Bibliografias temáticas / Flood estimation (Australia)

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Literatura científica selecionada sobre o tema "Flood estimation (Australia)"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 20 de fevereiro de 2023

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Artigos de revistas sobre o assunto "Flood estimation (Australia)"

Hasanzadeh Nafari, R., T. Ngo e W. Lehman. "Results comparison and model validation for flood loss functions in Australian geographical conditions". Natural Hazards and Earth System Sciences Discussions 3, n.º 6 (12 de junho de 2015): 3823–60. http://dx.doi.org/10.5194/nhessd-3-3823-2015.

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Abstract. Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods, namely flood frequency and intensity. Flood is a frequent natural hazard that has significant financial consequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster impacts in terms of loss reduction and damage mitigation has been less successful. This study aims to quantify the direct physical damage to residential structures that are prone to flood phenomena in Australia. In this paper, the physical consequences of two floods from Queensland have been simulated, and the results have been compared with the performance of two selected methodologies and one newly derived model. Based on this analysis, the adaptability and applicability of the selected methodologies will be assessed in terms of Australian geographical conditions. Results obtained from the new empirically-based function and non-adapted methodologies indicate that it is apparent that the precision of flood damage models are strongly dependent on selected stage damage curves, and flood damage estimation without model validation results in inaccurate prediction of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been adapted with real damage data.

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Hasanzadeh Nafari, R., T. Ngo e W. Lehman. "Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures". Natural Hazards and Earth System Sciences 16, n.º 1 (18 de janeiro de 2016): 15–27. http://dx.doi.org/10.5194/nhess-16-15-2016.

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Abstract. Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods. Flood is a frequent natural hazard that has significant financial consequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster impacts in terms of loss reduction and damage mitigation has been less successful. In this paper, a newly derived flood loss function for Australian residential structures (FLFArs) has been presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of the method developed in this work (contrasted to one Australian model and one model from USA) has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of the selected methodologies has been performed in terms of Australian geographical conditions. Results obtained from the new empirically based function (FLFArs) and the other models indicate that it is apparent that the precision of flood damage models is strongly dependent on selected stage damage curves, and flood damage estimation without model calibration might result in inaccurate predictions of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been calibrated with real damage data.

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Haddad, Khaled, e Ataur Rahman. "Development of a Large Flood Regionalisation Model Considering Spatial Dependence—Application to Ungauged Catchments in Australia". Water 11, n.º 4 (1 de abril de 2019): 677. http://dx.doi.org/10.3390/w11040677.

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Estimation of large floods is imperative in planning and designing large hydraulic structures. Due to the limited availability of observed flood data, estimating the frequencies of large floods requires significant extrapolation beyond the available data. This paper presents the development of a large flood regionalisation model (LFRM) based on observed flood data. The LFRM assumes that the maximum observed flood data over a large number of sites in a region can be pooled together by accounting for the at-site variations in the mean and coefficient of variation. The LFRM is enhanced by adding a spatial dependence model, which accounts for the net information available for regional analysis. It was found that the LFRM, which accounts for spatial dependence and that pools 1 or 3 maxima from a site, was able to estimate the 1 in 1000 annual exceedance probability flood quantile with consistency, showing a positive bias on average (5–7%) and modest median relative errors (30–33%).

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Wu, Wenyan, Seth Westra e Michael Leonard. "Estimating the probability of compound floods in estuarine regions". Hydrology and Earth System Sciences 25, n.º 5 (26 de maio de 2021): 2821–41. http://dx.doi.org/10.5194/hess-25-2821-2021.

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Abstract. The quantification of flood risk in estuarine regions relies on accurate estimation of flood probability, which is often challenging due to the rareness of hazardous flood events and their multi-causal (or “compound”) nature. Failure to consider the compounding nature of estuarine floods can lead to significant underestimation of flood risk in these regions. This study provides a comparative review of alternative approaches for estuarine flood estimation – namely, traditional univariate flood frequency analysis applied to both observed historical data and simulated data, as well as multivariate frequency analysis applied to flood events. Three specific implementations of the above approaches are evaluated on a case study – the estuarine portion of Swan River in Western Australia – highlighting the advantages and disadvantages of each approach. The theoretical understanding of the three approaches, combined with findings from the case study, enable the generation of guidance on method selection for estuarine flood probability estimation, recognizing issues such as data availability, the complexity of the application/analysis process, the location of interest within the estuarine region, the computational demands, and whether or not future conditions need to be assessed.

