RAGHAVENDRA P SINGH
Articles written in Journal of Earth System Science
Volume 126 Issue 8 December 2017 Article ID 0113
Trend analysis of evapotranspiration over India: Observed from long-term satellite measurements
Sheshakumar Goroshi Rohit Pradhan Raghavendra P Singh K K Singh Jai Singh Parihar
Owing to the lack of consistent spatial time series data on actual evapotranspiration (
Volume 129 All articles Published: 1 January 2020 Article ID 0035 Research Article
ROHIT PRADHAN NIMISHA SINGH RAGHAVENDRA P SINGH
Isotopic composition of atmospheric water vapor provides information on transport, mixing and phase change of water in the atmosphere. It provides a useful tool for understanding various aspects of the hydrological cycle. SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT-1 was a spectrometer designed to measure the composition of trace gases in troposphere and stratosphere. It provided global measurements of total columnar HDO and $H_{2}O$ concentrations using the spectral window between 2338.5 and 2382.5 nm. Temporal variability of columnar $\delta\rm{D}$ was studied over Northeast (NE) India and mean columnar $\delta\rm{D}$ for pre-monsoon and monsoon months were correlated with precipitation data obtained from Global System for Mapping of Precipitation (GSMaP). It was observed that $\delta\rm{D}$ during the pre-monsoon months of April–May showed good correlation ($r$ > 0.7, $p$ < 0.05) with total precipitation during June–August for the corresponding year over forested regions of Meghalaya and parts of Assam. Analysis was also carried out to understand the relationship between SCIAMACHY derived gridded monthly $\delta\rm{D}$ and Multivariate El-Ni$\tilde{n}$o Index (MEI) with zero and one month lag periods. Positive correlation was observed between $\delta\rm{D}$ and MEI over parts of Central India, Myanmar and Thailand. Isotope ratio of water vapor provides additional information compared to traditional meteorological observations and holds the potential to improve forecasting models.
Volume 129, 2020
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