1- Civil Eng. Department/ Kharazmi Uni.
2- Civil Engineering Department/ Kharazmi university ,hossein.mohajeri@gmail.com
3- Kharazmi university
2- Civil Engineering Department/ Kharazmi university ,
3- Kharazmi university
Abstract: (16 Views)
This study leverages satellite imagery and on-site meteorological data to empirically assess reservoir evaporation using the PM FAO 56 method and an artificial neural network. Focused on Sistan and Baluchistan provinces, it categorizes indicators into meteorological factors—such as wind speed, air pressure, relative humidity, and lake surface temperature—and hydrological connectivity indices, including the topographic wetness and flow length indices. These indices are evaluated under various hydrological conditions like the 120-day wind period, non-windy periods, and flood discharge periods. Results highlight the significant influence of the topographic wetness and flow length indices on evaporation, especially during flood discharge periods where their impact is 5% higher than in water storage periods. Additionally, meteorological indices have a 10% greater effect during windy conditions, with wind speed being notably more influential during the 120-day wind period. This research underlines the importance of integrating meteorological and hydrological data for comprehensive water resource management and suggests the potential of using similar approaches in other regions and under different climatic conditions, paving the way for future studies in water conservation and management strategies in response to global environmental changes.
Keywords: Hydrological connectivity, Evaporation, Water surface temperature, Artificial neural network, Relative importance, Wind of 120 days
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