جستجوی مقالات مرتبط با کلیدواژه "سطح پوشش برف" در نشریات گروه "آب و خاک"

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.

Snow is one of the huge sources of water around the world, and estimation of the equivalent water of snowmelt is regarded as one of the most crucial activities of the hydrologists. Through snow melting, the soil moisture, underground water reservoirs, lakes and rivers water reservoirs are supplied; and the more readily, the snowmelt runoff is regarded as a main factor for controlling the flow regime in mountains and high basins. Therefore, the estimation of the snow cover and its runoff is very important issue in the mountainous basins. But, this effort is very difficult to be done due to lack of adequate land information in this regard. Using snowmelt hydrological models and satellite imagery are very useful to cope with this problem. In this study, it was tried to simulate and evaluate the daily runoff of snowmelt by the use of a snowmelt runoff model (SRM) and the information extracted from MODIS eight-day images, for Nahava d watershed wh ch s o e of the Karkheh r ver bas ’s parts located o wester Ira . The model was run and calibrated for the water years from 2003 to 2010; and it was validated for the water years 2011 to 2013. The highest correlation coefficients and volume difference for the model in the calibration period were 0.75 and -3.62, respectively; and 0.79 and 26.67 for the validation period, respectively. A sensitivity analysis was done for degree day, snow runoff coefficient and rain runoff factors and its results shows that a lower sensitivity of model to increased or decreased of rain runoff coefficient among the other parameters. In general, obtained results show a high level of accuracy of this model by the use of satellite imagery as an input for estimating the snowmelt runoff for the studied watershed and the capability of this model for other similar basins as well.

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.

This study aims to compare the ability of dynamic artificial neural network (DANN) and multivariate linear regression (LR) in forecasting monthly inflow to Shahcheraghi reservoir in Semnan province, Iran. The input data consisted monthly flow discharge, precipitation, mean temperature and snow cover area. Snow cover area was estimated using NOAA-AVHRR images, based on thresholds in histograms of different phenomena in visible and thermal channels. Dynamic artificial neural networks were determined with one hidden layer, Levenberg-Marquardt as training function, and sigmoid as transfer function Moreover, five DANN and five LR models were run with different input data and the results were compared. Root mean square (RMSE), mean bias error (MBE), mean absolute relative error (MARE), maximum relative error (REmax) and R2 (coefficient of determination) are the criteria that were used for models evaluation. The best result is gained with three inputs (inflow discharge, precipitation and snow cover area) by DANN. Regarding linear regression as a classic model in inflow forecasting, the improvement of the results by using DANN was obvious. The REmax of the selected DANN model was almost 85% less than REmax of the selected LR.