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The agriculture sector in India accounts for more than 85% of the total water use for irrigation. Within irrigation, the share of groundwater in the net irrigated area is around 50% to 55%, which is responsible for two-thirds of the total agricultural production. Agriculture also accounts for non-point-source pollution that arises from excessive use of fertilizers and pesticides. The Green Revolution was partly based on increased fertilizer use and intensive irrigation. During the past three decades, food production was tripled and poverty witnessed a decline. However, agriculture intensification led to overexploitation and pollution of groundwater resources, causing degradation of the environment and depletion of natural resources. Over and above, absence of significant public investment in surface water-based irrigation infrastructure, free or highly subsidized electricity, unregulated groundwater pumping, and irrational pricing policies have promoted cropping choices independent of resource endowments of water and energy. Over the long term, the current trends toward resource depletion and degradation, combined with a lack of proper reforms and policies that ensure agricultural sustainability and threats from climate change, pose a huge risk to farmers' income and can force poorer farmers out of the agricultural sector. In turn, these problems would lead to increased migration and poverty and decreased crop production, which are serious threats to food security. In this paper, we have discussed various challenges to water sustainability and food security of India. [ABSTRACT FROM AUTHOR]

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions. [ABSTRACT FROM AUTHOR]

This article assesses the impact of the tensiometer on the consumption of groundwater and electric power in paddy cultivation in Indian Punjab, and its subsequent economic benefits. We find that compared to the continuous flooding method, the tensiometer-based application of irrigation reduces water and power consumption by 13%, cutting variable costs by 7% without any yield penalty. If 30% of the paddy area is irrigated following tensiometer-based schedules, then the state could save a total of 0.67 million ha m of water and 1516 million kWh of electric power in 2010-2025, with aggregate economic benefits of US$ 459 million. [ABSTRACT FROM AUTHOR]

Weather-state models have been shown to be effective in downscaling the synoptic atmospheric information to local daily precipitation patterns. We explore the ability of non-homogeneous hidden Markov models (NHMM) to downscale regional seasonal climate data to daily rainfall at a collection of gauging sites. The predictors used are: ensemble means of seasonal rainfall as forecast by the DEMETER and ECHAM models, and the preceding seasonal outgoing long-wave radiation (OLR). As the downscaling of seasonal GCM-based predictions lacks the ability to capture the intra-seasonal variability, we augment the seasonal GCM-driven inputs with statistically-driven predictions of the monthly rainfall amounts. The pooling effect of combining seasonal and monthly estimates of the regional rainfall enhances the capacity of the NHMM to simulate the stochastic characteristics of rainfall fields. The monthly rainfall prediction is derived from a wide range of climate precursors such as the El Nino-Southern Oscillation, local sea-level pressure, and sea-surface temperature. Application of the methodology to data from the Everglades National Park region in South Florida, USA is presented for the seasons May-July and August-September using a 22-year sequence of seasonal data from eight rainfall stations. The model skill in capturing the seasonal and intra-seasonal rainfall attributes at each station is demonstrated graphically and using simple statistical measures of efficiency. The hidden states derived from NHMM are qualitatively analysed and shown to correspond to the dominant synoptic-scale features of rainfall generating mechanisms, which reinforces the argument that physical processes are appropriately captured. Citation Khalil, A. F., Kwon, H.-H., Lall, U. & Kaheil, Y. H. (2010) Predictive downscaling based on non-homogeneous hidden Markov models. Hydrol. Sci. J. 55(3), 333-350. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]