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Zalnezhad, Amir, Ataur Rahman, Nastaran Nasiri, Mehdi Vafakhah, Bijan Samali e Farhad Ahamed. "Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia". Water 14, n.º 20 (20 de outubro de 2022): 3323. http://dx.doi.org/10.3390/w14203323.

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Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33–54% as compared to 37–64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia.

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Loveridge, Melanie, e Ataur Rahman. "Effects of Probability-Distributed Losses on Flood Estimates Using Event-Based Rainfall-Runoff Models". Water 13, n.º 15 (27 de julho de 2021): 2049. http://dx.doi.org/10.3390/w13152049.

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Probability distributions of initial losses are investigated using a large dataset of catchments throughout Australia. The variability in design flood estimates caused by probability-distributed initial losses and associated uncertainties are investigated. Based on historic data sets in Australia, the Gamma and Beta distributions are found to be suitable for describing initial loss data. It has also been found that the central tendency of probability-distributed initial loss is more important in design flood estimation than the form of the probability density function. Findings from this study have notable implications on the regionalization of initial loss data, which is required for the application of Monte Carlo methods for design flood estimation in ungauged catchments.

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Khan, Zaved, Ataur Rahman e Fazlul Karim. "An Assessment of Uncertainties in Flood Frequency Estimation Using Bootstrapping and Monte Carlo Simulation". Hydrology 10, n.º 1 (10 de janeiro de 2023): 18. http://dx.doi.org/10.3390/hydrology10010018.

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Reducing uncertainty in design flood estimates is an essential part of flood risk planning and management. This study presents results from flood frequency estimates and associated uncertainties for five commonly used probability distribution functions, extreme value type 1 (EV1), generalized extreme value (GEV), generalized pareto distribution (GPD), log normal (LN) and log Pearson type 3 (LP3). The study was conducted using Monte Carlo simulation (MCS) and bootstrapping (BS) methods for the 10 river catchments in eastern Australia. The parameters were estimated by applying the method of moments (for LP3, LN, and EV1) and L-moments (for GEV and GPD). Three-parameter distributions (e.g., LP3, GEV, and GPD) demonstrate a consistent estimation of confidence interval (CI), whereas two-parameter distributions show biased estimation. The results of this study also highlight the difficulty in flood frequency analysis, e.g., different probability distributions perform quite differently even in a smaller geographical area.

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Loveridge, Melanie, Ataur Rahman e Peter Hill. "Applicability of a physically based soil water model (SWMOD) in design flood estimation in eastern Australia". Hydrology Research 48, n.º 6 (28 de dezembro de 2016): 1652–65. http://dx.doi.org/10.2166/nh.2016.118.

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Abstract Event-based rainfall–runoff models are useful tools for hydrologic design. Of the many loss models, the ‘initial loss-continuing loss’ model is widely adopted in practice. Some of the key limitations with these types of loss models include the arbitrary selection of initial moisture (IM) conditions and lack of physically meaningful parameters. This paper investigates the applicability of a physically based soil water balance model (SWMOD) with distributed IM conditions for flood modelling. Four catchments from the east coast of New South Wales, Australia, are modelled. The IM content in SWMOD represents the antecedent moisture condition. A quasi-Monte Carlo simulation framework is adopted, where the IM is stochastically varied according to a lognormal probability distribution. In calibration, it is found that the adopted modelling framework is able to simulate the majority of the observed flood hydrographs with a higher degree of accuracy; however, in a design context, when compared to the results of conventional flood frequency analysis, discrepancies are noted for a range of annual exceedance probabilities. The quasi-Monte Carlo simulation framework proved to be useful in assessing the effect of the IM content on design flood estimates.

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Wahalathantri, Buddhi, Weena Lokuge, Warna Karunasena e Sujeeva Setunge. "Quantitative assessment of flood discharges and floodway failures through cross-cultivation of advancement in knowledge and traditional practices". International Journal of Disaster Resilience in the Built Environment 9, n.º 4/5 (16 de novembro de 2018): 435–56. http://dx.doi.org/10.1108/ijdrbe-09-2017-0051.

