A. Firoozi; seyed majid mirlatifi; Hamed Ebrahimian
Abstract
Introduction: Agriculture consumes a large portion of groundwater resources. In order to understand the status of groundwater resources in a basin and to optimize its management, it is necessary to carry out an accurate examination of the fluctuations in the groundwater levels. Recharging groundwater ...
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Introduction: Agriculture consumes a large portion of groundwater resources. In order to understand the status of groundwater resources in a basin and to optimize its management, it is necessary to carry out an accurate examination of the fluctuations in the groundwater levels. Recharging groundwater aquifers is one of the main strategies for water resources management which its accurate estimation plays a crucial role in the proper management of ground water resources. That portion of the excess irrigation water which becomes in the form of deep percolation should not be considered as wasted water, if its quality is not adversely reduced and it enters and recharges groundwater aquifers. The question is whether deep percolations resulting from irrigating farms with low application efficiencies and poor irrigation management in the Urmia basin would finally recharge ground water aquifers or not. In order to provide a solution to the aforementioned question, after calibrating HYDRUS-1D model, it was used to estimate the fluctuations of the levels of the water tables as a results of irrigations or rainfalls in a number of wheat, barley and sugar beet fields located in Miandoab and Mahabad regions where all agricultural practices were managed and carried out by the local farmers.
Materials and Methods: In order to ascertain the effects of irrigation on the groundwater recharge, the required field data was collected from nine agricultural fields including one wheat farm, three barley farms, and three sugar beet farms, all located in the Miandoab region and two wheat fields located in the Mahabad region. All the water balance parameters for each one of the fields were measured in the studied fields, including the depth of irrigation at each irrigation event by using WSC flumes. The Surface runoff from the studied farms was considered as negligible, since all the fields were irrigated using closed end borders. The evapotranspiration of wheat, barley and sugar beet were calculated in the regions using the CROPWAT8.0 model.
The soil texture of each of the study fields were determined by hydrometric method in the laboratory and then soil hydraulic parameters were estimated by ROSETTA model. The soil moisture of all the fields during the growing season were measured using a PR2 moisture meter instrument measuring soil moisture at various depths up to 105 cm below the soil surface. The amount of deep percolation occurring during the growing season was simulated by the HYDRUS-1D model. The soil water content measured by PR2 (Delta-T Device) probe were used for HYDRUS-1D model calibration and validation using the inverse solution method. Because of the occurrence of rainfall, irrigation and evapotranspiration, the atmospheric boundary condition was selected as the upper boundary condition and free drainage was considered as the lower boundary condition in order to estimate the groundwater recharge, assuming that water passes through and below the root zone. In areas with shallow ground water depth, constant flow with zero flux was chosen as the lower boundary condition in order to determine the fluctuations of the ground water level. Since the groundwater level in this case study was shallow, zero flux was considered as the lower boundary condition. The soil moisture content before irrigation was selected as the modelling initial condition.
Result and Discussion: The HYDRUS-1D model was calibrated by comparing the model estimated soil moisture contents with the corresponding measured values which indicated the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.6 to 0.85 and 0.17 to 0.033 (), respectively. Another set of measured soil moisture data which was collected by using PR2 instrument and was not used for calibrating the model, was applied to verify the model simulation of the soil moisture content. Comparing the measured and simulated soil moisture contents at this verification stage resulted in coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.62 to 0.88 and 0.002 to 0.023 (), respectively. There was no significant difference between the predicted and measured soil moisture data in all the fields (P-value> 0.05). The minimum and the maximum coefficient of determinations in the validation stage were obtained in the T5 field with a silty loam soil and in the H3 field having a sandy loam soil. The accuracy of the model performance after it was calibrated and verified using the collected field data, was appropriate for estimating the soil water content during the growing season. The model was used to simulate the soil water contents from the soil surface to the depth of the water table during the growing season to evaluate the degree of aquifer recharge if any happened. The soil moisture front advanced to a depth of 0.7 m below the soil surface in the M1 field and to 4.7 m in the T1field. The amount of groundwater recharge varied from field to field depending on each field’s soil type, cultivation and irrigation management including the depth and the time of the irrigations. The amount of groundwater recharge increased by decreasing crop evapotranspiration. The percentage of ground water recharge in N1, M1 and M2 fields due to limited availability of water resources which resulted in deficit irrigation was very low. The irrigation water requirements estimated by the CROPWAT model for the aforementioned fields were more than the depths of the irrigation water applied by the farmers. The CROPWAT model estimated the irrigation water requirements during the growing season for wheat, barley and sugar beet in the Miandoab region to be 308, 273 and 736 mm, respectively. However, the depths of irrigation applied to such farms ranged from 306 to 500 mm.
