Academic literature on the topic 'Flood forecasting Victoria Mathematical models'

The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.

Abstract In this study, a novel method for forecasting the flood risk in a tropical country is proposed, called CIHAM-UC-FFR. The method is based on the rainfall–runoff process. The CIHAM-UC-FFR method consists of three stages: (1) calibration and validation for the effective precipitation model, called CIHAM-UC-EP model, (2) calibration of forecasting models for components of the CIHAM-UC-EP model, (3) proposed model for forecasting of gridded flood risk called CIHAM-UC-FR. The CIHAM-UC-EP model has a mathematical structure derived from a conceptual model obtained by applying the principle of mass conservation combined with the adapted principle of Fick's law. The CIHAM-UC-FR model is a stochastic equation based on the exceedance probability of the forecast effective precipitation. Various scenarios are shown for a future time where the flood risk is progressively decreased as the expected life parameter of the hydraulic structure is increased.

Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.

Abstract Flood problems are complex phenomena with a direct relationship with the hydrological cycle; these are natural processes occurring in water systems, that interact at different spatial and temporal scales. In modeling the hydrological phenomena, traditional approaches, like physics-based mathematical equations and data-driven modeling (DDM) are used. Advances in hydroinformatics are helping to understand these physical processes, with improvements in the collection and analysis of hydrological data, information and communication technologies (ICT), and geographic information systems (GIS), offering opportunities for innovations in model implementation, to improve decision support for the response to societally important floods impacting our societies. This paper offers a brief review of agent-based models (ABMs) and multi-agent systems (MASs) methodologies' applications for solutions to flood problems, their management, assessment, and efforts for forecasting stream flow and flood events. Significant observations from this review include: (i) contributions of agent technologies, as a growing methodology in hydrology; (ii) limitations; (iii) capabilities of dealing with distributed and complex domains; and (iv), the capabilities of MAS as an increasingly accepted point of view applied to flood modeling, with examples presented to show the variety of system combinations that are practical on a specialized architectural level for developing and deploying sophisticated flood forecasting systems.

Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash–Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.

The mitigation of societal damage from urban floods requires fast hydraulic models for emergency and planning purposes. The simplified mathematical model Cellular Automata is combined with Motion Cost fields, which score the difficulty to traverse an area, to the urban inundation model CAMC. It is implemented with simple matrix and logic operations to achieve high computational efficiency. The development concentrated on an application in dense urban built-up areas with numerous buildings. CAMC is efficient and flexible enough to be used in a “live” urban flood warning system with current weather conditions. A case study is conducted in the German city of Wuppertal with about 12,000 buildings. The water depth estimation of every time step are visualized in a web-interface on the basis of the virtual globe NASA WorldWind. CAMC is compared with the shallow water equations-based model ANUGA. CAMC is approximatively 5 times faster than ANUGA at high spatial resolution and able to maintain numerical stability. The Nash-Sutcliffe coefficient (0.61), Root Mean Square Error (0.39 m) and Index of Agreement (0.65) indicate acceptable agreement for water depth estimation but identify different areas where important deviations occur. The estimation of velocity performs considerably less well (0.34 for Nash-Sutcliffe coefficient, 0.13 ms − 1 for Root Mean Square Error, and 0.39 for Index of Agreement) because CA ignores momentum conservation.

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.

The paper presents approach used for the development of the forecasting system of extreme hydro-meteorological events in the region of the Sea of Azov. Due to numerous dangerous extreme events that occurred in the beginning of XXI century the issue of creation such system has become very relevant and important. The forecasting system, named EX-MARE, was started developing in 2014 as a complex of mathematical models. For each type of hydro-meteorological events, the modeling component was designed. The EX-MARE system is based on a scenario approach implied the consideration a variety of possible futures taking into account the existing uncertainty. Accurate extreme events estimation requires automated monitoring systems and longterm database application. In the paper, the detail description of the system components and the data sources is examined. Three case studies about the sea surges, flash flood and ice conditions researches demonstrate the application of the EX-MARE system and the benefits of its using. Further development of the EX-MARE system assumes adding data on exposure and vulnerability to perform the risk assessment, as well as focusing on multi-hazards exploring methodology.

The article provides an analysis of the forecast of hydrocarbon ratios based on the data extracted. Various methods are used in the technological processes of gas extraction in order to build forecast models of the final gas-condensate yield coefficients. It is important to select the most accurate and reliable models in order to determine the recoverable reserves of gas and condensate fields operated in flood and depletion regimes or to assess the effectiveness of geological and technical measures taken to increase development efficiency. Mathematical modeling methods are widely used for analysis, control of gas and condensate field development and qualitative and quantitative forecasting of its technological parameters. At the same time, long-term operation of gas-condensate fields shows that the application of erolusion modeling methodology, which is one of the most accurate methods for determining the forecast values of recoverable gas reserves for different stages of development, is more appropriate. Keywords: Extracted reserves, hydrocarbon ratios, forecast, regimes, water pressure, gas-condensate fields.

