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Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height–area–storage (H–A–S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H–A–S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H–A–S models underestimate the exposed surface area of the Pong reservoir by up to 11.19%; the bias at Bhakra was much less. The GRanD H–A–S model performed very poorly at both reservoirs, producing overprediction in exposed reservoir area of up to 100% and 415% at the Pong and Bhakra reservoirs, respectively. Analyses also showed that up to 29% increase in reservoir capacity is required to compensate for the effect of net evaporation loss at low demand levels. As demand increases, the required evaporation-correction capacity decreases in proportional terms and is indistinguishable for all H–A–S models. Finally, recommendations are made on using the results for evaporation adjustment at nongauged sites in the region. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a water and energy-saving reliable irrigation system using state-of-the-art computing, communication, and optimal energy management framework. The framework integrates real-time soil moisture and weather forecasting information to decide the time of irrigation and quantity of water required for potato crops, which is made available to the users across a region through the cloud-based irrigation decision support system. This is accomplished through various modules such as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme. The main emphasizes is on the electrical segment which demonstrates an energy management scheme for PV-battery based grid-connected system to operate the irrigation system valves and water pump. The proposed scheme is verified through simulation and dSpace-based real-time experiment studies. Overall, the proposed energy management system demonstrates an improvement in the optimal onsite solar power generation and storage capacity to power the solar pump which save the electrical energy as well as the water in order to establish an improved solar-irrigation system. Finally, the proposed system achieved water and energy savings of around 9.24 % for potato crop with full irrigation enhancing the Water-Energy-Food Nexus at field scale., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:No conflicts of interest to disclose., (© 2024 The Authors.)