Performance Evaluation of High-Resolution Land Data Assimilation System (HRLDAS) Over Indian Region

Abstract

The present study evaluates the skill of a High-Resolution Land Data Assimilation System (HRLDAS) in simulating soil moisture (SM), soil temperature (ST) and sensible heat flux (SHF) for the Indian region (5°–39°N; 60°–100°E). The HRLDAS framework uses uncoupled Noah Land Surface Model (LSM) that integrates near-surface atmospheric parameters and land surface parameters from observations and analysis for the period January 2001–October 2013 at 20 km spatial resolution. The HRLDAS takes about 1 year to reach its quasi-equilibrium state for clay soil. The HRLDAS simulated ST and SM reasonably agree with the in situ observations. The simulated ST shows a negative bias in the monsoon season over the Gujarat, Mandla, and Kharagpur. The SM is under-estimated and the under-estimation increases with soil depth at Kharagpur, India. The negative bias in TRMM precipitation forcing causes under-estimation of SM. The simulated SM shown higher saturation point than observations. The daytime SHF has positive bias during the pre-monsoon, monsoon seasons and agrees well with observations in the post-monsoon season at Ranchi, India. The Noah 1D sensitivity experiments revealed that there is a need to revisit soil field capacity and porosity parameter for improving the skill of the HRLDAS.