F. Fathian; S. Morid; S. Arshad
Abstract
The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the ...
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The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the basin as well as tracking changes in the land use of the study area, using satellite images. Four non-parametric statistical tests, the Mann-Kendall, Theil-Sen, Spearman Rho and Sen's T test, were applied to estimate the trends in the annual time series of streamflow, precipitation and temperature at 18 stations throughout the case study. Furthermore, by using the LANDSAT satellite images of 1976, 1989, 2002 and 2011, land use classification was determined using maximum likelihood, minimum distance and mahalanobis distance methods. The results showed significant increasing temperature trend throughout the region and an area-specific precipitation trend. The trend tests also confirmed a general decreasing trend in region streamflows that was more pronounced in the downstream stations. The results showed that the classification by the maximum likelihood method wass associated with minimum error. The results of processing the images showed that the irrigated crops, horticultural and dry lands have increased during last 35 years by 412, 485 and 672 percent, respectively. But, the pasture area is decreased by 34 percent. Finally, correlation between streamflow changes with simultaneous changes in climatic variables and land use showed it is significant in case of temperature and land use; and insignificant in case of precipitation. However, the determination coefficient of land use is higher than temperature.
B. Hassanpour; F. Mirzaei; S. Arshad; H. Kossari
Abstract
In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL ...
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In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL and S-SEBI algorithms. These algorithms are different in predicting sensible heat flux. The results show that RSME value for the net radiation and soil heat flux was respectively 46 and 43 (w/m2). SEBAL algorithm is capable to estimate sensible heat flux more accurate than S-SEBI so it is able to estimate latent heat flux more accurate. The RSME amount in sensible heat flux and latent heat flux for SEBAL algorithm are 58 and 31 (w/m2) respectively. These amounts in S-SEBI algorithm are 111 and 74 (w/m2). The differences between two algorithms could be because of the use of meteorological data in predicting sensible heat flux and aerodynamic resistance in SEBAL algorithm. Also the results show that SEBAL algorithm estimates hourly evapotranspiration by the difference of 0.05 mm/hour which is about 1% of hourly evapotranspiration Whereas S-SEBI predicted it by the difference of 0.11 and 11%. The difference between measured daily evapotranspiration and SEBAL -based daily evapotranspiration was 0.4 mm that is about 1% less than measured. Whereas these differences by S_SEBI are 1mm and 12%.
