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Showing 2 results for Snow Water Equivalent
Comparison of Non-Linear Regression and Computational Intelligence Methods in Estimating Spatial Distribution of Snow Water Equivalent in Karoon Upstream
H Tabari, S Marofi, H Zare Abiane, R Amiri Chayjan, M Sharifi, A.m Akhondali,
Volume 13, Issue 50 (1-2010)
Abstract
In mountainous basins, snow water equivalent is usually used to evaluate water resources related to snow. In this research, based on the observed data, the snow depth and its water equivalent was studied through application of non-linear regression, artificial neural network as well as optimization of network's parameters with genetic algorithm. To this end, the estimated values by artificial neural network, neural network-genetic algorithm combined method and regression method were compared with the observed data. The field measurement were carried out in the Samsami basin in February 2006. Correlation coefficient (r) mean square error (MSE) and mean absolute error (MAE) were used to evaluate efficiency of the various models of artificial neural networks and nonlinear regression models. The results showed that artificial neural network and genetic algorithm combined methods were suitable to estimate snow water equivalent. In general, among the methods used, neural network-genetic algorithm combined method presented the best result (r= 0.84, MSE= 0.041 and MAE= 0.051). Of the parameters considered, elevation from sea level is the most important and effective to estimate snow water equivalent.
Estimation of the SZIsnow Snow Index on Basin Scale Using the GLDAS Database
F. Afsharipour, M.r. Sharifi, A. Motamedi,
Volume 28, Issue 4 (12-2024)
Abstract
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.

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