Conclusion: This research was conducted to ascertain the effects of local farmer’s irrigation management practices considered as poor management in some areas with plenty of water resources available and rainfall on the amount of the groundwater recharge occurring in the regions studied located in the Lake Urmia basin. The simulated groundwater recharge by the HYDRUS-1D model indicated that the amount of recharge varied from field to field depending on soil type, cultivation and irrigation management practices. In all the fields, the highest amount of groundwater recharge occurred when the crop evapotranspiration was low and therefore, enhancing deep percolation to take place. The highest percentage of groundwater recharge was 28% of the sum of the irrigation and rainfall depths which occurred in the barley field (H3).
Farzin Parchami-Araghi; seyed majid mirlatifi; Shoja Ghorbani Dashtaki; Majid Vazifehdoust; Adnan Sadeghi-Lari
Abstract
Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based ...
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Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based framework was developed to disaggregate daily weather data needed for estimation of subdaily reference evapotranspiration, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. In this paper, the results of performance comparison of the utilized disaggregation approaches are presented.
Materials and Methods: In developed framework, missing daily weather data are filled by implementation of a search-optimization algorithm. Meanwhile, disaggregation models can be calibrated using Unified Particle Swarm Optimization (UPSO) algorithm. Daily and subdaily solar radiation is estimated, using a general physically based model proposed by Yang et al. (YNG model). Long-term daily and three-hourly weather data obtained from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were used to evaluate the performance of the developed framework. In order to evaluate the accuracy of the different disaggregation models, the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (r), and model efficiency coefficient (EF) statistics were calculated. Different contributions to the overall mean square error was decomposed, using a regression-based method.
Results and Discussion: The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a EF of 0.9775 to 0.9924. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations of the air temperature. HUM III model (based on cosinusoidal disaggregation of daily actual vapour pressure) had the best performance to disaggregate daily dew point, actual vapour pressure, and relative humidity with an EF of 0.7266 to 0.8896. In addition, subdaily wind speeds were predicted with an EF of 0.3357 to 0.6300. The results showed high agreement between daily and sum-of-subdaily solar radiation (with an EF of 0.9801 to 0.9729). The use of the WAVE II and HUM II (based on linear disaggregation of relative humidity) models can be recommended for the regions with no subdaily weather data needed for calibration of the weather data disaggregation models. The results indicate the need for calibration of Green and Kozek model for disaggregation of the daily wind speed at different regions.
M. Gheysari; M.M. Majidi; seyed majid mirlatifi; M.J. Zareian; S. Amiri; S.M. Banifatemeh
Abstract
The response of root to water stress is one of the most important parameters for researchers. Study of growth and distribution of root under different irrigation managements helpsresearchersto a better understanding of soil water content, and the availability of water and nutrition in water stress condition. ...
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The response of root to water stress is one of the most important parameters for researchers. Study of growth and distribution of root under different irrigation managements helpsresearchersto a better understanding of soil water content, and the availability of water and nutrition in water stress condition. To investigate the effects of four levels of irrigation under two different deficit irrigation managements on the root length of maize, a study was conducted in 2009. Irrigation managements included fixed irrigation interval-variable irrigation depth (M1) and variable irrigation interval-fixed irrigation depth (M2). Maize plants were planted in 120 large 110-liter containers in a strip-plot design in a randomized complete block with three replications. Root data sampling was done after root washing in five growth stages. The results showed that the effect of irrigation levels on root length was significant (P
Farzin Parchami-Araghi; seyed majid mirlatifi; Shoja Ghorbani Dashtaki; Adnan Sadeghi-Lari
Abstract
Introduction: Subdaily estimates of reference evapotranspiration (ETo) are needed in many applications such as dynamic agro-hydrological modeling. The ASCE and FAO56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) has received favorable acceptance and application over much of the world, ...
