A Spreadsheet Approach for Prediction of Rating Curve Parameters

  • Muzzammil M
  • Alam J
  • Zakwan M
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Abstract

According to the official document of UNESCO (1985) - 'Teaching aids in Hydrology': "The main purpose of using hydrological models in the teaching process is not to duplicate the complicated hydrological process in detail by a sophisticated model, but to demonstrate the principal elements of the process, their combination into a simple or comprehensive model, and the importance of the model in solving typical problems of engineering hydrology". The course “Hydrological analysis and modelling” at the Department of Earth Sciences, Uppsala University consists of two parts, the first part deals with statistical analysis of hydrological data and the second part deals with modelling of runoff processes. This monograph serves as the lecture book for the second part. In particular, it is concerned with hydrologic models of precipitation-runoff process on catchment scale (groundwater modelling and urban storm flow modelling are not included). We will discuss their uses, formulations and methodology for model evaluation. The subject matter is divided into eight chapters. Chapter 1 is to give students a basic knowledge about modelling in hydrology. That includes: (1) the reasons of using models in problem solving in hydrology; (2) some definitions commonly used in the literature of hydrological modelling; (3) classification of runoff models; and (4) objectives of hydrologic models. Chapter 2 is to give an introduction to the students about the concept of the time series analysis and stochastic models. By this chapter, students will learn how to analyse a given time series of a hydrological variable, how stochastic models are formulated and what are their application fields. Chapters 3 through 5 show how the principal hydrological processes, such as, precipitation (rainfall and snowfall), evapotranspiration, streamflow, infiltration, etc., are generally treated in different kinds of runoff models. Chapter 6 is to study the methodology of model evaluation, which includes the issues in model selection, in model calibration (parameter estimation), in model verification, and estimation of its range of applicability. Although it is important to recognise that all four evaluations are of equal fundamental importance, estimation of the parameters and verification of model performance will receive more attention in this chapter. Some topics in optimisation are discussed in Chapter 7. Since the automatic optimisation procedures have attracted increasing interests in the field of conceptual catchment modelling, and they are, nowadays, widely used to minimise differences between selected features of modelled and observed streamflows by systematic trial alterations in the values of the model parameters. Although it is not necessary that the user himself writes programs, since a great number of these programmes are stored in many computer libraries. Nevertheless it is useful to have an idea of the principles of these algorithms in order to judge the results obtained, and moreover to choose the algorithm in such a way that it is adapted to the problem proposed. In chapter 8, some particular catchment models are discussed in more detail to illustrate how such models are formulated and what are their uses. The proposed models for discussion include, but not limited to, the HBV model (a simple conceptualdeterministic model), WASMOD model (a simple stochastic-conceptual snow and water balance model), the TOPMODEL (a relatively simple physically-based model), and the SHE model (a physically-based, distributed-parameter model). Moreover, this course includes a certain amount of individual computer work, which is not specified in this text: preparation of the inputs, running of different models and analysis of the outputs, etc. Several computer exercises will be done. By these means, the students learn to use hydrological models and develop them into powerful tools in the process of decision making. The content presented in the above chapters may be refined during the lecture time according to the interests of the students, and the orders of the appearance of the chapters may be changed.

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Muzzammil, M., Alam, J., & Zakwan, M. (2018). A Spreadsheet Approach for Prediction of Rating Curve Parameters (pp. 525–533). https://doi.org/10.1007/978-981-10-5801-1_36

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