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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]

• Water-level based rainfall-runoff calibration can be improved using discharge index. • Efficacy of regionalized discharge index is comparable with that of observed values. • Water-level based calibration with discharge index works better for wet catchments. • Regionalized Qmean is a better choice than regionalized Q95 as discharge index. Rainfall-runoff models are generally calibrated by using continuous stream discharge data. However, most catchments around the globe remain ungauged due to the difficulty in installing gauges in river channels with complex morphology and due to the high costs of installation and maintenance. Recently, new calibration methods that use water level data instead of gauged discharge to calibrate rainfall-runoff models have been proposed. With emerging low-cost local water-level monitoring technologies or with satellite altimetry, these water-level based calibration methods have the potential to significantly extend runoff prediction capability. However, rainfall-runoff models calibrated from using water-level information alone may lead to biased discharge even when water-level variations are accurately reproduced. It has been shown that this bias problem can be significantly alleviated when just a few discharge measurements, especially of high flows, are available and incorporated into the model calibration. However, it is rare to have discharge measurement data when catchments are ungauged. In this study, we incorporate discharge estimates derived from regionalization into a water-level based calibration using an inverse rating curve (IRC) method. Specifically, discharge indices such as the 95th percentile of daily discharge (Q95) and the mean daily discharge (Qmean) are estimated from catchment and climate characteristics based on regionalized relationships. These estimated discharge indices are then used to constrain the IRC calibration. We evaluate both constrained and unconstrained IRC methods over 130 study catchments where observed discharge time series are available but used only for validation in this study. Modelled discharge time series from the constrained IRC methods have median Nash Sutcliffe Efficiency (NSE) values of 0.61–0.63, compared with 0.54 from the unconstrained IRC method. Improvement is seen in around 75% of the catchments, with more than 0.2 increase in NSE values in 25% of the catchments. Improvement in the modelled discharges from using regionalized discharge indices is comparable to using observed discharge indices. Using regionalized Qmean yields greater improvement than using regionalized Q95. The constrained IRC methods tend to perform better in wetter catchments than in drier ones. This method can significantly extend rainfall-runoff model calibration in a wider range of catchments, including real ungauged catchments. [ABSTRACT FROM AUTHOR]

There is a growing concern about water scarcity and the associated decline in Australia's agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery. [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]

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage. • Factors driving seasonal irrigation usage were explored in an irrigation district. • A Bayesian hierarchical model (BHM) was developed for 1099 crop water use records. • Seasonal irrigation usage was mainly influenced by soil and irrigation practices. • The BHM showed good accuracy in estimating seasonal irrigation usage (R2 = 0.62). [ABSTRACT FROM AUTHOR]