H. Bagheri; H. Zare Abyaneh; azizallah izady
Abstract
Introduction: Vermicompost is a type of biological organic fertilizer obtained from earthworm activity. Vermicompost is used in sustainable agriculture due to its beneficial effects on diversity of plant nutrients and physical-hydraulic modification of soil. However, high presence of solutes ...
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Introduction: Vermicompost is a type of biological organic fertilizer obtained from earthworm activity. Vermicompost is used in sustainable agriculture due to its beneficial effects on diversity of plant nutrients and physical-hydraulic modification of soil. However, high presence of solutes in the structure of vermicompost causes soil salinity, increases soil sodium content and changes soil pH. Soil flushing is one of the well known strategies to minimize the mentioned disadvantages of vermicomposting. Although flushing can reduce the soil salinity and sodium content, it leads to transportation of some soil substances such as nitrate, dissolved organic carbon and colloids which their tracing is necessary because of soil quality monitoring and possibility of water resources pollution. The objective of the current study was to investigate the effects of vermicomposting on soil chemical, physical and hydraulic properties and its role on the amount of soil total dissolved salts (TDS), sodium, nitrate, dissolved organic carbon and leaching behavior of colloids.
Materials and Methods: To treat the soil, 1.45 weight percent of vermicompost (17.68 tones/hectare) was mixed with regular soil. Physical, chemical and hydraulic properties of soil were determined. PVC columns with length of 20 cm and internal diameter of 5.95 cm were used and filled with soil to perform leaching during 24 hrs in saturated condition experiment. The effluent of columns were collected at various interval times, and their sodium, nitrate, dissolved organic carbon, TDS and colloid contents were measured and the cumulative amounts of them were calculated at 6 and 24 hrs. All experiments were carried out in three replications, and the mean comparison of leaching parameters was done according to Duncan's multiple range test at probability level of 5%.
Results: Vermicompost increased the studied soil chemical properties i.e, organic matter, organic carbon, extractable nitrate, soluble sodium, soluble and exchangeable sodium, EC and TDS to 12.42, 12.9, 118.96, 80.43, 44.48, 109.4 and 109.4 %, respectively and decreased soil pH to 2.35 %. Soil bulk density reduction to 3.81 % and enhancement of soil porosity, saturated hydraulic conductivity and the pore water velocity to 1.38, 7.25 and 5.6 %, respectively are the other results of vermicompost application. The used vermicompost fertilizer caused displacement of soil water retention curve to more moisture around of saturated and permanent wilting points and reduction of air entry potential. In this regard, vermicomposting increased all of soil hydraulic coefficients of van Genuchten model including θr, θs, α and n, and its effect was specially more on θr and α. The result of leaching experiments showed that the amounts of leached TDS, sodium, nitrate, dissolved organic carbon and colloid in vermicompost-containing soil during 6 hrs were 491.4, 65.22, 116.71, 47.68 and 24.86, and during 24 hrs were 946.3, 72.16, 146.26, 95.11 and 41.97 mg/Kg, respectively. For the natural soil, these amounts during 6 hrs were 240.9, 11.84, 20.08, 23.2 and 15.11, and during 24 hrs were 665.6, 15.69, 44.48, 58.34 and 29.39 mg/Kg, respectively. Therefore, vermicompost significantly increased the amounts of leached TDS, sodium, nitrate, dissolved organic carbon and colloid, because of containing more contents of solute, sodium, nitrate and organic matter in its structure. It also increased the porosity and hydraulic conductivity of soil, and made changes in soil water retention curve (P<0.05). The presence of more sodium in vermicompost together with its effect on soil porosity enhancement increased the colloid dispersion and consequently its leaching. In addition, the leaching rate of all of parameters at 24 hrs in comparison to 6 hrs decreased significantly due to high amount of solute leaching through mass flow at initial time of leaching experiment and leaching residual solute by time-consuming process of diffusion.
Conclusion: Although vermicompost can enriched the soil due to increasing nitrate and organic matter contents, it leads to soil salinity and increases sodium contents. Flushing the soil treated by vermicompost removed the amounts of TDS, sodium, nitrate to 10.4, 76.2 and 44.6 % during 24 hrs. Therefore, leaching had a considerable effect on soil sodium reduction and a little effect on soil salinity reduction. Moreover, in comparison to chemical fertilizers, the high nitrate fraction of applied vermicompost resulted in sustainability of soil fertility. It is expected soil salinity and nitrate leaching fraction of vermicompost will be reduce by managing leaching methods, treating vermicompost before using and reducing fertilizer application rate. Thus, the results of current study warn the farmers who used vermicompost in soil to control the soil salinity, ground water pollution and vertical colloid migration.
