H. Mirhashemi
Abstract
Introduction: Potential evaporation is the result of the combined effects of several meteorological elements, including air temperature, relative humidity (or vapor pressure for saturation), wind speed, sunshine hours and air pressure. The amount of potential evaporation depends on how these variables ...
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Introduction: Potential evaporation is the result of the combined effects of several meteorological elements, including air temperature, relative humidity (or vapor pressure for saturation), wind speed, sunshine hours and air pressure. The amount of potential evaporation depends on how these variables interact in each climate region. Potential evaporation response of each of these variables depends on the importance that variable plays in the environment. For example, in windy places, the importance of wind speeds in the potential evaporation rate increases relative to places with calm air. By changing each of these meteorological elements, while the rest of the elements react to the given change, the overall effect of these changes and reactions is reflected in the amount of potential evaporation. It is therefore obvious that the potential evaporation response to meteorological variables due to spatial and time variations of these variables is of a complex nature. Materials and Methods: For this study, monthly data of air temperature, air pressure at sea level, wind speed, relative humidity and sunshine hours were used as independent variables and monthly data of evaporation pan at Tabriz Synoptic Station as response or dependent variable. In this study, firstly, the nonlinear and linear relationship between meteorological elements and potential evaporation were identified through Generalized Additive Model (GAM), MARSplines Model, and Generalized Linear Model (GLM), respectively. In the next step, by applying the simplex algorithm on the MARSplines model, the evaporation response gradient levels were determined individually for the meteorological variables. Also, to understand the process of pure evaporation response to each of these variables under different climatic conditions, first three weather conditions based on Tabriz Synoptic Station data were defined in three scenarios as S-1, S-2 and S-3. Then, by controlling and maintaining the meteorological variables under these three scenarios and combining the simplex algorithm with the MARSplines Model, the net evaporation reaction curves for the meteorological variables changes were evaluated. Results and Discussion: The computational results show that in all combinations, the computational error of the GAM model is less than the GLM model. Also considering the significant variables in each model, the combination of temperature, pressure, wind speed and sunshine are considered as the best subset of the effective variables in the distribution of potential evaporation in both models. On the one hand, relative humidity in these two linear and nonlinear models, in combination with other variables, does not show a significant relationship with potential evaporation. The results of the graphs of Splin smoothing components of the GAM model show that the overall effect of temperature on the evaporation is incremental. But the unit amount of this effect increases with increasing temperature. The individual evaporation reaction against air temperature is similar to its combined reaction. It is thus clear that other meteorological variables do not play a significant role in the influence of air temperature on the evaporation gradient. The overall and hybrid effect of air pressure variations on the amount of evaporation is singular and decreasing. Instead, the individual effect of this variable on evaporation is very intense, decreasing, and partly linear. Therefore, the major influence of air pressure on evaporation in the environment is due to the performance of other variables that interfere with the relationship between these two variables. The evaporation hybrid response to wind velocity was also incremental, although the single and nonlinear evaporation response to wind velocity was not significant, but its tendency was to increase its slope with respect to wind velocity changes. Sunny hours also have a net effect on the amount of evaporation. However, the slope of the solitary effect of this variable, like wind speed, is more than its combined effect. Based on the GLM model results, except for relative humidity, the other variables have a significant linear effect on the potential evaporation. Evaporation response to changes in meteorological variables under S-1, S-2 and S-3 scenarios, while accurately determining the interaction of these variables in plotting absolute evaporation, implicitly implying the synergistic role of these variables in determining absolute evaporation. The lowest distance between the absolute values of evaporation under these three scenarios is related to air temperature, which implies less influence of air temperature than the other variables. That is, the effect of each of the meteorological variables on the amount of evaporation depends to a large extent on the relationship of this variable to other meteorological variables, if such a matter is less weighted for temperature. Conclusion: The results of this study show that, except for air pressure, which has an increment-reducing effect on evaporation, other variables have only an incremental influence on evaporation and the intensity of this relationship has changed. This process has resulted in a nonlinear component in the relation of independent variables to evaporation. Since hybrid spline smoothing graphs determine evapotranspiration response to each of the predictor variables by eliminating the effect of other variables, therefore, consideration of the composition of these meteorological variables provides more accurate information on evaporation behavior against environmental changes. Through individually fitting evaporation against these meteorological elements, one cannot find how evaporation works against environmental changes. Comparing individual and combined evaporation responses to meteorological variables, while identifying the net effect of each of these variables, explains why evaporation responses within a given unit differ from changing meteorological variables over different times and locations.
Darush Yarahmadi; Hamid Mirhashemi
Abstract
Introduction: Forecasting and modeling of river flow is an essential step towards planning, designing and utilizing water resources management system which is subject to issues such as droughts and destructive floods in river basins. The river flow deficit and excess could result in financial and human ...