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Introduction: Subdaily estimates of reference evapotranspiration (ETo) are needed in many applications such as dynamic agro-hydrological modeling. The ASCE and FAO56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) has received favorable acceptance and application over much of the world, including the United States, for establishing a reference evapotranspiration (ETo) index as a function of weather parameters. In the past several years various studies have evaluated ASCE-PM and FAO56-PM models for calculating the commonest hourly or 15-min ETo either by comparing them with lysimetric measurements or by comparison with one another (2, 3, 5, 9, 10, 11, 16, 17, 19). In this study, sub-daily ET o estimates made by the ASCE-PM and FAO56-PM models at different timescales (1-360 min) were compared through conduction of a computational experiment, using a daily to sub-daily disaggregation framework developed by Parchami-Araghi et al. (14).
Materials and Methods: Daily and sub-daily weather data at different timescales (1-360 min) were generated via a daily-to-sub-daily weather data disaggregation framework developed by Parchami-Araghi et al. (14), using long-term (59 years) daily weather data obtained from Abadan synoptic weather station. Daily/sub-daily net long wave radiation (Rnl) was estimated through 6 different approaches, including using two different criteria for identifying the daytime/nighttime periods : 1) the standard criteria implemented in both ASCE-PM and FAO56-PM models and 2) criterion of actual time of sunset and sunrise in combination with 1) estimation of clear-sky radiation (Rso) based on the standard approach implemented in both ASCE-PM and FAO56-PM models (1st and 2nd Rnl estimation approaches, respectively), 2) integral of the Rso estimates derived via a physically based solar radiation model developed by Yang et al. (25), YNG model, for one-second time-steps (3rd and 4th Rnl estimation approaches, respectively), and 3) integral of the calculated Rnl based on Rso estimates derived via YNG model for one-second time-steps (5th and 6th Rnl estimation approaches, respectively). The capability of the two models for retrieving the daily ETo was evaluated, using root mean square error RMSE (mm), the mean error ME (mm), the mean absolute error ME (mm), Pearson correlation coefficient r (-), and Nash–Sutcliffe model efficiency coefficient EF (-). Different contributions to the overall error were decomposed using a regression-based method (7).
Results and Discussion: Results showed that during the summer days, 24h sum of sub-daily radiation and aerodynamic components of ETo and the estimated ETo derived from both models were in a better agreement with the respective daily values. The reason for this result can be attributed to the nighttime value of cloudiness function (f) and the longer nighttime during the cold seasons. Because the nighttime values for f are equal the f value at the end of the previous daylight period until the next daylight period. The difference between sub-daily ETo derived from the ASCE-PM and FAO56-PM models during the day and night was highly dependent on the wind speed. In case of both models, daily aerodynamic component of ETo (ETod,aero) were reproduced more efficiently, compared to radiation component (ETod,rad). Except in the case of 6th Rnl estimation approach, FAO56-PM model (with a mean model efficiency (MEF) of 0.9934 to 0.9972) had better performance in reproducing the daily values of ETo (ETod), compared to ASCE-PM model (with a MEF of 0.9910 to 0.9970). The agreement between 24h sum and daily values of aerodynamic component had a lower sensitivity to the adopted time-scale, compared to the radiation component. Compared to the FAO56-PM model the performance of the ASCE-PM model in reproducing the ETod,rad, ETod,aero and ETod had higher sensitivity to the approach utilized for calculation of Rnl and hence, to the uncertainty of net radiation. Results showed that a smaller time step does not necessarily leads to an improvement in agreement between 24h sum of subdaily and daily values of ETo. Deficiency of the standard daytime/nighttime identification criteria resulted in a higher daily averaged daytime (1.3831 to 1.6753 h) used in cloudiness function calculations, compared to the respective value used in calculations of the radiation and aerodynamic components. In order to estimate the sub-daily ETo under climatic condition of the studied region, the use of ASCE-PM model based on the 6th Rnl estimation approach, (ASCE-PM)6, with a MEF of 0.9970 is preferred, compared to other studied alternatives. Another advantage of the (ASCE-PM)6 and (FAO56-PM)6 models is their computational efficiency in case of their implementation in hydrological models.
