Showing 6 results for Snow
B. Rayegani, S. J. Khajeddin, S. Soltani , S. Barati,
Volume 12, Issue 44 (7-2008)
Abstract
Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-cover or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-cover. Today monitoring snow-cover patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow cover imagery. Existence of cloud in the study area is a major problem for snow cover monitoring. Therefore, in this research snow cover area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow cover area. In comparison with real image, precision of the method was confirmed.
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.
H. Hajihoseini, M. Hajihoseini, S. Morid, M. Delavar,
Volume 19, Issue 72 (8-2015)
Abstract
One of the major challenges in water resources management is the operation of trans boundary watershed. This has been experienced in case of Helmand River between Iran and Afghanistan since the last century. For such a situation, application of a conceptual rainfall-runoff models that can simulate management scenarios is a relevant tool. The SWAT model can be a relevant option in this regard. However, the required hydro-climatic data for them is a serious obstacle. Especially, this problem gets exacerbated in the case of Afghanistan with poor infrastructures. So, application of this type of model would be more problematic. This paper aims to investigate capabilities of SWAT for the simulation of rainfall-runoff processes in such a data-scarce region and the upper catchment of Helmand River is used as the case study. For this purpose, discharge data of Dehraut station from 1969 to 1979 along with some metrological data were prepared and used to calibrate and validate the simulations. The results were acceptable and the coefficients of determinations (R2) during calibration and validation periods were 0.76 and 0.70, respectively. Notably, with respect to snowy condition of the basin, the elevation band option of the snow module of model had a significant effect on the results, especially in the base flows. Moreover, two Landsat satellite images during February 1973 and 1977 when the basin was partly covered with snow was prepared and compared with the SWAT outputs. Similarly, the results showed good performance of the model such that R2 were 0.87 and 0.82, respectively.
S. Jahanbakhsh Asl, B. Sari Saraf, T. Raziei, A. Parandeh Khouzani,
Volume 23, Issue 4 (12-2019)
Abstract
In this study, the temporal and spatial variation of snow depth over the mountainous region of Zagros, in the western Iran, for the period 1979–2010 was investigated for the cold season when the probability of snow occurrences was high. For this purpose, daily gridded snow depth data relative to Era-Interim/land were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) and used for spatiotemporal analysis of snow in the region. Furthermore, monthly maximum, minimum and mean air temperature relative to the weather stations distributed over the region were also used to investigate the relationship between snow depth and air temperature variability in the region. In each grid point, the rate of temporal changes in the snow depth was estimated using the Sen’s slope estimator, while the modified Mann-Kendall Test was applied to assess if the change identified was statistically significant. The results showed that in almost all of the studied months, especially February and March, the snow depth was significantly reduced in the region, which was statistically significant at 5% significant level. Unlike the observed statistically significant decreasing trend in the depth snow in the region, a significant increase in the maximum, minimum and average temperature was observed for all the studied months and the stations. The result suggested that the observed decrease in the snow depth in the region was related to the increasing trend in the temperature during the study period, which could be attributed to the global warming and climate change.
A. Motamedi, M. Galoie,
Volume 25, Issue 2 (9-2021)
Abstract
The annual soil erosion in different regions of the world has been estimated using various empirical and numerical methods whose accuracy is very dependent on their utilized parameters. One of the most common methods in the evaluation of the mean annual soil erosion especially in sheet and furrow regions is the USLE method. In this relationship, almost all factors that normally affect the soil loss process such as land cover, slope, precipitation, soil type, and support practice parameter of soil have been employed but, in this research, it was shown that the accuracy of this method in mountainous areas covered by rock and snow is somewhat low. To do this, a part of the Tibet plateau in China, where observation soil loss data were available, was selected for investigation. To implement the numerical and analytical analysis, many maps including DEM, NDVI, orientation, soil type, mean monthly and annual precipitation for 30 years were collected. For increasing the accuracy of the model, the cover management parameter was extracted from high accuracy NDVI maps and all USLE parameters were calculated in ArcGIS. The final results were shown that the amount of annual soil loss which was estimated by the USLE method is more than the observed data which were collected by Chinese researchers. This is because the large areas of the study area are covered by lichen and snow where soil loss due to the erosion process is very low but these regions cannot be recognized from NDVI maps. Also, the analysis of the NDVI maps was shown that the relationships of Fu, Patil, and Sharma were not suitable for soil loss estimation in elevated mountainous areas. If the other relationships such as Lin, Zhu, and Durigon are used for the regions with a height of more than 5500 m, a new correction coefficient needs to be used for the C factor which was calculated as 0.2 for the study area.
S. Asghari Saraskanrood, R. Modirzadeh,
Volume 25, Issue 3 (12-2021)
Abstract
Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.
