Laboratory Study of Watershed Hydrology (HES 14)

National Science Foundation, Research Grant GP-1464unpublishednot peer reviewe

Methodologies for water resources planning: DDDP and MLOM (TLOM)

U.S. Geological SurveyU.S. Department of the InteriorOpe

Stochastic analysis of hydrologic systems

Hydrologic phenomena are in reality stochastic in nature; that is, their behavior changes with the time in accordance with the law of probability as well as with the sequential relationship between the occurrences of the phenomenon. In order to analyze the hydrologic phenomenon, a mathematic model of the stochastic hydrologic system to simulate the phenomenon must be formulated. In this study, a watershed is treated as the stochastic hydrologic system whose components of precipitation, runoff, storage and evapotranspiration are simulated as stochastic processes by time series models to be determined by correlograms and spectral analysis. The hydrologic system model is then formulated on the basis of the principle of conservation of mass and composed of the component stochastic processes. To demonstrate the practical application of the method of analysis so developed, the upper Sangamon River basin above Monticello in east central Illinois is used as the sample watershed. The watershed system model so formulated can be employed to generate stochastic streamflows for practical use in the analysis of water resources systems. This is of particular value in the economic planning of water supply and irrigation projects which is concerned with the long-range water yield of the watershed.U.S. Geological SurveyU.S. Department of the InteriorOpe

Application of DDDP in water resources planning

This is the completion report for the second phase of a research program on advanced methodologies for water resources planning. It summarizes the various achievements accomplished during the period of the project. The main portion of the report, however, is devoted to the presentation of a working manual for use by practicing water resources engineers and analysts, showing the application of the discrete differential dynamic programming (DDDP) which has been developed in the project. For this portion of the report, the brief theoretical background of the DDDP methodology and a review of the principal aspects of its theory are included. Then, a detailed description of the DDDP methodology is given, giving emphasis to the key steps of its procedure. The DDDP methodology as a means to solve already formulated dynamic programming problems is proposed. For illustrative purposes, three examples are given to show the application of the DDDP methodology in the solution of optimization problems arising from the planning and operation of complex water resources projects.U.S. Geological SurveyU.S. Department of the InteriorOpe

On the determination of transmissibility and storage coefficients from pumping test data

Reprint. Originally published: Transactions, American Geophysical Union ; v. 33, no. 3 (June 1952).Cover title.Includes bibliographical references

Analysis of multiple-input stochastic hydrologic systems

This report describes the development of a multiple-input stochastic hydrologic system model for the analysis of hydrologic behavior of watersheds. Various components of the hydrologic system are expressed by time series, each containing a trend component, a periodic component, and a stochastic residual component. For modeling the multiple-input system, a Markov-type mathematical formulation is proposed. For illustrative purposes, the model so formulated is applied to the analysis of monthly precipitations and streamflows of the upper Sangamon River basin in Illinois. The results of this study indicate that the proposed model is feasible for the basin and thus can be used for filling missing streamflow data or generating stochastic streamflow sequences.U.S. Geological SurveyU.S. Department of the InteriorOpe

Characterization of critical shear stresses and bank material erosion rates on gravelly stream banks

Meandering river migration over large spatial and temporal scales has traditionally been numerically simulated using a bank erosion submodel that calculates the eroding bank migration rate as the product of the near-bank excess flow velocity and a dimensionless migration coefficient. The latter value is an empirical parameter calibrated to historical observations. In efforts to improve upon the traditional model, recent research has followed two approaches: (a) provide a means of estimating the dimensionless migration coefficient based on field measurements; and (b) discard the traditional migration coefficient approach to develop a bank erosion submodel based on the actual formulations that dictate fluvial erosion rates and mass failure which determine bank migration. The latter physics-based approach was recently implemented into the numerical model RVR Meander developed by the Ven Te Chow Hydrosystems Laboratory at the University of Illinois in Urbana-Champaign (Motta et al, 2012a); however, the governing equations used for fluvial erosion strictly apply only to banks comprised of cohesive soils. In that formulation the fluvial erosion rate is linearly dependent on the excess boundary shear stress. This study explores whether a similarly simple formulation can describe in a gross sense the migration of river banks comprised entirely of non-cohesive soil or composite banks consisting of non-cohesive soil at the base overlain by cohesive soil. Numerical modeling of both fluvial erosion and shallow avalanche mass failures that occur simultaneously during non-cohesive bank deformation reveal that the bank migration rate is strongly non-linear with respect to the boundary shear stress (exponent greater than 1) when considering non-cohesive bank materials. A methodology is described for developing a site specific non-cohesive bank erosion submodel that is valid and computationally practicable over the desired large spatial and temporal scales relevant to models such as RVR Meander. The new methodology allows issues such as flow regime modifications to be incorporated to change the model parameters, which was not possible using the traditional empirical approach. The numerical modeling performed in this study also provides fundamental insights into deformation of non-cohesive river banks: it demonstrates that high flow events tend to cause bank slope reduction, with lower flow events tending to rejuvenate the steepness of the bank; it quantifies the importance of prior erosional history in influencing bank migration rates; and it quantifies the feedback of basal armoring on deformation of the unarmored region.U.S. Department of the InteriorU.S. Geological SurveyOpe