F. Parchami Araghi; seyed majid mirlatifi; Sh. Ghorbani Dashtaki; M. Vazifehdoust; A. Sadeghi Lari
Abstract
Introduction: Subdaily estimates of reference evapotranspiration (ET o) are needed in many applications such as dynamic agro-hydrological modeling. However, in many regions, the lack of subdaily weather data availability has hampered the efforts to quantify the subdaily ET o. In the first presented paper, ...
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Introduction: Subdaily estimates of reference evapotranspiration (ET o) are needed in many applications such as dynamic agro-hydrological modeling. However, in many regions, the lack of subdaily weather data availability has hampered the efforts to quantify the subdaily ET o. In the first presented paper, a physically based framework was developed to desegregate daily weather data needed for estimation of subdaily reference ET o, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. The main purpose of this study was to estimate the subdaily ETo using disaggregated daily data derived from developed disaggregation framework in the first presented paper.
Materials and Methods: Subdaily ET o estimates were made, using ASCE and FAO-56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) and subdaily weather data derived from the developed daily-to-subdaily weather data disaggregation framework. To this end, long-term daily weather data got from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were collected. Sensitivity analysis of Penman–Monteith model to the different meteorological variables (including, daily air temperature, wind speed at 2 m height, actual vapor pressure, and solar radiation) was carried out, using partial derivatives of Penman–Monteith equation. The capability of the two models for retrieving the daily ETo was evaluated, using root mean square error RMSE (mm), the mean error ME (mm), the mean absolute error ME (mm), Pearson correlation coefficient r (-), and Nash–Sutcliffe model efficiency coefficient EF (-). Different contributions to the overall error were decomposed using a regression-based method.
Results and Discussion: The results of the sensitivity analysis showed that the daily air temperature and the actual vapor pressure are the most significant meteorological variables, which affect the ETo estimates. In contrast, low sensitivity coefficients got for wind speed and the solar radiation. The similar patterns of ETo sensitivity coefficient to the air temperature ( ) and the air temperature (TA) showed that the extent of the seasonal variation of was mainly determined by the TA. Results showed a good agreement between daily and 24h sum of subdaily ETo derived from ASCE-PM (with an EF of 0.990 to 0.994) and FAO56-PM (with an EF of 0.992 to 0.995) models. The results showed a good generalization capability of the disaggregation models to estimate the subdaily ETo for the validation data set (Ahvaz). The 24h sum of subdaily ETo derived from both models underestimated and overestimated the daily ETo in calibration (Abadan) and validation (Ahvaz) data sets, respectively. In case of both models, the daily values of aerodynamic component of ETo were reproduced more efficiently, compared to radiation part. In case of the FAO56-PM model, the goodness of agreement between 24h sum of subdaily and daily values of aerodynamic part of the ETo showed a low sensitivity to variation of the time scale of weather data. With the increase of the time scale of the subdaily weather data, the ability of both models in retrieving the radiation component of the daily ETo was improved. Generally, there was no apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data. Results showed that adoption of a smaller time step does not always leads to an improvement in the agreement between 24h sum of subdaily and daily values of ETo. For most of the studied subdaily time scales (1 to 360 min), the FAO56-PM model had better performance in retrieving the daily ETo, compared to the ASCE-PM model.
Conclusion: The results of this study showed that the developed disagregation framework was able to estimate the subdaily ET o. In this study, the promising results got in retrieving the daily ETo can be attributed mainly to the high sensitivity of ETo to the air temperature and actual vapor pressure (which were desegregated with a reasonable accuracy) and low sensitivity to the wind speed (which were desegregated with a low accuracy) and the solar radiation (which were disaggregated with a reasonable accuracy). The main reason for the absence of an apparent relationship apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data can be attributed to adopted nighttime and daytime criteria in both models which is highly affected by time-scale of weather data and the estimated net long wave radiation.
