Effect of reservoir zones and hedging factor dynamism on reservoir adaptive capacity for climate change impacts

When based on the zones of available water in storage, hedging has traditionally used a single hedged zone and a constant rationing ratio for constraining supply during droughts. Given the usual seasonality of reservoir inflows, it is also possible that hedging could feature multiple hedged zones and temporally varying rationing ratios but very few studies addressing this have been reported especially in relation to adaptation to projected climate change. This study developed and tested Genetic Algorithms (GA) optimised zone-based operating policies of various configurations using data for the Pong reservoir, Himachal Pradesh, India. The results show that hedging does lessen vulnerability, which dropped from  ≥  60 % without hedging to below 25 % with the single stage hedging. More complex hedging policies, e.g. two stage and/or temporally varying rationing ratios only produced marginal improvements in performance. All this shows that water hedging policies do not have to be overly complex to effectively offset reservoir vulnerability caused by water shortage resulting from e.g. projected climate change

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling

Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications

Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations

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

Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment

This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall–runoff (R–R) model, the baseline runoff scenario was first simulated. The R–R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce ‘populations’ of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir