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The Middle East region, where arid and semi‐arid regions occupy most of the land, is extremely vulnerable to any natural or anthropogenic reductions in available water resources. Much of the observed interannual‐decadal variability in Middle Eastern streamflow is physically linked to a large‐scale atmospheric circulation patterns such as the North Atlantic Oscillation (NAO). In this work, the relationship between the NAO index and the seasonal and annual streamflows in the west of Iran was statistically examined during the last four decades. The correlations were constructed for two scenarios (with and without time lag). The associations between the annual and seasonal streamflows and the simultaneous NAO index were found to be poor and insignificant. The possibility of streamflow forecasting was also explored, and the results of lag correlations revealed that streamflow responses at the NAO signal with two and three seasons delays. The highest Spearman correlation coefficient of 0.379 was found between the spring NAO index and the autumn streamflow series at Taghsimab station, indicating that roughly 14% of the variance in the streamflow series is associated with NAO forcing. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran 总被引：5，自引：0，他引：5
Drought, a normal recurrent event in arid and semiarid lands such as Iran, is typically of a temporary nature usually leaving little permanent aftermath. In the current study, the rainfall and drought severity time series were analyzed at 10 stations in the eastern half of Iran for the period 1966–2005. The drought severity was computed using the Standardized Precipitation Index (SPI) for a 12‐month timescale. The trend analyses of the data were also performed using the Kendall and Spearman tests. The results of this study showed that the rainfall and drought severity data had high variations to average values in the study period, and these variations increased with increasing aridity towards the south of the study area. The negative serial correlations found in the seasonal and annual rainfall time series were mostly insignificant. The trend tests detected a significant decreasing trend in the spring rainfall series of Birjand station at the rate of 8.56 mm per season per decade and a significant increasing trend in the summer rainfall series of Torbateheydarieh station at the rate of 0.14 mm per season per decade, whereas the rest of the trends were insignificant. Furthermore, the 12‐month values of the standardized precipitation index decreased at all the stations except Zabol during the past four decades. During the study period, all of the stations experienced at least one extreme drought which mainly occurred in the winter season. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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M. Rezaeianzadeh A. Stein H. Tabari H. Abghari N. Jalalkamali E. Z. Hosseinipour V. P. Singh 《International Journal of Environmental Science and Technology》2013,10(6):1181-1192
Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R 2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m3 s?1 and 0.81, 2.297 m3 s?1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R 2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m3 s?1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting. 相似文献
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Rezaeian-Zadeh Mehdi Tabari Hossein Abghari Hirad 《Arabian Journal of Geosciences》2013,6(7):2529-2537
Arabian Journal of Geosciences - Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and... 相似文献
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Hossein Tabari P. Hosseinzadeh Talaee Hirad Abghari 《Meteorology and Atmospheric Physics》2012,116(3-4):147-154
Estimation of pan evaporation (E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions (r?=?0.97, RMSE?=?0.81?mm?day?1, MAE?=?0.63?mm?day?1 and PE?=?0.58?%). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process. 相似文献
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Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions 总被引：2，自引：0，他引：2
Mehdi Rezaeian-Zadeh Shahrookh Zand-Parsa Hirad Abghari Masih Zolghadr Vijay P. Singh 《Theoretical and Applied Climatology》2012,109(3-4):519-528
This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation. 相似文献
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Noori Hirad Shamimi Shirinabadi Reza Moosavi Ehsan Gholinejad Mehran 《Geotechnical and Geological Engineering》2022,40(4):2051-2062
Geotechnical and Geological Engineering - In this study, peak particle velocity (PPV) values for driving three piles with diameters of 40 cm, 50 cm, and 70 cm in a clayey... 相似文献
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