Volume 28, Issue 4 (Winter 2025)
jwss 2025, 28(4): 45-58 |
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Afsharipour F, Sharifi M, Motamedi A. Estimation of the SZIsnow Snow Index on Basin Scale Using the GLDAS Database. jwss 2025; 28 (4) :45-58
URL: http://jstnar.iut.ac.ir/article-1-4426-en.html
URL: http://jstnar.iut.ac.ir/article-1-4426-en.html
Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. , mshaifi@scu.ac.ir
Abstract: (90 Views)
Drought monitoring in snowy basins requires modifications in common drought indices, called snow drought indices. The latest developed snow index is SZIsnow. The SZIsnow index calculating with special algorithm requires access to the values of 22 different climatic and physical variables, including soil moisture at a depth of 0 to 10 centimeters, soil moisture at a depth of 100 to 200 centimeters, air temperature, water equivalent to snow, runoff from snow melting, snowfall, rainfall, total precipitation rate, evaporation and transpiration, wind speed, surface runoff, groundwater runoff, potential evaporation, air pressure, relative humidity, net latent heat flux, ground heat flux, net sensible heat flux, evaporation from bare soil, evaporation from the canopy, and potential evapotranspiration. So far, the mentioned index has been calculated only on a continental scale. Drought monitoring at the basin scale is important as one of the management aspects of water resources. On the other hand, due to the lack of sufficient information to estimate the mentioned parameters, the use of information from global databases will be a solution. Therefore, in this research, while introducing the process of calculating the SZIsnow index, in the Dez catchment area, extracting the required parameters of the index in a time scale of 3, 6, and 12 months and a period of 41 years (1982 to 2023) using data GLDAS and then drought monitoring of the basin was studied. The results showed that the new SZIsnow index is a multi-variable index that provides the possibility of calculating the index due to the existence of parameters that lack ground observations and on the other hand, the availability of the reliable GLDAS database. Also, the results showed that in the time steps of 3, 6, and 12 months, July at -0.59, June at -0.45, and October at -0.35 had the highest amount of drought, respectively.
Type of Study: Research |
Subject:
Ggeneral
Received: 2024/05/25 | Accepted: 2024/08/21 | Published: 2025/01/29
Received: 2024/05/25 | Accepted: 2024/08/21 | Published: 2025/01/29
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