No description available.

Methods of estimating floods with return periods of up to one hundred years are reasonably well established, and in the main rely on extrapolation of historical flood data at the site of interest. However, extrapolating the tails of fitted probability distributions to higher return periods is very unreliable and cannot provide a satisfactory basis for extreme flood estimation. The probable maximum flood concept is an alternative approach, which is often used for critical cases such as the location of nuclear power plants, and is viewed as a consequence of a combination of a probable maximum precipitation with the worst possible prevailing catchment conditions. Return periods are not usually quoted although they are implicitly thought to be of the order of tens of thousand of years. There are many less critical situations which still justify greater flood protection than would be provided for an estimated one-hundred year flood. There is therefore a need for techniques which can be used to estimate floods with return periods of up to several thousand years. The predictive approach adopted here involves a combination of a probabilistic storm transposition technique with a physically-based distributed rainfall-runoff model. Extreme historical storms within a meteorologically homogeneous region are, conceptually, moved to the catchment of interest, and their return periods are estimated within a probabilistic framework. Known features of storms such as depth, duration, and perhaps approximate shape will, together with catchment characteristics, determine much of the runoff response. But there are other variables which also have an effect and these include the space-time distribution of rainfall within the storm, storm velocity and antecedent catchment conditions. The effects of all these variables on catchment response are explored.

This thesis describes research in to flood forecasting on rapid response catchments, using knowledge based principles. Extensive use was made of high resolution single site radar data from the radar site at Hameldon Hill in North West England. Actual storm events and synthetic precipitation data were used in an attempt to identify 'knowledge' of the rainfall - runoff process. Modelling was carried out with the use of transfer functions, and an analysis is presented of the problems in using this type of model in hydrological forecasting. A physically realisable' transfer function model is outlined, and storm characteristics were analysed to establish information about model tuning. The knowledge gained was built into a knowledge based system (KBS) to enable real-time optimisation of model parameters. A rainfall movement forecasting program was used to provide input to the system. Forecasts using the KBS tuned parameters proved better than those from a naive transfer function model in most cases. In order to further improve flow forecasts a simple catchment wetness procedure was developed and included in the system, based on antecedent precipitation index, using radar rainfall input. A new method of intensity - duration - frequency analysis was developed using distributed radar data at a 2Km by 2Km resolution. This allowed a new application of return periods in real time, in assessing storm severity as it occurs. A catchment transposition procedure was developed allowing subjective catchment placement infront of an approaching event, to assess rainfall `risk', in terms of catchment history, before the event reaches it. A knowledge based approach, to work in real time, was found to be successful. The main drawback is the initial procurement of knowledge, or information about thresholds, linkages and relationships.

No description available.

>Magister Scientiae - MSc / Improved knowledge is required on the quantity and source of water resources, particularly evident during periods of drought currently being faced in South Africa. There is inadequate knowledge with regards to the flood attenuating properties of wetlands, particularly evident in the ungauged catchments of Southern Africa. This study aims to improve the knowledge on the contribution of flow from tributaries with headwaters in mountainous regions to low lying areas and the effects of wetlands on river flow patterns. Several river flow monitoring sites were established along the major upper tributaries of the Nuwejaars River at which daily water levels were recorded and bi-weekly discharge measurements were conducted. Weather data was collected using four automatic weather stations and three automatic rain gauges’ setup throughout the catchment. Rainfall data coupled with rating curves and daily discharges were used to assess the flow responses of these tributaries to rainfall events. Additionally, stable isotope analysis and basic water quality analysis was used to determine the major sources of flow within the major tributaries. The rainfall and river flow data collected, coupled with the characterization of the wetland was used to determine the flood attenuation capabilities of the wetland. Lastly, a conceptual model based on a basic water balance was developed to further explain the role of the wetland and its effects on river flows. The results showed a 27-hour lag time in peak flows from the upper tributaries at the inflows of the wetland to the outflow. Two of the upper tributaries had flow throughout the year and were fed by springs in the upper mountainous regions of the catchment and all tributaries were largely reliant on rainfall for peak flows. The temporary storage of flows within the wetland occurred as a result of the Nuwejaars River bursting its banks, filling of pools, or ponds and the Voëlvlei Lake. It was concluded that the wetland increased the travel time and decreased the magnitude of flows of the Nuwejaars River. However, due to the fact that wetlands are interlinked on a catchment scale and have a collective effect on flood attenuation this study may be improved by looking at the wetlands within the catchment holistically.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18563号 / 工博第3924号 / 新制||工||1603(附属図書館) / 31463 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 藤田 正治, 教授 中川 一, 准教授 竹林 洋史 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Warning systems with the ability to predict floods days in advance can benefit tens of millions of people. Because of these potential impacts there have been efforts to improve prediction systems such as the United States’ Advanced Hydrologic Prediction Service and European-developed Global Flood Awareness System. However, these projects are currently limited to relatively coarse resolutions. This thesis presents a method for downscaling and routing global runoff forecasts generated by the European Centre for Medium-Range Weather Forecasts using the Routing Application for Parallel computatIon of Discharge program that make possible orders of magnitude increases in the density of the resolution of stream forecasts. The processing method involves using the Amazon Web Services to distribute execution in a cloud-computing environment to make it possible to solve for large watersheds with high-density stream networks. Using the Amazon Web Services, the number of streams that can be used in the downscaling process in a twelve-hour period is approximated to be close to five million. In addition, an application for visualizing large high-density stream networks has been created using the Tethys Platform of water resources modeling developed as part of the CI-WATER NSF grant. The web application is tested with the HUC-2 Region 12 watershed network with over 67,000 reaches and is able to display analyzed results to the user for each reach.

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

No description available.

Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the relationships between variables in a system are dynamic and disproportionate, however completely deterministic. Classical linear time series models have proved inadequate in analysis and prediction of complex geophysical phenomena. Nonlinear approaches such as Artificial Neural Networks, Hidden Markov Models and Nonlinear Prediction are useful in forecasting of daily discharge values in a river. The focus of these methods is on forecasting magnitudes of future discharge values and not the prediction of floods. Chaos theory provides a structured explanation for irregular behavior and anomalies in systems that are not inherently stochastic. Time Series Data Mining methodology combines chaos theory and data mining to characterize and predict complex, nonperiodic and chaotic time series. Time Series Data Mining focuses on the prediction of events.