This paper deals with stochastic modelling of monthly inflows into a reservoir system in the monsoon climatic coditions using a multiplicative seasonal ARIMA model based on 25 years of data with logarithmic transformation. The developed model was applied to forecast the monthly inflows for 27 years. The comparison of these forecasted flows with the actual flows reveals that the ARIMA family models are adequate for longterm forecasting of inflows. The parameter uncertainity was also evaluated and found to be minimal thus avoiding the frequent updating of the model for forecasting. The use of the model in evolving optimal cropping patterns and optimal operational policies is also highlighted. © 1995 Kluwer Academic Publishers.

S. Mohan

Department Of Civil Engineering

Forecasting

Modelling

Reservoirs

Seasonal variation

ARIMA

Inflow

RESERVOIR SYSTEM

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Multiplicative seasonal Arima model for longterm forecasting of inflows

Water Resources Management

Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter’s exponential smoothing model, have been investigated for their applicability for forecasting weekly reference crop ET. A seasonal ARIMA model with one autoregressive and one moving average process and with a seasonality of 52 weeks was found to be an appropriate stochastic model. The ARIMA and Winter’s models were compared with a simple ET model to assess their performance in forecasting. The forecast errors produced by these models were very small and the models would be promisingly of great use in real-time irrigation management. © 1995 Taylor & Francis Group, LLC.

Hydrological Sciences Journal

Forecasting weekly reference crop evapotranspiration series

Demand for water is increasing continually, whereas available supplies are more or less constant. Under these circumstances there is an urgent need to introduce efficient techniques in water resources management for optimal utilization of available water. Water management under drought conditions assumes great importance in a tropical region such as India, where one-third of the cropped area is affected by frequent droughts. This paper deals with the development and application of an expert system for drought management. A linear programming model was used to generate optimal cropping patterns from past drought experiences as also from synthetic drought occurrences. These policies together with the knowledge of the experts were incorporated in an expert system. Using this, one can identify the degree of drought in the current situation and its similarity to the identified drought events and be able to get the corresponding management strategy. © ASCE.

Journal of Irrigation and Drainage Engineering

Decision support for crop planning during droughts

Time Series Modelling of Water Resources and Environmental Systems Developments in Water Science

Acknowledgements

Real-time multipurpose reservoir operation: a case study

Water Resources Research

Selecting seasonal streamflow models

Monthly Weather Review

On the Use of Autoregressive-Moving Average Processes to Model Meteorological Time Series

Journal	Water Resources Management
Publisher	Kluwer Academic Publishers
ISSN	09204741
Open Access	No