M. Panahi; S.M. Mirlatifi; F. Abbasi
Abstract
Abstract
This study addresses two dimensional infiltration from irrigated furrows. The basic approach is to develop a two-dimensional infiltration as a combination of the corresponding one-dimensional vertical and an edge effect. The edge effect is the difference between the cumulative infiltration ...
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Abstract
This study addresses two dimensional infiltration from irrigated furrows. The basic approach is to develop a two-dimensional infiltration as a combination of the corresponding one-dimensional vertical and an edge effect. The edge effect is the difference between the cumulative infiltration per unit of adjusted wetting perimeter and the corresponding one-dimensional infiltration. This approach was evaluated using field measured furrow experiments and double ring infiltration tests. In this study, two series of experiments was conducted in 2010 on a clay loam soil. The first series of the tests included five experiments with inflow rate (0.3-0.8 ls-1) on free draining furrows having 110 meters in length, 75 cm wide and general slope of 0.008 m m-1. The second series of the experiments were carried out using double ring. A general conclusion was that the edge effect was linearly related to time. Using minimizing root mean square error (RMSE) the two empirical coefficients of the model including γ and W*/W were determined. The values of 0.62 and 1.15 were determined for the two empirical parameters in the clay loam soil studied. The results showed that the RMSE and the absolute error (AE) were 0.0031 and5.9 %, respectively. Model sensitivity analysis showed that the lowest sensitivity was to initial water content and the highest sensitivity was to saturation water content. The approach leads to an infiltration function for irrigation furrows without the need to perform a fully two-dimensional simulation.
Keywords: Furrow irrigation, Two-dimensional infiltration models, Warrick model, Edge effect
K. Ahmadzadeh Gharah Gwiz; S.M. Mirlatifi; K. Mohammadi
Abstract
چکیده
تبخیر-تعرق یکی از اجزای اصلی چرخه هیدرولوژی و تخمین نیاز آبیاری است. در سال های اخیر استفاده از سیستم های هوشمند برای برآورد پدیده های هیدرولوژی افزایش چشمگیری ...
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چکیده
تبخیر-تعرق یکی از اجزای اصلی چرخه هیدرولوژی و تخمین نیاز آبیاری است. در سال های اخیر استفاده از سیستم های هوشمند برای برآورد پدیده های هیدرولوژی افزایش چشمگیری داشته است. این پژوهش با هدف امکان تخمین تبخیر-تعرق مرجع (ETo) روزانه با استفاده از سیستم های هوش مصنوعی و مقایسه این سیستم ها با هم، به انجام رسید. بدین منظور پتانسیل سیستم استنتاج تطبیقی عصبی-فازی (ANFIS) و شبکه عصبی مصنوعی (ANN) در برآورد تبخیر-تعرق مرجع روزانه مورد بررسی قرار گرفت. از داده های روزانه هواشناسی سه ایستگاه سینوپتیک اصفهان، کرمان و یزد، شامل ساعات آفتابی، دمای هوا، رطوبت نسبی و سرعت باد به عنوان ورودی، و تبخیر-تعرق مرجع روزانه محاسبه شده با روش استاندارد فائو پنمن-مانتیث به عنوان خروجی روش های ANN و ANFIS استفاده شد. ایستگاه های مورد مطالعه بر اساس روش پهنه بندی اقلیمی دین پژوه در اقلیم بسیار خشک دسته بندی شدند. برآوردهای ETo از روش های ANN و ANFIS با مدل های تجربی ماکینک، پرستلی-تیلور، هارگریوز-سامانی، فائو بلانی-کریدل و ریچی مقایسه شد. کارایی روشهای مورد مقایسه، با استفاده از آمارههای ریشه میانگین مجذور خطا (RMSE)، خطای انحراف میانگین (MBE) و ضریب تعیین (R2)، مورد ارزیابی قرار گرفت. روش های ANN و ANFIS توانستند با موفقیت تبخیر-تعرق مرجع روزانه را برآورد کنند. مدل ANFIS85 تنها با سه پارامتر ورودی شامل تشعشع خورشیدی، دمای حداکثر هوا و سرعت باد نسبت به تمامی روشهای تجربی مورد استفاده، از دقت بالاتری برخوردار است. روش فائو بلانی-کریدل نسبت به دیگر روشهای تجربی دارای دقت بالاتری بود.
