parva mohammadi

The purpose of this study is to investigate landuse changes in Gorganrood basin in 2001, 2010 and 2019. Using Landsat and Product-Modes satellite images, used maps were prepared using the classification method of random forest algorithm in Google Earth Engine. Satellite imagery was classified into eight classes including forest, cropland, shrubland, grassland, wetland, urban, barren, and water. The results showed that the highest landuse area in 2001, 2010 and 2019 is related to cropland, respectively, equal to 3150.58, 3403.8 and 3031.82 square kilometers, and lowest amount in 2001 is related to water with an area of 84.67 and in 2010 and 2010 is related to wetlands with an area of 167.98 and 118.7 square kilometers. Comparison of the results showed that the urban landuse study period in 2019 compared to 2001, it has grown by about 34% (341 square kilometers). Also, forest lands at a rate of 5.5 percent (148.92 kilometers), shrubs at a rate of 8.7 percent (135.79 kilometers) and arable land at a rate of 3.8 percent (118.76 kilometers) had a negative growth.

Global warming has changed rainfall patterns and reduced snow sources. The main objective of this research was to investigate the impact of temperature increase on snowmelt and river runoff in hot months of the year along with analysis of climate variables in Tamar basin, Iran. For this purpose, the snow cover area was extracted from the daily images of Modis Satellite, based on elevation. Then, the study area was divided into four districts. Discharge flow measured data of Tamar hydrometric station, precipitation of Qarnaq and average temperature of Gidagh stations were used from 2013 to 2016 for calibration and from 2017 to 2019 for verification, to simulate snowmelt runoff. Snow cover extent, in calibration stage and january, reached the highest values of 28% and 28.8% in the third and fourth districts, respectively. Furthermore, in the verification stage, the third and fourth districts in February had the highest percentages of 45.8 and 30.2, respectively. Comparison of simulated and measured runoff data revealed that the portion of runoff due to snowmelt is significant by passing from February to April. So that the highest increase (from 1.8% to 39.1%) in runoff amount was corresponded to the water year of 2018-2019. The highest and the lowest percentages of snowmelt in water years of 2014-2015 and 2017-2018 were 19 and 3.3 percent, respectively. The results of sensitivity analysis of model parameters including temperature decrease rate, critical temperature, delay time, degree-day factor, X and Y coefficients, precipitation runoff coefficient and snow runoff coefficient showed that the rainfall coefficient and X parameters are the most effective parameters. The effect of temperature and rainfall on runoff process was varied in different months.

Surface water, especially rivers, is one of the most important water resources that play an important role in supplying water for various activities. The purpose of this study was to investigate the temporal and spatial variability of water quality parameters in three different basins, in terms of land use, at 50 hydrometric stations in 9 rivers in the period 1992-2015 by using multivariate and GIS statistical methods. In this study, factor analysis based on 10 qualitative parameters was performed to determine the most important parameters affecting the surface water quality of the study area. The results of Principal component analysis showed that the North, Northwest and Southwest basins have two significant main components with 82.38%, 79.86% and 81.60% of the total variance, respectively. Cluster analysis of hydrometric stations located in northwestern and southwestern regions was divided into two clusters and north into three clusters. In the cluster analysis, some stations in Aji Chay, Atrak and Zayanderood rivers had different water quality characteristics than other stations. These stations are located downstream of the rivers. Based on the pattern of mean water quality parameters in GIS and land use map, station 2 had the lowest values for most parameters.

Rainfall is among the most important climatic factors affecting the rainfed cultivation. Thus, in order to maintain water consumption in current agriculture, with the view of water resources management, the country needs to convert some irrigated land areas to rainfed cultivation in the near future. Indeed, it is necessary to conduct an analytical study on rainfed agriculture and identify appropriate areas for rainfed agriculture in the country, especially in Urmia Lake basin. Principal component analysis (PCA), K-Means and Ward have been already used to assess climate regionalization in different regions such as Spain (Diaz and Rodrigo, 2004), Greece (Kitsara et al, 2005), central-northeastern region of Mexico (Pineda-Martinez et al, 2007), Luanhe basin (Hassan and Ping, 2012) and Iberian Peninsula (Parracho et al, 2015). This study was, thus, intended to study the regionalization of the eastern part of Urmia Lake basin on the basis of the precipitation and yield of rainfed wheat using PCA, K-Means and Ward methods. To that end, the maps were drawn in the GIS environment and three methods of clustering were compared. Finally, using the clustering of precipitation and rainfed yield, wheat cultivability was investigated in the eastern part of Urmia Lake. To that end, the daily rainfall dataset of 26 rain gauge stations were used and the yield of rainfed wheat was considered during the period. Then, PCA, K-Means and Ward clustering were performed and the results were compiled. The homogenousity of the resulting clusters were analyzed by H and S statistical tests and homogeneous clusters were drawn in the GIS environment. The analytical factor coefficients to the main components, through K-Means clustering method, showed that the clusters point of view, precipitation and rainfed yield were more consistent and the results were close to each other.

