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Aytac Guven - One of the best experts on this subject based on the ideXlab platform.
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potential changes in inflow Design Flood under future climate projections for darbandikhan dam
Journal of Hydrology, 2015Co-Authors: F A Tofiq, Aytac GuvenAbstract:Summary Insignificant changes in the future system might be assumed due to the impacts of climate changes. Temperature changes and anticipated precipitation suggest possible changes in Floods. Extreme events will have bigger impacts on sectors with direct connections to climate, such as water sector; there is great belief that climate changes have the potential to critically affect water management systems. This study investigated how future climate change might affect the Inflow Design Flood (IDF) of Darbandikhan Dam. The study uses the projected downscaled daily inflow from Global Climate Models (GCMs) in comparative to the historical peak inflows. Flood frequency analysis based on historical data, both observed and statistically downscaled, was used to obtain the new Design Flood values. Analysis of the future projections of Flood frequency analysis (FFA) reveals a general tendency toward changes in the magnitude of IDF.
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prediction of Design Flood discharge by statistical downscaling and general circulation models
Journal of Hydrology, 2014Co-Authors: F A Tofiq, Aytac GuvenAbstract:Summary The global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. Downscaling, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to reDesign an existing dam but at least precaution parameters can be taken for the worse scenarios of Flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical downscaling using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the Flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in downscaling the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall–runoff models.
Ataur Rahman - One of the best experts on this subject based on the ideXlab platform.
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Monte Carlo simulation for Design Flood estimation: a review of Australian practice
Australasian Journal of Water Resources, 2018Co-Authors: Melanie Loveridge, Ataur RahmanAbstract:ABSTRACTRainfall-based Design Flood estimation methods in Australia traditionally follow the Design event approach. However, the basic assumption of a probability neutral transformation in the Design event approach has been widely criticised. For this reason, joint probability approaches (like Monte Carlo simulation) were proposed in the 1970s to account for the probabilistic nature of key inputs in rainfall–runoff modelling. However, these techniques were not seriously tested until the 1990s, when a simple Monte Carlo simulation technique was developed that used existing Design data and models, for Australian hydrologic practice. This paper summarises the evolution of Monte Carlo simulation techniques for Design Flood estimation with a particular emphasis on Australian practice. It has been found that significant advancements have been made in the development and testing of Monte Carlo simulation in Australia; but, there is still a lack of commercial software hindering the routine application of holistic...
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application of monte carlo simulation technique to Design Flood estimation a case study for north johnstone river in queensland australia
Water Resources Management, 2013Co-Authors: James Charalambous, Ataur Rahman, Don CarrollAbstract:The traditional rainfall-runoff modelling based on the Design Event Approach has some serious limitations as this ignores the probabilistic nature of the key Flood producing variables in the modelling except for rainfall depth. A more holistic approach of Design Flood estimation such as the Joint Probability Approach/Monte Carlo simulation can overcome some of the limitations associated with the Design Event Approach. The Monte Carlo simulation technique is based on the principle that Flood producing variables are random variables instead of fixed values. This allows accounting for the inherent variability in the Flood producing variables in the rainfall-runoff modelling. This paper applies the Monte Carlo simulation technique and hydrologic model URBS to a large catchment with multiple pluviograph and stream gauging stations. It has been found that it is quite feasible to apply the Monte Carlo simulation technique to large catchments. The Monte Carlo simulation technique has much greater flexibility than the Design Event approach and can provide more realistic Design Flood estimates with multiple scenarios, which is likely to replace the Design Event Approach. The method developed here can be applied to other catchments in Australia and other countries.
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probabilistic Flood hydrographs using monte carlo simulation potential impact to Flood inundation mapping
Congress on Modelling and Simulation, 2013Co-Authors: Melanie Loveridge, Ataur Rahman, Mark BabisterAbstract:Flood inundation modelling generally involves two steps. The first involves the use of a hydrologic model, such as RORB, to estimate the Design Flood hydrograph for a given Design storm event. These models require several inputs, such as Design rainfalls (i.e. duration, intensity & temporal pattern), losses, baseflow and routing parameters; each of which has an associated degree of uncertainty that can affect the shape and magnitude of the estimated Design Flood hydrograph. The second involves the use of these Design Flood hydrographs as inputs into a hydraulic model, to estimate the Flood inundation extent. Given the uncertainties in hydrologic modelling and their importance in mapping inundation extents, it is of interest to determine the potential impacts of hydrological uncertainties on Flood inundation mapping. This paper, therefore, considers how the uncertainties in Design losses can affect the hydraulic analysis. The Orara River catchment in north-east NSW was selected for this study, which covers an area of 135 km 2 . The data during the period of 1970 to 2009 was used, with both streamflow (204025) and a pluviograph station (59026) available throughout this period. For 43 storm events, rainfall spatial patterns are produced using ordinary kriging, with 23 daily rainfall stations, and baseflow was separated using a recursive digital filter. The RORB rainfall-runoff model was adopted, with the non-linearity exponent fixed at 0.8 and the routing parameter fixed at 15. Both the initial and continuing losses were calibrated for each event and then examined to find the best fit probability distribution. From the 27 parametric distributions, it was found that the initial loss (IL) can be approximated by the 2-parameter Gamma distribution and the continuing loss (CL) can be approximated by the 3-parameter Weibull distribution. A Monte Carlo framework was adopted to quantify uncertainties in the losses. Ten thousand randomly generated initial and continuing loss values were run through RORB in order to derive confidence limits for the peak flow, Flood volume and time to peak flow characteristics. These derived Flood frequency curves (DFFC) are then compared to observed Floods and an at-site Flood frequency analysis (FFA). The median relative errors of the DFFC when compared to the at-site FFA were found to be 13.5% and - 23.1%, for the peak flow and Flood volumes, respectively. The Flood volumes were found to be more consistent across all probabilities with a range of -3.6% to -26.6%, as compared to the peak flows that ranged from 9% to 39.5%. The confidence band (referring to the 5 th and 95 th percentiles) were found to be smallest about the time to peak flow, which only varied up to 10%, followed by the peak flows which showed around ±55% variability. The Flood volumes saw the widest confidence bands, with a median variation of about ±63%, which increased to a maximum of about ±105%. It has been found that the Monte Carlo framework adopted in this study has the ability to produce more accurate and realistic Design Flood estimates, however, these improvements have not yet been carried through to the hydraulic model. Flood inundation maps are generally still depicted as a single deterministic Flood inundation prediction for a given deterministic Design hydrograph. As found in a study by Merwade et al. (2008) when the standard errors in peak flows ranged from -36.1% to 56.5%, this caused a shift in the water surface elevation from -0.4 m to 1 m and the extent of Floodplain inundation varied in width from 54.3 m to 90.2 m. With peak flows ranging up to ±55% in this study, potentially causing these types of errors in the inundation extents, it is clear that probability-weighted Flood inundation extents need to be modelled rather than a single deterministic prediction.
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Design Flood estimation in ungauged catchments a comparison between the probabilistic rational method and quantile regression technique for nsw
Australian journal of water resources, 2011Co-Authors: Ataur Rahman, Khaled Haddad, Mohona Zaman, George Kuczera, P E WeinmannAbstract:Design Flood estimation for ungauged catchments is often required in hydrologic Design. The most commonly adopted regional Flood frequency analysis methods used for this purpose include the index Flood method, regression based techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile regression technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods.
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Design Flood estimation in ungauged catchments by quantile regression technique ordinary least squares and generalised least squares compared
30th Hydrology & Water Resources Symposium: Past Present & Future Hotel Grand Chancellor Launceston 4-7 December 2006. Conference Proceedings, 2006Co-Authors: Khaled Haddad, Ataur Rahman, Erwin WeinmannAbstract:Design Flood estimation in small-to-medium sized catchments is frequently required in hydrologic analysis and Design and is of notable economic significance. Australian Rainfall and Run-off 1987 recommends the Probabilistic Rational Method for general use in south-eastern Australia. The central component of this method is a run-off coefficient which is assumed to vary smoothly over a geographical area and over a range of average recurrence intervals but there has been criticism of the run-off coefficients because it does not show meaningful links with catchment characteristics. More recent Design Flood estimation techniques have the potential to provide more meaningful and accurate Design Flood estimation in small-to-medium sized ungauged catchments; for instance, the L moments based index Flood method and the quantile regression technique. This paper is concerned with the quantile regression technique and compares two methods: ordinary least squares and generalised least squares estimators. This study uses data from 98 catchments in south-eastern Australia to develop prediction equations involving readily obtainable catchment characteristics data. Even though the differences in the model parameter estimates are modest, the generalised least squares technique is shown to be better than the ordinary least squares technique in terms of average variance of prediction.
Shenglian Guo - One of the best experts on this subject based on the ideXlab platform.
