Land use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. However, current water‐quality models lack a framework to capture these legacy effects. Here we have modified the SWAT (Soil Water Assessment Tool) model to capture the effects of nitrogen (N) legacies on water quality under multiple land‐management scenarios. Our new SWAT‐LAG model includes (1) a modified carbon‐nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502‐km2 Iowa watershed as a case study, we found that between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation − Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT‐LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the 2 years predicted in the original SWAT model. The framework proposed here constitutes a first step toward modifying a widely used modeling approach to assess the effects of legacy N on the time required to achieve water‐quality goals.

The COVID-19 pandemic has disrupted daily activities across multiple sectors globally. The extent of its impact on the global economy and its key sectors, especially water, wastewater, and associated sectors such as agriculture, is still unclear. In this paper, the preliminary impacts of COVID-19 on water resources of India, especially on the river water quality, water usage in domestic and commercial sectors, wastewater treatment sector, and agriculture sector, are discussed. The limitations in the functioning of the existing system and management of water resources are identified. The need for improvements to strengthen the water resources monitoring and developing process-based models are highlighted. This paper also discusses the need for further investigation to identify the extent of impact and contributing factors to improve our understanding of the natural system for preparing, monitoring, and implementing the policies to manage the water resources during any pandemic/epidemics in the future.

In most of the Indian cities, around half of the urban water requirement is fulfilled by groundwater. Recently, seasonal urban droughts have been frequently witnessed globally, which adds more stress to groundwater systems. Excessive pumping and increasing demands in several Indian cities impose a high risk of running out of groundwater storage, which could potentially affect millions of lives in the future. In this paper, groundwater level changes have been comprehensively assessed for seven densely populated and rapidly growing secondary cities across India. Several statistical analyses were performed to detect the trends and non-stationarity in the groundwater level (GWL). Also, the influence of rainfall and land use/land cover changes (LULC) on the GWL was explored. The results suggest that overall, the groundwater level was found to vary between ±10 cm/year in the majority of the wells. Further, the non-stationarity analysis revealed a high impact of rainfall and LULC due to climate variability and anthropogenic activities respectively on the GWL change dynamics. Statistical correlation analysis showed evidence supporting that climate variability could potentially be a major component affecting the rainfall and groundwater recharge relationship. Additionally, from the LULC analysis, a decrease in the green cover area (R = 0.93) was found to have a higher correlation with decreasing groundwater level than that of urban area growth across seven rapidly developing cities.

More than a century of land-use changes and intensive agriculture across the Mississippi River Basin (MRB) has led to a degradation of soil and water resources. Nitrogen (N) leaching from the excess application of fertilizers has been implicated in algal blooms and the development of large, coastal “dead zones.” It is, however, increasingly recognized that water quality today is a function not only of the current-year inputs but also of legacy N within the watershed—legacy that has accumulated in soil and groundwater over decades of high-input agricultural practices. Although attempts have been made to quantify the extent to which soil organic nitrogen (SON) is being sequestered in agricultural soils with intensive fertilization, improved residue management, and the adoption of conservation tillage practices, the controls on accumulation dynamics as well as linkages between legacy N accumulation and water quality remain unclear. Here, we have used the process-based model CENTURY to quantify accumulation and depletion trajectories for soil N across a range of climate and soil types characteristic of the MRB. The model was calibrated against crop yield data and soil nitrogen accumulation data from a long-term field site. Model runs highlighted that under current management scenarios, N accumulation is greatest in regions with the highest crop yield, and this can be attributed to the higher residue rates with greater yields. We thus find that humans, through management practices, have homogenized spatial patterns of SON across the landscape by increasing SON magnitudes in warmer and drier regions. Results also suggest a regime shift in the relationship between soil organic N and N mineralization fluxes, such that N fluxes are greater now than in the 1930s, despite similar soil organic N magnitudes, mainly due to higher proportions of labile, unprotected soil organic matter. This regime shift leads to elevated N leaching to tiles and groundwater in landscapes under intensive agriculture.

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4The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously 1 5 to control the spread by imposing intervention measures and increasing the medical facilities. In 1 6 order to tackle the crisis effectively we need to know the trajectories of number of the people 1 7 infected (i.e. confirmed cases). Such information is crucial to government agencies for 1 8 developing effective preparedness plans and strategies. We used a statistical modeling approach 1 9 4 1 number of confirmed, recovered, and mortality cases were 1,595,350, 353,975 and 95,455 4 2 respectively [3]. The reported cases globally are drastically increasing from 4,219/day in 4 3 February 2020 to 50,784/day in early April 2020. The trajectory of the infection spread during 4 4the coming days is important information in order to plan, prepare, and scale up the intervention 4 5