Regional Land Surface Conditions Developed Using the High-Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region

The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintaining land surface observations, thus creating uncertainties in surface energy and hydrological processes. The present study demonstrates the value of the high-resolution land data assimilation system (HRLDAS) integrated at 2 km grid spacing from 2011 to 2021 over Uttarakhand and validated against in situ, satellite, and reanalyzes products. Diurnal variation of sensible heat flux (SHF), and latent heat flux (LHF) are closer to the in situ observations (−35 to 64 Wm⁻²) than the global and regional analysis (−125 to 129 Wm⁻² and −40 to 172 Wm⁻²) during monsoon season. The HRLDAS soil moisture (SM) is overestimated against in situ and exhibited less error against European Space Agency Climate Change Initiative (ESACCI) (0.02 m³ m⁻³ with 30%) and Cyclone Global Navigation Satellite System (CYGNSS) (−0.02 m³ m⁻³ error with 21%). The HRLDAS performs better for soil temperature (ST) with high correlation and less bias (0.94°C and −0.34°C) than the GLDAS (0.83°C and −0.61°C) and IMDAA (0.86°C and 2.2°C), when verified against in situ observations. The spatial distribution of HRLDAS shows maximum ST in the southern parts and minimum ST in the northern parts of the Uttarakhand region and is consistent with the GLDAS and IMDAA during monsoon. HRLDAS shows lesser biases in net radiation (12 Wm⁻²), SHF (−10 Wm⁻²), and LHF (9.7 Wm⁻²) compared to GLDAS (25, −17, 10.3 Wm⁻²), and IMDAA (38, −11, 16 Wm⁻²), respectively. Besides the performance, the HRLDAS products represent better spatial heterogeneity than the coarser global and regional analysis and are useful to initialize numerical models.