Snow is one of the main components of hydrological cycle in most of mountainous basins. Since collecting the Snow data (e.g. Snow Water Equivalent data) is very difficult and time consuming, some effort is necessary to develop methods to estimate spatially variation of Snow depth distribution. In the present study, the at-site SWE data of 14 stations located in the west of Isfahan providence for the period 1989-2010 were spatialized applying four methods composing the Kriging, the Co-Kriging, the Radial Basis Functions (RBF) and the Inverse Distance Weighting (IDW). In order to reach this purpose, first, the normality of data was checked using the Kolmogorov– Smirnov test. The homogeneity, the stability and the trend of data were tested employing the semivariogram approach. Then the appropriate data of each year was entered into the ArcGIS 9.3 to conduct the methods. Finally, the best method for spatializing the SWE data was selected based on the RMSE values. The results showed that the RBF method provided the best results for most of the years. Furthermore, it was found that the amount of SWE reduced from the south and west to the north and east of the basin.

In mountainous area of the northwest of Iran, Snowfall could be regarded as an important climatic element particularly during cold seasons. Snow cover and Snow depth, in turn, could influence the other hydrological components such as: stream flow charge, ground Water reservoir, flood occurrence and vegetative canopy, by means of significant interconnected reflections throughout a Water basin.The current research was accordingly introduced to provide some Snow cover maps using MODIS imageries in the Agi-Chay basin which is located in the east of the Orumiyyeh Lake, with two high mountains namely: Sahand and Sabalan in the catchments. First, to design the Snow cover map, different image processing algorithms were progressively applied to the same time series imageries (provided for year 2005) and available ground-based data by applying an ERDAS Imagine software. Then, using ArcGIS software some required spatial analysis such as: transforming of existing analogue topographic maps to digital format, designing of a DEM model, extracting of Watershed divides and generating of Thiesen polygons have been subsequently completed. At the last stage, an overlaying operation was applied to estimate the rate of Snow Water Equivalent (nearly about sixty nine billions m3 for year 2005)in the study area referencing to the ground observation data.Final results reveal the importance of models which have been provided by processing of MODIS images because of its spatial and temporal resolutions and the great number of bands. By examining Snowmap algorithm, it was found that this function could be regarded as a precise method in the analyzing of imageries at a regional scale. Easy calculations, simplicity, high accuracy and automated operations are the most implications of a methodology which was introduced in the current investigation.

A significant portion of the annual precipitation of the mountainous West Azarbaijan (WA) of the I.R.of Iran occurs as Snowfalls. Therefore, an assessment of the Water Equivalent of the Snowpack and its density is of utmost importance in estimating the Water yield of the WA basins. AS Snowfall gradually increase with latitude and elevation, and since establishing Snow courses and regular Snow sampling in extensive areas is both costly and difficult, it is prudent to use a limited amount of data, suitable algorithms, and modeling to predict these 2 parameters for inaccessible places in WA. In this study, using the artificial neural network, variable Snow density and Snow Water Equivalent in the basins were estimated. In designing the artificial neural network three independent variables of longitude, latitude and altitude of the Snow survey are used as the inputs. Artificial neural network design consisted of three neurons in input layer, two neurons in output layer, 6 to 18 neurons in one or more of the middle layers.The results showed that the structure of 2-6-3 to 2-6-6-3 and 2-6-6-6-3, yielded more logical answers. Thus, in the 2-6-3 structure, network design were converged after 270 iterations. Using the Levenberg-Merquate algorithm and the results obtained from the 2-6-3 structure, the neural network successfully estimated 92% of variations in Snow density and Snow Water Equivalent in WA. Furthermore, the stability results indicated that the 2-6-3 structure when used for analyzing the data from 35 Snow measuring stations could satisfactorily predict 91% of variations in Snow density and Snow Water Equivalent for the 2009-2010 period. As the neural network uses fewer, easily accessible inputs as compared with the models used in similar studies, it has a better merit in Snow-related hydrological studies.

Karoonbasin is one of the most important Watersheds in southwestern of Iran. Great amount of precipitation in this region fall in the form of Snow. Studying about accumulated Snow and Snow Water Equivalent in this basin can help to knowledge about hydrologic condition and better management on Water sources. This research was carried out in the Col Chari region as an indicator region for Snow measuring in north Karon basin. In this research Snow height were measured directly with ruler. Also Snow volume (with specific cylinder), weight (with digital scale) and Snow Water Equivalent (according to Snow bulk density) were determined. In order to Snow measuring, seven sites were selected and three scaled indexes (accurate to one cm and four m in length) were established in each site. This research was performed for six years. In each year, inventory and data collection were started after the first Snow and Snow height were measured weekly. Also three samples of Snow profile were supplied. The results indicated that Snow stability had been differed from 104 to 193 days during six years. Maximum height of Snow was observed in February. Also, we concluded that, maximum height of accumulated Snow and mean of annually Snow height were varied from 103 to 197 cm and from 39 to 100 cm respectively. Maximum amount of Snow Water Equivalent and its annually mean were determined between 37 and 110 cm and between 14 and 44 cm respectively also.

