Sensitivity of Vegetation Type to the Simulation of Land Surface Conditions in the Foothills of Himalayas: Evaluation with In-Situ Observations and Reanalyses

Abstract

The Uttarakhand state of the Indian Himalayan region is primarily occupied by needle-leaf and broad-leaf forests Understanding the behavior of a land surface model (LSM) to different vegetation types and forcings is crucial to configuring LSM for this particular region, which is the aim of the study. Various reanalysis products (ERA5-Land, IMDAA, GLDAS, MERRA2, and NEP FNL) are validated against in situ observations at three stations (Kosi-Katarmal, Kantli, and Gangolihat stations) to find the best forcing, and ERA5-Land performed the best. Therefore, one-dimensional Noah-multi parameterization (Noah-MP) LSM is forced with ERA5-Land analysis and IMERG rainfall during 2011–2021. Three vegetation types (Deciduous needle-leaf forest, Evergreen broad-leaf forest, and Barren/Sparsely vegetated type) along with no-vegetation type are considered in LSM and referred to as EXP2, EXP3, EXP4, and EXP1, respectively. Note that the vegetation types in EXP2 and EXP3 are closely related to the actual vegetation observed at in- situ stations, while EXP1 and EXP4 provide sensitivity of land surface conditions to the tree density. The diurnal variation of soil temperature (ST) from EXP2 and EXP3 reasonably agrees with in-situ observations and is better than global/regional analyses, unlike EXP1 and EXP4. EXP2 and EXP3 are comparable for surface sensible and latent heat fluxes in pre-monsoon and southwest-monsoon seasons and could be due to matching with vegetation type and density. The Noah-MP soil moisture (SM) is overestimated (~ 0.09 to 0.15 m³ m^-3) against observation, comparable with ESACCI and CYGNSS (− 0.065 to 0.03 m³ m^-3) on daily and monthly scales. The SM variations are marginal among the seasons, unlike the ST and surface fluxes. The Noah-MP simulated evapotranspiration is comparable to in-situ observation in EXP2 and EXP3. The study demonstrates the value of LSM in simulating land-surface processes when driven by correct vegetation type, density, and best forcing.