Drought mitigation through a hedging-based model of reservoir-farm systems considering climate and streamflow variations

• Published in: Theoretical and applied climatology 2023-04, Vol.152 (1-2), p.723-737
• Main Authors: Anvari, Sedigheh, Moghaddasi, Mahnoosh, Bagheri, Mohammad Hossein
• Format: Article
• Language: English
• Subjects: Analysis; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climate; Climate models; Climate science; Climatology; Daily weather; Drought; Drought index; Droughts; Earth and Environmental Science; Earth Sciences; Evapotranspiration; Evapotranspiration-precipitation relationships; Farming systems; Farms; Hydrologic drought; Hydrology; Irrigation; Irrigation scheduling; Irrigation water; Linear programming; Mathematical analysis; Mathematical optimization; Meteorological conditions; Mitigation; Modelling; Nonlinear programming; Particle swarm optimization; Reservoir operation; Reservoirs; Stream discharge; Stream flow; Streamflow; Streamflow variations; System effectiveness; Variation; Vulnerability; Waste Water Technology; Water demand; Water Management; Water Pollution Control; Water shortages; Water supply; Weather
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-023-04402-7

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (α t ) and volume-based (α v ) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of α t , α v , and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, α t had the highest variations, while α v had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM).