India has made considerable progress as far as creation of irrigation potential is concerned. The gap between irrigation potential created and utilized is a matter of concern. The success of irrigation system operation and planning depends on the quantification of supply and demand and equitable distribution of supply to meet the demand if possible, or, to minimize the gap between the supply and demand. Hence, it is essential to forecast reservoir inflow for proper planning and management of canal irrigation projects. Autoregressive Integrated Moving Average (ARIMA) and X-12-ARIMA are one of the extensively used software packages for time series forecasting. This study focused on the Application of these software packages for Monthly Stream Flow Forecasting of Kangsabati River of India. Here, ARIMA (2, 1, 1) (2, 1, 2) and ARIMA-X-12 (2, 1, 1) (2, 1, 2) models were found to have less Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC) and many other statistical values, selected for mean monthly foresting. In the comparison of ARIMA and X-12-ARIMA models, the X-12-ARIMA model is found more accurate then the ARIMA model for monthly stream flow forecasting. This study suggests that the selected models can be used successfully for monthly stream flow forecasting of Kangsabati river.

The study was undertaken to develop and evaluate evapotranspiration model for black gram (Vigna Mungo L.) crop under climatic conditions of Udaipur, India. Pan evaporation data for the duration of twenty three years (1978-2001) and measured black gram evapotranspiration data by electronic lysimeter for duration of kharif season of 2001 were used for analysis. Black gram is an important crop of Udaipur region. No sys-tematic study on modelling of black gram evapotranspiration was conducted in past under above said cli-matic conditions. Therefore, stochastic model was developed for the estimation of daily black gram evapotranspiration using 24 years data. Validation of the developed models was done by the comparison of the estimated values with the measured values. The developed stochastic model for black gram evapotran-spiration was found to predict the daily black gram evapotranspiration very accurately