AKTU Stochastic Hydrology (HTSW601) Syllabus PDF – B.Tech Honors Degree in Soil and Water Conservation Engineering
Stochastic Hydrology HTSW601
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- Semester 6
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Syllabus — Stochastic Hydrology
Download PDFHTSW601 · 4 credits · L-T-P 3-1-0
| Pre-requisites (if any) | Basic knowledge of watershed hydrology. |
Course Outcomes
Course Outcomes
Course Outcomes
| CO 1 | Understand hydrologic cycle, systems theory, hydrologic models classification, statistical methods, deterministic approaches, time series analysis, and extreme hydrologic events. | K2 & K3 |
| CO 2 | Understand multivariate regression, correlation analysis, interpret correlation coefficients in regional studies, and construct reliable prediction equations using simple and multiple linear regressions. | K2 & K6 |
| CO 3 | Familiar with stochastic processes, time series analysis, autocorrelation, moving averages, spectral analysis, and multivariate hydrologic series investigation methods. | K2 |
| CO 4 | Understand and apply the Fiering and Box-Jenkins models, parameter estimation, calibration, validation, hydrologic data application, generation, forecasting, flood frequency analysis, transformations, and hypothesis testing. | K2 & K3 |
| CO 5 | Understand hydrologic uncertainty modeling, first-order Markov process, Markov chains, data generation, and analysis of hydrologic time series models. | K2 |
K1- Remember, K2- Understand, K3- Apply, K4- Analyze, K5-Evaulate, K6- Create Hydrologic cycle, Systems concept, Hydrologic systems model. Classification of hydrologic models, Statistical, stochastic and deterministic approaches. Statistical characteristics of hydrological data, probability distribution of hydrologic variables. Deterministic and stochastic hydrology, Cause and effect analysis. Hydrologic time series analysis – nature, stationarity and ergodicity, components of time series, trend, periodicity and stochastic parts, parameter estimation of probability distributions. Analysis of hydrologic extremes.
Multivariate regression analysis, correlation analysis, correlation coefficient and its significance in regional analysis. Developing prediction equation by simple and multiple linear regression. Reliability of the Model.
Stochastic Process: Classification, stationary process. Time series: Classification, component of time series.
Methods of investigation: Auto correlation coefficient, moving average process, auto regressive process, auto regressive moving average process, auto regressive integrated moving average process. Spectral analysis, analysis of multivariate hydrologic series.
Thomas Fiering model, Box Jenkins model. Model formulation: Parameter estimation, calibration and validation. Application to hydrologic data. Generation and forecasting. Regional flood frequency analysis.
Transformations, Hypothesis testing.
Modeling hydrologic uncertainty. First order Markov process, Markov chain, Data generation, Hydrologic time series analysis, Modelling of hydrologic time series.
- Clarke RT. Mathematical Models in Hydrology. FAO Publication.
- Haan CT. 2002. Statistical Methods in Hydrology. Iowa State Press.
- Kotteguda NT. 1982. Stochastic Water Resources Technology. The Macmillan Press, New York.
- McCuen RH and Snyder WM. Hydrological Modelling–Statistical Methods and Applications. Prentice Hall Inc., New York.
- Yevjevich V Stochastic Processes in Hydrology. Water Resources Publications, Colorado.
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