Journal of Spatial Analysis

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Simulation of runoff from Gamasiab basin snowmelt with SRM model
 
 
Abstract
Snow cover in a basin affect its water balance and energy balance. So, snow cover variation is a major factor in climate change of a region. Study of temporal variation of snowmelt and snow water equivalent depth is very important in flood forecasting, reservoir management and agricultural activities of an area. In the most of the mountainous basins of the country, information on snow cover were not available. Also, the number of meteorological stations in high altitude areas do not match with information needed for snowmelt simulation. Therefore, indirect methods such as the analysis of satellite images to obtain the needed parameters for simulation is necessary, which is the one of the most effective methods in estimation of runoff originated from snow. Using the NOAA satellite data for zoning the snow cover of area started firstly in the USA since the 1961 and continuous until today (spatial and temporal resolution of satellite images increased by starting the MODIS work).
Gamasiab River is one of the important branches of Karkheh basin. Its basin area is about 11040 km² between latitude 47 degrees 7 minutes to 49 degrees 10 minutes east and latitude 33 degrees 48 minutes 4 degrees 85 minutes north. The altitude of this basin is 1275 to 3680 meters above sea level. In this study, for simulation of runoff originated from melting snow, firstly snow cover in the basin of Gamasiab in 2014 to 2017 calculated by using the satellite images of MODIS in the google earth engine system. Also, air temperature and precipitation data of synoptic stations in the area of study and daily stream flow discharges of Polechehr hydrometric station, from November of 2014 to July of 2017 was used. Then, weather and snow cover area included as the input of SRM for simulation of snowmelt runoff. To obtain the information needed to the model, physiographic characteristics of the basin including the area and different classes of height obtained from the Arc-Hydro and Hec_GeoHMS in DEM maps of GIS software. Then the snow cover areas obtained from the images of MODIS in daily interval that obtained by google earth engine system.
Using the digital elevation map (DEM) and the accession of the Arc-Hydro and Hec_GeoHMS software of GIS, firstly flow direction map plotted. Secondly flow accumulation and stream flow network maps plotted, and by introducing the basin output to the program (Polechehr hydrometric station) borders of the basin identified and classification of the basin accomplished according to the three distinct height classes. Monitoring the snow surface cover during the daily time interval showed that the area covered with snow in winter season. This area decreases as the air temperature increases. The SRM model simulated the snowmelt of Gamasiab basin with good accurately, in which, the percent of volume error or V_d was lose than 2% and the R²   was above 0.9.
The results of this research showed that the using the images of MODIS yields a reasonable estimation of the snow cover area of Gamasiab with local of data. Also simulation results showed the high capability of the SRM in snowmelt runoff of the area under study. Result showed that the coefficient of determination and volume percent of error of model was 0.93 and %0.3 for 2014-2015 and it was 0.9 and 3.33 for 2015-2016 years, respectively. The results of this study, was in consistent with the previous studies fading in which in addition of model's parameters, physiographic characteristics, basin play a major role in the accuracy of the simulation. According to the calculated and observed runoff diagram, in both years of study, peak temperatures begin in March, as the weather warms and the snow melts, and will continue until April. Considering the snow cover, it can be concluded that the main runoff of March Peak is related to snowmelt, but with the change in the shape of precipitation from snow to rain and the warming of the weather, April peak is related to rain. Regardless of acceptable simulation results of the model, the lack of snow survey station in the study area, (yield the model to face with difficulty) in process. To overcome this shortcoming, we used the presumptions of the model and recommended values of the model.
 
Keywords: MODIS; Remote sensing; Runoff Snow; SRM; Gamasiab.       

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Introduction
Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is undergoing change over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. With regard to the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost. To be prevented.

Materials & Methods
In order to classify and detect the marginal land of the river, TM and OLI image images were selected for a specific month (August, August) for the years 1987 and 2017. The purpose of this study was to investigate the changes occurring in the studied area with an emphasis on agricultural lands. To do this, the images before processing in the ENVI software took radiometric, atmospheric and geometric corrections on them. After that, the main components of the river route were extracted. Five basic algorithms were used to classify the base pixel, but eCognition software was used to classify the object. Supervised classification identifies homogeneous regions with examples of land use and land cover, in which pixels are assigned in known information classes. Education is a process that determines the criteria for these patterns. Learning output is a set of spectral signatures of proposed classes. The first step in object-oriented classification is the segmentation of the image and the creation of distinct objects, consisting of homogeneous pixels. The main purpose of image segmentation is to combine pixels or small objects to create large image objects based on the spectral and spatial characteristics of the image. In order to evaluate the accuracy and compare the resulting maps, the overall accuracy and Kappa coefficient are used. When the sampling of pixels is done as a spectral or informational class pattern, the evaluation of the spectral reflection of classes and their resolution can also be done. An algorithm with the highest accuracy and accuracy will be the basis for the detection. Detection of changes, which leads to a two-way matrix and shows variations of the main types of land use in the study area, was carried out in this study. Pixel-based cross-tabulation analysis on pixels facilitates the determination of the conversion value from a specific user class to another user category and areas associated with these changes over the given time period.

Results & Discussion
The results showed that the object-oriented method is more accurate than the base pixel algorithms for providing user-defined maps. The amount of accuracy in the method based on object-oriented classification depends largely on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. The Kappa coefficient for each image is approximately 0.90. So these maps are the basis for the discovery of change. According to the results, the agricultural and residential lands have been increased and this increase has been accompanied by a decrease in rangelands. A general overview of this 30-year period shows that the arable and dry farming, respectively, increased by 2418.79 and 719.61 hectares and the rangelands had a decrease of 2848.86 hectares. However, the residential class and human effects show an increase of 428.88 hectares or a growth of 178.87%, which indicates the importance of agriculture in the studied area.

Conclusion
Identifying and discovering land cover changes can help planners and planners identify effective factors in land use change and land cover, and have a useful planning to control them. For this reason, maps are needed with precision and speed, and object-oriented processing methods make this possible with very high precision. The results of this study, in addition to proving the precision and efficiency of object-oriented processing in land cover estimation, between 1987 and 2017, have witnessed a decrease in the area of rangeland lands and, on the other hand, agricultural and residential lands, which is indicative of the overall trend Destruction in the area through the replacement of pastures by other uses such as rainfed farming.

Keywords: Land Use, Gamasiab, Object Oriented, Pixel Base, Kappa Coefficient