Hamid Zare Abyaneh; Farzaneh Heidari; Gholamreza Heidari; mehdi jovzi
Abstract
In this pot experiment, the effects of three levels of zero (A0), three (A1) and five gram (A2) aquasorb superabsorbent per kg of soil, three levels of 70 (W1), 85 (W2) and 100 (W3) percent of irrigation requirements and two levels of 75 (F1) and 100 (F2) percent of nitrogen fertilizer requirements were ...
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In this pot experiment, the effects of three levels of zero (A0), three (A1) and five gram (A2) aquasorb superabsorbent per kg of soil, three levels of 70 (W1), 85 (W2) and 100 (W3) percent of irrigation requirements and two levels of 75 (F1) and 100 (F2) percent of nitrogen fertilizer requirements were studied on some traits of bell pepper plant. The experiment was factorial based on randomized complete block design with 18 treatments and three replications. The results showed significant effect of superabsorbent and irrigation treatments on all components except stem diameter. Among the superabsorbent treatments, the highest fruit yields (666.2 gr) and water productivity (12.36 kg/m3) were obtained in A2 treatment. Among the irrigation treatments, the highest values of the mentioned functions were obtained in the W3 and W1 treatments with 621.81 g and 10.57 Kg/m3 respectively. The effect of fertilizer treatments on shoot weight, root and fruit yield was significant. The highest fruit yield was 638.70 g in F2 treatment. The interaction of two variables of water with superabsorbent with effect on fresh and dry weight of shoot and root and on yield and water productivity yielded the highest fruit yield (916.65g) and productivity (14.55kg/m3) in A2W3 treatment. The interaction effects of superabsorbent and fertilizer showed that the highest yield and water productivity were equal to 670.51 grams and 12.44 kg / m3 in A2F2 treatment. The interactions of water and fertilizer showed that the highest yield and water productivity were 625.59 g in W3F2 and 12.32 kg/m3 in W1F2 treatment. The interaction of three superabsorbent, water and fertilizer variables on all studied traits was not significant.
Mohammad Ghabaei S; Hamid Zare Abyaneh; Abolfazl Mosaedi; S. Zahra Samadi
Abstract
Introduction: Drought is a recurrent feature of climate that caused by deficiency of precipitation over time. Due to the rise in water demand and alarming climate change, recent year’s observer much focus on drought and drought conditions. A multiple types of deficits and relevant temporal scales can ...
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Introduction: Drought is a recurrent feature of climate that caused by deficiency of precipitation over time. Due to the rise in water demand and alarming climate change, recent year’s observer much focus on drought and drought conditions. A multiple types of deficits and relevant temporal scales can be achieved through the construction of a joint indicator that draws on information from multiple sources and will therefore enable better assessment of drought characteristics including return period, persistent and severity. The Standardized Precipitation Evapotranspiration Index (SPEI) combines information from precipitation and temperature in the form of water surplus or deficit according to Standardized Precipitation Index (SPI). Rainfall over some regions of Iran during some resent year was below average while mean and maximum temperatures were very high during this period, as was evaporation. This would suggest that drought conditions were worse than in previous recent periods with similarly low rainfall. The main objective of this study is to assess the influences of humidity on the SPEI index and investigate its relation with SPI and Reconnaissance Drought Index (RDI) over six different climatic regions in Iran.
Materials and Methods: Iran has different climatic conditions which vary from desert in central part to costal wet near the Caspian Sea. In this study the selection of stations was done based on Alijani et al (2008) climatic classification. We chose 11 synoptic stations from six different climatic classes including costal wet (Rasht and Babolsar), semi mountains (Mashhad and Tabriz), mountains (Shiraz and Khoram Abad), semi-arid (Tehran and Semnan), arid (Kerman and Yazd) and costal desert (Bandar Abas). The Meteorological datasets for the aforementioned stations were obtained from the Iran Meteorological Organization (IRIMO) for the period 1960-2010. The compiled data included average monthly values of precipitation, minimum and maximum air temperature, mean relative humidity, sunshine hours) and wind speed at 2 m height. A probability-based overall water deficit assessment was achieved from multiple drought-related indices (i.e. SPEI, SPI and RDI). The humidity conditions were monitored for given stations based on each index during annual, short term (1, 3 and 6 months) and long term (9 , 12, 18 and 24 months) periods. This research further examine the Locally Weighted Scatter plot Smoothing (LOWESS) graphical method and nonparametric Man- Kendal test to evaluate the trends associated with humidity deficiency in annual and monthly time scales during 51 years period (i.e. 1960-2010).
