Predicting crop yield response to irrigation level is increasingly important to
optimize irrigation under limited available water and for enhancing
sustainability and profitable production. This study was carried out to evaluate
the performance of
CropWat model in predicting deficit irrigation effect on
cotton crop, and to explore some alternatives for cotton irrigation. Crop yield
and water use data were collected from a 3-yr (2007-2009) field experiment to
assess the response of drip-irrigated cotton to deficit irrigation (DI).
Continuous calculations of evapotranspiration (LE) using eddy covariance
method and energy budget were performed over more than one year above the
heterogeneous canopy of an arid oasis ecosystem in the central Syrian desert.
Irrigation practice wa
s traditional flooding with a 28 days turn of water
delivery. The work focuses on seasonal variations of energy budget over a 2
years period with emphasis on effects of rainfall, wind speed and radiative
budget on evapotranspiration. Maximum evapotranspiration was only
5mm/day. Even under irrigation practices, winter rainfall seems to have an
important impact on LE: comparisons of two situations in June 2002 and June
2003 showed an increase of 13% in values of LE/Rn-G. Maximum averaged
hourly values of evapotranspiration were found for wind speed values closed to
3m/s. This suggests that when the evaporative demand from the air (or vapour
pressure deficit (vpd) ) is increasing above a certain limit, the vegetation closes
its stomata and reduces transpiration. Results from the energy balance closure
showed significant differences in the slope of H+LE against Rn-G relationships
between cold and hot month which was explained by specific radiative budget
of desert areas.
Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of t
he major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications.
So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models.
The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models.
The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models.
Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters
(R= 98.55%, RMSE = 12.49 mm/month) during the test period.
The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions.
This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.
