Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions.

Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD), secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs.

Identifying the suitable managed aquifer recharge (MAR) strategy in an overexploited and contaminated river basin.

Managed aquifer recharge (MAR) is a promising adaptation measure to reduce vulnerability to climate change and hydrological variability. However, in areas where the basin is highly polluted, densely populated, and intensely cultivated, implementing suitable MAR strategies is a significant challenge. This study used a geographic information system-based multicriteria decision analysis (GIS-MCDA) approach to delineate the MAR potential sites using seven thematic layers describing surface and subsurface features. Further, basin-specific MAR approach was developed using information such as polluted water areas, canal network distribution for water supply, and cropping patterns. The results of this study indicate that only 17% of the area is highly suitable, while 54% and 29% were found moderately suitable and unsuitable for the MAR approach. Since most highly and moderately suitable sites were falling in the agricultural areas, agricultural-based MAR (AgMAR) was considered a preferred option. AquaCrop model for sugarcane was developed considering excess canal water supply during the grand growth stage to understand the AgMAR potential in the study area. It was observed that the potential recharge under normal irrigation scenarios varies from 135.5 to 272 mm/year, which can be increased through AgMAR up to 545 mm/year depending on the water availability for excess irrigations. This study provides an improved understanding of the parameters that should be considered for MAR site selection and post-GIS-MCDA analysis to assess the basin-specific MAR strategy.

Leakage of CO2 from geological storage and its impacts on fresh soil-water systems: a review.

Leakage of CO2 from the geological storage is a serious issue for the sustainability of the receiving fresh soil-water systems. Subsurface water quality issues are no longer related to one type of pollution in many regions around the globe. Thus, an effort has been made to review studies performed to investigate supercritical CO2 (scCO2) and CO2 enrich brine migration and it's leakage from geological storage formations. Further, the study also reviewed it's impacts on fresh soil-water systems, soil microbes, and vegetation. The first part of the study discussed scCO2/CO2 enrich brine migration and its leakage from storage formations along with it's impact on pore dynamics of hydrological regimes. Later, a state-of-the-art literature survey has been performed to understand the role of CO2-brine leakage on groundwater dynamics and its quality along with soil microbes and plants. It is observed in the literature survey that most of the studies on CO2-brine migration in storage formations reported significant CO2-brine leakage due to over-pressurization through wells (injections and abandoned), fracture, and faults during CO2 injection. Thus, changes in the groundwater flow and water table dynamics can be the first impact of the CO2-brine leakage. Subsequently, three major alterations may also occur-(i) drop in pH of subsurface water, (ii) enhancement of organic compounds, and (iii) mobilization of metals and metalloids. Geochemical alteration depends on the amount of CO2 leaked and interactions with host rocks. Therefore, such alteration may significantly affect soil microbial dynamics and vegetation in and around CO2 leakage sites. In-depth analysis of the available literature fortifies that a proper subsurface characterization along with the bio-geochemical analysis is extremely important and should be mandatory to predict the more accurate risk of CO2 capture and storage activities on soil-water systems.

Ensemble modelling framework for groundwater level prediction in urban areas of India.

India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information theory (MI), genetic algorithm (GA), artificial neural network (ANN) and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data which consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate were pre-processed using mutual information theory, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre-processing predict groundwater levels accurately (R > 85%). It is also evident from the results that the pre-processing techniques enhance the prediction accuracy and results were improved for 66% of the monitored wells. Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels. The developed approach in this study for urban groundwater prediction can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.