Surplus application of nitrate can lead to contamination of groundwater resources. Accurate knowledge of nitrate distribution in the root zone is essential for the design and management of drip irrigation systems. In this study, artificial neural network (ANN) model is used based on strong pattern recognition technique that made reasonable relations between input and output parameters. In order to simulate the nitrate distribution pattern, the Experiments were set on three different soil textures for surface and subsurface drip irrigation systems. The drippers were installed at 3 different soil depths (i.e. 15, 30 and 45 cm). The emitter outflows were considered as 2.4, 4 and 6 lit/hr. The fertigation treatments include treatments with nitrate concentrations of 125, 250 and 375 mg/liter. The effective variables include the initial nitrate of soil, nitrate concentrations in fertigation, initial soil moisture content, radial distance of points, applied water volume, saturated hydraulic conductivity, emitter discharge, emitter installation depth, soil bulk density, and the proportions of sand, silt and clay of soil. The mentioned variables were utilized for estimation of nitrate distribution pattern of surface and subsurface drip irrigation systems using ANN model. The comparisons results of simulated and measured values indicated that ANN models were capable methods for prediction of nitrate distribution. The values of correlation coefficient (R) were ranged 0.9-0.94 and 0.8-0.96 for surface and subsurface drip irrigation systems, respectively.

Considering the importance of precipitation and scarcity of water resources, spatial analysis of daily rainfall together with corresponding time intervals is one of the requirements. Among the atmospheric factors affecting the rainfed farming, precipitation is the most important factor in numerous studies about the rainfed cultivation. In order to reduce the destruction threat of water resources and resolve the future’s food needs of the people, the importance of rainfed agriculture would be inevitable. In this regard, one of the effective measurements that can be done to find rainy areas with the possibility of rainfed cultivation. The greatest water consumption is corresponding to the agricultural sector. According to the water crisis in the country, decline in the groundwater levels and the importance of agriculture in job creation, the water resource management is essential. Therefore, it will have a value of higher management to determine for the fertile lands in the current situation. Despite the reduction in rainfall, it is also necessary to analytically study the rainfed and determine the fertile lands in the country, especially in the Urmia Lake basin. This study aimed to introduce an index for identification of the suitable areas from the viewpoint of rainfed ability, by using daily rainfall in corresponding with the time intervals for each year in the east part of the Urmia Lake basin. First a program is written in Fortran, using the rainfall data, the proportion of rainfall intervals is determined, then the Fortran program is developed for this propose, based on the concept of the temporal intensity precipitation and their related mechanistic, an index is defined which is the main outcome of the Fortran programming. The index was a criterion for analysis and disintegration of the rainfed quantity. The idea of introducing an index is based on the ratio of rainfall to the interval corresponding. If the ratio quantity was high, it represents the water supply, in other words, the rainfed capability will be great which can play the role of an index rating. In this index, the rainfall depth has positive role and intervals have negative role in the assessment. When the index amount is high, the rainfed capability will increase. Because the amount of precipitation must be bigger (which is the sign of the large amount of rainfall or the smaller interval time) an increase in the amount of rainfall would be followed, or in compound, causes an increase in the proportion, which shows the increase of rainfed ability. The statistical period was 1992-2014 for 23 rainfall stations. The index values were calculated for each year of all stations. Clustering is one of the most useful classification methods. In the cluster analysis, one attempts to actual observations of each cluster, which have the most similarity in terms of variables together. In this study, the clustering methods of the K-Means and Ward were carried out to deline the homogeneous regions based on the developed index values. Clustering in the rainfall stations was considered as a variable, the correlation matrix 23 * 23, where 23 is related to the number of the years stations used. The homogeneity of the clusters were checked through the H-Statistics method and the homogeneous clusters were shown in the GIS environment. In order to use the K- Means method, the whole study area was considered as two clusters and the results of the H-Statistics homogeneity test showed that the two clusters are homogeneous. The first cluster has a mild homogeneity while the second one is completely homogeneous. In the clustering index using the Ward method, the study area was divided into two clusters. The observation of thehomogeneous clusters with the H-Statistics homogeneity test showed that the both clusters are mild homogeneous. On the other hand, from the viewpoint of spatial variation, Iso-Index lines were drawn over the study area. Identifing the rainy areas was conducted by examining the lines of these areas w are suitable for the rainfed agriculture and have a better temporal rainfall distribution. The comparison of two maps regionalization with two methods and the Iso-Index lines show that the regionalization by K- means method, the first part of this regionalization with Iso-Index lines have the same incremental direction, that suggests more rain stations in this area in comparison with the second area. The second cluster of homogeneous region was obtained using the Ward method, having similarity with Iso-Index only in the limited stations. The comparison of the regions resulted from clustering methods with the spatial distribution of the Iso-Index lines over the study area implied that the K-Means method isolated the regions better than the Ward method. The results show that the south, northeast, northwest and the center parts of the study area were more eligible for the rainfed agriculture than the other parts. These areas, regarding to agriculture, temporal distribution of precipitation and richness of groundwater, are better.

