جستجوی مقالات مرتبط با کلیدواژه « Urmia » در نشریات گروه « آب و خاک »

Empirical and theoretical methods (energy balance) are widely used to calculate the Crop Water Stress Index (CWSI) and irrigation scheduling to describe crop water status. In this study, irrigation scheduling was performed at the research farm of College of Agriculture, Urmia University, using a manual infrared thermometer and the empirical method of Idso et al. (1981) for the black gram under different irrigation regimes using drip irrigation in 2017. The experimental design was carried out in a randomized complete block design with three levels of irrigation I1, I2 and I3 which were 50, 75 and 100 percent water requirement in three replications, respectively. Using the baselines obtained for each treatment, the average CWSI values during the growth season of black gram for I1, I2 and I3 treatments were calculated to be 0.37, 0.23 and 0.15 respectively. The relationship between CWSI and total irrigation depth (mm) was determined as CWSI = -0.0008 (I) + 0.58, and the relationship between black gram grain yield (ton/hec) and CWSI was determined as Yield = -1.8237 (CWSI) + 2.1435 which their correlation coefficients (R2) were 0.98 and 0.99 respectively, which shows the high accuracy of regression models. In general, if the amount of water decreases with stress during the plant growth, the CWSI value increases, and as a result of increasing CWSI, the crop grain yield decreases. Finally, the no stress treatment (I3) with CWSI=0.15 was the basis for irrigation scheduling and then some relationships were established for determining the irrigation time using CWSI in Urmia climate for four stages of black gram growth; flowral induction-flowering, pod formation, seed and pod filling, and physiological maturity as (Tc  ̶ Ta)C = 1.9498  ̶ 0.1579(AVPD), (Tc  ̶ Ta)C = 4.4395 ̶ 0.1585(AVPD), (Tc  ̶ Ta)C = 2.4676  ̶ 0.0578(AVPD) and (Tc  ̶ Ta)C = 5.7532  ̶  0.1462(AVPD), respectively.

Long-term forecasting of hydroclimatic variables such as maximum monthly precipitation (MMP) is very important in water resources management. The previous researches have shown that discovering association between the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) and hydroclimatic variables such as precipitation could provide important predictive information. In this paper, the application of two data mining techniques is offered to discover affiliation between MMP values of Urmia and Tabriz synoptic stations and SSTs of the Black, Mediterranean and Red Seas. Two major steps of the modeling in this study are the classification of SST data and selecting the most effective groups and extracting hidden predictive information involved in the data. Decision tree algorithms were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. The results show a relative correlation between the Black, Mediterranean and Red Sea SSTs and MMP of Urmia and Tabriz synoptic stations so that the confidence between the MMP values and the SST of seas is higher than 60% forstations.

Soil organic matter (SOM) is a key index in evaluation of the soil degradation and soil carbon sequestration. Therefore, determination of the SOM spatial patterns is essential for developing the suitable strategies of soil and ecosystem management. Geostatistical methods such as kriging have been widely employed to investigate the spatial pattern of different soil properties like SOM. The quality of the spatial maps is significantly influenced by the statistical properties of the raw data. Thus, identification and elimination of spatial outlier data, which has rarely been considered in previous works, is an important step in preparation of accurate and suitable maps of soil organic matter. The aim of the present study was to evaluate the effect of spatial outliers on the spatial pattern of soil organic carbon at the Rozeh-Chay watershed, Urmia, west Azarbayjan province. A total of 89 surface soil samples (0-10 cm) were collected based on the stratified random sampling scheme. After the normalization of the raw SOM data, global outliers were eliminated by box plot method. Spatial outliers were identified by the global and local Moran’s I indices. Spatial correlogram of the global Moran’s I showed the highest spatial autocorrelation at distance of 900 m, which was used as a distance band for preparation of spatial clusters map by local Moran’s I index. Cluster map of the local Moran’s I index showed four spatial outliers which were eliminated to assess their effects on the accuracy of kriging map of SOM. With the elimination of the spatial outliers, the MAE and RMSE of the SOM map were decreased from 0.97 and 1.31 to 0.85 and 1.12, respectively. Therefore, the accuracy of the kriging map increased by 13.5 percent. Generally, it can be concluded that the combination of Moran’s I index and kriging method improves the efficiency of organic matter map in the study area.

