Dolon Banerjee

One of our planet’s most significant and important widely available resources is groundwater, which is also a major global source of water for domestic, agricultural and industrial uses. Subterranean aquifers typically provide over 40% of the water consumed by California’s towns and farmers, and a substantial amount more during the drought years. Since aquifers are influenced by both natural and anthropogenic processes, groundwater pollution is a major issue worldwide. Ecosystem functions, human health, and socio-economic development, all depend on the quality of the water that it used in different sectors. In order to guarantee the safe and sustainable use of these resources for a variety of purposes, it is crucial to evaluate and monitor the quality of groundwater. In this study, the groundwater quality for the state of California, United States was evaluated using Weighted Index Overlay Analysis to establish the suitability of it for human consumption. The dataset utilized in the study was hosted by the California State Water Resources Control Board (CSWRCB).Groundwater data collected from CSWRCB website on physiochemical parameters, such as total dissolved solids, total hardness and main cations like Ca2+, Mg2+, Na+ & K+ as well as anions like HCO3-, Cl-, SO42- & NO3- were analysed to determine the quality of groundwater in California. Using the Inverse Distance Weighted (IDW) approach for interpolation, spatial maps were generated in ArcMap. In accordance with WHO drinking water quality guidelines, weights have been allocated to several physiochemical characteristics for the Weighted Index Overlay Analysis (WIOA). California's groundwater quality's temporal variation is assessed for last 15 years to investigate the evolution of groundwater usability for drinking in the state. Figure 1. Flowchart showcasing WIOA working procedure for groundwater quality assessment for drinking purpose

Over last few decades the Punjab region of India has been one of the country's leading contributors to agricultural products. The agricultural farms in the region are supplied with water from a well-established canal system and groundwater reserve in the state. The share of irrigated area in the region fed by canals and groundwater wells are 28 and 72%, respectively. The over and unscientific usage of groundwater over the years has resulted in groundwater depletion at an alarming rate. To help policymakers address the situation and develop effective plans, forecasting groundwater recharge for the future is utmost essential. The recharge process primarily governs the growth or depletion of groundwater reserve. Groundwater recharge is one of the most difficult phenomena to be quantified as it cannot be measured directly and is influenced by several processes varying spatially and temporally. Extensive research work for quantifying the groundwater recharge have been performed in the past. These investigations introduced a number of methodologies, including chemical tracers and physical procedures. These methods, however, being experimental in nature, involve significant time and investment. The use of machine learning algorithms to predict the recharge is promoted as a solution to these problems. These algorithms have proven to be efficient enough to deduce the recharge with very high accuracy. Through a variety of models, ranging from the most basic to one of the more intricate, we have attempted to forecast the recharge scenario in the Punjab region, India. Four machine learning algorithms, namely the Multi-linear regression model, Non-linear regression model (Random Forest), Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) have been employed in this study. The aim was to comprehend the dependence of groundwater recharge on the factors of temperature, precipitation, soil type, LULC, and ground slope. The observed recharge for every subsequent month in a 30-year period is calculated using the observed monthly groundwater level data from observation wells located throughout Punjab. The monthly temperature and precipitation data are used for the study while soil type and ground slope for the location of the observation stations are extracted from digital elevation models (DEMs). At intervals of three years, the LULC maps are created. The models are then used to forecast and compare with the available observation data after the entire data set was split into a training and testing set using the 80/20 method. The models were then assessed for their ability to predict observational data using the Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) in each case. The groundwater recharge prediction is then performed using the model with the highest accuracy.

Groundwater and surface water, though thought to be different entities in the past, are connected throughout the different landforms of the world. Despite being studied for quite some time, the interaction between groundwater and surface water (GW–SW) has received attention recently because of the heavy exploitation of both of these resources. This interaction is responsible for a phenomenon like contaminant transport, and understanding it helps to estimate the effects of climate change, land use on chemical behavior, and the nature of water. Hence, knowledge of GW–SW interactions is required for hydrologists to optimize resources and analyze the related processes. In this review article, different aspects of the interaction are discussed. Starting from the basics of the phenomenon, this work highlights the importance of GW–SW interactions in the hydrological cycle. Different mechanisms of GW–SW interactions are briefly examined to describe the phenomenon. The scales of interaction are also elucidated where the classification is addressed along with a brief introduction to the large scale and sediment reach scales. The study then moves on to the investigation methodologies used for the process of SW–GW interaction and their classifications based on whether they are field methods or modeling techniques. Various literature is then explored in terms of research approaches. Finally, we highlight the applicability of the methods for different scenarios. This work is aimed to summarize advances made in the field, finding research gaps and suggest the way forward, which would be helpful for hydrologists, policymakers and practicing engineers for planning water resources development and management.

Groundwater is an important resource in India as it is used extensively for industrial, agricultural and drinking purposes. With the increase in demand due to growth in population, industrialization and improvement of living standards, the groundwater resources in India are depleting. For instance, the long-term trend of groundwater level observed in the Ropar district of Punjab, India for a span of ten years shows a gradual decline. The maximum fall of groundwater level is observed to be 1.05 m/year. In Ropar, the natural recharge process is diminishing because of rapid urbanization, variation in rainfall, and temperature patterns. Therefore the available water is found to be insufficient to fulfil the rise in water demand.To replenish the declining groundwater table and thus maintain the balance between the water supply and demand, artificial recharge techniques are proven to be beneficial in various studies. In this study, areas suitable for artificial recharge have been proposed.Remote Sensing(RS) techniques and the Geographic Information System(GIS) has been used to prepare various thematic maps constituting slope, land use & land cover, soil, geomorphology, the thickness of granular zone (permeable zones), the distance between recharge structure and the Sutlej river, rainfall map, drainage density, and population density. Lithological mapping in and around the Ropar district has been analyzed using borehole logs and reports from Central Ground Water Board, Govt. of India. Analytical Hierarchical Process (AHP) and Artificial Neural Networks (ANN) model have been used to determine the weightage of different parameters to map the suitable areas required for artificial recharge in the Ropar District. Finally, the best type of artificial recharge structure has been chosen based on higher stream order, drainage density and lithology for the present scenario.