Climatology
Yagob Dinpashoh; Saeid Jahanbakhsh-Asl; Asma Azadeh Garebagh
Abstract
In this study the values of potential reference crop evapotranspiration were calculated using the FAO-56 Penman Monteith method in six stations located in southern shores of Caspian Sea. Trends in annual ET0 values of the stations were analyzed using the Mann-Kendall test. Then to determine the relative ...
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In this study the values of potential reference crop evapotranspiration were calculated using the FAO-56 Penman Monteith method in six stations located in southern shores of Caspian Sea. Trends in annual ET0 values of the stations were analyzed using the Mann-Kendall test. Then to determine the relative importance of climatic variables on ET0 in a certain station factor analysis conducted. To do this, correlation matrix (R) of seven variables also called similarity matrix was constructed. The significance of correlation coefficients were tested. Results of trends in ET0 showed that in all the stations (except Noshahr) trends of annual ET0 were upward and significant. The slopes of trend lines were positive in all the stations. Factor analysis showed that the first two factors accounted the total variance in the range of 56.5 per cent in the Rasht to 79.6 per cent in the Sari. The largest loading of the first factor is attributed to sunshine hours in the station Rasht, however, it was maximum air temperature in all other sites. In the case of the second factor, the largest loading belonged to wind speed (in Rasht, Gorgan, Sari and Noshahr) and precipitation (in Ramsar and Astara). The findings of this study can be helpful in optimum management of regional water resources.
Climatology
Monir Shirzad; Hajar Feyzi; Majid Rezaei Banafsheh
Abstract
Introduction Reference evaporation and transpiration is one of the important elements of the hydrological cycle, which plays an important role in agricultural studies, water resource management plans, irrigation and drainage network design and water structures (Nuri et al., 2013, Volume twenty, ...
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Introduction Reference evaporation and transpiration is one of the important elements of the hydrological cycle, which plays an important role in agricultural studies, water resource management plans, irrigation and drainage network design and water structures (Nuri et al., 2013, Volume twenty, number five, page 12). Due to the small amount of precipitation and the limitation of water resources in Iran, the correct management of water resources is very important and it is necessary to be careful in using water.Data and MethodIn order to carry out this research, daily climatic data during the years 2014 to 2015 of East Azerbaijan (four stations of Maragheh, Midane, Jolfa and Ahar) were prepared from the regional meteorological organization. After normalization and determination of correlation, the data were used in MATLAB software with artificial neural network method with Lunberg-Marquardt training to 70-30 combination for training and simulation. The input data for the simulation of evaporation and transpiration (temperature, sunshine hours, humidity, wind speed) and the work evaluation criteria are RMSE, R2 and MAE, which we gave priority to the data with less error. Results and DiscussionIn this research, the method based on artificial intelligence (ANN) and three experimental models (Penman Monteith Fau (PMF56), Blaney Kridel (B-C) and Kimberly Penman (K-P) were used to model the non-linear transpiration evaporation system of the reference plant. The results showed that the artificial intelligence method has better accuracy and speed in estimating ET0 compared to experimental methodsConclusionThe results showed that the artificial intelligence method has better accuracy and speed. Also, comparing the method of artificial neural networks with classical methods, the results indicate the appropriateness of the performance of artificial neural networks.
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 ...
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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
Khalil Valizadeh Kamran; maryam longbaf
Volume 22, Issue 65 , November 2018, , Pages 287-299
Abstract
The agriculture is the sector that uses most of fresh water resources. Since the water resources are always subjected to severe depletion, the agriculture sector requires using the water with high efficiency and more effective ways One of the procedures leading to improvement of water management productivity ...
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The agriculture is the sector that uses most of fresh water resources. Since the water resources are always subjected to severe depletion, the agriculture sector requires using the water with high efficiency and more effective ways One of the procedures leading to improvement of water management productivity and ultimately to increase of water efficiency is the accurate estimation of the evapo-transpiration or estimation of water use efficiency of the crops. The remote sensing by giving an estimation of the degree of evapotranspiration (with little use of ground data) has a high potential for modification of cultivation patterns and management of water resources This research aims to determine the actual evapo-transpiration (need of water) of maize, which is an indigenous plant in the northern Khuzestan province, using the image processing of Landsat 8 in four passes include: 13 Aug, 14 Sep, 16 Oct and 17 Nov 2013 and also using the required metrological data based on Surface Energy Balance Algorithm for Land (SEBAL). The results showed that the amounts of needed water estimated by SEBAL model for maize in the initial growth stage, development stage, middle stage and the end stage are 5.04, 8.23, 5.55, and 1.46 mm per day respectively. The values from remote sensing were compared for values assessed by FAO- Penman-Monteith and evaporation pan methods and it was observed that MAE and RMSE are 0.45 and 0.18 mm per day compared to FAO- Penman - Montieth method. In sum, the results indicated that the SEBAL model is able to give answers with high accuracy and in short time and can be used as a beneficial and efficient tool in organizing water resources and meeting the plant water needs.
