Publications

Sensitivity of Permissible Aquifer Recharge Capacity towards Hydrogeological and Well Design Parameters. (under revision)

Abstract

The maximum injection rate to an aquifer for a given operational time, hydrogeological and well characteristics, and under the constraints of a permissible head is defined as the Permissible Aquifer Recharge Capacity (PARC). A local and global sensitivity analysis has been presented to address the important hydrogeological and well parameters in determining PARC for confined and unconfined aquifers. A novel methodology to determine PARC for 3D numerical groundwater models has been discussed, and its implementation in Lower Ain Valley has been presented. PARC’s sensitivity varies with specific parameter interactions, particularly between hydraulic conductivity and vertical anisotropy in unconfined aquifers, necessitating careful management, whereas confined aquifers show a broader range of influential factors. The methodology for determining PARC with an adaptive learning rate based on the analytical solution to the well is more efficient and requires fewer iterations. The Lower Ain River Basin study demonstrates the methodology’s applicability. It reveals significant spatial variability in PARC with aquifer characteristics, highlighting the basin’s strong aquifer storage potential for addressing severe groundwater deficiencies.

Multi-Facet Analysis of Analytical and Numerical Models to Resolve Sustainable Artificial Recharge Rates in Unconfined Aquifers.

Abstract

Determination of transient recharge rates in case of Managed Aquifer Recharge (MAR) is a complex task. It involves anticipating the response of aquifers to the induced volumes of water. This paper presents a novel methodology to determine the suitable recharge rates of surplus runoff water at sub-basin scale for sustainable development of aquifers. Sub-basin scale availability of monthly surplus surface runoff was estimated at 75% dependability, using the Soil & Water Assessment Tool (SWAT) model. Linear relationship between injection rates and the developed groundwater mound was established using an analytical solution to determine the Permissible Aquifer Recharge Capacity (PARC). Finally, MODFLOW model was employed to determine the suitable MAR rates based on the available surplus runoff and PARC values. The developed methodology was applied in the semi-arid region of Lower Betwa River Basin (LBRB), India. The estimated surplus runoff rates exhibited an average of about 5000 m3/d water was available over 85% of the basin. Analysis of the PARC results revealed that over 80% of the basin had storage capacity of 270 to 9400 m3 per day. Estimation of suitable recharge rates revealed that over 50% of the LBRB had the capability of being self-sufficient with 1000 – 5000 m3/d of surplus water. The MAR-Runoff ratio estimates revealed that 51% of the LBRB had excess surplus water, but lacked adequate groundwater storage capacity, while it was vice-versa for 4% of the basin. Overall, the developed methodology was effective and provided an efficient way to study aquifer response to applied interventions.

HRU-based Downscaling of GRACE-TWS to quantify the hydrogeological fluxes and Specific Yield in the Lower Middle Ganga Basin.

Abstract

Precise volumetric assessments of different hydrological variables, such as precipitation, evapotranspiration, and groundwater components, are necessary for comprehensive water resource management. This presents several challenges, including topographical complexity and economic limitations, mainly when aiming for high temporal and spatial resolution. The satellite mission Gravity Recovery and Climate Experiment (GRACE) has greatly improved the ability to quantify variations in GWS. We have described a two-stage downscaling system that integrates SWAT Hydrological Response Units (HRUs) with GRACE-derived Terrestrial Water Storage Anomalies (GRACE-TWSA) using Artificial Neural Networks (ANN). Using data from the Global Land Data Assimilation System (GLDAS), GWS was derived with GRACE-TWSA and further downscaled to the HRU level. The GWS trend in most of the study areas has not shown any significant trend from 2001 to 2014. The mean increasing trend was 8.14 mm/year, while the mean decreasing was 1.28 mm/year. Using a decision-tree-based CatBoost model, the GWS has been used as an independent variable to determine distributed groundwater levels within the study area. A strong correlation between measured groundwater levels and GWS was observed, and a regularised robust optimisation was used to determine the Specific yield. The surface water and groundwater budgeting indicate that most Gangetic area blocks are water-stressed. The study offers an extensive approach to integrated water resource management by providing insights into groundwater availability, aquifer storage characteristics, and budgeting with HRU scale GWS estimates.

Cluster-based delineation of optimal sites for managed aquifer recharge: a case study of Lower Betwa River Basin, India.

