Accurate catchment level water resource assessment is the base for integrated river basin management. Due to the complexity in model structure and requirement of a large amount of input data for semi-distributed/distributed models, the conceptual models are gaining much attention in catchment modelling these days. The present study compares the performance of three conceptual models, namely GR4J, Australian Water Balance Model (AWBM) and Sacramento for runoff simulation. Four small catchments and one medium catchment in the upper Godavari river basin are selected for this study. Gap-filled daily rainfall data and potential evapotranspiration (PET) measured from the same catchment or adjacent location are the major inputs to these models. These models are calibrated using daily Nash–Sutcliffe efficiency (NSE) with bias penalty as the objective function. GR4J, AWBM and Sacramento models have four, eight and twenty-two parameters, respectively, to optimise during the calibration. Various statistical measures such as NSE, the coefficient of determination, bias and linear correlation coefficient are computed to evaluate the efficacy of model runoff predictions. From the obtained results, it is found that all the models provide satisfactory results at the selected catchments in this study. However, it is found that the performance of GR4J model is more appropriate in terms of prediction and computational efficiency compared to AWBM and Sacramento models.

The Godavari river basin (GRB), the second largest river basin (312,800 km$^{2}$) in India, was considered in this study to quantify the relative hydrological impact of recent land cover (LC) changes and rainfall trends using the variable infiltration capacity hydrologic model. Three scenarios, namely, (i) LC change, (ii) climate change and (iii) LC and climate changes, were considered to isolate the hydrological implications of the LC changes from those of climate change. Results revealed that evapotranspiration is predominantly governed by LC change and that small changes in rainfall cause greater changes in the runoff. Although the spatial extent of LC change is higher, the climate change is the dominant driver of hydrological changes within the GRB. Thus, climate projections are the key inputs to study the impact on the river basin hydrology. The results provide insights into the impacts of the climate and LC changes on the basin. The methodology and results of the present study can be further considered for water resource planning within the river basin in view of the changing environment.

Classifying watersheds prior to regionalization improves streamflow predictions in ungauged basin. Present study aims to assess the ability of combining watershed classification using dimensionality reduction techniques with regionalization methods for reliable streamflow prediction using soil and water assessment tool (SWAT). Isomap and principal component analysis (PCA) are applied to watershed attributes of 30 watersheds from Godavari river basin in India to classify them. The best classification technique is determined by calculating similarity index (SI). The results showed that Isomap is better at classifying hydrologically similar watersheds than PCA with an average SI value of 0.448. The regionalization methods such as global mean, inverse distance weighted (IDW) and physical similarity were applied to transfer the parameters from watersheds of best watershed classification group to the pseudo-ungauged watersheds, using SWAT model. The present study suggests that classifying watersheds with Isomap and regionalization using physical similarity improves the efficiency of streamflow estimation in ungauged basins.