The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.

The study is aimed at finding the best distribution to match the steam flow and calculation of magnitude and frequency of flow. In the current study, we have used several statistical distributions to find the best fit distribution for stream flow and used flood frequency analysis techniques to find the magnitude and frequency of stream flow and non-exceedance probability of peak discharge. The study has been performed at Sikandarpur and Rosera gauging sites of BurhiGandak River. Historical (50 years) maximum annual peak discharge data of each station are used for statistical analysis for estimating maximum peak discharge in 5, 10, 25, 50, 100 year return period. In this study, Lognormal distribution, Galton distribution, Gamma distribution, Log Pearson Type III distribution, Gumbell distribution, Generalised extreme values distribution have been considered to describe the annual maximum stream flow. Flood frequency analysis methods were used for estimating the magnitude of the extreme flow events and their associated return periods. For both Sikandarpur and Rosera stations, Log Pearson type III distributions showed the lowest value of K–S and Chi-square test statistic. The annual probable peak discharge for 5, 10, 25, 50, and 100 years return period is calculated for each distribution. The most suitable distribution for both the stations is found to be the log-Pearson type III distribution.

Bansagar Dam is an inter-state multipurpose large river basin project constructed at a place called Devalond in Shahdol district of Madhya Pradesh state. The dam is built on the Son River (Sonbhadrashila) in Madhya Pradesh. Devalond is located on the Rewa-Shahdol road, about 56 km from Rewa. The height of this dam is 67 meters. We have surveyed the villages which are partially or completely affected by the Bansagar dam, as any development program, as we know, has two stages of construction. There are positive as well as negative effects, so we survey the affected area to compare the pre and post position of the submerged area according to the hypothesis or previous assumption.

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