Spatio-temporal coupling of land-surface and energy balance parameters with monsoon rainfall using remote-sensing technology

Abstract

This research paper focuses on the spatio-temporal coupling of monsoon rainfall with land-surface and energy balance parameters, which are important for understanding hydrological, climatological, and agricultural aspects at local, regional, and global scales. The dynamics of land-surface and energy balance parameters influence summer monsoon over India. Time scales of the land-surface response to monsoon forcing are different for different land-surface conditions due to different physical processes governing the land-surface–atmosphere exchange through energy balance components. A synergy of satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) (0.05° × 0.05°) for obtaining land-surface and energy balance parameters, and the Atmospheric Infrared Sounder (AIRS) (1° × 1°) for obtaining atmospheric parameter and gridded rainfall data (1° × 1°) from the Indian Meteorological Department (IMD) during June to September for three consecutive years (2009–2011) representing low to normal rainfall, were used to develop a coupling model in the spatio-temporal domain. Surface energy fluxes were estimated using a surface energy balance model by partitioning available energy at the surface into latent heat flux (LE) and sensible heat flux (H) through the evaporative fraction (EF) concept of a 2D land-surface temperature (LST)-albedo scatter plot. The coupling models were based on statistical methods developed at both temporal and spatial scales to explain the linking of various parameters with monsoon rainfall. A significant positive relationship was obtained between rainfall and land-surface parameters such as normalized difference vegetation indices (NDVIs), and soil wetness/energy balance parameters such as LE and EF, whereas a strong negative relationship was obtained between rainfall and surface radiation parameters (LST and albedo)/energy balance parameters such as soil heat flux (G) and net radiation (Rn). This approach has demonstrated its simplicity with remote sensing technology and could identify ‘at risk’ regions at spatio-temporal scales based on coupling models.