Adaptive hedging rules for a data-scarce dryland reservoir: integrating simple drought index, water user participation, and short-term hydrological monitoring

A numerical hedging model for a dryland reservoir, featuring an early rationing system based on the identification of drought events, is presented. Key advantages include the optimization of decision variables (trigger volumes and rationing coefficients) using a genetic algorithm (NSGA-II), integration of water user participation, water allocation connected with drought assessments through a simple drought index, streamflow prediction based on river-aquifer dynamics, and the use of short-term field-measured hydrological data. The results show that the proposed hedging rule maintained system vulnerability below 10% using both simulated and measured inflow data, and the objective function (the modified shortage index) was successfully optimized even when early rationing occurred during the rainy season. The quantitative analysis suggests that for adaptive hedging in data-scarce drylands, the calculation method (the rule itself) is more critical than the availability of onsite inflow measurements. Therefore, operating rules for a dryland reservoir optimized using simulated data may be effective in satisfying water demands and stakeholder requirements, even when integrated with a simple drought index and in the presence of data uncertainties.