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Purpose The 2011 and 2013 Queensland, Australia flood events caused massive infrastructure damage for low-level stream crossings such as floodways and culverts in regional Queensland. Failures of newly built floodways during the 2013 Queensland flood event in the Lockyer Valley Regional Council area raised significant concerns with respect to floodway design practices adopted in Australia and attracted significant research interest to enhance the resilience of floodways. Review of existing floodway design guidelines indicates that floodway design process is closely related to hydraulic and hydrological aspects. However, conducting a hydrological analysis is a challenging in rural areas, mainly owing to information scarcity. Floodways in rural areas often require a simple and economical solution contrast to more detailed hydrological analysis approaches adopted in urbanised areas. This paper aims to identify and apply the rational method to estimate maximum flood discharges at selected floodway locations in the Lockyer Valley Regional Council area. The paper further attempts to provide the first insight of flood characteristics during the 2011 and 2013 Queensland flood events at three catchment outputs across the selected case study area. It also highlights modern day challenges for practising engineers and researchers when estimating flood characteristics in rural areas. The paper shows that cross-cultivation of advancement in engineering practices and traditional approaches can promote quantitative approaches when assessing floodway damage in regional areas. Design/methodology/approach The research identifies limitations when assessing flood impact in rural regions in collaboration with experience from industry partners and authors themselves. The authors developed a framework to overcome those limitations arising from information scarcity to minimise the trial and error design approaches utilised in the current design practices for floodways. Findings This paper developed a simple and effective hydrological method with minimum inputs. It also provides an example on collating available but scattered resources and traditional method to quantitatively assess flood discharges of a rural catchment in Australia. Flood discharges at three catchment outlets along the Left-Hand Branch Road in the Lockyer Valley Region during both 2011 and 2013 Queensland flood events are estimated for the first time. The findings highlight the impact of flood discharges and flooded period on floodway failures. Research limitations/implications The current research is based on a selected case study area in Australia. However, similar challenges are expected all across the world, due to the scarcity of rainfall and flood measurement gauges. Practical implications Floodway designers can apply similar framework to estimate the flood discharges instead of current practice of trial and error process. This will provide more scientific and reliable estimation and assessment process. Social implications One of the social impacts identified in the broader research is the community outrages and disagreement between floodway design engineers and the community. Following the developed framework in the manuscript, design engineers will be able to justify their assumptions and design work. Originality/value The paper presents a novel framework on collating different and scattered information towards estimating flood discharges in rural areas. The manuscript presents the first insights on estimated flood discharges in the selected case study area during the 2011 and 2013 Queensland flood events. This will enable further research to be performed in a quantitative manner rather than the present approach of qualitative manner.

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Franks, S. W., C. J. White e M. Gensen. "Estimating extreme flood events – assumptions, uncertainty and error". Proceedings of the International Association of Hydrological Sciences 369 (11 de junho de 2015): 31–36. http://dx.doi.org/10.5194/piahs-369-31-2015.

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Abstract. Hydrological extremes are amongst the most devastating forms of natural disasters both in terms of lives lost and socio-economic impacts. There is consequently an imperative to robustly estimate the frequency and magnitude of hydrological extremes. Traditionally, engineers have employed purely statistical approaches to the estimation of flood risk. For example, for an observed hydrological timeseries, each annual maximum flood is extracted and a frequency distribution is fit to these data. The fitted distribution is then extrapolated to provide an estimate of the required design risk (i.e. the 1% Annual Exceedance Probability – AEP). Such traditional approaches are overly simplistic in that risk is implicitly assumed to be static, in other words, that climatological processes are assumed to be randomly distributed in time. In this study, flood risk estimates are evaluated with regards to traditional statistical approaches as well as Pacific Decadal Oscillation (PDO)/El Niño-Southern Oscillation (ENSO) conditional estimates for a flood-prone catchment in eastern Australia. A paleo-reconstruction of pre-instrumental PDO/ENSO occurrence is then employed to estimate uncertainty associated with the estimation of the 1% AEP flood. The results indicate a significant underestimation of the uncertainty associated with extreme flood events when employing the traditional engineering estimates.

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Mais fontes

Teses / dissertações sobre o assunto "Flood estimation (Australia)"

Haddad, Khaled. "Design flood estimation for ungauged catchments in Victoria : ordinary and generalised least squares methods compared". Thesis, View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/30369.