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A general framework of Design Flood estimation for cascade reservoirs in operation period
Journal of Hydrology, 2019Co-Authors: Feng Xiong, Shenglian Guo, Pan Liu, Yixuan Zhong, Jiabo YinAbstract:Abstract The hydrological regimes of downstream reservoirs have been significantly altered due to the operation and regulation of upstream cascade reservoirs. The original Design Flood quantiles, namely “Design Flood in construction period”, do not consider anthropogenic impacts in reservoir operation period, and have led to enormous conflicts between Flood control and conservation. In this study, the “Design Flood and Flood limited water level in operation period” are defined for practical application. We establish a general framework to measure the spatiotemporal pattern of streamflow and to estimate Design Floods of cascade reservoirs in operation period. The multivariate t-copula and a genetic algorithm strategy are proposed to solve the curse of dimensionality encountered in the derivation of most likely regional composition. The Jinsha River and Yalong River cascade reservoir system in China, which consists of 13 large reservoirs with the total storage capacity of 74.06 billion m3 and hydropower capacity of 71.47 GW, is selected as a case study. Results indicate that: (1) The curse of dimensionality can be well addressed by applying multivariate t-copula to build high dimensional joint distribution and using the genetic algorithm to achieve the most likely regional composition. (2) Compared with the Design Floods in construction period, the Design Floods of downstream reservoirs in operation period have been significantly reduced due to the upstream reservoir regulation. The 1000-year Design peak Flood discharge, 3-day, 7-day and 30-day Flood volumes of Xiangjiaba reservoir decrease by 38.7%, 37.4%, 34.2% and 13.8%, respectively. (3) The Flood limited water level of these reservoirs can be raised without increasing Flood control risks in operation period. The cascade reservoirs in the Jinsha River and Yalong River can generate 3.28 billion kW h more hydropower (or increase 4.3%) annually during Flood season.
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uncertainty analysis of bivariate Design Flood estimation and its impacts on reservoir routing
Water Resources Management, 2018Co-Authors: Jiabo Yin, Shenglian Guo, Yixuan Zhong, Zhangjun Liu, Guang Yang, Dedi LiuAbstract:The bivariate hydrological quantile estimation may inevitably induce large sampling uncertainty due to short sample size. It is crucial to quantify such uncertainty and its impacts on reservoir routing. In this study, a copula-based parametric bootstrapping uncertainty (C-PBU) method is proposed to characterize the bivariate quantile estimation uncertainty and the impact of such uncertainty on the highest reservoir water level is also investigated. The Geheyan reservoir in China is selected as a case study. Four evaluation indexes, i.e. area of confidence region, mean horizontal deviation, mean vertical deviation and average Euclidean distance, are adopted to quantify the quantile estimation uncertainty. The results indicate that the uncertainty of quantile estimation and the highest reservoir water level increases with larger return period. The 90% confidence interval (CI) of highest reservoir water level reaches 1.56 m and 2.52 m under 20-year and 50-year JRP respectively for the sample size of 100. It is also indicated that the peak over threshold (POT) sampling method contribute to uncertainty reduction comparing with the annual maximum (AM) method. This study could provide not only the point estimator of Design Floods and corresponding Design water level, but also the rich uncertainty information (e.g. 90% confidence interval) for the references of reservoir Flood risk assessment, scheduling and management.
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bivariate Design Flood quantile selection using copulas
Hydrology Research, 2017Co-Authors: Shenglian Guo, Zhangjun Liu, Lihua Xiong, Jiabo YinAbstract:Flood event consists of peak discharge and Flood volume that are mutually correlated and can be described by a copula function. For a given bivariate joint distribution, a choice of Design return period will lead to infinite combinations of peak discharge and Flood volume. A boundary identification method is developed to define the feasible ranges of Flood peak and volume suitable for combination, and two combination methods, i.e., equivalent frequency combination (EFC) method and conditional expectation combination method for estimating unique bivariate Flood quantiles are also proposed. Monte Carlo simulation method is used to evaluate the performance of these combination methods. The Geheyan reservoir in China was selected as case study. It is shown that the joint Design values estimated by the two proposed combination methods are both within the feasible range, which means that the methods could be selected for Designing unique Flood quantiles. The proposed bivariate combination methods are also compared with univariate method, and the reservoir water level estimated by EFC method is higher than the other methods, which means the EFC method is safer for reservoir Design. The developed approach provides an applicable way for the identification of feasible range and Flood quantile estimation.
