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The management of water, viewed either as a natural hazard or a vital resource, is critical for the safety and prosperity of communities. The risks associated with managing water availability, whether in scarcity or excess, are critical concerns for the design and operation of infrastructure as well as the implementation of public policy. The spatial variability of rainfall is a known driving force of catchment dynamics and water availability, but despite this, it is often poorly represented in hydrologic studies and designs. This thesis focuses on improvements to the estimation, simulation and evaluation of spatial rainfall. Specifically these developments include: (i) the development of a generalised approach for spatial extreme rainfall estimation; (ii) the development of a flexible, continuous, and spatial stochastic model of rainfall and its corresponding evaluation; and (iii) an innovative framework for critically evaluating the performance of stochastic rainfall models via the assessment of simulated streamflow. Australian case study locations, with varying climates, are used to present and investigate these approaches. A new approach for estimating extreme spatial rainfall intensities and a critical evaluation of current approaches for estimation are presented. Current techniques for estimating extreme spatial rainfall are reliant on areal reduction factors (ARF) to convert intensity estimates of extreme point rainfall to extreme spatial rainfall. It is common practice to ignore spatial variation in rainfall intensity and assume a constant ARF over a large region. Approaches using ARFs for estimating extreme spatial rainfall were demonstrated to be in error by 5% to 15%. A new approach that explicitly incorporates the variation of spatial rainfall over an area, referred to as Intensity Frequency Duration Area (IFDA) was developed to address this issue. IFDAs use spatially interpolated rainfall grids to directly estimate how extreme rainfall intensity varies with frequency, duration and area for a given location. The IFDA approach overcomes the shortcomings of existing approaches by avoiding the need to assume a fixed regional ARF value. IFDA provide direct and unbiased estimates of extreme spatial rainfall. An alternative approach to spatially interpolated observations of extremes is to use data generated by a stochastic spatial rainfall model. A new model for continuously simulating fields of daily spatial rainfall in a parsimonious manner is developed in this thesis. A Gaussian latent variable approach is used because it is able to simultaneously generate rainfall occurrences as well as amounts. Parameter surfaces are produced via kriging which enables the model to produce stochastic replicates for any location of interest in the catchment. Additional benefits of the model are that it removes the need for interpolation to construct catchment average rainfall estimates, preserves the rainfall's volumetric properties and can be used with distributed hydrologic models. A comprehensive evaluation approach was developed to identify model strengths and weaknesses. This included a performance classification system that provided a systematic, succinct and transparent method to assess and summarize model performance over a range of statistics, sites and scales. The model showed many strengths in reproducing observed rainfall characteristics with the majority of statistics classified as either statistically indistinguishable from the observed or within 5% of the observed across the majority of sites and seasons. A significant challenge when evaluating rainfall models is that the key variable of interest is resultant streamflow, not generated rainfall. Typical evaluation methods use a variety of rainfall statistics, but they provide limited understanding on (i) how rainfall influences streamflow generation; (ii) which rainfall characteristics are most important; and (iii) the trade-offs made when one or more features of rainfall are poorly reproduced. An innovative virtual hydrological evaluation framework is developed to evaluate whether deficiencies in simulated rainfall lead to deficiencies in resultant streamflow. The key feature of the framework is the use of a hydrological model to compare streamflow derived from observed and simulated rainfall at the same location. The framework allows the impact of an influencing month of simulated rainfall on streamflow in an evaluated month of interest to be isolated. Application of the virtual hydrological evaluation framework identified the importance of transition months May and June (late autumn/early winter) in the 'wetting-up' phase of the catchment cycle. Despite their low monthly flow volumes, the transition months contributed significantly to error in the annual total flow. With improved representation and evaluation of spatial rainfall, this thesis ultimately demonstrates more realistic and accurate methods for hydrological estimation.
Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.
This book communicates some contemporary mathematical and statistical developments in river basin hydrology as they pertain to space-time rainfall, spatial landform and network structures and their role in understanding averages and fluctuations in the hydrologic water balance of river basins. While many of the mathematical and statistical nations have quite classical mathematical roots, the river basin data structure has led to many variations on the problems and theory.