A significant part of the precipitation in the upstream areas of the Watersheds of West Azerbaijan province is in the form of Snow, especially in the cold months of the year. Analyzing the data of these sources is very important for the optimal management of reservoir dams in the region, especially in spring, which is the season of Snow melting in the respective basins. In common traditional methods for estimating Water Equivalent of Snow storage, the height gradient relationship (linear regression relationship between Water Equivalent of Snow and the height of Snow measuring points from the surface of the oceans) is used, which sometimes due to lack of proper fit, some points are removed from the calculations. In this study, for the data of 65 Snow measuring stations in Snowy months of the Water year 2020-2021, instead of the relation of altitude gradient, the four-variable regression of Water Equivalent to altitude, longitude and latitude (in the UTM coordinate system) was used. It improved the regression characteristics (an increase of more than 3 times the A.R.S., i.e. the square of the adjusted correlation coefficient, or the coefficient of determination, and a decrease of more than 10,000 times the significance level, i.e. the alpha of the correlation coefficient). Also, in order to further investigate, the simulation of relationships with artificial neural networks (selected network: three-layer perceptron with 3, 8 and 1 neurons in the input, middle and output layers) was used; So that the correlation coefficient of the estimated data with the available observations for the selected artificial neural network model was 0.97.

In mountainous basins, Snow Water Equivalent (SWE) estimation is an important step of Water resources management. The main purpose of this research was the investigation of the weighting method efficiency using GIS in estimating spatial distribution of SWE. To this regard, using the observed Snow depth and density, the SWE was initially calculated. Then, the weighting method was used to estimate spatial distribution of SWE. The results of the weighting method were evaluated by the interpolation technique map. Weighted SWE map indicated that very high and high SWE zones were dominant in areas with high elevations, moderate slope and high upwind slopes. In general, the results showed that weighting method is enable to estimate spatial distribution of SWE satisfactorily.

Background and Objectives: Snow Water Equivalent (SWE) is a key parameter in hydrological cycle. In Iran, measurement of Snow depth and its Water Equivalent is usually is limited due to lack of automated Snow measuring instruments. According to research conducted in the field of Snow Water Equivalent, wind speed, temperature, precipitation and elevation are the factors affecting the amount of Snow Water Equivalent. Because values for wind speed, temperature and precipitation can affect the long-term Snow Water Equivalent, therefore the aim of this study was using meteorological and geographical parameters to estimate Snow Water Equivalent of Snow stations in the study area.Materials and Methods: In the current study, based on meteorological data and interpolation method Snow Water Equivalent was estimated. In this regard, first, average amounts of precipitation, air temperature and wind speed were computed during periods of 10, 20, 30, 40 and 50 days. Then, binary correlations between Snow Water Equivalent and the parameters were estimated. Parameters that had the highest correlation were selected. Then in SPSS software between these parameters and the elevation of the stations, the Snow Water Equivalent to a multiple regression was obtained. The regression equation were validated with Snow Water Equivalent data measurement in Snow stations Results: Based on these results, the average precipitation, 40-days temperature and wind speed of 30-days, showed the highest correlation with Snow Water Equivalent, respectively. The best Snow Water Equivalent equation was obtained using the relevant parameters. Estimated data was also compared with the observed data, based on the Nash- Sutcliffe criteria (NS=0.83) and regression coefficient (r=0.91). The results showed an acceptable accuracy of the equation on Snow Water Equivalent estimation.Conclusion: In this study, due to the lack of meteorological measuring in Snow stations, the interpolation methods for estimating the amount of precipitation, wind speed and temperature parameters the location station was used. The results indicated that among the interpolation methods, radial basis functions with model of Inverse Multiquadric for average wind speed of 10 to 50 days, Completely Regularized Spline model to estimate the average temperature of 10 to 50 days and kriging method with Gaussian model for estimating the average precipitation 10 to 50 days, had the high accuracy in the Snow stations. Using the parameters that were most correlated with Snow Water Equivalent, a regression equation to estimate Snow Water Equivalent was obtained. Evaluation showed regression equation can be used to estimate Snow Water Equivalent in the respective stations.

Snow Water Equivalent (SWE) is one of the important parameters in climatic and hydrologic studies and knowledge on SWE values is essential for supplying, season floods control and Water providing for human consumptions use. In this research, first, correlations between SWE and corresponding elevation were determined using different regression relationships and then the best equation was selected based on correlation coefficient (r). In the next step, based on the equation and using GIS, spatial distribution of Snow Water Equivalent was determined and its local map was developed. Finally, the results were evaluated using the map of exact observed data which was interpolated by ordinary Kriging geostatistical model. The results indicated that the best correlation relationships between SWE and elevation were polynomial non-linear, logarithmic non-linear and linear regressions (P value<0.05), respectively. Also, the results showed a strong corresponding between the SWE mapping which was developed by polynomial non-linear method and the interpolated map and decreasing elevation from west to east of the study area, SWE value extremely decreased.

