The paper deals with the projected rainfall for eight rain gauge stations located in the upper Mahanadi catchment in Chhattisgarh state of India and corresponding changes on the water availability in few reservoirs of the catchment. Rescaled predictors obtained from NCEP were used and statistically tested for selection of best-fit set of predictors using percentage reduction methods. The calibrated and validated models were used to generate multiple series for early, mid and late century periods, i.e. for 2020–2035 (FP-1), 2046–2064 (FP-2) and 2081–2099 (FP-3) under CMIP5 climatic forcing conditions of RCP2.6, RCP4.5 and RCP8.5 using predictors data of CanESM2, Canadian GCM. The comparisons of future predicted rainfall with the base period (1981–2003) showed mixed trends, viz. declining trend at five stations, both declining and increasing trend at two stations, and increasing trend at one station. The predicted reduced rainfall during August and September attribute to a significant impact on paddy cultivation and industrial development. The analysis of future catchment rainfall on five important reservoirs in this region indicated a reduction of 12–29% seasonal rainfall with respect to the base period rainfall; while for one reservoir not much variation (–7 to 5%) in the rainfall was noted, possibly due to the large catchment area.

In this paper, the Kolar River watershed, Madhya Pradesh is taken as the study area. This study area is located in Narmada River in Central India. The data set consists of monthly rainfall of three meteorological stations, Ichhawar, Brijesh Nagar, and Birpur rainfall stations from 2000 to 2018, runoff data at Birpur and temperature data of Sehore district. In this paper, radial basis function neural network models have been studied for generation of rainfall–runoff modeling along with wavelet input and without wavelet input to the RBF neural network. A total of 15 models were developed in this experiment based on various combinations of inputs and spread constant of RBF model. The evaluation criteria for the best models selected are based on R2, AARE, and MSE. The best predicting model among the networks is model 8, which has input of R(t-1), R(t-2), R(t-3), R(t-4), and Q(t-1). For the RBFNN model, the maximum value of R2 is 0.9567 and the lowest values of AARE and MSE are observed. Similarly, for the WRBFNN model, the maximum value of R2 is 0.9889 and the lowest values of AARE and MSE are observed. WRBF performs better than RBF with any data processing techniques which shows the proposed model possesses better predictive capability.

In present study, MIKE 11NAM rainfall runoff(R-R) and MIKE BASIN models were used to develop R–R relationship and to analyze the water availability status of the Shipra river basin under changing hydrological conditions. The model was developed using discharge data observed for 9 years at the Ujjain G/d site. The coefficient of determination(R²) values for NAM R-R model calibration and validation were 0.685 and 0.642, respectively, indicating good agreement between the simulated and observed runoff. As the Shipra River is subjected to a number of hydrological interventions, the MIKE BASIN model was used to develop various scenarios to study the influence of river diversion, on-stream dam, river interlinking on water availability in different time-scale at desired locations. Scenario analysis of MIKEBASIN results indicated that the Khan diversion, which was executed to avoid entry of contaminated Khan river water into Shipra has reduced the water availability in the Shipra river. However, the simulation results indicated that the water availability in Shipra has been improved due to Narmada-Shipra link, a minimum of 1.7 m³/s flow is available at Ujjain even during the low-flow period. Gambhir reservoir was developed to evaluate how demand deficit can be fulfilled through proper reservoir operation during a drought period.