واژه های کلیدی: ایران، بسیار خشک، تبخیر-تعرق مرجع، سیستم استنتاج تطبیقی عصبی-فازی، مدل های تجربی
F. Parchami-Araghi; S.M. Mirlatifi; Sh. Ghorbani Dashtaki; M.H. Mahdian
Abstract
Abstract
Infiltration process is one of the most important components of the hydrological cycle. On the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. In this study, the possibility of predicting cumulative infiltration in specific time intervals, ...
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Abstract
Infiltration process is one of the most important components of the hydrological cycle. On the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. In this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and Artificial Neural Networks (ANNs) was investigated. For this purpose, 210 double ring infiltration data were collected from different regions of Iran. Basic soil properties of the two upper pedogenic layers including initial water content, bulk density, particle-size distributions, organic carbon, gravel content (>2mm size), CaCO3 percent and soil water contents at field capacity and permanent wilting point were determined on each soil sample. The feedforward multilayer perceptron was used for predicting the cumulative infiltration at times 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 minutes after the start of the infiltration test and the time of basic infiltration rate. The developed ANNs were categorized into two groups; type 1 and type 2 ANNs. For developing type 1 ANNs, the basic soil properties of the first upper soil horizon were used as inputs, hierarchically. In developing the type 2 ANNs, the available soil properties of the two upper soil horizons were used as inputs, using principal component analysis technique. Results of Reliability test for developed ANNs indicated that type 1 ANNs with a RMSE of 1.136 to 9.312 cm had the best performance in estimating the cumulative infiltration. Also, type 1 ANNs with the mean RMSD of 6.307 cm had the best performance in estimating the cumulative infiltration curve.
Keywords: Artificial Neural Networks, Cumulative Infiltration, Infiltration Process, Multilayer Perceptron
A. Seifi; S.M. Mirlatifi; H. Riahi
Abstract
Abstract
Reference evapotranspiration (ETo) is an essential parameter required for proper management of agricultural crop irrigation. ETo is influenced by many different hydrological variables and as a result is a very complex procces. ETo is usually estimated by empirical or process-orinented models ...
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Abstract
Reference evapotranspiration (ETo) is an essential parameter required for proper management of agricultural crop irrigation. ETo is influenced by many different hydrological variables and as a result is a very complex procces. ETo is usually estimated by empirical or process-orinented models (mathematical relationships) from historical weather data. The need for accurate estimates of ETo and the complexity of developing models to describe such complex process magnifies the need for developing new data mining methods. In this paper, the possibility of using a combined method of multiple linear regressions with principal componenets analysis (MLR-PCA) for estimating reference evapotranaspiration was investigated. In this analysis, measured daily meteorological data of Kerman synoptic weather station recorded from 1996 to 2005 were used. Three principal componenets that explained 80% of the total variance of the data were recognized as the principle componenets and others as disorder. Using the extracted principle componenets, a multiple linear regression model was developed to estimate ETo. The statistic index of t for assessing the results of a fixed constant and each componenets of PC1 and PC2 were determined. According to the results, all coefficients were significant at the level of 95% and PC1 had more importance than the other component namely PC2. This revealed that the variables of radiation intensity, relative humidity, sunshine hours, minimum temperature and maximum temperature had more importance in estimating reference evapotranspiration than other climatological parameters. Comparison of MLR-PCA model with Penman-Monteith results showed that about 82% of the total amount of the ETo variance is defined by the three aformenstioned principle componenets.
Keywords: Reference Evapotranspiration, FAO Penman-Monteith, Multiple Regression, Principle Componenet Analysis.