Vulnerability of groundwater from elevated nitrate pollution across India: Insights from spatio-temporal patterns using large-scale monitoring data.

Agriculture-sourced, non-point groundwater contamination (e.g., nitrate) is a serious concern from the drinking water crisis aspect across the agrarian world. India is one of the largest consumers of nitrogen fertilizers in South-Asia as well as in the world but groundwater nitrate lacks critical attention as a wide-scale drinking water pollutant in the country. Our study provides the first documentation of the distribution of groundwater nitrate and the extent of elevated nitrate contamination across India, along with the delineation of the temporal trends and the natural and anthropogenic factors that influence such occurrence of groundwater nitrate. High resolution, annual-scale spatio-temporal variability of groundwater nitrate concentration and consequent contamination was delineated using groundwater nitrate measurements from ~3 million drinking water wells spread across 7038 administrative blocks between 2010 and 2017 in India. An average 8% of the studied blocks were found affected by elevated groundwater nitrate (> 45 mg/L). Depth-dependent trend demonstrated that nitrate concentrations were about 14% higher in shallow water wells (≤ 35 m) than deep wells (>35 m). The overall temporal trend of groundwater nitrate concentration was decreasing slightly nationwide in the study period. The correlation tests and causality test results indicated that the spatial distribution of groundwater nitrate was significantly associated with agricultural N-fertilizer usage, whereas the decreasing temporal trend corresponded with the overall reduced N-fertilizer usage during the study period. Spatial autocorrelation analysis identified the clustering of high nitrate areas in central, north, and southern India, specifically in areas with higher fertilizer usage. We estimate about 71 million Indians possibly exposed to elevated groundwater nitrate concentrations and the majority of them reside in rural areas. Thus, this study provides the previously unrecognized, wide-scale, anthropogenic, diffused groundwater nitrate contamination across India.

Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin.

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January-March 2020), syn-lockdown (April-May), and post-lockdown periods (June-July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.

Using night time lights to find regional inequality in India and its relationship with economic development.

Due to unavailability of consistent income data at the sub-state or district level in developing countries, it is difficult to generate consistent and reliable economic inequality estimates at the disaggregated level. To address this issue, this paper employs the association between night time lights and economic activities for India at the sub-state or district-level, and calculates regional income inequality using Gini coefficients. Additionally, we estimate the relationship between night time lights and socio-economic development for regions in India. We employ a newly available data on regional socio-economic development (Social Progress Index), as well as an index that represents institutional quality or governance. Robust to the choice of socio-economic development indicators, our findings indicate that regional inequality measured by night time lights follow the Kuznets curve pattern. This implies that starting from low levels of socio-economic development or quality of institutions, inequality rises as regional socio-economic factors or quality of institutions improve, and with subsequent progress in socio-economic factors or quality of institutions, regional inequality declines.

Groundwater storage change detection from in situ and GRACE-based estimates in major river basins across India.

India has been the subject of many recent groundwater studies due to the rapid depletion of groundwater in large parts of the country. However, few if any of these studies have examined groundwater storage conditions in all of India's river basins individually. Herein we assess groundwater storage changes in all 22 of India's major river basins using in situ data from 3420 observation locations for the period 2003-2014. One-month and 12-month standardized precipitation index measures (SPI-1 and SPI-12) indicate fluctuations in the long-term pattern. The Ganges and Brahmaputra basins experienced long-term decreasing trends in precipitation in both 1961-2014 and the study period, 2003-2014. Indeterminate or increasing precipitation trends occurred in other basins. Satellite-based and in situ groundwater storage time series exhibited similar patterns, with increases in most of the basins. However, diminishing groundwater storage (at rates of >0.4 km3/year) was revealed in the Ganges-Brahmaputra river basin based on in situ observations, which is particularly important due to its agricultural productivity.

Achieving Sustainable Development Goal for Clean Water in India: Influence of Natural and Anthropogenic Factors on Groundwater Microbial Pollution.