One of the most important parameters in designing, managing, and operating surface and subsurface drip irrigation systems is the advance velocity of the wetting (moisture) front in soil, which enormously affects the performance of these systems. Emitter discharge, soil type (soil texture and structure) and initial moisture content are the main factors affecting advance velocity under drip irrigation. Experiments were carried out in a transparent plexiglass tank (0.5 m*1.22 m*3 m) using three different soil textures (light, heavy, and medium). The drippers were installed at 4 different soil depths (surface, 15 cm, 30 cm, and 45 cm). The emitter outflows were considered 2.4, 4, and 6 L/hr. A simulation model was developed using artificial neural network (ANN) for predicting advance velocity of the wetting front (horizontal, downward, and upward direction) under point sources in surface and subsurface drip irrigation. The variables affecting wetting pattern included emitter discharge, emitter installation depth, application time, saturated hydraulic conductivity, soil bulk density, initial soil moisture content, and the proportions of sand, silt and clay in the soil. The results of the comparisons between the simulated and measured values showed that the ANN model was capable of predicting the advance velocity of the wetting front in different directions with high accuracy. The values of Root Mean Square Error (RMSE) varied from 0.09 to 0.35, from 0.02 to 0.17, and from 0.08 to 0.25 cm/min for horizontal, downward and upward velocity, respectively. Also, the values of Mean Absolute Error (MAE) varied from 0.06 to 0.27, from 0.02 to 0.07, and from 0.05 to 0.12 cm/min for horizontal, downward, and upward velocity, respectively. Using these models in designing and operating surface and subsurface drip irrigation systems could improve system performance.

The supply to manage and optimal use of water in agriculture is essential for management and conserve water resources. This study aimed to evaluate the effect of seasonal precipitation fluctuations on annual rainfed yield. Monthly precipitation data for 26 rainfall stations with Statistical period 1992 to 2014 of the east part of Urmia Lake basin were used. Clustering were done with Ward and K-means methods. The homogeneity of cluster were checked through H-Statistics method and homogeneous clusters were shown in GIS environment. The effect of seasonal precipitation were compared with annual rainfed yield. Results showed that the effect of precipitation in the spring, autumn and winter in the amount of rainfed yield is positive and precipitation in these seasons has a remarkable effect on increasing yield, In the three seasons precipitation cause increase of soil moisture storage that summer is consumer this storage But in the summer there is an inverse relationship between precipitation and annual rainfed yield. In order to identify areas combined with increased precipitation and rainfed yield Iso-line of precipitation seasons with the rainfed yield of study area was drawn. The results of this lines showed that in the spring, autumn and winter at the Daryan, Lighvan, Khormazard, Sahlan, harvy, Saidabad, ShirinKandy, Ghorigol, Tabriz and Moghanjigh increased amount of pricipitation is directly related to the amount of yield. These areas can be introduced more eligible rainfed Cultivation in East Lake Urmia and recognition the effects of precipitation on yield rainfed areas can be more practical and reliable.