Recent U.S. Geological Survey water‐use report suggests that increasing water‐use efficiency could mitigate the supply‐and‐demand imbalance arising from changing climate and growing population. However, this rich data have neither analyzed to understand the underlying patterns, nor have been investigated to identify the factors contributing to this increased efficiency. A national‐scale synthesis of public supply withdrawals (“withdrawals”) reveals a strong North–south gradient in public supply water use with the increasing population in the South contributing to increased withdrawal. Contrastingly, a reverse South–north gradient exists in per capita withdrawals (“efficiency”), with northern states consistently improving the efficiency, while the southern states' efficiency declined. Our analyses of spatial patterns of per capita withdrawals further demonstrate that urban counties exhibit improved efficiency over rural counties. Improved efficiency is also demonstrated over high‐income and well‐educated counties. Given the potential implications of the findings in developing long‐term water conservation measures (i.e., increasing block rates), we argue the need for frequent updates, perhaps monthly to annual, of water‐use data for identifying effective strategies that control the water‐use efficiency in various geographic settings under a changing climate.

The present study examines the Kerala Flood Event (KFE, 15–16 August 2018, in India) that occurred along the west coast of India and resulted in ~400 mm of rainfall in one day. The KFE was unique in comparison to previous floods in India, not only due to the rainfall duration and amount, but also due to the fact that the dams failed to mitigate the flood, which made it the worst in history. The main goal of this study is to analyze and elucidate the KFE based on meteorological and hydrological parameters. A propagating low-pressure system (LPS) from the Bay of Bengal (BoB) caused the streak of plenty of rainfall over Kerala, the west coast, central India, and the BoB. Additionally, the upper-tropospheric anti-cyclonic system over the Middle East region inhibited a northward advancement of LPS. On the western coast of India, a non-propagating (with diurnal fluctuations) offshore trough was observed over the west coast (from Kerala to Gujarat state). Therefore, a synergic interaction between LPS, an intrusion of dry air in the middle-troposphere, and the offshore trough was the main reason for KFE. However, after around ten days, rainfall saturated the dam capacities; thus, the released water, along with the amount of precipitation on the day of the event, was one of the other possible reasons which worsened the flood over Kerala.

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.

The intensity and frequency variability of cyclones in the North Indian Ocean (NIO) have been amplified over the last few decades. The number of very severe cyclonic storms (VSCSs) over the North Indian Ocean has increased over recent decades. “Phailin”, an extreme severe cyclonic storm (ESCS), occurred during 8–13 October 2013 over the Bay of Bengal and made landfall near the Gopalpur coast of Odisha at 12 UTC on 12 October. It caused severe damage here, as well as in the coastal Odisha, Andhra Pradesh, and adjoining regions due to strong wind gusts (~115 knot/h), heavy precipitation, and devastating storm surges. The fidelity of the WRF model in simulating the track and intensity of tropical cyclones depends on different cloud microphysical parameterization schemes. Thus, four sensitivity simulations were conducted for Phailin using double-moment and single-moment microphysical (MP) parameterization schemes. The experiments were conducted to quantify and characterize the performance of such MP schemes for Phailin. The simulations were performed by the advanced weather research and forecasting (WRF-ARW) model. The model has two interactive domains covering the entire Bay of Bengal and adjoining coastal Odisha on 25 km and 8.333 km resolutions. Milbrandt–Yau (MY) double-moment and WRF single-moment microphysical schemes, with 6, 5, and 3 classes of hydrometeors, i.e., WSM6, WSM5, and WSM3, were used for the simulation. Experiments for Phailin were conducted for 126 h, starting from 00 UTC 08 October to 06 UTC 13 October 2013. It was found that the track, intensity, and structure of Phailin are highly sensitive to the different microphysical parameterization schemes. Further, the precipitation and cloud distribution were studied during the ESCS stage of Phailin. The microphysics schemes (MY, WSM3, WSM5, WSM6), along with Grell–Devenyi ensemble convection scheme predicted landfall of Phailin over the Odisha coast with significant track errors. Supply of moisture remains a more crucial component than SST and wind shear for rapid intensification of the Phailin 12 h before landfall over the Bay of Bengal. Finally, the comparison of cyclone formation between two decades 2001–2010 and 2011–2020 over the Bay of Bengal inferred that the increased numbers of VSCS are attributed to the supply of abundant moisture at low levels in the recent decade 2011–2020.

Conventional physically based models have long yielded promising results, as they have been the main tool to depict the underpinnings of the physics governing the hydrological events. These models, however, suffer from certain issues such as the intense calibration time or the uncertainty in the estimation of hydrological variables. The development of the sophisticated data-driven techniques, and machine learning models in particular, combined with rapid increases in computational abilities (graphics processing units, computer clusters. etc.), has enabled hydrologists to utilize the data driven models in tandem with the well-established hydrological models to simulate miscellaneous environmental processes nimbly, and therefore circumvent the aforementioned conundrums associated with the physically based models. To this end, the present study aims at exploring a sophisticated neural network called variational Bayesian neural network, to improve the accuracy of physically based predictions such as runoff. Our neural network was able to accurately forecast the runoff rates with the mean Pearson correlation coefficient of within a randomly selected subset of cells in the Brazos River Basin. As these cells are selected randomly across the basin, we exclude the possibility of biasing our neural network by any specific cell. Moreover, this work for the very first time, to the best of our knowledge, suggests a similarity-based solution to transfer the learning model developed in a basin to be deployed across a different basin. In other words, there would be no need to develop a learning model for each basin from scratch. We, instead, utilize the models learnt from the previously studied basins. We cross-validated our proposed transfer learning solution via leave-one-out strategy within the grid cells of the Brazos River basin achieving a mean Pearson correlation coefficient of . Keywords: Variational Bayesian neural network, VIC Model, Similarity, Transfer-Learning, Pearson correlation coefficient.

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.