Worldwide, >2 billion people (~1/3 world population), mostly living in economically stressed areas of Africa and South Asia, still do not have access to basic sanitation, and ~1 billion still practice open defecation. Water pollution due to open defecation may primarily be linked to economy, and other factors such as social and hygiene practices, land use and hydrogeological parameters could also have sufficient influence. The present study describes the effect of human development index (HDI, 2001-2015) and economic development (NL, 1992-2013) on groundwater microbial pollution (FC, 2002-2017) across India. Economic development pattern suggested discernable inverse relationship with FC in most areas, although areas with inferior water quality, improper human practices were found to outweigh economic development. Vulnerability modelling, using these data, along with measured FC in groundwater-sourced drinking water locations (n = 235) demonstrated the heterogeneity of FC distribution potential in areas of homogenous economy, social practices, and land use. High-resolution numerical modelling of the advective transport of the hypothetical FC particles in the aquifers, suggest up to ~24 times faster movement of pollutants under irrigation-induced pumping regimes. Hence, the results of our study highlight and quantify the potential pitfalls that are possible hindrance for achieving the United Nations sustainable development goal, despite social and economic development, across the spatial scales.

Estimating influences of environmental drivers on soil heterotrophic respiration in the Athabasca River Basin, Canada.

Soil heterotrophic respiration (RH) is a crucial component of the atmospheric carbon dioxide (CO2) budget, as RH accounts for ∼10 times more CO2 than burning fossil fuels. However, modelling of RH is primarily based upon empirical/semi-empirical approaches. Here, we developed a mechanistic model based on microbial kinetics and thermodynamics processes (MKT) to model soil chemical environment and soil RH in the Athabasca River Basin, Canada. MKT was coupled with the Soil and Water Assessment Tool (SWAT) for a regional-scale hydro-biogeochemical simulation. Dissolved oxygen, redox potential and meteorological variables were simulated for the first time at a regional scale. Annual mean simulated RH varied from 20 to 320 kg CO2-C/ha/yr across Athabasca River basin (ARB) in 2000-2013. Our results show that dissolved oxygen, air temperature, and soil temperature have more influence on RH than redox potential, precipitation, and water-filled pore space (WFPS). A significant (p < 0.01) causal relationship exists between the dissolved oxygen, air temperature, soil temperature, redox potential and precipitation with RH. Our results show that the role of environmental drivers are essential and should be considered in future estimations of RH.

Author Correction: Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight.

Globally, ~1 billion people, mostly residing in Africa and South Asia (e.g. India), still lack access to clean drinking water and sanitation. Resulting, unsafe disposal of fecal waste from open-defecation to nearby drinking water sources severely endanger public health. Until recently, India had a huge open-defecating population, leading declining public health from water-borne diseases like diarrhoea by ingesting polluted water, mostly sourced to groundwater. However, in recent past, sanitation development to achieve Sustainable Development Goals (SDGs) has been encouraged throughout India, but their effect to groundwater quality and human health conditions are yet-unquantified. Here, for the first time, using long term, high-spatial resolution measurements (>1.7 million) across India and analyses, we quantified that over the years, groundwater fecal coliform concentration (2002-2017, -2.56 ± 0.06%/year) and acute diarrheal cases (1990-2016, -3.05 ± 0.01%/year) have significantly reduced, potentially influenced by sanitation development (1990-2017, 2.63 ± 0.01%/year). Enhanced alleviation of groundwater quality and human health have been observed since 2014, with initiation of acceletated constructions of sanitation infrastructures through Clean India (Swachh Bharat) Mission. However, the goal of completely faecal-pollution free, clean drinking water is yet to be achieved. We also evaluated the suitability of using satellite-derived night-time light (NLan, 1992-2013, 4.26 ± 0.05%/year) as potential predictor for such economic development. We observed that in more than 80% of the study region, night-time light demonstrated to be a strong predictor for observed changes in groundwater quality, sanitation development and water-borne disease cases. While sanitation and economic development can improve public health, poor education level and improper human practices can strongly influence on water-borne diseases loads and thus health in parts of India.

Microbial kinetics and thermodynamic (MKT) processes for soil organic matter decomposition and dynamic oxidation-reduction potential: Model descriptions and applications to soil N2O emissions.