One of the essential prerequisites for improving water consumption management in a farm is the accurate estimation of the crop water consumptive use. Accurate estimation of the consumed water by the crop evapotranspiration is one of the important factors in planning for agriculture development and increasing agricultural production. The FAO Penman– Monteith (FPM) method has been proposed as a universal standard method to estimate the reference crop evapotranspiration (ETo) and to evaluate the other procedures of EToestimation, which needs solar radiation, wind speed, air temperature and relative humidity data. Unfortunately, there are no facilities for measurement of some weather parameters at some weather stations. Therefore, identifying the correctness of the ETo estimation methods by using the estimated climatic data is very important. Based on this, the objective of the present study was to evaluate the performance of the FPM method for estimating ETo when solar radiation (Rs), vapor pressure (ed) and relative humidity (RH) data were not available under climatological conditions of the northwest of Iran. Data of Urmia and Tabriz stations were used to compare the estimated ETo by the FPM method for the complete and limited data sets. The results showed that when RH and ed data were missing, the FPM method was still a very good option for estimating ETo in Urmia and Tabriz stations, with RMSE value smaller than 0.4 mm day-1. When Rs was missing, the FPM method was not good enough for estimating ETo, and RMSE increased to 0.7 mm day-1. The results also showed that solar radiation was the most effective parameter on ETo estimation and both vapor pressure and relative humidity had the same effect on ETo estimation.

Climate change via variations in temperature pattern، precipitation and other climate variables patterns affects all the hydrologic processes. One of the main consequences of the climate change is its impacts on agricultural water use، which can cause serious challenges for water resources management. The purpose of this research was to investigate the global warming impacts on reference evapotranspiration، precipitation deficit (PD) and vapor pressure deficit (VPD) in Urmia synoptic station. To achieve this goal، using the HadCM3 global circulation model and LARS-WG5 weather generator، the minimum and maximum temperature and precipitation values for 2011-2030، 2046-2065 and 2080-2099 periods based on A1B، A2 and B1 scenarios were simulated. Then، using the forecasted parameters، the reference evapotranspiration، PD and VPD were calculated and compared with the corresponding base data (1971-2010). The results show that in each of the three periods the precipitation in the spring will be decreased but the autumn precipitation will be increased in comparison with the base period. Meanwhile، the maximum event of the precipitation will be shifted from the spring season in the base period to the autumn season in 2090. Moreover، similar to air temperature، the reference evapotranspiration، PD and VPD will be increased in 2090 and such increment will be more in the warm months than the cold months. The amounts of these parameters’ increments in the A2 scenario are more than those in the A1B scenario and also in the A1B scenario are more than those in the B1 scenario.

Monitoring and prediction of droughts، especially accurate determination of its emersion time and duration، are very important in water resources management and developing drought mitigation strategies. In this study، meteorological drought conditions in Urmia station during 1951-2011 were evaluated by means of the modified Standardized Precipitation Index (SPImod) and the Joint Deficit Index (JDI). Results showed that use of the modified SPI covered some flaws of the traditional SPI، and took into account the seasonal variation of precipitation. Nevertheless، the modified SPI same as the traditional SPI is sensitive to the time scale and it can lead to confusion because inconsistent results may emerge under different time scales. In order to resolve this issue، the JDI was used. The JDI was calculated through constructing the joint distributions of multiple modified SPIs with time scales 1 to 12 months using copula functions. The results indicated that the Urmia station had experienced a prolonged and sever (JDI<-1) drought during 1996-2011، as 101 out of 192 months were dry (JDI<0). Also، the dry periods had more severity than the wet periods. These drought events led to serious environmental consequences such as Urmia Lake drying up.