An empirical method is developed for constructing likelihood functions required in a Bayesian probabilistic flash flood forecasting model using data on objective quantitative precipitation forecasts and their verification. Likelihoods based on categorical and probabilistic forecast information for several forecast periods, seasons, and locations are shown and compared. Data record length, forecast information type and magnitude, grid area, and discretized interval size are shown to affect probabilistic differentiation of amounts of potential rainfall. Use of these likelihoods in Bayes' Theorem to update prior probability distributions of potential rainfall, based on preliminary data, to posterior probability distributions, reflecting the latest forecast information, demonstrates that an abbreviated version of the flash flood forecasting methodology is currently practicable. For this application, likelihoods based on the categorical forecast are indicated. Apart from flash flood forecasting, it is shown that likelihoods can provide detailed insight into the value of information contained in particular forecast products.

The aim of this research project was to estimate parameters for the distribution of annual maximum flood levels for the Zambezi River at Katima Mulilo. The estimation of parameters was done by using the maximum likelihood method. The study aimed to explore data of the Zambezi's annual maximum flood heights at Katima Mulilo by means of fitting the Gumbel, Weibull and the generalized extreme value distributions and evaluated their goodness of fit.

Flash flood is among the most hazardous natural disasters, and it can cause severe damages to the environment and human life. Flash floods are mainly caused by intense rainfall and due to their rapid onset (within six hours of rainfall), very limited opportunity can be left for effective response. Understanding the socio-economic characteristics involving natural hazards potential, vulnerability, and resilience is necessary to address the damages to economy and casualties from extreme natural hazards. The vulnerability to flash floods is dependent on both biophysical and socio-economic factors. This study provides a comprehensive assessment of socio-economic vulnerability to flash flood alongside a novel framework for flash flood early warning system. A socio-economic vulnerability index was developed for each state and county in the Contiguous United States (CONUS). For this purpose, extensive ensembles of social and economic variables from US Census and the Bureau of Economic Analysis were assessed. The coincidence of socio-economic vulnerability and flash flood events were investigated to diagnose the critical and non-critical regions. In addition, a data-analytic approach is developed to assess the interaction between flash flood characteristics and the hydroclimatic variables, which is then applied as the foundation of the flash flood warning system. A novel framework based on the D-vine copula quantile regression algorithm is developed to detect the most significant hydroclimatic variables that describe the flash flood magnitude and duration as response variables and estimate the conditional quantiles of the flash flood characteristics. This study can help mitigate flash flood risks and improve recovery planning, and it can be useful for reducing flash flood impacts on vulnerable regions and population.

Thesis (MEng) -- Stellenbosch University, 2014.
ENGLISH ABSTRACT: The Francou and Rodier (1967) empirical approach uses the original concept of envelope curves for the definition of the regional maximum flood (RMF). Kovacs (1980) adopted the Francou and Rodier empirical flood calculation method and applied it to 355 catchments in South Africa. He revised his study in 1988 to also include the southern portions of the Southern Africa subcontinent. No method other than the Francou and Rodier empirical flood approach in the reviewed literature was found to be suitable for the purpose of this study. Therefore the Francou and Rodier empirical approach, as applied by Kovacs in 1988, was reapplied and used in this study to update the RMF for Lesotho. Maximum recorded flood peaks were derived from annual maximum time series and an up to date catalogue of flood peaks for 29 catchments was compiled for Lesotho. The maximum recorded flood peaks were then plotted on the logarithmic scale against their corresponding catchment areas. There are 3 major river systems that divide Lesotho into hydrologically homogenous basins. Envelope curves were drawn on the upper bound of the cloud of plotted points for these 3 river basins. These envelope curves represent the maximum flood peaks that can reasonably be expected to occur within the respective river basins in Lesotho.
AFRIKAANSE OPSOMMING: Francou en Rodier (1967) se empiriese benadering maak gebruik van die oorspronklike konsep van boonste limiet kurwes vir die definisie van die streeks maksimum vloed (SMV). Kovacs (1980) het die Francou en Rodier empiriese vloed berekening metode toegepas op 355 opvanggebiede in Suid-Afrika. Hy hersien sy studie in 1988 om ook die suidelike gedeeltes van die Suider-Afrikaanse subkontinent in te sluit. Geen ander metode as die Francou en Rodier empiriese vloed benadering is in die literatuur gevind wat as geskik aanvaar kan word vir die doel van hierdie studie nie. Daarom is die Francou en Rodier empiriese benadering, soos toegepas deur Kovacs in 1988, weer in hierdie studie toegepas en gebruik om die SMV metode vir Lesotho op te dateer. Maksimum aangetekende vloedpieke is verkry vanuit jaarlikse maksimum tyd-reekse en ʼn opgedateerde katalogus van vloedpieke vir 29 opvanggebiede saamgestel vir Lesotho. Die maksimum aangetekende vloedpieke is grafies aangetoon op logaritmiese skaal teenoor hul opvanggebiede. Daar is 3 groot rivierstelsels wat Lesotho in hidrologiese homogene gebiede verdeel. Boonste limiet kurwes is opgestel om die boonste grens van die gestipte punte vir hierdie 3 gebiede aan te toon. Hierdie krommes verteenwoordig die maksimum vloedpieke wat redelikerwys verwag kan word om binne die onderskeie rivierstelsels in Lesotho voor te kan kom.