A conversion of the global terrestrial carbon sink to a source is critically dependent on the microbially mediated decomposition of soil organic matter (SOM). We have developed a detailed, process-based, mechanistic model for simulating SOM decomposition and its associated processes, based on Microbial Kinetics and Thermodynamics, called the MKT model. We formulated the sequential oxidation-reduction potential (ORP) and chemical reactions undergoing at the soil-water zone using dual Michaelis-Menten kinetics. Soil environmental variables, as required in the MKT model, are simulated using one of the most widely used watershed-scale models - the soil water assessment tool (SWAT). The MKT model was calibrated and validated using field-scale data of soil temperature, soil moisture, and N2O emissions from three locations in the province of Saskatchewan, Canada. The model evaluation statistics show good performance of the MKT model for daily soil N2O simulations. The results show that the proposed MKT model can perform better than the more widely used process-based and SWAT-based models for soil N2O simulations. This is because the multiple processes of microbial activities and environmental constraints, which govern the availability of substrates to enzymes were explicitly represented. Most importantly, the MKT model represents a step forward from conceptual carbon pools at varying rates.

Evaluating the uncertainty of terrestrial water budget components over High Mountain Asia.

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff(R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11±0.45, 1.26±0.11, and 0.85±0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Groundwater depletion causing reduction of baseflow triggering Ganges river summer drying.

In summer (pre-monsoon) of recent years, low water level among the last few decades, has been observed in several lower Indian reaches of the Ganges (or Ganga) river (with estimated river water level depletion rates at the range of -0.5 to -38.1 cm/year between summers of 1999 and 2013 in the studied reaches). Here, we show this Ganges river depletion is related to groundwater baseflow reduction caused by ongoing observed groundwater storage depletion in the adjoining Gangetic aquifers (Ganges basin, -0.30 ± 0.07 cm/year or -2.39 ± 0.56 km3/year). Our estimates show, 2016-baseflow amount (~1.0 × 106 m3/d) has reduced by ~59%, from the beginning of the irrigation-pumping age of 1970s (2.4 × 106 m3/d) in some of the lower reaches. The net Ganges river water reduction could jeopardize domestic water supply, irrigation water requirements, river transport, ecology etc. of densely populated northern Indian plains. River water reduction has direct impact on food production indicating vulnerability to more than 100 million of the population residing in the region. The results of this study could be used to decipher the groundwater-linked river water depletion as well as the regional water security in other densely populated parts of the globe.

Groundwater rejuvenation in parts of India influenced by water-policy change implementation.

The dwindling groundwater resource of India, supporting almost one fifth of the global population and also the largest groundwater user, has been of great concern in recent years. However, in contrary to the well documented Indian groundwater depletion due to rapid and unmanaged groundwater withdrawal, here for the first time, we report regional-scale groundwater storage (GWS) replenishment through long-term (1996-2014, using more than 19000 observation locations) in situ and decadal (2003-2014) satellite-based groundwater storage measurements in western and southern parts of India. In parts of western and southern India, in situ GWS (GWSobs) has been decreasing at the rate of -5.81 ± 0.38 km3/year (in 1996-2001) and -0.92 ± 0.12 km3/year (in 1996-2002), and reversed to replenish at the rate of 2.04 ± 0.20 km3/year (in 2002-2014) and 0.76 ± 0.08 km3/year (in 2003-2014), respectively. Here, using statistical analyses and simulation results of groundwater management policy change effect on groundwater storage in western and southern India, we show that paradigm shift in Indian groundwater withdrawal and management policies for sustainable water utilization appear to have started replenishing the aquifers in western and southern parts of India.

Benefits and Pitfalls of GRACE Data Assimilation: a Case Study of Terrestrial Water Storage Depletion in India.

This study investigates some of the benefits and drawbacks of assimilating Terrestrial Water Storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) into a land surface model over India. GRACE observes TWS depletion associated with anthropogenic groundwater extraction in northwest India. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. Moreover, in situ measurements of shallow groundwater show no trend, suggesting that the trends are erroneously introduced by the assimilation into the modeled shallow groundwater, when in reality the groundwater is depleted in deeper aquifers. The results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.

Spatio-temporal variability of groundwater storage in India.

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.