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For hydrological analysis, it is essential to have continuous and long-term precipitation data. However, the precipitation data from rain gauge stations are often insufficient and not continuous. At present, ground-based gridded data and satellite-based gridded data are often used as an alternative. However, these data sets have to be evaluated for their suitability in hydrological studies. The current study compared three different rainfall data sources with the observed station data for the Kallada River basin of Kerala, India. The ground-based gridded rainfall data from the India Meteorological Department (IMD), the high-resolution satellite product Tropical Rainfall Measuring Mission (TRMM 3B43, version 7), and the reanalysis data Modern-Era Retrospective Analysis for Research and Applications (MERRA) are used in the analysis. The correlation coefficient, normalized root mean square error, Nash-Sutcliffe efficiency, modified index of agreement, and volumetric efficiency are used as performance indicators. The performance indicator's weights are based on the entropy method. The multi-criteria decision-making techniques like compromise programming and Preference Ranking Organization Method (PROMETHEE II) are used for ranking the precipitation data sources. It is found that IMD ground-based gridded data is ranked 1 among the three data sets. The IMD ground-based gridded data are not homogeneous based on the absolute homogeneity test, even though they had the highest rank. The IMD gridded data are further corrected based on double mass curve analysis. The corrected data were analyzed using the precipitation concentration index (PCI) to assess the temporal variation in precipitation, and it was found that the location falls under a uniform distribution zone.

Accurate surface runoff prediction is vital for water resources engineers for various applications. Advances in the artificial intelligence techniques can act as robust tools for modelling hydrological processes. The present study focuses on testing the reliability of different data sources and choosing the correct source to model the rainfall-runoff process under data scarce situations using AI techniques. In this study, an absolute homogeneity test was performed for TRMM, gridded and observed precipitation data and found that the observed precipitation dataset is homogeneous and best suitable for modelling rainfall-runoff process in Kallada river basin, Kerala. Emotional artificial neural network (EANN) is a novel hybrid neural network and it is suggested in the present study for accurate monthly surface runoff prediction. This study was also conceived to address and investigate the efficiency of EANN for forecasting monthly surface runoff and compare the performances with conventional feed forward neural network (FFNN) and multivariate adaptive regression spline (MARS) models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination (R2) and graphical indicators are used for assessing the efficacy of the developed models. The results showed that the EANN model performs better with R2 = 0.80 for the training phase and R2 = 0.77 for validation phase compared to other models. The improvement in the performance of EANN model over FFNN model is 12% and 5.8% for coefficient of determination in the training and validation phase, respectively. Further, the Taylor diagram indicates that there is a close match between the observed and EANN model predicted values in terms of statistical parameters. Overall, this study demonstrated the effectiveness of EANN in modelling the rainfall-runoff process and also could be a useful technique in other fields of water resources engineering.

India is an immense nation with high spatial and temporal inconsistency of precipitation. The end target is to tap the available water assets and to use the water as per the necessity. Numerous dams have already constructed to tap the available water resources. But the storage capacity of these reservoirs is gradually reducing due to sedimentation. To decide the useful existence of reservoir and to evaluate the sedimentation rate in the reservoir, it is fundamental duty to conduct the surveys intermittently. If the actual sedimentation process evaluated in the reservoirs, measures can propose ahead of time, and reservoir operational plan can arrange for typical usage of water. Some traditional procedures of measurement of reservoir sedimentation like hydrographic surveys and inflow-outflow techniques are expensive and also tedious. Remote sensing method turns into a helpful instrument for assessment of reservoir sedimentation because of its spatial, spectral and time-related features, which give succinct, monotonous and auspicious information concerning the area of water spread of the reservoir. Because of the deposition of sediments in a reservoir, the water spread at a height continuously alters. Presently by looking at the decreasing area of water spread with time, the sedimentation rate in a reservoir can be determined easily. In the current research, the sedimentation rate and volume ascertain within the Thenmala reservoir were carried out utilising satellite data. The data of IRS-P6 satellite with LISS-III sensor for 6 different date of pass was used to extract the area of water spread. The initial elevation-area-capacity curves and the reservoir levels on the six date of pass were acquired from the authorities of the dam. Utilising the cone formula, the capacity in zone of live storage is calculated. Because of silt deposition inside the reservoir, 44.063 (M Cu.m) storage capacity lost in a traverse of 30 years, i.e. 1985–2014.