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Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products of different sources and precision. We evaluate the influence of different precipitation product on model parameters and streamflow predictive uncertainty using a soil water assessment tool (SWAT) model for a forest dominated catchment in India. We used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected TRMM (corrected satellite product) and NCEP-CFSR (reanalysis dataset) over a period from 1998–2012 for simulating streamflow. The precipitation analysis using statistical measures revealed that the TRMM and CFSR data slightly overestimate rainfall compared to the ground-based IMD data. However, the TRMM estimates improved, applying a bias correction. The Nash–Sutcliffe (and R2) values for TRMM, TRMMbias and CFSR, are 0.58 (0.62), 0.62 (0.63) and 0.52 (0.54), respectively at model calibrated with IMD data (Scenario A). The models of each precipitation product (Scenario B) yielded Nash–Sutcliffe (and R2) values 0.71 (0.76), 0.74 (0.78) and 0.76 (0.77) for TRMM, TRMMbias and CFSR datasets, respectively. Thus, the hydrological model-based evaluation revealed that the model calibration with individual rainfall data as input showed increased accuracy in the streamflow simulation. IMD and TRMM forced models to perform better in capturing the streamflow simulations than the CFSR reanalysis-driven model. Overall, our results showed that TRMM data after proper correction could be a good alternative for ground observations for driving hydrological models.

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Assessment of potential zone of groundwater recharge is extremely important for the protection of water quality and the management of groundwater systems. Groundwater Potential Zones (GPZ) are demarcated with the help of geospatial techniques. The parameters, considered for identifying the GPZ such as geology, geomorphology, slope, drainage density, lineament density, rainfall, soil maps and LULC are generated using the satellite data and toposheet. Later they are integrated with each other applying weighted overlay in ArcGIS. Suitable ranks are assigned for each category of these parameters. For the various geomorphic units, weight factors are decided based on their capability to store groundwater. This procedure is repeated for all the other layers and resultant layers are reclassified. The groundwater potential zones are classified into three categories like poor, good and excellent. The use of aforesaid methodology is demonstrated in a selected study area in Reamal Block of Deogarh District, Odisha. The thematic layers were first digitized from satellite imagery, supported by ancillary data such as toposheet, finally all thematic layers were integrated using ArcGIS software to identify the groundwater recharge potential zones for the study area and generate a map showing these groundwater recharge potential zones namely 'poorly suitable', to 'most suitable' on knowledge based weightage factors.