A linear reservoir cell model is presented which is proposed as a good candidate for real time flood forecasting applications. The model is designed to be computationally efficient since it should be able to run on a P.C and must operate online in real time. The model parameters and forecasts can be easily updated in order to allow for a more accurate forecast based on real time observations of streamflow and rainfall. The final model, once calibrated, should be able to operate effectively without requiring highly skilled and knowledgeable operators. Thus it is hoped to provide a tool which can be incorporated into an early warning system for mitigation of flood damage, giving water resources managers the extra lead-time to implement any contingency plans which may be neccssary to ensure the safety of people and prevent damage to property. The use of linear models for describing hydrological systems is not new, however the model presented in this thesis departs from previous implementations. A particular departure is the novel method used in the conversion of observed to effective rainlfall. The physical processes that result in the rainfall to runoff conversion are non-linear in nature. Most of the significant non-linearity results from rainfall losses, which occur largely due to evaporation and human extraction. The remaining rainfall is converted to runoff. These losses are particularly significant in the South African climate and in some regions may be as much as 70-90 % of the total observed rainfall. Loss parameters are an integral part of the model formulation and allow for losses to be dealt with directly. Thus, input to the model is observed rainfall and not the "effective" rainfall normally associated with conceptual catchment models. The model is formulated in Finite Difference form similar to an Auto Regressive Moving Average (ARMA) model; it is this formulation which provides the required computational efficiency. The ARMA equation is a discretely coincident form of the State-Space equations that govern the response of an arrangement of linear reservoirs. This results in a functional relationship between the reservoir response constants and the ARMA coefficients, which guarantees stationarity of the ARMA model.
Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.

Design flood estimation is often required in hydrologic practice. For catchments with sufficient streamflow data, design floods can be obtained using flood frequency analysis. For catchments with no or little streamflow data (ungauged catchments), design flood estimation is a difficult task. The currently recommended method in Australia for design flood estimation in ungauged catchments is known as the Probabilistic Rational Method. There are alternatives to this method such as Quantile Regression Technique or Index Flood Method. All these methods give the flood peak estimate but the full streamflow hydrograph is required for many applications. The currently recommended rainfall based flood estimation method in Australia that can estimate full streamflow hydrograph is known as the Design Event Approach. This considers the probabilistic nature of rainfall depth but ignores the probabilistic behavior of other flood producing variables such as rainfall temporal pattern and initial loss, and thus this is likely to produce probability bias in final flood estimates. Joint Probability Approach is a superior method of design flood estimation which considers the probabilistic nature of the input variables (such as rainfall temporal pattern and initial loss) in the rainfall-runoff modelling. Rahman et al. (2002) developed a simple Monte Carlo Simulation technique based on the principles of joint probability, which is applicable to gauged catchments. This thesis extends the Monte Carlo Simulation technique to ungauged catchments. The Joint Probability Approach/ Monte Carlo Simulation Technique requires identification of the distributions of the input variables to the rainfall-runoff model e.g. rainfall duration, rainfall intensity, rainfall temporal pattern, and initial loss. For gauged catchments, these probability distributions are identified from observed rainfall and/or streamflow data. For application of the Joint Probability Approach to ungauged catchments, the distributions of the input variables need to be regionalised. This thesis, in particular, investigates the regionalisation of the distribution of rainfall duration and intensity. In this thesis, it is hypothesised that the distribution of storm duration can be described by Exponential distribution. The developed new technique of design flood estimation can provide the full hydrograph rather than only peak value as with the Probabilistic Rational Method and Quantile Regression Technique. The developed new technique can further be improved by addition of new and improved regional estimation equations for the initial loss, continuing loss and storage delay parameter (k) as and when these are available.
(M. Eng.) (Hons)

Many techniques have been developed over the years in first world countries for the estimation of flood hydrographs from small catchments for application in design, management and operations of water related issues. However, relatively little attention has been directed towards the transfer and adaptation of such techniques to developing countries in which major hydrological decisions are crucially needed, but in which a scarcity of quality hydrological data often occurs. As a result, hydrologists and engineers in developing countries are frequently unable to alleviate the problems that extreme rainfall events can create through destructive flood flows or, alternatively, they do not possess the appropriate tools with which to design economically viable hydraulic structures. Eritrea is a typical example of a developing country which faces difficulties in regard to the adaptation of an appropriate design flood estimation technique for application on small catchments. As a result, the need has arisen to adapt a relatively simple and robust design flood model that can aid hydrologists and engineers in making economic and safe designs of hydraulic structures in small catchments. One objective of this study was, therefore, to review approaches to hydrological modelling and design flood estimation techniques on small catchments, in order to identify the barriers regarding their adaptation, as well as to assist in the selection of an appropriate technique for application, in Eritrea. The southern African adaptation of the SCS (i.e. Soil Conservation Service) design hydrograph technique, which has become a standard method for design flood estimation from small catchments in that region, was selected for application on small catchments in Eritrea for several reasons. It relies on the determination of a simple catchment response index in the form of an initial Curve Number (CN), which reflects both the abstraction characteristics and the non-linear stormflow responses of the catchment from a discrete rainfall event. Many studies on the use of SCS-based hydrological models have identified that adjustment of the initial CN to a catchment's antecedent soil moisture (ASM) to be crucial, as the ASM has been found to be one of the most sensitive parameters for accurate estimates of design flood volumes and peak discharges. In hydrologically heterogeneous regions like Eritrea, the hypothesis was postulated that simulations using a suitable soil water budgeting procedure for CN adjustment would lead to improved estimates of design flood volumes and peak discharges when compared with adjustments using the conventional SCS antecedent moisture conditions (SCS-AMC) method. The primary objective of this dissertation was to develop a surrogate methodology for the soil water budgeting procedure of CN adjustment, because any direct applications of soil water budgeting techniques are impractical in most parts of Eritrea owing to a scarcity of adequate and quality controlled hydrological information. It was furthermore hypothesised that within reasonably similar climatic regions, median changes in soil moisture storage from the socalled "initial" catchment soil moisture conditions, i.e. LIS, were likely to be similar, while between different climatic regions median LISs were likely to be different. Additionally, it was postulated that climatic regions may be represented by a standard climate classification system. Based on the above hypotheses, the Koppen climate classification, which can be derived from mean monthly rainfall and temperature information, was first applied to the 712 relatively homogeneous hydrological response zones which had previously been identified in southern Africa. A high degree of homogeneity of median values of LIS, derived by the daily time step ACRU soil moisture budgeting model, was observed for zones occurring within each individual Koppen climate class (KCC) - this after a homogeneity test had been performed to check if zones falling in a specific KCC had similar values of median LIS. Further assessment within each KCC found in southern Africa then showed that a strong relationship existed between LIS and Mean Annual Precipitation (MAP). This relationship was, however, different between KCCs. By developing regression equations, good simulations of median LIS from MAP were observed in each KCC, illustrating the potential application of the Koppen climate classification system as an indicator of regional median LIS, when only very basic monthly climatological information is available. The next critical task undertaken was to test whether the estimate of median LIS from MAP by regression equation for a specific Koppen climate class identified in southern Africa would remain similar for an identical Koppen climatic region in Eritrea. As already mentioned, LIS may be determined from daily time step hydrological soil moisture budget models such as ACRU model. The performance of the ACRU stormflow modelling approach was, therefore, first verified on an Eritrean gauged research catchment, viz. the Afdeyu, in order to have confidence in the use of values of LIS generated by it. A SCS-ACRU stormflow modelling approach was then tested on the same catchment by using the new approach of CN adjustment, termed the ACRU-Koppen method, and results were compared against stormflow volumes obtained using the SCS-AMC classes and the Hawkins' soil water budgeting procedures for CN adjustment, as well as when CNs remain unadjusted. Despite the relatively limited level of information on climate, soils and land use for the Afdeyu research catchment, the ACRU model simulated both daily and monthly flows well. By comparing the outputs generated from the SCS model when using the different methods of CN adjustment, the ACRU-Koppen method displayed better levels of performances than either of the other two SCS-based methods. A further statistical comparison was made among the ACRU, the SCS adjusted by ACRU-Koppen, the SCS adjusted by AMC classes and the unadjusted SCS models for the five highest stormflows produced from the five highest daily rainfall amounts of each year on the Afdeyu catchment. The ACRU model produced highly acceptable statistics from stormflow simulations on the Afdeyu catchment when compared to the SCS-based estimates. In comparing results from the ACRU-Koppen method to those from the SCS-AMC and unadjusted CN methods it was found that, statistically, the ACRU-Koppen performed much better than either of the other two SCS based methods. On the strength of these results the following conclusions were drawn: • Changes in soil moisture storage from so-called "initial" catchment soil moisture conditions, i.e. L1S, are similar in similar climatic regions; and • Using the ACRU-Koppen method ofCN adjustment, the SCS-SA model can, therefore, be adapted for application in Eritrea, for which Koppen climates can be produced from monthly rainfall and temperature maps. Finally, future research needs for improvements in the SCS-ACRU-Koppen (SAK) approach in light of data availability and the estimation ofL1S were identified. From the findings of this research and South African experiences, a first version of a "SCSEritrea" user manual based on the SAK modelling approach has been produced to facilitate its use throughout Eritrea. This user manual, although not an integral part of this dissertation, is presented in its entirety as an Appendix. A first Version of the SCS-Eritrea software is also included.
Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.

