H. Ahmadzadeh; saeed morid; M. Delavar
Abstract
Streamflows, actual evapotranspiration and crops’ yield are the main variables to estimate agricultural water productivity. Thus, simulation of these variables is of great importance in evaluation of different measures to increase water productivity. For this, application of conceptual models is a ...
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Streamflows, actual evapotranspiration and crops’ yield are the main variables to estimate agricultural water productivity. Thus, simulation of these variables is of great importance in evaluation of different measures to increase water productivity. For this, application of conceptual models is a relevant approach and SWAT (soil and water assessment tool) is one of the well known models in this regard. The present paper aims to assess SWAT in simultaneous simulation of streamflows, actual evapotranspiration and the main crops’ yield of the Zarineh Rud basin. The reason for selection of this basin as the study area relates to its role to meet the Urmia Lake’s water requirement. The lake faces with serious water shortage in recent years and escalating water inflow depend to increase water productivity in the upper catchments. To setup SWAT, the basin was divided to 11 subbasins and 908 hydrological response units, which enables us to introduce more accurately the basin’s cropping pattern and water resources, which meet the requirements of the agricultural area. For simulation of the river flows, data from 6 gauging stations were used for calibration and validation of the model for periods of 1987 to 1999 and 2000 to 2006 respectively that resulted R2 and RMSE between 0.49 to 0.71 and 3.9 to 44.9 (m3/sec) for calibration period, and values of 0.54 to 0.77 and 2.07 to 55.7(m3/sec) for validation period respectively. There is no observed data for actual evapotranspiration in the basin. So, it was verified in the wet years by maximum evapotranspiration reported in National Water Document that results presented the values of 0.97 and 52.5(mm/year) for R2and RMSE respectively. Finally, the estimated yields of the 7 staple crops by the model were compared with the recorded data that showed very close values(R2=0.9 and RMSE=1.65(ton/ha)).
B. Mansouri; H. Ahmadzadeh; A. Massah Bavani; saeed morid; M. Delavar; S. Lotfi
Abstract
This paper evaluate impacts of climate change on temperature, rainfall and runoff in the future Using statistical model, LARS-WG, and conceptual hydrological model, SWAT. In order to the Zarrinehrud river basin, as the biggest catchment of the Lake Urmia basin was selected as a case study. At first, ...
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This paper evaluate impacts of climate change on temperature, rainfall and runoff in the future Using statistical model, LARS-WG, and conceptual hydrological model, SWAT. In order to the Zarrinehrud river basin, as the biggest catchment of the Lake Urmia basin was selected as a case study. At first, for the generation of future weather data in the basin, LARS-WG model was calibrated using meteorological data and then 14 models of AOGCM were applied and results of these models were downscaled using LARS-WG model in 6 synoptic stations for period of 2015 to 2030. SWAT model was used for evaluation of climate change impacts on runoff in the basin. In order to, the model was calibrated and validated using 6 gauging stations for period of 1987-2007 and the value of R2 was between 0.49 and 0.71 for calibration and between 0.54 and 0.77 for validation. Then by introducing average of downscaled results of AOGCM models to the SWAT, runoff changes of the basin were simulated during 2015-2030. Average of results of LARS-WG model indicated that the monthly mean of minimum and maximum temperatures will increase compared to the baseline period. Also monthly average of precipitation will decrease in spring season but will increase in summer and autumn. The results showed that in addition to the amount of precipitation, its pattern will change in the future period, too. The results of runoff simulation showed that the amount of inflow to the Zarrinehrud reservoir will reduce 28.4 percent compared to the baseline period.
