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.

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.

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.

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.