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Design flood estimation in small to medium sized ungauged catchments is frequently required in hydrologic analysis and design and is of notable economic significance. For this task Australian Rainfall and Runoff (ARR) 1987, the National Guideline for Design Flow Estimation, recommends the Probabilistic Rational Method (PRM) for general use in South- East Australia. However, there have been recent developments that indicated significant potential to provide more meaningful and accurate design flood estimation in small to medium sized ungauged catchments. These include the L moments based index flood method and a range of quantile regression techniques. This thesis focuses on the quantile regression techniques and compares two methods: ordinary least squares (OLS) and generalised least squares (GLS) based regression techniques. It also makes comparison with the currently recommended Probabilistic Rational Method. The OLS model is used by hydrologists to estimate the parameters of regional hydrological models. However, more recent studies have indicated that the parameter estimates are usually unstable and that the OLS procedure often violates the assumption of homoskedasticity. The GLS based regression procedure accounts for the varying sampling error, correlation between concurrent flows, correlations between the residuals and the fitted quantiles and model error in the regional model, thus one would expect more accurate flood quantile estimation by this method. This thesis uses data from 133 catchments in the state of Victoria to develop prediction equations involving readily obtainable catchment characteristics data. The GLS regression procedure is explored further by carrying out a 4-stage generalised least squares analysis where the development of the prediction equations is based on relating hydrological statistics such as mean flows, standard deviations, skewness and flow quantiles to catchment characteristics. This study also presents the validation of the two techniques by carrying out a split-sample validation on a set of independent test catchments. The PRM is also tested by deriving an updated PRM technique with the new data set and carrying out a split sample validation on the test catchments. The results show that GLS based regression provides more accurate design flood estimates than the OLS regression procedure and the PRM. Based on the average variance of prediction, standard error of estimate, traditional statistics and new statistics, rankings and the median relative error values, the GLS method provided more accurate flood frequency estimates especially for the smaller catchments in the range of 1-300 km2. The predictive ability of the GLS model is also evident in the regression coefficient values when comparing with the OLS method. However, the performance of the PRM method, particularly for the larger catchments appears to be satisfactory as well.

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Charalambous, James, University of Western Sydney, of Science Technology and Environment College e School of Engineering and Industrial Design. "Application of Monte Carlo Simulation Technique with URBS Runoff-Routing Model for design flood estimation in large catchments". THESIS_CSTE_EID_Charalambous_J.xml, 2004. http://handle.uws.edu.au:8081/1959.7/769.

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In recent years, there have been significant researches on holistic approaches to design flood estimation in Australia. The Monte Carlo Simulation technique, an approximate form of Joint Probability Approach, has been developed and tested to small gauged catchments. This thesis presents the extension of the Monte Carlo Simulation Technique to large catchments using runoff routing model URBS. The URBS-Monte Carlo Technique(UMCT),has been applied to the Johnstone River and Upper Mary River catchments in Queensland. The thesis shows that the UMCT can be applied to large catchments and be readily used by hydrologists and floodplain managers. Further the proposed technique provides deeper insight into the hydrologic behaviour of large catchments and allows assessment of the effects of errors in inputs variables on design flood estimates. The research also highlights the problems and potentials of the UMCT for application in practical situations.
Masters of Engineering (Hons.)

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Charalambous, James. "Application of Monte Carlo Simulation Technique with URBS Runoff-Routing Model for design flood estimation in large catchments". Thesis, View thesis, 2004. http://handle.uws.edu.au:8081/1959.7/769.

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Haddad, Khaled, University of Western Sydney, College of Health and Science e School of Engineering. "Design flood estimation for ungauged catchments in Victoria : ordinary and generalised least squares methods compared". 2008. http://handle.uws.edu.au:8081/1959.7/30369.

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Patel, Hitesh D. "Stochastic runoff routing model parameter for design flood estimation using joint probability approach". Thesis, 2010. http://handle.uws.edu.au:8081/1959.7/506747.

