Abstract
It is a great challenge to obtain reliable gridded meteorological data in some data-scarce and complex territories like the Himalaya region. Less dense observed raingauge data are unable to represent rainfall variability in the Beas river basin of North-Western Himalaya. In this study four reanalyses (MERRA, ERA-Interim, JRA-55 and CFSR) and one global meteorological forcing data WFDEI have been used to evaluate the potential of the products to represent orographic rainfall pattern of Beas river basin using hydrology model. The modeled climate data have compared with observed climate data for a long term basis. A comparison of various rainfall and temperature products helps to determine uniformity and disparity between various estimates. Results show that all temperature data have a good agreement with gridded observed data. ERA-Interim temperature data is better in terms of bias, RMSE (Root Mean Square Error), and correlation compared to other data. On the other hand, MERRA, ERA-Interim and JRA-55 models have overestimated rainfall values, but CFSR and WFDEI models have underestimated rainfall values to the measured values. Variable Infiltration Capacity (VIC), a macroscale distributed hydrology model has been successfully applied to indirectly estimate the performance of five gridded meteorological data to represent Beas river basin rainfall pattern. The simulation result of the VIC hydrology model forced by these data reveals that the discharge of ERA-Interim has a good agreement with observed streamflow. In contrast there is an overestimated streamflow observed for MERRA reanalysis estimate. JRA-55, WFDEI, and CFSR data underestimate the streamflow. The reanalysis products are also poor in capturing the seasonal hydrograph pattern. The ERA-Interim product better represents orographic rainfall for the Beas river basin. The reason may be the ERA-Interim uses a four-dimensional variational analysis model during assimilation. The major drawback of MERRA is the non-inclusion of observed precipitation data during assimilation and modeling error. The poor performance of JRA-55, CFSR and WFDEI is due to the gauge rainfall data assimilation error. This research finding will help for broader research on hydrology and meteorology of the Himalayan region.
Influence of anthropogenic activities and seasons on heavy metals in spring water along Amala and Nyangores tributaries of the Mara River Basin
