Role of Land Use and Land Cover Changes in Modulating Monsoon Depression Dynamics: Insights From a Regional High-Resolution Model

Abstract

Land use and land cover (LULC) changes significantly influence the dynamics of weather systems, particularly in regions prone to rapid land cover changes and extreme weather events like monsoon depression (MD). This study employs the Weather Research and Forecasting (WRF) model with a high-resolution (2 km) configuration to investigate the impact of updated LULC data on the predictability of MDs over India. Two experiments were conducted with LULC data from different sources: (i) the United States Geological Survey (USGS) with 1 km resolution and (ii) the National Remote Sensing Centre (NRSC) with 56 m resolution. The simulations are validated against observational and reanalysis datasets, including Automated Weather Stations (AWS), ERA5 reanalysis, IMD best track data, and GPM IMERG rainfall estimates. The results indicate that the NRSC dataset, which reflects current updated land cover conditions, provides a more accurate representation of land surface-atmosphere interactions, leading to improved simulations of MDs' track, intensity, and associated rainfall. Key meteorological parameters such as wind profiles, potential vorticity, moisture transport, and diabatic heating exhibit better agreement with observed/reanalysis data in the NRSC experiment than in the USGS. The NRSC experiment consistently shows lower mean Direct Position Errors (DPEs) in MD tracks throughout the forecast period, with an average improvement of 45–60 km over USGS. Additionally, RMSE in surface variables like temperature, humidity, and wind is reduced by 5%–10%. This study highlights the critical role of accurate and up-to-date LULC data in numerical models for enhancing their forecast capability of MDs, particularly in regions undergoing rapid LULC changes.