Abstract
Abstract. This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial and meteorological parameters. Single source surface energy balance model was used to derive surface energy balance parameters. Agro climatic region wise monthly information of terrestrial, energy balance and meteorological parameters were derived during June- September from decadal analysis of MODIS data (2003–2012) over India (68–100°E, 5–40°N) at 5 km spatial resolution. Information on rainfall was obtained from gridded rainfall data (1° × 1° spatial resolution) from Indian Meteorological Department (IMD). The spatiotemporal variability of the parameters such as rainfall, evapotranspiration (ET), evaporative fraction (EF), soil water index (SWI), land surface temperature (LST) and air temperature (Ta) showed strong influence on seasonal LE fluctuation. LE showed positive linear coupling with ET (0.52
Highlights
The meteorological component which deals with the development of the atmospheric boundary layer leading to cloud formation and precipitation is highly sensitive to the exchange of surface energy fluxes such as evapotranspiration or sensible heat flux
Hydrology of the study area was assessed by the seasonal total (June-September) rainfall obtained from Indian Meteorological Department (IMD) and seasonal average (June-September) ET obtained from MODIS ET during 2003-2011 in 14 agro climatic regions of India (Figure 2)
Scatter plot (Figure 6a) between the total seasonal rainfall obtained from IMD versus seasonal mean latent heat flux (LE) estimated from MODIS over the different ACRs was found to be positively linearly coupled with each other R2 varies
Summary
The meteorological component which deals with the development of the atmospheric boundary layer leading to cloud formation and precipitation is highly sensitive to the exchange of surface energy fluxes such as evapotranspiration (latent heat flux) or sensible heat flux. All these components are strongly coupled to each other and the predictability in the climate system can be determined through the interaction of these parameters through “Coupling hot spots”, where both terrestrial and meteorological components are strongly linked with each other. With the availability of remote sensing sensors at various optical, thermal to microwave spectral ranges, estimation of the above parameters are possible to understand their spatiotemporal changes and the resulting hydrological pattern
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