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Domestic water use is one of India's primary water uses that also includes irrigation, industrial, and environmental water uses. However, there is a lack of reliable data that hinders the estimation of domestic water use in India. Previous large-scale assessments often estimated domestic water use using population alone as a predictor. Economic and technological development and the improvement in the country's living standards may increase per-capita domestic water use. The present study's objective was to develop a large-scale domestic water use model in India, which considered the temporal variation of per-capita domestic water use. The economic indicator per-capita Gross Domestic Product (GDP) was used to model the time-variant per-capita domestic water use. The model results showed acceptable performance (NSE>0.7) at both the national and state levels. Maharashtra exhibited the highest per-capita domestic water use in the country because of the rapid economic and technological development relative to other States. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the major water exchange processes between the earth's surface and the atmosphere. ET is a combined process of evaporation from open water bodies, bare soil and plant surfaces, and transpiration from vegetation. Remote sensing‐based ET models have been developed to estimate spatially distributed ET over large regions, however, many of them reportedly underestimate ET over semi‐arid regions (Jamshidi et al., Journal of Hydrometeorology, 2019, 20, 947–964). In this work, we show that underestimation of ET can occur due to the open water evaporation from flooded rice paddies ignored in the existing ET models. To address the gap in ET estimation, we have developed a novel approach that accounts for the missing ET component over flooded rice paddies. Our method improved ET estimates by a modified Penman‐Monteith algorithm that considered the fraction of open water evaporation from flooded rice paddies. Daily ET was calculated using ground based meteorological data and the MODIS satellite data over the Krishna River Basin. Seasonal and annual ET values over the Krishna Basin were compared with two different ET algorithms. ET estimates from these two models were also compared for different crop combinations. Results were validated with flux tower‐based measurements from other studies. We have identified a 17 mm/year difference in average annual ET over the Krishna River Basin with this new ET algorithm. This is very critical in basin scale water balance analysis and water productivity studies. [ABSTRACT FROM AUTHOR]

• A new census data-based approach is proposed to estimate industrial water demand. • The model estimates the State-level industrial water demand at the monthly time scale. • The principal components are used as effective predictors to reduce input variables. Rising population and industrialisation aided by technological advancements have resulted in increased industrial water demand in India. Industrial water demand accounts for about 8–10 % of the total water demand in the country. Although some national and international organisations report industrial water demand, there is a wide variation in data due to the scarcity of the reliable data and inconsistencies in the methods adopted for quantification. Rather than using these estimates, the census-based statistical data could provide better figures. Therefore, the present study aims to quantify industrial water demand by compiling census-based statistical data collected from various Governmental agencies in India. Based on census datasets collected at National and State Governmental institutions, this study develops a new model to estimate industrial water demand. Industrial water demand is estimated in this study at State and monthly scale from 1991 to 2010. The study models industrial water demand as a function of industrial production data, industrial survey components, and economic/technological development indicators. Principal component analysis is employed to reduce these large number of input variables to a smaller number of latent variables, which account for the variance in the input variables. The model results are cross-validated with industrial water demand data obtained from National Commission on Integrated Water Resources Development (NCIWRD). Country-level estimates are disaggregated to State-level water demand estimates based on number of industrial units in each State. These State-level results are verified against an independent census dataset on industrial water demand collected from State Governmental organisations. It is found that industrial water demand increased at a rate of 2% per annum during the period of analysis, which exceeds the increase rate of agricultural and domestic water demands during the same period. [ABSTRACT FROM AUTHOR]

From the 1980s, Indian summer monsoon rainfall (ISMR) shows a decreasing trend over north and northwest India, and there was a significant observed reduction in July over central and south India in 1982–2003. The key drivers of the changed ISMR, however, remain unclear. It was hypothesized that the large-scale irrigation development that started in the 1950s has resulted in land surface cooling, which slowed large-scale atmospheric circulation, exerting significant influences on ISMR. To test this hypothesis, a fully coupled model, the CESM v1.0.3, was used with a global irrigation dataset. In this study, spatially varying irrigation-induced feedback mechanisms are investigated in detail at different stages of the monsoon. Results show that soil moisture and evapotranspiration increase significantly over India throughout the summertime because of the irrigation. However, 2-m air temperature shows a significant reduction only in a limited region because the temperature change is influenced simultaneously by surface incoming shortwave radiation and evaporative cooling resulting from the irrigation, especially over the heavily irrigated region. Irrigation also induces a 925-hPa northeasterly wind from 30°N toward the equator. This is opposite to the prevailing direction of the Indian summer monsoon (ISM) wind that brings moist air to India. The modeled rainfall in the irrigated case significantly decreases up to 1.5 mm day−1 over central and north India from July to September. This paper reveals that the irrigation can contribute to both increasing and decreasing the surface temperature via multiple feedback mechanisms. The net effect is to weaken the ISM with the high spatial and temporal heterogeneity. [ABSTRACT FROM AUTHOR]

• The study proposes and uses a new modelling framework for quantifying blue water scarcity. • This study reconstructs various water uses in India using census datasets. • Blue water scarcity is quantified in India at State and monthly scales. • More than 60% of the population in India faces severe water stress every year. • Groundwater abstraction in Gujarat, Rajasthan and Tamil Nadu exceeds the groundwater recharge. Sustainable water use and water scarcity are major concerns in developing countries such as India. Rapidly growing population along with increasing economic and technological development in India have resulted in increased water use leading to severe water scarcity. The present study aims to quantify and assess sustainable water use and water scarcity in India. A data-intensive approach is employed at a State spatial resolution and monthly temporal scales during the period 1991–1999. The Water Stress Index (WSI), which is defined as the ratio of total water use to available water, is used as a measure of blue water scarcity. The total available water includes surface water and groundwater while the total water uses include irrigation, industrial, domestic and environmental uses. A Community Land Model (CLM 4.0) developed by the National Centre for Atmospheric Research (NCAR), is used to quantify the total available water in India. Water uses in India are reconstructed using a census-based statistical database while the environmental water demand is modelled following the hydrology-based approach, which allocates environmental flows as a proportion of the available water, with seasonally defined environmental flow thresholds. Further, the excess water component, defined as the amount of water remaining after meeting the demands at each time step, is incorporated into the modelling framework by adding it to the available water pool. The study estimates that 60% of the population in India faces severe water stress during the analysis period, and is more predominant in the States of Rajasthan, Gujarat, Uttar Pradesh and Maharashtra. Moreover, non-renewable groundwater abstraction, which is defined as the amount of groundwater abstraction more than the groundwater recharge, is also quantified. This study identifies severe groundwater depletion in the north-western parts of India, consistent with the current estimates from GRACE satellite observations. [ABSTRACT FROM AUTHOR]