Some studies that investigate the climate change and hydrologic balance relationships utilize reference evapotranspiration to either calculate the changes in trends and magnitude of actual ET or to determine changes in atmospheric demand. Some studies utilize temperature or radiation-based empirical equation. Since many climate variables that affect ETo rates have been changing, single-variable equation for estimating the trend and magnitude of ETo should be avoided. The present and future temporal characteristics of ETo are examined in this paper. ETo are calculated during 1961-2005 by the Penman-Monteith recommended by FAO with historical weather data while ETo during 2011-2099 are downscaled from HadCM3 outputs under two emission scenarios(A2 and B2) by SDSM and under A2 by LARS-WG. Results showed that Downscaled ETo by SDSM will increasing during 2011-2099 at a Rate of 1.7 and 0.96 mmyr-1 under A2 and B2 scenario respectively. Averaged over the two emission scenarios, the projected increase of downscaled ETo by SDSM are 4.2%, 7.1% and 12.5% for the three periods 2020s, 2050s and 2080s respectively. Another model has projected 3.4%, 7.8% and 14.9% increases in ETo for these three periods respectively.

Global warming has brought about changes in meteorological variable time series. These changes, accompanied with serious human intervention in nature, has resulted in hydrological changes. So the effects can be tracked down by the trend of the time series. The purpose of this study is to analyze and compare the monthly and annual trends of temperature, precipitation as well as stream flow time series of the Urmia Lake basin, using nonparametric methods. Four non-parametric statistical tests, namely: Mann-Kendall, Theil-Sen, Spearman Rho, and Sen's T tests are investigated throughout the present study. The records of 11 temperature gauging stations, 35 rain gauge stations and 35 hydrometery stations showed significant increasing trends in the basin temperature. In the case of precipitation recordings, it was different, in a way that 8% of the rain stations indicated increasing trend, while the trend being decreased in 14% of the stations. Finally for stream flows, 60% of the hydrometery stations revealed a decreasing trend. A comparison of the methods revealed (at a monthly scale), that Mann-Kendall and Theil-Sen's performance were similar. Furthermore, Sen's T and Spearman Rho methods were detected to be the ones showing the number of stations to undergo minimum and maximum significant trends, respectively. As for annual time scale, Sen's T showed to represent the maximum number of stations to be suspicious of being of non-stationary as regards the trend.

Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions، such as PCA are important tools in water resources planning. In this study factor analysis method as a tool for projecting the information space on the limited and specific axes، has been applied. The main aim of this study is regionalization of Urmia Lake basin from the view of drought using factor analysis. For this purpose monthly precipitation data of 30 weather stations in the period 1972-2009 were used. For each of the selected stations three and twelve months SPI value were calculated. Factor analysis conducted on SPI values to delineate the study area with respect to drought characteristics. Homogeneity of obtained regions tested using the S-statistics proposed by Wiltshire. Resultes of factor analysis of 3 (12) months SPI values showed that 5 (6) factors having eigen values greater than 1 acounted for 68. 08 (78. 88) percent of total variance. Urmia Lake basin delineated to 5 (6) distinct regions considering the eigenvectors following rotation using the 3 (12) month time scale. Results of homogeneity test indicated that all of the obtained regions were homogeneous.

The intensify process of lowering Urmia lake level has led to reduce water quality, making it brackish and unusable in wells of Lake Marginal Lands. The present study investigated changes in groundwater salinity in Urmia plain using geostatistical models, and compares the results of the mapping in three time periods. To assess salinity of the groundwater, data from 57 wells in the period of 1380, 1384 and 1387 were analyzed, using Kriging in GIS, GS+ and ARCVIEW8 softwares. The results indicated that application of Kriging, experimental semi-variogram with spherical model provided better predictions with correlation coefficient of 0.99. According to the obtained groundwater salinity maps, land area with more than 2 dS/m groundwater salinity in 1380 was equal to 1924 hectares that has increased to 8331 hectares in 1387. During these years, in the study area the groundwater salinity with less than 1 dS/m was decreased about 14675 hectares. The observed maximum salinity value in 1380 has increased from 1.91 to 5.8 dS/m in 1387.