S. Vazirpour; H. Ebrahimian; H. Rafiee; F. Mirzaei Asl Shirkohi
Abstract
Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and ...
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Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and subsurface. Due to temporal and spatial variability, Many measurements are needed to explain the average soil infiltration characteristics under field conditions. Stochastic characteristics of the different natural phenomena led to the application of random variables and time series in predicting the performance of these phenomena. Time-series analysis is a simple and efficient method for prediction, which is widely used in various sciences. However, a few researches have investigated the time-series modeling to predict soil infiltration characteristics. In this study, capability of time series in estimating infiltration rate for different soil textures was evaluated.
Materials and methods: For this purpose, the 60 and 120 minutes data of double ring infiltrometer test in Lali plain, Khuzestan, Iran, with its proposed time intervals (0, 1, 3, 5, 10, 15, 20, 30, 45, 60, 80, 100, 120, 150, 180, 210, 240 minutes) were used to predict cumulative infiltration until the end of the experiment time for heavy (clay), medium (loam) and light (sand) soil textures. Moreover, used parameters of Kostiakov-Lewis equation recommended by NRCS, 24 hours cumulative infiltration curves were applied in time-series modeling for six different soil textures (clay, clay loam, silty, silty loam, sandy loam and sand). Different time-series models including Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), autoregressive integrated moving average (ARIMA), ARMA model with eXogenous variables (ARMAX) and AR model with eXogenous variables (ARX) were evaluated in predicting cumulative infiltration. Autocorrelation and partial autocorrelation charts for each variable time-series models were investigated. The evaluation indices were the coefficient of determination (R2), root of mean square error (RMSE) and standard error (SE).
Results and discussion: The results showed that the AR(p), ARX(p,x) and ARMAX(p,q,x) time series models with various degrees 1, 2, 3 successfully predicted infiltration rates for duration of the test in different soils. Significant correlation between actual and estimated values of cumulative infiltration was almost obtained. The values of SE varied between 2 and 5 percent for three soil textures in Lali plain. Reducing input data from two hours to one hour did not have major impact on infiltration prediction. The results of 24 hours cumulative infiltration also indicated standard error of estimated infiltration varied between 2 and 21% for six different soil textures. Similarly, there was a very good correlation between the actual and predicted values of 24 hours cumulative infiltration. The prediction error increased with increasing prediction time (4 hours vs. 24 hours). The time-series models had accurate performances to predict cumulative infiltration until 12 hours, therefore, they would be as a useful tool to predict soil infiltration characteristics for irrigation purposes. The RMSE values for predicting 24 hours cumulative infiltration were 0.5, 2.6, 4.1, 4.9, 7.5 and 11.8 cm for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. The SE values also were 2.6, 11.7, 13.9, 14.9, 17.2 and 21.6 % for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. Time-series modeling showed better performance in heavy and moderate soils than in light soils. However, the performance of the time-series modeling for predicting infiltration for the double ring test with four hours experiment time was better for light soil textures as compared to heavy and moderate soil textures. Therefore, more studies are needed to investigate the capability of time series modeling to predict infiltration with more experiment data, particularly for heavy and moderate soil textures.
Conclusion: The results indicated that the experiment time of the double ring test could be reduced from four to one hour by using time series models in various soil textures and consequently the cost of soil infiltration measurements would be decreased. Using initial 120 min infiltration data, the time-series models could successfully predict the 12 hours cumulative infiltration. Comparison between the results of times-series models and actual data indicated the application of time-series models in predicting soil infiltration characteristics was efficient.
S. Shahabi; M.J. Khanjani
Abstract
In this paper a method to perform Estimation of Flood Risk (EFR) is presented when the assumption of stationary is not important (or not valid). A wavelet transform model is developed to EFR. A full series is applied to EFR using energy function of wavelet. The data were decomposed into some details ...
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In this paper a method to perform Estimation of Flood Risk (EFR) is presented when the assumption of stationary is not important (or not valid). A wavelet transform model is developed to EFR. A full series is applied to EFR using energy function of wavelet. The data were decomposed into some details and an approximation through different wavelet functions and decomposition levels. The approximation series was employed to EFR. This was performed using daily maximum discharge data from of the Polroud River in the north of Iran. In this way, the data from 1956 to 2007 were evaluated by wavelet analysis. The study shows that wavelet full series model results (density function) are too small compared with the results of combined method and they are both lesser than traditional methods (AM and PD). In other hand, the results of energy function method are closed to the combined method when they are compared with the full series data results. These wavelet models were assessed with the AM and PD methods. The concrete result of this paper is that, the watershed hydrologic conditions and nature of the data are very important parameters to improve FFA and to select the best method of analysis.
B. Shabani; M. Mousavi Baygi; Mehdi Jabbari Nooghabi; B. Ghareman
Abstract
Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and ...
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Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and minimum monthly temperatures of Mashhad plain, the long- term data of Mashhad and Golmakan were used for the joint period from 1987 to 2008. The SARIMA(0,0,0)(0,1,1)12 model for maximum monthly temperature and the SARIMA(0,0,0)(2,1,1)12 model for minimum monthly temperature were determined as the final models using time series. High correlation coefficientsindicate acceptable adaptation of modeling and actual values in the calibration and validation of models. Finlay, predictions were performed based on models fitted for the next 10 years (2009-2018). Comparison of results for future period (2009-2018) and the base period (1987-2008) represents maximum temperature mean 1 °c increase and minimum temperature mean 1.4 °cincrease.
J. Behmanesh; M. Montaseri
Abstract
Potential evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. One of methods for prediction of evapotranspiration is to use the time series models. In this research, application of different time series models, ...
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Potential evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. One of methods for prediction of evapotranspiration is to use the time series models. In this research, application of different time series models, such as AR and ARMA, in order to predicting monthly potential evapotranspiration in Urmia synoptic station were evaluated. In this process, monthly potential evapotranspiration since 1971 to 2010 was determined and the first 35 years and last 5 years were used for model calibration and validation respectively. After selecting the best model, the potential evapotranspiration were predicted for the next 5 years. The results showed that AR(11) time series model had the best results in comparing the other models and the trend of AR(11) time series model had least error. The values of R2 and RMSE in AR(11) model were 0.96 and 1.85 mm/month, respectively.
