• T I Eldho

      Articles written in Journal of Earth System Science

    • Assessment of LULC and climate change on the hydrology of Ashti Catchment, India using VIC model

      Narendra Hengade T I Eldho

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      The assessment of land use land cover (LULC) and climate change over the hydrology of a catchment has become inevitable and is an essential aspect to understand the water resources-related problems within the catchment. For large catchments, mesoscale models such as variable infiltration capacity (VIC) model are required for appropriate hydrological assessment. In this study, Ashti Catchment (sub-catchment of Godavari Basin in India) is considered as a case study to evaluate the impacts of LULC changes and rainfall trends on the hydrological variables using VIC model. The land cover data and rainfall trends for 40 years (1971−2010) were used as driving input parameters to simulate the hydrological changes over the Ashti Catchment and the results are compared with observed runoff. The good agreement between observed and simulated streamflows emphasises that the VIC model is able to evaluate the hydrological changes within the major catchment, satisfactorily. Further, the study shows that evapotranspiration is predominantly governed by the vegetation classes. Evapotranspiration is higher for the forest cover as compared to the evapotranspiration for shrubland/grassland, as the trees with deeper roots draws the soil moisture from the deeper soil layers. The results show that the spatial extent of change in rainfall trends is small as compared to the total catchment. The hydrological response of the catchment shows that small changes in monsoon rainfall predominantly contribute to runoff, which results in higher changes in runoff as the potential evapotranspiration within the catchments is achieved. The study also emphasises that the hydrological implications of climate change are not very significant on the Ashti Catchment, during the last 40 years (1971−2010).

    • Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

      Jitendra Singh Sheeba Sekharan Subhankar Karmakar Subimal Ghosh P E Zope T I Eldho

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      Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 subhourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

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