• SANJEEV KUMAR JHA

      Articles written in Journal of Earth System Science

    • Assessment of groundwater mass balance and zone budget in the semi-arid region: A case study of Palar sub-basin, Tamil Nadu, India

      MATHIAZHAGAN MOOKIAH SANJEEV KUMAR JHA ASHIS BISWAS

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      The assessment of groundwater potential zones is crucial for estimating and managing available groundwater resources. In the proposed study, quantification of groundwater availability is performed using the information collected from the hydrogeological and geophysical (electrical resistivity) investigation of the aquifer. We delineate groundwater potential zones using a weighted overlay analysis based on the conventional method with 110 electrical resistivity surveys and 40 lithological data. MODFLOW is nused to calibrate and validate the flow pattern and groundwater characteristics. The study area comprises a complex geological formation. The groundwater potential map is prepared using the observed groundwater level instead of rainfall data as the study area lacks rainfall stations. The final potential map is validated with the specific capacity obtained from the pumping test. This map is divided into 13 zones and each zone is considered as boundaries for the MODFLOW simulation. The thickness of each zone is assessed using the electrical resistivity method. The calibration and validation of the groundwater model nare performed for one year and 1.5 years, respectively, between November 2012 and March 2015. We consider two layers, namely topsoil and unconfined/semi-confined aquifers in the groundwater model. During the calibration and validation periods, the groundwater volume is found to be 7.12 and 7.51 Mm$^3$, respectively. The groundwater mass balance assessment performed in this study will be helpful in the planning and management of groundwater resources in the area.

      $\bf{Highlights}$

      $\bullet$ Delineated groundwater potential zones by a weighted overlay analysis based on conventional method with 110 electrical resistivity surveys and 40 lithological data.

      $\bullet$ MODFLOW was used to calibrate and validate the flow pattern and characteristics of groundwater.

      $\bullet$ Groundwater potential map was validated with specific capacity and MODFLOW results were validated with pumping test results.

      $\bullet$ The groundwater volume during the calibration and validation period was found to be 7.12 and 7.52 Mm3, respectively.

      $\bullet$ The groundwater mass balance assessment performed in this study can be useful in the planning and management of groundwater resources.

    • Bias correction of WRF output for operational avalanche forecasting in the Indian Himalayan region

      NIBEDITA SAMAL SANJEEV KUMAR JHA

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      The forecast of snow avalanches in the Himalayan region has critical importance due to repetitive hazard scenarios and huge loss of property and lives. In recent years, avalanche forecast models have been developed using meteorological data collected from surface observatories (SO) at Defence Geoinformatics Research Establishment (erstwhile Snow and Avalanche Study Establishment (SASE) and now DGRE), India. For operational forecasting, outputs of a Weather Research Forecasting (WRF) model are used in real-time. The objective of this study is to determine a suitable bias correction approach for three key variables: mean temperature (T), relative humidity (RH), and wind speed (WS) which can be implemented in the operational forecast services at four observatory stations in Himachal Pradesh, India. We consider data of seven cold seasons (November–April) from 2011 to 2018 obtained from SO and WRF model output. Three quantilebased bias-correction approaches: Quantile Mapping (QM), Quantile–Quantile Mapping (QQ), and Quantile–Delta Mapping (QD), have been applied. In applying the QD method, two types of delta terms: multiplicative and additive, have been experimented according to the density distribution ofthe data. For evaluation, a leave-one-season-out-cross-validation approach is used. The results indicate that the QM and QD methods significantly reduced the bias associated with the mean temperature, relative humidity, and wind speed, whereas the performance of QQ method has limitations for all the variables. Furthermore, to meet the objective of generating bias-corrected datafor operational forecasting services, uniform parameter set is generated for each variable associated with all the observatory stations. This approach is effective only for temperature, and for other variables, the improvements were not significant, mainly because of the high topographical variability in the Himalayan terrain.

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