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Introduction: Forecasting and modeling of river flow is an essential step towards planning, designing and utilizing water resources management system which is subject to issues such as droughts and destructive floods in river basins. The river flow deficit and excess could result in financial and human losses. Such predictions of river flow not only provide the necessary warning signals about the flood risk, but also help to adjust the water outflows during low level of water flows which help to the water resource management. Due to the importance of river flows and its fluctuations in short and long term on different aspects of human lives, understanding its behavior and performance is crucial (necessary). Thus, with discovering its dynamic behavior, it is possible to predict its future performance. The aim of this study is to explore and simulation of Kashkan River’s performance using the statistical intelligent methods to provide models with lower uncertainty in order to improve the planes based on Kashkan’s River flows.
Materials and Methods: For this study, the series of daily discharge data from Poldokhtar- Kashkan station (located in the coastal river) over 1370-1393 were used as the primary input. Methods used in this study were based on memory uses the Hurst exponent of long memory time series. Runoff is the dynamics of the series. The current state of these series is dependent on its historical states. The delay time (lag time) of 1, 3, 5, 7, 10 and 15 days before the runoff were calculated. The amount of runoff was seen as a function of the time series. Considering the above-mentioned six time series as input signals, time series modeling using statistical methods K- nearest neighbor (K-NN), and artificial neural network, combined wavelet - K-NN and combining the wavelet nervous.
Results and Discussion: Kashkan’s Memory river flow system, using the Hurst exponent within 10 days and mid-4200 based on the amount of 0.6 was obtained (Figure 2). This amount indicates a non-linearly behavior and a dynamic learning system. In addition, it shows the presence of long memory in the river flow time series. Then, by allocating 80% of the data for training and the remaining 20 percent for testing the model and adopting ranges from 1 to 10 nearest neighbor and a range of 1,000 to 50,000 particles (for data on education) Model K-NN were prepared. Using the criteria to assess the efficiency and accuracy of a model in each performance of the mentioned domains, the best model with the 6 neighbors structure and 15,000, was obtained. In this model stimulated the runoff with the correlation of 0.90 and a 4.6 error was obtaied. On the other hand, artificial neural network architecture to simulate runoff with 6 input neurons in a hidden layer neurons and considering 3 to 20 and an output neurons leading to the 6-8-1 structure as the best model was fitted. This model has a correlation of 0.89 and the forecast error of 5.8 in the process of runoff simulation. Then using wavelet function, mortality, time-series signal runoff into 4 levels, including 8 under high frequency and low frequency signal was decomposed where high-frequency signals and low-frequency signal of 4 level were considered as the original signal for the input surface runoff. In this regard, the hybrid model K-NN-WT with runoff time series prediction error of 2.7 percent and the hybrid model ANN-WT with the correlation of 0.99 the estimation error of 1.2 were simulated.
Conclusion: Running 4 Artificial Neural Network (ANN), K-nearest neighbor (K-NN) and combining the wavelet analysis of the two models (ANN-WT and K-NN-WT) to predict the time series of runoff river showed that due to the existence of multiple time frequencies in the time series of the river signals, its decomposition it using wavelet analysis results in extraction of hidden information that are not available through the original signal. This information is the daily, monthly, quarterly and annual fluctuations. The hybrid models performance indicated higher accuracy and improved outcomes relative to individual models. In fact, the analysis of the original runoff signal by wavelet analysis in the process of simulation results in an appropriate weighting given to long-term and short term dynamic of runoff which led to significant lower error in modeling
Hamid Mirhashemi
Abstract
In this study, in order to analyze the changes trend of the crop water requirement as the aspect of climate change in East Azerbaijan, we have adopted FAO Penman- Monteith reference crop evapotranspiration and 15 variables associated with it. By using nonparametric methods of Spearman, Man- Kendall ...
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In this study, in order to analyze the changes trend of the crop water requirement as the aspect of climate change in East Azerbaijan, we have adopted FAO Penman- Monteith reference crop evapotranspiration and 15 variables associated with it. By using nonparametric methods of Spearman, Man- Kendall and Sen’s Estimator in twelve monthly and annual series for each station, the trends have been explored. The results of these two test statistics indicate that the significance of the respective phenomenon is being studied in two separate clusters. The first cluster consists of Tabriz and Maragheh stations the entire time series, the time series exceptions of April in Tabriz, the rest of the series with the significant increase in confidence levels are 95% and 99%. The second cluster consists of Ahar, Sarab, Mianeh and Julfa stations that mentioned phenomenon of jointly in the time series in March with a significant increase in the same levels of trust. Also the test results from other variables show that despite a series of meteorological variables, no significant trends were, but due to the influence of the reference crop evapotranspiration and the significant impact it has on the superposition. Finally, we should acknowledge that the study of climate change in the region, is not just dependent on the climate variables and could be caused by other variables too.
