New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R²). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.     

Physiological Parameters of Silicon-Treated Maize Under Salt Stress Conditions

Silicon - The effects of silicon (Si) on Zea mays under salt stress conditions were investigated and the data was analyzed by cluster heat maps. The results indicated that the application of Si in...     

Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information

Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict droughts. Agro-metrological drought index addressing seasonality and autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. Markov chain is adopted to model drought for joint occurrence of different classes of drought severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain model. The proposed model, which benefits suitability of Markov chain models for modeling droughts, showed improvement results in prediction scores relative to classic Markov chain model not including SST-M information, additionally.     

The Effect of Temperature Adjustment on Reference Evapotranspiration and Reconnaissance Drought Index (RDI) in Iran

The reference evapotranspiration (ET₀) is necessary to calculate Reconnaissance Drought Index (RDI). To estimate ET₀, FAO56 Penman-Monteith method which needs reference stations data is commonly used. Most of the meteorological stations in Iran are classified as non-reference satations and The use of their data in ET₀ calculation can affect the RDI. The objective of the present study is to evaluate the effect of temperature adjustment based on the reference condition on ET₀ and RDI values in non-reference stations of Iran. For this purpose, the meteorological data, recorded during 1960–2014 in 27 non-reference stations located in arid and semi-arid regions, were used. First, the values of ET₀ were determined using observed values of temperature. Using these values, RDI were computed by Log-Normal and Gamma distributions at annual and 6-month scales. Then the values of minimum, maximum and dew point temperatures were adjusted on the basis of the reference condition. The values of ET₀ and consequently RDI were calculated using adjusted data. On the basis of obtained results, at annual and 6-month scales, using observed values of temperature instead of adjusted values in non-reference stations cause to overestimate the value of ET₀. Also, using observed data with no adjustment can change the drought class which was determined on the basis of RDI. According to these results, temperature adjustment based on reference condition can change the values of ET₀ and RDI which was calculated by using Log-Normal or Gamma distributions at annual and 6-month scales.     

Investigation of Drought Trend on the Basis of the Best Obtained Drought Index

Water Resources Management - The drought phenomenon is a primary natural hazard in Iran. The drought can be analyzed using different indices. Therefore, the use of suitable indices will have an...     

Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization

The Muskingum model is a popular method for flood routing in river engineering. This model has several parameters, which should be estimated. Most of the techniques have applied to estimate these parameters to reduce the distance between observed flow and estimated flows. In this paper, for the first time, the parameters of a novel form of the nonlinear Muskingum model are estimated by the Particle Swarm Optimization (PSO) algorithm. The new Muskingum model, which have four parameters, is applied for three benchmark examples and one real case in Iran. The sum of the squared (SSQ) or absolute (SAD) deviations between the observed and estimated outflows was considered as objective functions. The results showed that although the new Muskingum model became more complex but this model by using PSO technique can improve the fit to observed flow especially in multiple-peak hydrographs.