During ten years serving with the USDA Soil Conservation Service (SCS), now known as the Natural Resources Conservation Service (NRCS), I became amazed at how millions of dollars in contract monies were spent based on simplistic hydrologic models. As project engineer in western Kansas, I was responsible for building flood control dams (authorized under Public Law 566) in the Wet Walnut River watershed. This watershed is within the Arkansas-Red River basin, as is the Illinois River basin referred to extensively in this book. After building nearly 18 of these structures, I became Assistant State Engineer in Michigan and, for a short time, State Engineer for NRCS. Again, we based our entire design and construction program on simplified relationships variously referred to as the SCS method. I recall announcing that I was going to pursue a doctoral degree and develop a new hydrologic model. One of my agency's chief engineers remarked, "Oh no, not another model!" Since then, I hope that I have not built just another model but have significantly advanced the state of hydrologic modeling for both researchers and practitioners. Using distributed hydrologic techniques described in this book, I also hope one day to forecast the response of the dams I built.
Objectives The current global environmental crisis has reinforced the need for developing flexible mathematical models to obtain a better understanding of environmental problems so that effective remedial action can be taken. Because natural phenomena occurring in hydrology and environmental engineering usually behave in random and probabilistic fashions, stochastic and statistical models have major roles to play in the protection and restoration of our natural environment. Consequently, the main objective of this edited volume is to present some of the most up-to-date and promising approaches to stochastic and statistical modelling, especially with respect to groundwater and surface water applications. Contents As shown in the Table of Contents, the book is subdivided into the following main parts: GENERAL ISSUES PART I PART II GROUNDWATER PART III SURFACE WATER PART IV STOCHASTIC OPTIMIZATION PART V MOMENT ANALYSIS PART VI OTHER TOPICS Part I raises some thought-provoking issues about probabilistic modelling of hydro logical and environmental systems. The first two papers in Part I are, in fact, keynote papers delivered at an international environmetrics conference held at the University of Waterloo in June, 1993, in honour of Professor T. E. Unny. In his keynote pa per, Dr. S. J. Burges of the University of Washington places into perspective the historical and future roles of stochastic modelling in hydrology and environmental engineering. Additionally, Dr. Burges stresses the need for developing a sound scien tific basis for the field of hydrology. Professor P. E.
Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane's Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling.
Amid climatic changes linked to global warming, ongoing changes in land-use patterns, and growing international concern with the environment it is increasingly important to understand the potential impact of these changes on the environment. Rainfall-runoff modeling is an important predictor of that impact. This book introduces rainfall-runoff models that have been developed over the past 24-30 years, giving examples of their practical applications. It provides a summary of available techniques for rainfall modeling based upon the most recent research, but in a way that serves as a primer for the subject. Provides an overview of how catchment rainfall-runoff systems work A history of rainfall-runoff models Examples of models can be downloaded over the Internet Looks at uncertainty in model prediction
The subject of rainfall-runoff modeling involves a wide spectrum of topics. Fundamental to each topic is the problem of accurately computing runoff at a point given rainfall data at another point. The fact that there is currently no one universally accepted approach to computing runoff, given rainfall data, indicates that a purely deter ministic solution to the problem has not yet been found. The technology employed in the modern rainfall-runoff models has evolved substantially over the last two decades, with computer models becoming increasingly more complex in their detail of describing the hydrologic and hydraulic processes which occur in the catchment. But despite the advances in including this additional detail, the level of error in runoff estimates (given rainfall) does not seem to be significantly changed with increasing model complexity; in fact it is not uncommon for the model's level of accuracy to deteriorate with increasing complexity. In a latter section of this chapter, a literature review of the state-of-the-art in rainfall-runoff modeling is compiled which includes many of the concerns noted by rainfall-runoff modelers. The review indicates that there is still no deterministic solution to the rainfall-runoff modeling problem, and that the error in runoff estimates produced from rainfall-runoff models is of such magnitude that they should not be simply ignored.
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Rainfall: Physical Process, Measurement, Data Analysis and Usage in Hydrological Investigations integrates different rainfall perspectives, from droplet formation and modeling developments to the experimental measurements and their analysis, to application in surface and subsurface hydrological investigations. Each chapter provides an updated representation of the involved subject with relative open problems and includes a case study at the end of the chapter. The book targets postgraduate readers studying meteorology, civil and environmental engineering, geophysics, agronomy and natural science, as well as practitioners working in the fields of hydrology, hydrogeology, agronomy and water resource management. Presents comprehensive coverage of rainfall-related topics, from the basic processes involved in the drop formation to data use and modeling Provides real-life examples for practical use in the form of a case study in each chapter