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The Design Event Approach is currently recommended rainfall-based flood estimation method in Australia according to Australian Rainfall and Runoff. However, Design Event Approach does not account for the probabilistic nature of the key flood producing variables except for the rainfall depth. This arbitrary treatment of key inputs and model parameters in Design Event Approach can lead to inconsistencies and significant bias in flood estimates for a given average recurrence interval. A significant improvement in design flood estimates can be achieved through a Joint Probability Approach, which is more holistic in nature that uses probability-distributed input variables/model parameters and their correlations to obtain probability-distributed flood output. More recently, there have been notable researches in Australia on Monte Carlo Simulation Technique for flood estimation based on the principles of Joint Probability that can employ many of the commonly adopted flood estimation models and design data in Australia. The previous studies in Australia on Joint Probability Approach/Monte Carlo Simulation Technique have considered rainfall duration, rainfall intensity, rainfall temporal pattern and initial loss as random variables in the simulation but the probabilistic nature of runoff routing model storage delay parameter k has been disregarded, which in many circumstances is likely to cause under- or over-estimation of design flood peaks. Application of Monte Carlo Simulation Technique with RORB model, the most widely used hydrologic model in Australia, has not been well investigated. At present, the RORB model has a limited capability in terms of implementation of the Monte Carlo Simulation Technique in flood modeling. In the current RORB modeling, storage delay parameter kc is considered to be a fixed input. In this study, the applicability of the Monte Carlo Simulation Technique with RORB model has been investigated using a large number of selected storm and runoff events from Lismore catchment in New South Wales. It has been found that the value of kc exhibits a high degree of variability from event to event, and use of different possible representative values of kc results in quite different design flood peak estimates and hence it should be considered as a stochastic variable in modeling under the framework of the Monte Carlo Simulation Technique. In this study, the previously developed Monte Carlo Simulation Technique in Australia (old Monte Carlo Simulation Technique) has been upgraded by incorporating runoff routing model storage delay parameter (k) as a stochastic variable. The updated Monte Carlo Simulation Technique has been tested on five catchments in New South Wales. The derived flood frequency curves from the new Monte Carlo Simulation Technique have been compared with at-site flood frequency estimates and the old Monte Carlo Simulation Technique. It has been found that the new Monte Carlo Simulation Technique provides more accurate design flood estimates as compared to the old Monte Carlo Simulation Technique.

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Loveridge, Melanie. "Loss models for design flood estimation : toward applications within a Monte Carlo environment". Thesis, 2016. http://hdl.handle.net/1959.7/uws:38506.

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This thesis focusses on more holistic approaches to flood modelling. For instance, joint probability/Monte Carlo approaches have received a great deal of attention in recent years, particularly since their use was advocated in Australian Rainfall and Runoff (ARR) 2015. In Monte Carlo simulation, rainfall-runoff model inputs are described by probability distributions, rather than fixed inputs as previously carried out with the Design Event Approach (DEA). There is no compelling evidence of the most appropriate loss model for flood estimation within a Monte Carlo framework. Furthermore, the functional form of key stochastic variables is in dispute. Previous recommendations on loss models were made for traditional techniques; however, since the advent of Monte Carlo simulation the most suitable loss model has not been rigorously assessed. Additionally, while many have investigated the probability density function of key parameters, there is some dispute over the most suitable model. Therefore, due to the lack of guidance currently available, models of runoff generation processes and parameter variability require thorough investigation for more rigorous rainfall-runoff modelling within a Monte Carlo environment.

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Capítulos de livros sobre o assunto "Flood estimation (Australia)"

Aziz, Kashif, Ataur Rahman e Asaad Shamseldin. "Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia". In Artificial Neural Network Modelling, 307–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_13.

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Stephens, D. A., M. J. Scorah, P. I. Hill e R. J. Nathan. "Australian experience with application of Monte Carlo approach to extreme flood estimation". In Sustainable and Safe Dams Around the World, 3261–75. CRC Press, 2019. http://dx.doi.org/10.1201/9780429319778-294.

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Trabalhos de conferências sobre o assunto "Flood estimation (Australia)"

Haddad, Khaled. "A NEW FLOOD REGIONALISATION MODEL FOR LARGE FLOOD ESTIMATION IN AUSTRALIA". In SGEM2011 11th International Multidisciplinary Scientific GeoConference and EXPO. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2011/s13.101.

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"Development of a regional flood frequency estimation model for Pilbara, Australia". In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.l6.haque.

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Aziz, Kashif, Ataur Rahman, Gu Fang, Khaled Haddad e Surendra Shrestha. "Design Flood Estimation for Ungauged Catchments: Application of Artificial Neural Networks for Eastern Australia". In World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)293.

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"Development and validation of Artificial Intelligence Based Regional Flood Estimation Model for Eastern Australia". In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.l6.aziz.

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Zaman, Mohammad, Ishita Rahman, Khaled Haddad e Ataur Rahman. "Scaling Issues in Design Flood Estimation for Ungauged Catchments: A Case Study for Eastern Australia". In World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)295.

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"Regional flood estimation in Australia: application of gene expression programming and artificial neural network techniques". In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l1.aziz.

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"A New Probabilistic Rational Method for design flood estimation in ungauged catchments for the State of New South Wales in Australia". In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.l6.alsuwaidi.

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"Features of Regional Flood Frequency Estimation (RFFE) Model in Australian rainfall and runoff". In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.l6.rahman2.

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