Decentralized rainwater harvesting (RWH) is a promising approach to mitigate drought in the drylands. However, an insufficient understanding of its impact on hydrological processes has resulted in poor resource planning in this area. This study is a meta-analysis of 25 agricultural watersheds representing a range of rainfall and soil types in the semi-arid tropics. Rainfall-runoff-soil loss relationship was calculated at daily, monthly and yearly levels, and the impact of RWH interventions on surface runoff and soil loss was quantified. A linear relationship was observed between daily rainfall and surface runoff up to 120 mm of rainfall intensity, which subsequently saw an exponential increase. About 200–300 mm of cumulative rainfall is the threshold to initiate surface runoff in the Indian semi-arid tropics. Rainwater harvesting was effective in terms of enhancing groundwater availability (2.6–6.9 m), crop intensification (40–100%) and farmers’ incomes (50–200%) in different benchmark watersheds. An average of 40 mm of surface runoff was harvested annually and it reduced soil loss by 70% (3 ton/ha/year compared to 1 ton/ha/year in non-intervention stage. The study further quantified runoff at 25th, 50th and 75th percentiles, and found that more than 70% of the area in the Indian semi-arid tropics has high to medium potential for implementing RWH interventions.

This paper presents a comparison of the predictive capability of three hydrological models, and a mean ensemble of these models, in a heavily influenced catchment in Peninsular India: GWAVA (Global Water AVailability Assessment) model, SWAT (Soil Water Assessment Tool) and VIC (Variable Infiltration Capacity) model. The performance of the three models and their ensemble were investigated in five sub-catchments in the upstream reaches of the Cauvery river catchment. Model performances for monthly streamflow simulations from 1983–2005 were analysed using Nash-Sutcliffe efficiency, Kling-Gupta efficiency and percent bias. The predictive capability for each model was compared, and the ability to accurately represent key catchment hydrological processes is discussed. This highlighted the importance of an accurate spatial representation of precipitation for input into hydrological models, and that comprehensive reservoir functionality is paramount to obtaining good results in this region. The performance of the mean ensemble was analysed to determine whether the application of a multi-model ensemble approach can be useful in overcoming the uncertainties associated with individual models. It was demonstrated that the ensemble mean has a better predictive ability in catchments with reservoirs than the individual models, with Nash-Sutcliffe values between 0.49 and 0.92. Therefore, utilising multiple models could be a suitable methodology to offset uncertainty in input data and poor reservoir operation functionality within individual models.