Evaluation of TRMM-Precipitation with Rain-Gauge Observation Using Hydrological Model J2000

Spatial precipitation is a major input to distributed hydrological models, and the accuracy of runoff predictions greatly depends on its accuracy. Satellite-based precipitation products are expected to offer an alternative to ground-based rainfall estimates in the present and the foreseeable future. In the present study, the suitability of tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA) rainfall in driving a distributed hydrological model for runoff prediction was evaluated. For this purpose, a hydrological model from the literature was calibrated and validated using raingauge data on daily time step for simulation of runoff in Kopili River basin ($=\mathrm{7,198}{\mathrm{km}}^2$) during 2003–2010. The calibrated model was then used for simulation of runoff employing specialized software and compared with the observed discharge at Kherunighat gauging site. Evaluation criteria, i.e., coefficient of correlation (CC), Nash-Sutcliffe coefficient (NSE), percent bias (PBIAS) and root mean square error (RMSE)-observations standard deviation ratio (RSR) were adopted to judge the performance of the model under different rainfall datasets. Simulation using gauge precipitation, the values of CC, NSE, PBIAS, and RSR were found to be 0.85, 0.67, 4.73, and 0.45 respectively during calibration and 092, 0.85, $-1.50$, and 0.29, respectively, during validation indicating overall good model performance. Furthermore, using the raw TMPA precipitation, the values of CC, NSE, PBIAS, and RSR were found to be 0.90, $-5.21$, 141.55, and 2.01, respectively during the period 2004 to 2007 and 0.85, $-13.49$, 179.89, and 2.80, respectively during simulation time period from 2008 to 2010. The moderate value of RSR indicates that the raw TMPA precipitation-based simulation represents the low flow, including rising and recession limbs fairly well, however, in case of the high-flow periods, overpredictions for all years were observed. The evaluation result concluded that the raw TRMM precipitation data are heavily biased from observed precipitation and are incompatible for daily runoff simulation in the study area and bias correction is essential for TRMM precipitation correction. However, after employing adequate bias-correction techniques, the TRMM precipitation performed well with higher degree of accuracy and can be used as an alternative to measured rainfall data due to its high spatial resolution where data are insufficient for runoff prediction, i.e., for ungauged basins. Moreover, performance of the TRMM precipitation improved when simulated in combination with the gauge precipitation. Therefore, along with the efforts to improve satellite-based precipitation-estimation techniques, it is also important to develop more-effective near-real-time precipitation bias-adjustment techniques for hydrological applications.