A physically-based bank erosion model for composite river banks: Application to Mackinaw River, Illinois

Meandering river migration over large spatial and temporal scales has traditionally been numerically simulated using a bank erosion submodel that calculates the eroding bank migration rate as the product of the near-bank excess flow velocity and a dimensionless migration coefficient. The latter value is an empirical parameter calibrated to historical observations. In efforts to improve upon the traditional model, recent research has followed two approaches: (a) provide a means of estimating the dimensionless migration coefficient based on field measurements; and (b) discard the traditional migration coefficient approach to develop a bank erosion submodel based on the actual formulations that dictate fluvial erosion rates and mass failure which determine bank migration. The latter physics-based approach was recently implemented into the numerical model RVR Meander developed by the Ven Te Chow Hydrosystems Laboratory at the University of Illinois in Urbana-Champaign (Motta et al, 2012a); however, the governing equations used for fluvial erosion strictly apply only to banks comprised of cohesive soils. In that formulation the fluvial erosion rate is linearly dependent on the excess boundary shear stress. This study explores whether a similarly simple formulation can describe in a gross sense the migration of river banks comprised entirely of non-cohesive soil or composite banks consisting of non-cohesive soil at the base overlain by cohesive soil. Numerical modeling of both fluvial erosion and shallow avalanche mass failures that occur simultaneously during non-cohesive bank deformation reveal that the bank migration rate is strongly non-linear with respect to the boundary shear stress (exponent greater than 1) when considering non-cohesive bank materials. A methodology is described for developing a site specific non-cohesive bank erosion submodel that is valid and computationally practicable over the desired large spatial and temporal scales relevant to models such as RVR Meander. The new methodology allows issues such as flow regime modifications to be incorporated to change the model parameters, which was not possible using the traditional empirical approach. The numerical modeling performed in this study also provides fundamental insights into deformation of non-cohesive river banks: it demonstrates that high flow events tend to cause bank slope reduction, with lower flow events tending to rejuvenate the steepness of the bank; it quantifies the importance of prior erosional history in influencing bank migration rates; and it quantifies the feedback of basal armoring on deformation of the unarmored region.U.S. Department of the InteriorU.S. Geological SurveyOpe

Scheme for stochastic state variable water resources systems optimization

This report describes the development of an analytical scheme for the formulation and optimization of water resources systems. The scheme being proposed and investigated is to model the stochastic input of annual as well as monthly streamflows to a hydrologic and water resources system, to formulate the system in a state variable format, and to optimize the stochastic state variable model so formulated by dynamic programming. For annual streamflows, a second-order autoregressive model with a data-based transformation is proposed, and both the maximum likelihood method and the Bayesian approach are used for estimating the model parameters. For monthly streamflows, two linear models are proposed, one is the regression model and the other is the functional relationship model, and their consideration of both uncorrelated and correlated errors and their techniques of generation by a stationary Markov process are discussed. The proposed state variable approach provides a generalized framework within which many different kinds of system models may be expressed and combined for the representation of a given hydrologic and water resources system. This simple yet general format is a major advantage of the proposed state variable modeling. While the annual or monthly streamflows are generated as stochastic inputs to the state variable system model by the proposed scheme, a new procedure of optimization of the system by stochastic dynamic programming is developed. Although the research effort should be further extended to the development of practical procedures for application, a few simple examples are given to illustrate the validity of such applications.U.S. Geological SurveyU.S. Department of the InteriorOpe

Hydrologic determination of waterway areas for the design of drainage structures in small drainage basins

Bibliography: p. 91-104