Evaluation of Regional Land Surface Conditions Developed Using The High-Resolution Land Data Assimilation System (HRLDAS) with Satellite and Global Analyses Over India

Soil moisture and temperature (SM and ST) have been identified for modeling of extreme weather and hydrological processes. The coarser resolution global analyses are limited in capturing realistic heterogeneity. This study focuses on evaluating regional land surface conditions developed from a high-resolution (4 km grid spacing) land data assimilation system (HRLDAS) over India from 2000 to 2013 against in situ and global analyses. Global analyses such as the European Space Agency Climate Change Initiative (ESACCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Forecasting System (CFS), and Global Land Data Assimilation System (GLDAS) have been considered to assess the credibility of the regional analysis. The regional SM from the HRLDAS is superior to global and satellite products, particularly in the orography (altitude > 300 m) regions followed by the plane regions (altitude ≤ 300 m). The probability distribution function (PDF) indicates that the regional SM and satellite analysis exhibited less error (~ 0.02 m3 m−3 at ~ 28%) in the plane regions. The regional SM analysis in the orography regions is reliable (0.015 m3 m−3 at 28% frequency) with a high equitable threat score (~ 0.6) compared to other analyses. The HRLDAS is consistently superior for soil temperature (ST) to other global analyses. The mean diurnal variation of HRLDAS-ST is close to in situ observation. The HRLDAS performs better for spatial representation of SM and ST for different months and monsoon seasons. The improved representation of land conditions from the HRLDAS could provide a realistic distribution of latent and sensible heat fluxes when compared with other global products. This study demonstrates the value of high-resolution regional analyses and recommends usefulness in hydrological applications.