Evaluation of reanalysis and global meteorological products in Beas river basin of North-Western Himalaya

Tanmoyee Bhattacharya,Manohar Arora,Deepak Khare

doi:10.1186/s40068-020-00186-1

Tanmoyee Bhattacharya, Manohar Arora + Show 1 more

Open Access

https://doi.org/10.1186/s40068-020-00186-1

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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.

Highlights

Rainfall and temperature data are considered as a significant input for water resource management and hydrological processes of the Himalayan river basin
Comparison of modeled and observed temperature The 2-m average maximum, minimum and mean temperatures for the Beas river basin are compared for Interim-ECMWF reanalysis (ERA)-Interim, JRA-55, Modern-Era Retrospective Analysis for Research and Applications (MERRA), Climate Forecast System Reanalysis (CFSR) and WATCH forcing data methodology applied to ERA-Interim (WFDEI)
The monthly maximum temperature for ERA-Interim, JRA-55, MERRA, CFSR and WFDEI varies from 4.18–21.49 °C, 4.52–23.91 °C, 6.56–25.86 °C, 1.82–20.73 °C and 7.07–24.47 °C

Summary

Introduction

Rainfall and temperature data are considered as a significant input for water resource management and hydrological processes of the Himalayan river basin. The satellite rainfall data have limitations of their short length of record (Derin and Yilmaz 2014) To address these challenges of datascarce basin high-resolution global reanalysis data have been widely used for hydrology models around the world (Zhao et al 2010). Several studies have conducted to evaluate the precipitation estimates based on streamflow simulation by hydrology modeling framework (Bai and Liu 2018; Sun et al 2018; Li et al 2015; Tong et al 2014a, b; Mei et al 2016) These researches have assumed that the error of rainfall products can be communicated into the simulated discharge. Even many studies have suggested that the best accessible evidence for catchment precipitation in the data-scarce basin is discharge which is superior to suggested meteorological observations (Duethmann et al 2013; Henn et al 2015; Sevruk and Mieglit 2002)

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