Abstract

Managed Aquifer Recharge (MAR) has emerged as a multi-facet, sustainable and effective technique to replenish dwindling groundwater resources. Suitable site selection is an important step in the design phase of MAR process. Conventional methods of site suitability studies using GIS and Multi Criteria Decision Analysis (MCDA) does not address the aquifer’s response to MAR, while limited modelling-based studies have considered the effect of surface hydraulic factors. In this paper, a fresh approach is presented, that utilises decision model, K-mean clustering technique and numerical model to identify optimal sites for MAR interventions. The methodology was applied in a semi-arid region of Lower Betwa River Basin (LBRB), India. Four different parameter combinations were employed that incorporated the impact of surface and subsurface parameters. Multiple model runs were executed using MODFLOW–NWT to assess the groundwater head response to the infiltrated volume of water. The results indicated that under the least head change category (< 0.7 m), the north and south–east regions of the LBRB were the most appropriate sites. The coalition of geomorphology and drainage density along with aquifer properties such as hydraulic conductivity, specific yield and aquifer thickness were found to be the best suited combination for site selection in LBRB, with maximum spatial coverage (16%) under it. It was observed that the choice of parameter combinations affected the range of groundwater head variations and depended upon the site-specific criteria of accepted head change ranges for determining the best suitable parameter combinations for MAR site selection.

Study of hydrologically critical subbasins under climate change

Climate Impact Study on Gopad River Basin

Abstract

This study aims to evaluate the impact of climate change on the surface water hydrology of the Gopad river basin in India. The outputs of four CMIP6 Global Climate Models have been downscaled using the statistical downscaling method to the basin level. A comparative analysis for the accuracy achieved in the bias correction for the combination of GCM and downscaling method has been performed before utilising the downscaled weather parameters for hydrological study. The MIROC6 and ACCESS-CM2 were found best for the simulation of precipitation and temperature, respectively. The Distribution Mapping and Variance Scaling methods have shown better accuracy w.r.t other statistical methods. The impact of climate change has been found significant since the temperature has been observed to be increased by 3.16 °C by the end of 2060; meanwhile, there is an average decrease of 9.2% in the annual rainfall from the baseline. The peak runoff has increased while there is a significant decrease in the groundwater recharge. Further, hydrologically critical subbasins (HCS) have been delineated based on the runoff, groundwater recharge, and baseflow. Most HCS was observed to be situated in the upper Gopad river basin, representing the area’s pristine conditions.

Suitable sites for groundwater development: a capture map-based approach integrated with weighted overlay analysis

Abstract

A novel methodology for suitable site selection for groundwater development based on river capture, pumping cost, and groundwater potential has been proposed for better groundwater utilisation. River capture and cost map have been generated from a calibrated groundwater model, simulated with forecasted hydrological time series data. The groundwater potential has been calculated with weighted overlay analysis. These three variables have been used to classify the model domain into five zones of groundwater development by K-Means clustering. The area with lower river capture, low cost of pumping, and high groundwater potential is found to be the best location for groundwater extraction. The methodology has been applied to the lower Ain River basin, in France.

A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region

A data augmentation approach to improve groundwater potential zonation

Abstract

Groundwater prospecting with reasonable accuracy is often a challenging task. The integration of geographic information system (GIS) with Machine Learning techniques has proven very reliable to delineate the nonlinear behaviour of groundwater occurrence. The weighted overlay analysis is one of the many essential tools used for groundwater potential zonation, based on remote sensing technology and has been extensively used in the several research work. However, the performance of these methods have not been evaluated for the data-scarce regions. The fuzzy-analytical hierarchy process (fuzzy-AHP) has been used to assign weights to the most used hydrological conditioning parameters to calculate groundwater potential index. Support vector classifier, k- nearest neighbors and random forest classifier have been used as machine learning (ML) algorithms to predict the groundwater potential. The models have been trained, tested and deployed based on 208 yield data of wells. All the affecting parameters have been developed using remotely sensed data. The results have been compared based on precision, recall, f1-score and Cohen’s kappa score. As only 208 well data was available for 16997 Km2 of the study area, it has been observed that ML models failed to delineate the groundwater potential zones compared to fuzzy-AHP-based weighted overlay analysis. Data augmentation has been used to generate the well data with distributed independent variables, which eventually increased the number of datasets and improved the performance of ML model significantly.

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