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Real-time availability of satellite-based precipitation products gives the hydrologic prediction community an opportunity to enhance hydrologic prediction for a watershed or river basin. In the present study, the latest tropical rainfall-measuring mission (TRMM) multisatellite precipitation analysis (TMPA) research product, 3B42V7, was assessed against a gauge-based Indian Meteorological Department (IMD) gauge-based gridded data set using statistical and contingency table methods for an agricultural watershed in India. A comparative analysis of the TRMM and IMD data sets was carried out on daily, monthly, seasonal, and yearly bases for 16years (1998-2013). The analysis revealed that the TRMM data set performed reasonably well but showed significant biases. Although it underestimated watershed-averaged daily precipitation (bias=-28.65%, correlation coefficient=0.42), its results improved for monthly precipitation (bias=-26.17%, correlation coefficient=0.86). Moreover, its rainfall detection capability was determined to be better during the monsoon season than during the nonmonsoon season. In all timescales, however, it usually underestimated heavy rains. The utility of the TRMM precipitation data set in hydrologic modeling was evaluated via the semidistributed soil and water assessment tool (SWAT) hydrologic model. Using the IMD gauged data-calibrated SWAT model, TRMM data set-based simulation showed limited hydrologic prediction on a daily scale, whereas its prediction capability was fairly good on a monthly scale. The SWAT model exhibited remarkable improvement in both daily and monthly simulation when recalibrated with TRMM precipitation data. However, when driven by IMD data, the model always performed better than its TRMM-driven counterpart. The analysis indicated that the use of the TRMM precipitation data set can be a compensating approach after suitable bias correction and that it has the potential for hydrologic prediction in data-sparse regions.

Digital elevation model (DEM) of a watershed forms key basis for hydrologic modelling and its resolution plays a key role in accurate prediction of various hydrological processes. This study appraises the effect of different DEMs with varied spatial resolutions (namely TOPO 20 m, CARTO 30 m, ASTER 30 m, SRTM 90 m, GEO-AUS 500 m and USGS 1000 m) on hydrological response of watershed using Soil and Water Assessment Tool (SWAT) and applied for a case study of Kaddam watershed in India for estimating runoff and sediment yield. From the results of case study, it was observed that reach lengths, reach slopes, minimum and maximum elevations, sub-watershed areas, land use mapping areas within the sub-watershed and number of HRUs varied substantially due to DEM resolutions, and consequently resulted in a considerable variability in estimated daily runoff and sediment yields. It was also observed that, daily runoff values have increased (decreased) on low (high) rainy days respectively with coarser resolution of DEM. The daily sediment yield values from each sub-watershed decreased with coarser resolution of the DEM. The study found that the performance of SWAT model prediction was not influenced much for finer resolution DEMs up to 90 m for estimation of runoff, but it certainly influenced the estimation of sediment yields. The DEMs of TOPO 20 m and CARTO 30 m provided better estimates of sub-watershed areas, runoff and sediment yield values over other DEMs.

Climate change impacts assessment involves downscaling of coarse-resolution climate variables simulated by general circulation models (GCMs) using dynamic (physics-based) or statistical (data-driven) approaches. Here we use a statistical downscaling technique for projections of all-India monsoon rainfall at a resolution of 0.5 degrees in latitude/longitude. The present statistical downscaling model utilizes classification and regression tree, and kernel regression and develops a statistical relationship between large-scale climate variables from reanalysis data and fine-resolution observed rainfall, and then applies the relationship to coarse-resolution GCM outputs. A GCM developed by the Canadian Centre for Climate Modeling and Analysis is employed for this study with its five ensemble runs for capturing intramodel uncertainty. The model appears to effectively capture individual station means, the spatial patterns of the standard deviations, and the cross correlation between station rainfalls. Computationally expensive dynamic downscaling models have been applied for India. However, our study is the first to attempt statistical downscaling for the entire country at a resolution of 0.5 degrees. The downscaling model seems to capture the orographic effect on rainfall in mountainous areas of the Western Ghats and northeast India. The model also reveals spatially nonuniform changes in rainfall, with a possible increase for the western coastline and northeastern India (rainfall surplus areas) and a decrease in northern India, western India (rainfall deficit areas), and on the southeastern coastline, highlighting the need for a detailed hydrologic study that includes future projections regarding water availability which may be useful for water resource policy decisions.