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risk analysis for Flood control operation of seasonal Flood limited water level incorporating inflow forecasting error
Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2014Co-Authors: Yanlai Zhou, Shenglian GuoAbstract:AbstractThe seasonal Flood-limited water level (FLWL), which reflects the seasonal Flood information, plays an important role in governing the trade-off between reservoir Flood control and conservation. A risk analysis model for Flood control operation of seasonal FLWL incorporating the inflow forecasting error was proposed and developed. The variable kernel estimation is implemented for deriving the inflow forecasting error density. The synthetic inflow incorporating forecasting error is simulated by Monte Carlo simulation (MCS) according to the inflow forecasting error density. The risk analysis for seasonal FLWL control was estimated by MCS based on a combination of the forecasting inflow lead-time, seasonal Design Flood hydrographs and seasonal operation rules. The Three Gorges reservoir is selected as a case study. The application results indicate that the seasonal FLWL control can effectively enhance Flood water utilization rate without lowering the annual Flood control standard. Editor D. Koutsoyia...
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measure of correlation between river flows using the copula entropy method
Journal of Hydrologic Engineering, 2013Co-Authors: Lu Chen, Vijay P Singh, Shenglian GuoAbstract:AbstractAnalysis of the dependence between the main stream and its upper tributaries is important for hydraulic Design, Flood prevention, and risk control. The concept of total correlation, computed by the copula-entropy method, was applied to measure the dependence. This method only needs to calculate the copula entropy instead of the marginal or joint entropy, which estimates the total correlation more directly and avoids the accumulation of systematic bias. To that end, bivariate and multivariate Archimedean and metaelliptical copulas were employed, and multiple-integration and Monte Carlo methods were used to calculate the copula entropy. The methodology was applied to the upper Yangtze River reach in China, which has five major tributaries: Jinsha, Min, Tuo, Jialing, and Wu. Results showed that the selected copulas fitted the empirical probability distributions satisfactorily. There was a significant difference in total correlation values, when different copula functions were used. The copula entropy...
F A Tofiq - One of the best experts on this subject based on the ideXlab platform.
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potential changes in inflow Design Flood under future climate projections for darbandikhan dam
Journal of Hydrology, 2015Co-Authors: F A Tofiq, Aytac GuvenAbstract:Summary Insignificant changes in the future system might be assumed due to the impacts of climate changes. Temperature changes and anticipated precipitation suggest possible changes in Floods. Extreme events will have bigger impacts on sectors with direct connections to climate, such as water sector; there is great belief that climate changes have the potential to critically affect water management systems. This study investigated how future climate change might affect the Inflow Design Flood (IDF) of Darbandikhan Dam. The study uses the projected downscaled daily inflow from Global Climate Models (GCMs) in comparative to the historical peak inflows. Flood frequency analysis based on historical data, both observed and statistically downscaled, was used to obtain the new Design Flood values. Analysis of the future projections of Flood frequency analysis (FFA) reveals a general tendency toward changes in the magnitude of IDF.
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prediction of Design Flood discharge by statistical downscaling and general circulation models
Journal of Hydrology, 2014Co-Authors: F A Tofiq, Aytac GuvenAbstract:Summary The global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. Downscaling, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to reDesign an existing dam but at least precaution parameters can be taken for the worse scenarios of Flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical downscaling using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the Flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in downscaling the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall–runoff models.
Byungsik Kim - One of the best experts on this subject based on the ideXlab platform.
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assessment of change in Design Flood frequency under climate change using a multivariate downscaling model and a precipitation runoff model
Stochastic Environmental Research and Risk Assessment, 2011Co-Authors: Hyunhan Kwon, Bellie Sivakumar, Youngil Moon, Byungsik KimAbstract:Precipitation and runoff are key elements in the hydrologic cycle because of their important roles in water supply, Flood prevention, river restoration, and ecosystem management. Global climate change, widely accepted to be happening, is anticipated to have enormous consequences on future hydrologic patterns. Studies on the potential changes in global, regional, and local hydrologic patterns under global climate change scenarios have been an intense area of research in recent years. The present study contributes to this research topic through evaluation of Design Flood under climate change. The study utilizes a weather state-based, stochastic multivariate model as a conditional probability model for simulating the precipitation field. An important premise of this study is that large-scale climatic patterns serve as a major driver of persistent year-to-year changes in precipitation probabilities. Since uncertainty estimation in the study of climate change is needed to examine the reliability of the outcomes, this study also applies a Bayesian Markov chain Monte Carlo scheme to the widely used SAC-SMA (Sacramento soil moisture accounting) precipitation-runoff model. A case study is also performed with the Soyang Dam watershed in South Korea as the study basin. Finally, a comprehensive discussion on Design Flood under climate change is made.