Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Aliakbar Rasouly; Hashem Rostamzadeh
Abstract
Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included ...
Read More
Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included the selection of variables, bandwidth and spatial correlation errors. Using the GWR, Franklin analyzed the spatial characteristics of the rainfall along with the elevation changes. Elvi also used this model to study the spatial factors that affect land prices. The GWR produces spatial information that expresses spatial variations between variables' relationships. Therefore, the maps produced from these analyzes play a key role in the spatial non-static description and interpretation of variables (Mennis 2006) and an equation Generates a separate regression for each observation instead of calibrating an equation, so it allows the parameter values to be continuously changed in the geographic space. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away.
Data and Method
The Surface Energy Balance Algorithm for Land (SEBAL) calculates the surface heat flux instantaneously as well as 24-hour. The latent heat flux shows the energy required for true evapotranspiration and is calculated as the remainder of the equilibrium energy equation (Mobasheri, 2005). In remote sensing estimates of surface Albedo, surface temperature and surface leakage in the thermal infrared region, reflectance is used to calculate spatial variations in short-wave radiation and long-wave radiation emitted from the surface of the earth. A combination of short-wave and long-wave radiation combines the ability to calculate the pure absorbed surface radiation for each image pixel. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. In other words, the GWR only uses geographically close observations to estimate local coefficients. This method of weighting is based on the idea that the use of geographically close observations is the best way to estimate local coefficients. The GWR method not only does not consider the effects of self-variables on the independent variable, but also the effects of neighboring situations. The values of the geographic weighting model can be used to describe the spatial correlation of the factors used. Therefore, we extend the study area to several sections We divide the values of the geographic weight coefficients in each of the sections in relation to each of the environmental parameters. Unlike regular regression models, they provide an equation for describing general relationships between variables. GWR allows the parameter values to be changed continuously in the geographic space. Each of the equations is obtained using a different weight of the observations contained in the total data.
Results and Discussion
The analysis of the relationships between selected indices by geographic weighted regression model and the classification of output values through the normalization of data in seven categories. The values obtained vary between 1 and 1, and the smaller the index, the spatial disjunction is variable, and the larger it shows the presence of spatial clusters. It was found that all three indexes of evapotranspiration, surface temperature and vegetation index have cluster spatial pattern. Therefore, the null hypothesis is based on the spatial correlation itself, and as a result, three of the above indicators can be used for spatial analysis of the actual evaporation. Based on the correlation between the factors affecting the macroeconomic factors, the factor of vegetation index has the most effect on the magnitude of the spatial distribution in the studied area (53% with an area of 471782864 square meters). However, as the results are clear, this number is an overall number and covers the overall situation in the area. And does not refer to spatial features of the area. In the results of weighted regression, the effect of elements can be observed spatially. Accordingly, according to the geographic weighted regression method, the relationship between evapotranspiration and surface temperature was negatively affected and negatively affected. The relationship between dehiscence and vegetation index was studied in different years. The highest digit on the seventh floor is 13/99 and in the area of 266611500, which shows a high positive effect. The relationship between evapotranspiration and the Albedo shows the highest value in the first and second classes. The values of 18 and 10 in the area of 490428000 and 1170753300 m 2, respectively, show a very negative impact and a significant negative effect.
Conclusion
Geographic weighted regression method is a statistical method that is adapted to study local patterns. This method is, in fact, a technical technique that analyzes the relationship between spatial variables in a hypothetical unpopular space. In this research, we tried to express the effect of several indicators on actual evaporation. These indicators are not all indicators that have had an impact on actual evapotranspiration Because actual evapotranspiration is closely related to other climatic factors. Because of the unique ability of spatial weighted regression to identify and analyze the relationships between variables, it is recommended to use it in quantitative analyzes. The Z classes resulting from the GWR analysis of the actual evapotranspiration in different years have different states that indicate the spatial effect of the surface temperature in different conditions.
Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Ali Akbar Rasuly; Hashem Rostamzadeh
Abstract
Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship ...
Read More
Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship between land use, water allocation, and water consumption. Evapotranspiration is the second element of the water cycle (after precipitation) and its accurate estimation on a regional scale is necessary to design appropriate management strategies. Evapotranspiration is a function of the amount of energy available for vegetation and its exchange. Because of this dependence, it can be estimated using the principle of energy conservation. Due to the limited number of meteorological stations in the country and the high cost of collecting ground data, the cost-effectiveness of the use of satellite data is one of its advantages, and the possibility of retrieving data from all levels of the region at one time is its next advantage. Having timely information makes horizontal monitoring of meteorological and environmental parameters possible. The ability of remote sensing to measure some terrestrial parameters has had an important effect on estimating actual evapotranspiration. The SEBAL model is one of the remote sensing algorithms that calculate plant evapotranspiration based on the momentary energy balance at the level of each pixel of a satellite image. The study area of the current research was the eastern cities of Lake Urmia. The reason for studying this section was the impact of recent droughts on these areas and the reduction of surface and groundwater, which has increased the need to manage water resources in these areas. Methodology In the first step of radiometric corrections, the amount of spectral radiance in the thermal band and at the next step, the reflectance in the visible bands, near-infrared, and short-wavelength infrared bands were calculated. As mentioned above, in the SEBAL model, actual evapotranspiration is calculated through satellite imagery and meteorological data is calculated using the surface energy balance. When satellite imagery provides information for its transit time, SEBAL calculates the instantaneous evapotranspiration flux for that time. Landsat 8 images for 2017-2016-2014-2013 years and meteorological data such as Minimum temperature, maximum temperature, dew point temperature, evaporation pan data, sunny hours, and wind speed were analyzed using ENVI 4.8 - Excel 2013- Arc GIS 10.3 software. Results and Discussion SEBAL is an image processing model that measures evapotranspiration and other energy conversions on the Earth's surface using digital data measured by remote sensing satellites that emit visible, near-infrared, and thermal infrared radiation. This method uses surface temperature, surface reflection, and normalized plant differential index (NDVI) and their internal relationships to estimate surface fluxes for different types of land cover. In this section, using the values obtained from latent heat flux and evaporation heat flux, first, the amount of instantaneous evapotranspiration for each pixel was calculated. Then, using Ref_ET software, the total 24-hour evapotranspiration was calculated and the daily evapotranspiration rate was obtained for the whole image. Conclusion The results showed that there was a good correlation between the values estimated by the remote sensing algorithm (SEBAL) and the FAO-Penman-Monteith method as well as the evaporation pan method. The difference between the amount of SEBAL and the FAO-Penman-Monteith method in the reference plant was less than 4.21 mm/day; the largest difference was related to the 22nd of October. In total, SEBAL and Penman-Monteith methods had an average absolute difference of 4.28 mm/day. According to the results of this study, it can be observed that using the SEBAL model, the actual evapotranspiration and water needs of crops and even orchards and rangelands can be calculated on a large scale. This case could prove the suitability of this model for estimating actual evapotranspiration at different levels of the farm and irrigation networks. Therefore, remote sensing has a very high potential to improve the management of irrigation resources in very large areas using various algorithms and providing an estimate of the amount of ET with minimal use of ground data. Using remote sensing technology and GIS, acceptable results can be obtained in estimating the actual evapotranspiration rate, especially in large areas. If the parameters of the energy balance equations and Penman-Monteith could be calculated from satellite images spatially, with a suitable plant coefficient, the two methods would have similar results in estimating the rate of evapotranspiration. Using this method, the plant coefficient, which is one of the important factors in calculating the evapotranspiration of plants, can be accurately determined.
