Water-Saving Agricultural Technologies: Regional Hydrology Outcomes and Knowledge Gaps in the Eastern Gangetic Plains—A Review

Increasing food demand has exerted tremendous stress on agricultural water usages worldwide, often with a threat to sustainability in agricultural production and, hence, food security. Various resource-conservation technologies like conservation agriculture (CA) and water-saving measures are being increasingly adopted to overcome these problems. While these technologies provide some short- and long-term benefits of reduced labor costs, stabilized or increased crop yield, increased water productivity, and improved soil health at farm scale, their overall impacts on hydrology outcomes remain unclear at larger temporal and spatial scales. Although directly linked to the regional hydrological cycle, irrigation remains a less understood component. The ecological conditions arising from the hydrology outcomes of resource-conservation technologies are associated with sustainability in agricultural production. In this paper, the philosophies and benefits of resource-conservation technologies and expert perceptions on their impacts on temporal and spatial scales have been reviewed comprehensively focusing on regional hydrology outcomes in the Eastern Gangetic Plain (EGP). Due to data inadequacy and lack of knowledge-sharing among disciplines, little is yet known about actual water saving by these resource-conservation technologies and the level of their contribution in groundwater and surface water storage over large temporal and spatial scales. Inadequate knowledge of the hydrological effects of water applied in the agricultural field leads to the implementation of water management policy based on local perspectives only, often with the possibility of deteriorating the water-scarcity situation. Therefore, multidisciplinary future research should quantify regional hydrology outcomes by measuring the components of regional water balance in order to develop a proper water management policy for sustainable agricultural production.

Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks

Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5° spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.