فهرست مطالب sajjad abdollahi asadabadi

Meteorological drought is defined as a lack of rainfall over long periods, which reduces soil moisture and river flow. One of the critical drought assessment tools is drought indices (Tsakiris & Vangelis, 2005). So far, many drought indicators have been developed by researchers, for example, the RDI )Reconnaissance Drought Index) (Tsakiris & Vangelis, 2005). The difference between this index and other drought indices is that it is estimated based on two variables of precipitation and potential evapotranspiration. For this reason, it is more accurate than indices that are calculated only based on precipitation. So far, some studies have been used the RDI for drought assessment. Zarei et al. (2016) studied the spatial pattern of drought using the RDI index in southern Iran. The results showed that the area with dry conditions had an increasing trend. Asadi Zarch (2017) investigated the drought trend in Yazd province between 1966-2009 using the RDI index. The results showed that drought occurrence in Yazd increased during the studied period. Because, unlike other natural disasters, it is difficult to accurately determine the onset and the end of the drought period (Moried et al., 2005). Accordingly, it is difficult to diagnose and evaluate the drought phenomenon. Therefore, monitoring and predicting drought is very important in water resources management. The use of wavelets is a new and very effective way of analyzing signals and time series. Application of Wavelet in Wavelet- Artificial Neural Network (WANN) models as a function for training has recently been used as an alternative for Artificial Neural Network (ANN) models. In recent years, the combination of wavelet theory and artificial neural networks has led to the development of wavelet neural networks (Thuillard, 2000). Zhang et al. (2017) applied the ARIMA, ANN, WANN, and Support Vector Regression (SVR) models to predict droughts in China's northern Haihe River basin using the SPI index. The results showed that the WANN model performed better than other considered models for predicting the SPI values at 6 and 12 months time scales. This study aimed to predict the meteorological droughts in the Kuhrang region using ANN and WANN models. To this end, the efficiency of ANN and WANN models in predicting precipitation and potential evapotranspiration will be evaluated. Then, the Resilience Drought Index (RDI) will be calculated based on the predicted values by ANN and WANN to describe and prediction of Kuhrang wetness conditions.

Precipitation is one of the most important hydrological events and its prediction can be used as a practical tool for optimum utilization and management of water resources. In the present study, artificial neural network (ANN) and wavelet-artificial neural network (WANN) were used for monthly precipitation perdiction at selected synoptic stations in Ardabil province, including Ardabil, Khalkhal, Meshginshahr and Parsabad during the 225 months for the years 1996-2016. For the short-term forecast of monthly precipitation (one month later), different scenarios were defined based on precipitation delays. Results indicated that the WANN model with the highest determination coefficient (R2) and minimum root mean square error (RMSE) was acceptable for all stations. The values of R2 and RMSE for Ardabil station were equal to 0.88 and 7.13 mm, for Khalkhal station were equal to 0.91 and 6.36 mm, for Meshginshahr station were equal to 0.92 and 6.97 mm and for Parsabad station were equal to 0.86 and 8.51 mm, respectively. In all stations, utilization of  the superior model (WANN model) with the combination scenarios i.e. rainfall delays, the minimum and maximum temperature improved the results of the model, but on the other hand, increased the computational cost of the model. Also, in all stations, the addition of relative humidity and wind speed as input variables somewhat reduced the performance of the model. The general results of this study showed that the WANN model with appropriate rainfall delays on a monthly scale can be utilized to predict monthly precipitation of selected stations in Ardabil province, including Ardabil, Khalkhal, Meshginshahr and Parsabad with acceptable accuracy.

Recently, the use of copula functions as a practical and flexible tool for constructing joint probability distribution of multivariate hydrologic phenomena, such as flood, has attracted great attention of hydrologists. The main objective of this study is to extract and analysis of the joint and conditional return periods of some dependent characteristics of runoff hydrograph, including runoff volume, peak discharge, base time and time to peak discharge. These characteristics extracted from 60 flood events recorded in Valikbon hydrometric station, located in outlet of Kasiliyan reference watershed during 1975-2007. Three copulas, including Clyton, Ali-Mikhail-Haq and Frank were considered for constructing the joint distribution of the paired hydrograph characteristics. The Frank copula was selected as the best copula for constructing the joint distribution from paired characteristics of runoff volume and peak discharge of hydrograph and also runoff volume and base time of hydrograph. While the Clyton copula was recognized as the best copula for other two dependent characteristics, namely time of peak discharge and base time of hydrograph. After constructing joint distributions, several valuable information such as joint probability, joint and conditional return periods were calculated and plotted.

Recently, copula functions have attracted great attention of hydrologists as a practical tool for multivariate frequency analysis of climatological phenomena. In this study, we focus on the joint frequency analysis of two dependent characteristics of rainfall, including depth (mm) and duration (hr) using copulas for 522 events recorded in Sangdeh rain gauge station located in Kasiliyan watershed. To join the marginal distributions and constructing the joint distribution, seven copulas including Clyton, Ali-Mikhail-Haq, Farlie-Gumbel-Morgenstern, Frank, Galambos, Gumbel-Hougaard and Placket were used and evaluated. By comparing the mentioned parametric copulas with an empirical copula, we found that the Placket is the the best fitted copula on the considered variables. Finally, the joint probabilities, joint return periods and conditional joint return periods were calculated and plotted. For example, joint probability values for two events with duration of 12 and 24 (hr) given rainfall depth that exceeds 15 (mm) were calculated as 0.2663 and 0.7693, respectively. Also, conditional return period was calculated equal to 9.19 year for an event with depth of 30 (mm), given rainfall duration that exceeds 24 (hr) and equal to 14.94 year for an event with duration of 24 (hr), given rainfall depth that exceeds 30 (mm).

Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. Initial phase, phase differences of subseries obtained from wavelet analysis, and calibration coefficients were calculated. Then structural series were reconstructed and average of structural components calculated. The river discharges were predicted for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg–Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons. The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods.