seyed mohamad tajbakhsh

Ecosystem services are fundamental for human well-being and are the basis of rural livelihoods, particularly for poor people. Rainwater harvesting can serve as an opportunity to enhance ecosystem productivity, thereby improving livelihoods, human well-being, and economies. Rainwater harvesting has been shown to create synergies between landscape management and human well-being. These synergies are particularly obvious when rainwater harvesting improves rainfed agriculture, is applied in watershed management, and when rainwater harvesting interventions address household water supplies in urban and rural areas. Rainwater harvesting has often been a neglected opportunity in water resource management: only water from surface and ground water sources is conventionally considered. Managing rainfall will also present new management opportunities, including rainwater harvesting. Improved water supply, enhanced agricultural production, and sustainable ecosystem services can be attained through adoption of rainwater harvesting with relatively low investments over fairly short time spans (5-10 years). Rainwater harvesting is a coping strategy in variable rainfall areas. In the future climate change will increase rainfall variability and evaporation, and population growth will increase demand on ecosystem services, in particular for water. Rainwater harvesting will become a key intervention in adaptation and reducing vulnerabilities. Moreover, in addition to increasing public knowledge about the ecosystem function of these systems, basic steps should be taken in the audit, evaluation and application of local knowledge of rainwater harvesting in the country.

Most of the rainfall in arid and semi-arid regions, which becomes runoff immediately after rainfall, eventually evaporates and becomes inaccessible. Harvesting water from impermeable rock surfaces and storage in reservoirs or aquifer feeding is an effective way to prevent evaporation losses in these areas. The purpose of this study is to model the amount of runoff in sub-basins with impermeable surfaces in the Haft Bagh Alavi area of Kerman to estimate the volume of harvestable runoff in this area. Haft Bagh Alavi watershed is located 26 km from Kerman and has an area of 14923 hectares. About 18.3%, equivalent to 2732 hectares of the basin area has a rock mass level of more than 90%. HEC-HMS software has been used to model rainfall-runoff in the basin. The area curve number of soil, slope, and land use information was entered into the model. 30-year mean evapotranspiration data, with the 30-year mean of 10-minute precipitation data, were collected from the automatic station and used as input. The results showed that by harvesting only 10% of the total average annual cumulative runoff of four sub-basins, it is possible to collect 8.055 million cubic meters of water and provide water for supplementary irrigation of 16,110 hectares of gardens.

Correct and consistent uses of natural resources preserve this valuable wealth. Using optimization knowledge can assist us to achieve this object. Thus, this study aims to compare linear programming as a classical method of optimization with genetic evolutionary algorithm for land use optimization of the Bayg watershed. Results showed that linear programming reduced dry farming acreage and increased the acreage of irrigated agriculture. After minimization, surface runoff and sediment yield declined by 1.16, 12.91 percent, respectively. Genetic algorithm led to an increase in rangeland, irrigated agriculture and horticulture acreages, while almond orchard and dry farming acreages were reduced. Furthermore, surface runoff and sediment yield declined by 13.95 and 31.99 percent, respectively. Linear programming acted stronger in satisfying the constraints, as compared with genetic algorithm. The constraint “total acreage” was satisfied by linear programming, while genetic algorithm could not meet this constraint. Sensitivity analysis of linear programming showed that the most critical factor in minimizing runoff and sediment yield function was the coefficient of dry farming with a reduced cost of 67.52. Results also established that the constraints “total acreage and minimum acreage of rangeland” with the shadow prices of 397.40 and 233.28, respectively had the highest negative impact on the optimal solution. Meanwhile, the constraints “maximum acreages of irrigated horticulture and almonds garden” with the shadow prices of -134.97 and -118.44, respectively had the highest positive impact on the optimal solution. As a general conclusion it can be stated that in land use optimization problems with a large number of constraints, genetic algorithm show poorer performance in satisfying constraints, as compared with linear programming.