hadi ansari; safar marofi
Abstract
Introduction: Snow water equivalent (SWE) provides important information for water resources management and recently has attracted the attention of many researchers using remote sensing. Remote sensing presents a possibility for observation of snow characteristics, like water equivalent, over larger ...
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Introduction: Snow water equivalent (SWE) provides important information for water resources management and recently has attracted the attention of many researchers using remote sensing. Remote sensing presents a possibility for observation of snow characteristics, like water equivalent, over larger areas. Validation of remote sensing data of snow water equivalent (SWE) has always been an important issue for the researchers. Previous studies have assessed the global SWE data. Although it has been tried by using large-scale models of the world to estimate SWE, but regional effects such as snow density, topography and local meteorological conditions may lead to uncertainty.
Materials and Methods: The Northwestern Iran was selected as the study area in this research. Reasons for choosing this area are being mountainous with much snowfall. Also this region compared to the other parts of Iran, has more dense snow survey stations. In this study the AMSR-E sensor data and Global Land Data Assimilation System (GLDAS) was used to estimate SWE in the basins of the northwestern Iran. After processing AMSR-E sensor data and GLDAS model with related software, SWE was estimated in the snow survey stations and evaluated with observed data. To specify the snow density effect on SWE data in AMSR-E sensor from the snow density data, the stations were used. To determine the accuracy of estimation of SWE at different heights, snow survey stations is arranged by considering height and were divided into four height classes that contain enough observational data to evaluate computational data in each height class. To verify SWE obtained estimations in the stations, Root Mean Square Error (RMSE) and Pearson correlation coefficient (r) assessment criteria were used. After evaluating, the SWE data of AMSR-E sensor and GLDAS model for the GLDAS model monthly data to estimate SWE was used for the period 2000 to 2015. With calculating average annual SWE from monthly data, SWE trend changes in mentioned period, the moving averages graphs 3, 5 and 7-year-old was drawn.
Results and Discussion: According to the obtained results, SWE computational data with observational data had significant correlation at the 1% level. Using in situ snow densities, the correlation coefficient between AMSR-E and situ SWE increased from 0.27 to 0.55. The results showed that the best estimation of SWE is in the stations, which have the height of 1,350 to 1600 meters. Also with increasing altitude, the estimation accuracy is significantly reduced. In most years maximum of the SWE was obtained in January and February and in the period of June to September, the area was out of snow storage. According to the average annual SWE and moving averages graphs 3, 5 and 7-years old, the SWE of Northwestern Iran basins in period 2015-2001 has a reducing trend.
Conclusions: In the regions like the Northwestern Iran mountainous where snowfall constitutes a significant fraction of total precipitation, the snowpack delays the resulting runoff into the time of year where water demand is greater. So measurement of snow on the ground has been an important component of hydrologic forecasting for a century. Various remotely sensed snow data have been widely utilized for cold regions to explore the relationships between snow distribution, river discharge, and climate change. The accuracy of remotely sensed snow products should be well understood and incorporated in any investigations using such data. The main objective of the present study was to quantitatively compare the AMSR-E and GLDAS model for an understudied region of the earth. AMSR-E global SWE data and GLDAS data were compared by situ SWE measurements performed in the snow courses. The results showed that the snow density is an effective factor in derived algorithm for the SWE AMSR-E data. Also with increasing height, precision of the estimation significantly decreased. The determination of SWE from satellite imagery in progress updated with new learning. The obtained results from passive microwave in smooth terrain are promising, but involvement of different mechanisms become more complicated as the terrain gets more complex. Nevertheless, it is believed that if the above discussions are taken into account, AMSR-E would provide valuable SWE information even for a mountainous region like Northwestern Iran. It is also hoped that this study would be a starting point in the water scarce, developing Iran to plan and use the limited supply in a suitable manner.
P. Mohajeri; P. Alamdari; A. Golchin
Abstract
Introduction: Topography is one of the most important factors of soil formation and evolution. Soil properties vary spatially and are influenced by some environmental factors such as landscape features, including topography, slope aspect and position, elevation, climate, parent material and vegetation. ...
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Introduction: Topography is one of the most important factors of soil formation and evolution. Soil properties vary spatially and are influenced by some environmental factors such as landscape features, including topography, slope aspect and position, elevation, climate, parent material and vegetation. Variations in landscape features can influence many phenomena and ecological processes including soil nutrients and water interactions. This factor affects soil properties by changing the altitude, steepness and slope direction of lands. In spite of the importance of understanding the variability of soils for better management, few studies have been done to assess the quality of soils located on a toposequence and most of these studies include just pedological properties. The aim of this study was to investigate physical and chemical properties of soils located on different slope positions and different depths of a toposequence in Deilaman area of Gilan province, that located in north of Iran.
Materials and Methods: The lands on toposequence that were same in climate, parent material, vegetation and time factors but topographical factor was different, were divided into five sections including steep peak, shoulder slope, back slope, foot slope and toe slope. In order to topsoil sampling, transverse sections of this toposequence were divided into three parts lengthways, each forming one replicate or block. 10*10 square was selected and after removing a layer of undecomposed organic residues such as leaf litter, three depths of 0 to 20, 20 to 40 and 40 to60 cm soil samples were collected. physical and chemical characteristics such as soil texture, bulk density, aggregate stability, percent of organic matter, cation exchange capacity, available phosphorous and total nitrogen were measured.
Results and Discussion: The results showed that, because of high organic matter content and fine textured soils on the lower slope positions including foot slope and toe slope, aggregate stability, cation exchange capacity, available phosphorous and total nitrogen were maximum in these positions, whereas, bulk density had a reverse trend and was higher in the upper slope positions than the lower slope positions. The high content of organic carbon, phosphorus and total nitrogen in the soil of foot and toe slope positions, can be attributed to soil erosion and transferred from top of the slope and their accumulation in these situations. The results also revealed that, with increasing depth, aggregate stability, organic carbon content, cation exchange capacity, available phosphorous and total nitrogen content of soils decreased, whereas, clay content and bulk density had a reverse trend and increased with increasing the depth. Reducing the amount of organic carbon with increasing depth was because of the remains of plants and roots in the surface horizons and the presence of more organic carbon. Since phosphorus and nitrogen in the soils are highly dependent on organic matter, Thus, changes in these indicators are mainly obeys from this materials.
Conclusion: In general, it became appears from this study, that the topography factor had important effect on studied soil properties. The changes observed in the quality of soils located on different slope positions can be attributed to the differences of the soil in erosion rate and moisture content and different sediment receptions in different positions of toposequence as affected by the amount and distribution of rainfall. Considering the effect of the position of the landscape on the physical and chemical properties of soil, recommended analysis of the landscape is better to be done in the sustainable land management and also for soil and water conservation programs. Because of the different management practices in different parts of landscape is difficult and perhaps impossible, in order to maintain soil, conservation management must be done based on soil quality in areas with maximum damage and minimum quality.