"May 2002" Includes list of papers published during this study Errata slip inserted inside back cover of v. 1 Includes bibliographical references (leaves 331-357) V. 1. [Text} -- v. 2. Appendices Develops an alternative design flood estimation methodology. Establishing a relationship between catchment characteristics and the rainfall excess frequency duration proportions enables the definition of these proportions for generic catchment types, increasing the potential for translation to catchments with limited data but similar hydrographic properties, thereby improving design process.

There is a frequent need for estimates of design floods by hydrologists and engineers for the design of hydraulic structures. There are various techniques for estimating these design floods which are dependent largely on the availability of data. The two main approaches to design flood estimation are categorised as methods based on the analysis of floods and those based on rainfall-runoff relationships. Amongst the methods based on the analysis of floods, regional flood frequency analysis is seen as a reliable and robust method and is the recommended approach. Design event models are commonly used for design flood estimation in rainfall-runoff based analyses. However, these have several simplifying assumptions which are important in design flood estimation. A continuous simulation approach to design flood estimation has many advantages and overcomes many of the limitations of the design event approach. A major concern with continuous simulation using a hydrological model is the scale at which should take place. According to Martina (2004) the “level” of representation that will preserve the “physical chain” of the hydrological processes, both in terms of scale of representation and level of description of the physical parameters for the modelling process, is a critical question to be addressed. The objectives of this study were to review the literature on different approaches commonly used in South Africa and internationally for design flood estimation and, based on the literature, assess the potential for the use of a continuous simulation approach to design flood estimation. Objectives of both case studies undertaken in this research were to determine the optimum levels of catchment discretisation, optimum levels of soil and land cover information required and, to assess the optimum use of daily rainfall stations for the configuration of the ACRU agrohydrological model when used as a continuous simulation model for design flood estimation. The last objective was to compare design flood estimates from flows simulated by the ACRU model with design flood estimates obtained from observed data. Results obtained for selected quaternary catchments in the Thukela Catchment and Lions River catchment indicated that modelling at the level of hydrological response units (HRU’s), using area weighted soils information and more than one driver rainfall station where possible, produced the most realistic results when comparing observed and simulated streamflows. Design flood estimates from simulated flows compared reasonably well with design flood estimates obtained from observed data only for QC59 and QCU20B.
Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

A method for predicting threats in complex distributed systems is proposed, based on the intelligent analysis of large data arrays on the results of monitoring changes in water level in water bodies and air temperature at the measurement point, which makes it possible to increase the efficiency of planning and implementing measures to fend off such and similar threats. The method is based on general approaches and mathematical models previously used by the authors to develop adaptive algorithms for controlling gas turbine engines, which is especially relevant in the context of the increasingly widespread introduction of automatic means for monitoring the state of complex distributed systems and the exponential growth in the number of data used to support decision-making. The choice of the future value of the water level at the measurement point is carried out based on the results of processing the data accumulated for all previous flood periods on the compliance of the water level and its changes per day with the values of air temperature and its changes for the same day. The results of an experimental assessment of the accuracy of predicting the water level in the water bodies of the Republic of Bashkortostan in the flood period of 2021 are presented, which confirm the applicability of the proposed forecasting method to support decision-making to fend off threats in complex distributed systems from a sharp rise in water.