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This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow-and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0°to +2°C) and rainfall (from −10 to +10 % of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without hedging; this was improved to 26 % with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study.

Optimization of irrigation water is an important issue in agricultural production for maximizing the return from the limited water availability. The current study proposes a simulation-optimization framework for developing optimal irrigation schedules for rice crop (Oryza sativa) under water deficit conditions. The framework utilizes a rice crop growth simulation model to identify the critical periods of growth that are highly sensitive to the reduction in final crop yield, and a genetic algorithm based optimizer develops the optimal water allocations during the crop growing period. The model ORYZA2000, which is employed as the crop growth simulation model, is calibrated and validated using field experimental data prior to incorporating in the proposed framework. The proposed framework was applied to a real world case study of a command area in southern India, and it was found that significant improvement in total yield can be achieved by the model compared to other water saving irrigation methods. The results of the study were highly encouraging and suggest that by employing a calibrated crop growth model combined with an optimization algorithm can lead to achieve maximum water use efficiency.

The COVID-19 pandemic has disrupted daily activities across multiple sectors globally. The extent of its impact on the global economy and its key sectors, especially water, wastewater, and associated sectors such as agriculture, is still unclear. In this paper, the preliminary impacts of COVID-19 on water resources of India, especially on the river water quality, water usage in domestic and commercial sectors, wastewater treatment sector, and agriculture sector, are discussed. The limitations in the functioning of the existing system and management of water resources are identified. The need for improvements to strengthen the water resources monitoring and developing process-based models are highlighted. This paper also discusses the need for further investigation to identify the extent of impact and contributing factors to improve our understanding of the natural system for preparing, monitoring, and implementing the policies to manage the water resources during any pandemic/epidemics in the future.

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As the main water resources infrastructure in the region, the Ubonratana reservoir has played and continues to play a significant role in the socio-economic well-being of north-eastern Thailand. For such a multi-purpose system serving flood protection and various water demand needs, it is important that the reservoir is effectively operated to ensure that the overall performance of the system is enhanced. Consequently, this study has evaluated the performance of the Ubonratana reservoir with four competing operating policies, namely: (a) the pre-2002 policy (P1); (b) a post-2002 policy, following the catastrophic flood of 2002 (P2); (c) a policy derived in the current study to address the limitations of P2 in relation to water shortages (P3); and (d) the standard operating policy, SOP (P4). The simulation analyses were implemented using a water evaluation and planning system model of the reservoir meeting domestic (first priority), industrial (second priority), irrigation (third priority) and in-stream (fourth priority) needs. The performance was summarised in terms of reliability, vulnerability, resilience and sustainability. The results showed that overall, P4 was the best, followed by P3, P1 and P2 in that order. This is a useful demonstration of how rule curves can successfully guide the operation of multi-purpose reservoir systems.

Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model's input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation-optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making.

The suitability of the Dirma River Basin in Ethiopia for surface irrigation potential development and identification of potentially irrigable sites were the subject of the present study. The river basin area suitability was evaluated based on slope, soil and surface water resource availability using ArcGIS 10.1. Potentially suitable sites for surface irrigation development were evaluated for potato and tomato crops. The reference crop evapotranspiration, effective rainfall, net irrigation and gross irrigation water requirement were estimated using CROPWAT software. The results of the suitability model developed indicated that about 68.3% of the total river basin area falls in marginally to highly suitable categories for surface irrigation development. The water resource availability reveals that due to very high intra‐annual variability in river flows, only 6% of the mean annual flow is available for the period from January to May, and with this up to 30% of land that is suitable for surface irrigation can be irrigated. However, with suitable water storage infrastructure and deficit irrigation practices, utilization of land and water resources of the Dirma River Basin can be increased significantly. Therefore, this study will help decision makers, investors, planners and policymakers at the local, regional and national levels in better identification of profitable and sustainable irrigation investment opportunities in the region. © 2019 John Wiley & Sons, Ltd.