systems of the municipalities of Sainte-Julie and Lac-Etchemin (Quebec). The model gives a reliable prediction of lagoons output fluctuations, and represents a new tool for uncertainty modeling of facultative aerated lagoons.

On Automatic Calibration of the SWMM Model

Conceptual urban runoff (CUR) models, such as the U.S. Environmental Protection Agency Storm Water Management Model (Huber and Dickinson, 1988), or SWMM, are c…

Comparison of Homogenous Region Delineation Approaches for Regional Flood Frequency Analysis at Ungauged Sites

AbstractThe occurrence of floods and their impacts on hydrologic systems and society are critical considerations in the design and management of a large number of water resources projects. As streamflow records are often limited or unavailable at many watersheds, it is necessary to develop better methods for regional estimation of floods at these partially-gauged or ungauged sites. In this paper, a comparative study of homogeneous region delineation approaches in the context of regional flood quantile estimation is presented. Three approaches were considered in this study using the available annual maximum series (AMS) of flood peaks for 57 watersheds in Quebec, Canada. The first approach was the scaling method, which is a relatively new approach for homogeneous region delineation. This approach is based on the scaling behavior of the flood series with the basin area. The other two approaches were the region of influence (ROI) and canonical correlation analysis (CCA) methods. Regional flood estimation was...

Regional Estimation of Floods for Ungauged Sites Using Partial Duration Series and Scaling Approach

AbstractOne of the main obstacles in making reliable predictions of extreme events is the apparent shortness of the time series available in hydrology. There is a common advantage of both regional flood-frequency analysis and partial duration series (PDS) in which both of them allow a reduction of uncertainty by introducing more data to the site of interest. Therefore, a new regional index flood method for ungauged sites based on the PDS model is presented. The PDS model considered in this case assumes a Poisson-distributed number of threshold exceedances and generalized Pareto-distributed peak magnitudes. A new objective approach for the selection of the threshold in the context of regionalization is introduced. This approach estimates a range of reasonable thresholds (or an average annual number of events) for every site. Consequently, the regional average annual number of events can be determined as a common value for all sites in the homogeneous region. The delineation of hydrologically homogeneous re...

Modeling of icing events based on passive ice meter observations in Quebec

The objective of this study is to estimate an event-based model for the distribution of glaze ice and of the associated wind speed in different regions of Quebec. The available meteorological data covers periods of observations up to 21 yr at 180 stations deployed throughout the province. Four statistical distributions for ice thickness (extreme type I, extreme type II, 3-p lognormal, and log Pearson type III) are investigated. The log Pearson distribution is found to provide the best fit for the majority of the stations. Statistical analyses are also performed on the associated wind speed, the recurrence rate of icing events, and the residency period of ice. For the associated wind, 5 statistical distributions were investigated (extreme type I, extreme type II, Rayleigh, 3-p lognormal, and log Pearson type III). All 5 distributions were found to provide a good fit for the data.

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32. https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.