Estimating Snow melt Water Equivalent is one of the most important challenges in determining the hydrological regime in mountainous basins. There are some problematic obstacles in field observations, due to inaccessibility as well due to difficulties in Snow sampling at Snow covered peaks making researchers try to find indirect approaches. One of these methods in use is Degree- day model that is simple and used in Snow melt estimations. In this study the model is applied along with MODIS images for predicting Snow Water Equivalent at times of peak Snow accumulation. For this purpose, following a determination of the time of Snow accumulation peak, the degree-days needed for the Snow to be completely melted is computed and then, the Snow Water Equivalent at any point calculated by reversing degree-day equation. The results show that there is 13 percent difference between the observed and estimated data. This indicates that MODIS data and degree-day model can acceptably estimate Snow Water Equivalent in not easily accessible areas. Therefore, the degree-day model would be helpful in hydrological monitoring programs in not easily accessible mountainous basins.

Snow cover, as an important component of land cover, is one of the most active natural and plays an important role in hydrological processes and climate. Variability in Snow covered area has a significant influence on Water and energy cycles, as well as socioeconomic and environmental repercussions. Frequent and long-term Snow observation, accurate Snow cover (SC) mapping and Snow Water Equivalent (SWE) estimation are crucial for operational flood control, Water delay planning, and resource management in Snowmelt-dominated basins. Today, satellite-derived Snow products obtained from visible and infrared imagery, as well as passive microwaves are available on the Internet, with few recently availability in near-real time. Optical sensors (e. g., Landsat, Advanced Very High Resolution Radiometer-AVHRR, Moderate Resolution Imaging Spectroradiometer-MODIS, Systeme Probatoire d’ Observation de la Tarre-SPOT) have been well developed to provide Snow information with good temporal and/or spatial resolution. But, cloud cover is a major factor in optical remote sensing that limits our capability to map the Earth’ s surface. It is often not an easy task to collect a time-series cloud-free images for a particular area of interest, using optical remote sensing, which limits their wider applications for SC monitoring. Space-borne passive-microwave radiometers (e. g., Scanning Multichannel Microwave Radiometer-SMMR, Special Sensor Microwave/Imager-SSM/I, Advanced Microwave Scanning Radiometer-Earth Observing System-AMSR-E), can penetrate cloud to detect microwave energy emitted by Snow and ice and provide information on SC and SWE. These passive microwave data are well suited to Snow cover monitoring because of the characteristics such as all-weather imaging, large swath width with frequent overpass times. But, the coarse spatial resolution (e. g., 25 km of AMSR-E daily SWE product) hinders their applications in operational hydrological modeling. It seems that the combination of MODIS and AMSR-E can take advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data. In this study, daily cloud-free SC and SWE maps at the 500-m resolution were produced for Karun Watershed, Ahwaz, Iran (January 25– 32, 2004). The daily MODIS-Terra, MODIS-Aqua, and AMSR-E Snow data products were used via a fusion-disaggregation algorithm. The developed SC and SWE maps were evaluated, using total accuracy of Snow mapping in clear-sky (Os) and all-sky (Oa) conditions, underestimation (UEc) and Overestimation (OEc) of Snow covered area in clear-sky condition, Snow accuracy in clear-sky (Sc) or in all-sky (Sa) conditions, and no Snow accuracy in clear-sky (NSc) or in all-sky (NSa) conditions. The results of this study showed that the combination of MODIS-Terra and MODIS-Aqua considerably reduced the cloud coverage in such high resolution optical data. Although MAC-SC and MAC-SWE products have been developed to have 500 m spatial resolution, the massive and continuous cloud cover (larger than 25 km in size) in the MODIS-Terra and MODIS-Aqua and, hence, TAC products were simply replaced by the coarse AMSR-E pixels. In this case, although those cloud coverages were removed in the MAC-SC and MAC-SWE products, the actual resolution of the Snow or no-Snow pixels kept 25 km and such pixels had the false spatial resolution of 500 m. The SWE redistribution of AMSR-E based on MAC-SC products enhanced (to some extent) the spatial resolution of the AMSR-E SWE products. However, there was no measured data to evaluate the accuracy of the enhanced SWE products. It can be concluded that for pixels with scattered cloud cover (less than 25 km in size) in the TAC products, the MAC-SC and MAC-SWE products indeed improve the spatial resolution of those pixels to 500 m, while for massive cloud cover (larger than 25 km in size), the actual resolution of those pixels in the MAC-SC and MAC-SWE products are 25 km, even in 500 m pixel size. Despite of these limitations, the MAC-SC and MAC-SWE maps are suitable for hydrological, meteorological modeling on a daily basis in the study area.