Results and Discussion: Our results revealed that the maximum correlation between SPEI index with indices of SPI and RDI was achieved in the coastal wet region and with a declining trend in relative humidity condition in the rest of the regions, this correlation is down over both short- and long-term periods. A comparison between SPI and SPEI also performed that the SPI index was able to reflect prolonged drought over the costal wet region where it showed significant inconstancy in desert and semi desert regions. SPEI result suggested substantial deficiencies in relative humidity at the beginning of 1997 during long term period which indicated an increasing trend of drought statues during last decades. Overall, according to the results of SPEI index in 1month periods monthly drought assessment showed a declining trend in drought magnitude during autumn, winter and spring season months (October to June) at investigated stations excepts Tehran and Shiraz stations and with a potential deficiency in relative humidity conditions. Unlikely, annual trend showed increasing trends in drought frequency and persistent over last decade.
Conclusion: Our results can be summarized as below:
Focusing on various types of deficits, the result of humidity based deficiencies indicated that for semi-mountains, mountains, semi-arid, arid and costal desert regions the period of 1997 to 2010 has a large total moisture shortage over all climatic regions. Most of the climate stations showed moisture deficits (decline trends) during October to June (9-month) at many stations expect Tehran and Shiraz stations which revealed a significant increasing over 51 years. We recommend using SPEI index for arid and semi-arid regions because it includes temperature variability in drought model so it reflects drought conditions better than other indices. Furthermore, three drought indices (i.e. SPEI, SPI and RDI) have similar sensitivity to water deficits over wet climatic regions; therefore, each of those indices can be used.
H. Zare Abyaneh; A. Afruzi; M. Mirzaei; H. Bagheri
Abstract
Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average ...
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Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA) model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations.
Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1), the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO) for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data.
Table 1. The geographical location and climate conditions of the synoptic stations
Station Geographical location Altitude (m) Mean air temperature (°C) Mean precipitation (mm) Climate, according to the De Martonne index classification
Longitude (E) Latitude (N) Annual Min. and Max.
Esfahan 51° 40' 32° 37' 1550.4 16.36 9.4-23.3 122 Arid
Semnan 53° 33' 35° 35' 1130.8 18.0 12.4-23.8 140 Arid
Shiraz 52° 36' 29° 32' 1484 18.0 10.2-25.9 324 Semi-arid
Kerman 56° 58' 30° 15' 1753.8 15.6 6.7-24.6 142 Arid
Yazd 54° 17' 31° 54' 1237.2 19.2 11.8-26.0 61 Arid
Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference evapotranspiration were obtained. The mean values of evapotranspiration in the study period were 4.42, 3.93, 5.05, 5.49, and 5.60 mm day−1 in Esfahan, Semnan, Shiraz, Kerman, and Yazd, respectively. The Augmented Dickey-Fuller (ADF) test was performed to the time series. The results showed that in all stations except Shiraz, time series had unit root and were non-stationary. The non-stationary time series became stationary at 1st difference. Using the EViews 7 software, the seasonal ARIMA models were applied to the evapotranspiration time series and R2 coefficient of determination, Durbin–Watson statistic (DW), Hannan-Quinn (HQ), Schwarz (SC) and Akaike information criteria (AIC) were used to determine, the best models for the stations were selected. The selected models were listed in Table 2. Moreover, information criteria (AIC, SC, and HQ) were used to assess model parsimony. The independence assumption of the model residuals was confirmed by a sensitive diagnostic check. Furthermore, the homoscedasticity and normality assumptions were tested using other diagnostics tests.
Table 2- The selected time series models for the stations
Station Seasonal ARIMA model Information criteria R2 DW
SC HQ AIC
Esfahan ARIMA(1, 1, 1)×(1, 0, 1)12 1.2571 1.2840 1.2396 0.8800 1.9987
Semnan ARIMA(5, 1, 2)×(1, 0, 1)12 1.5665 1.5122 1.4770 0.8543 1.9911
Shiraz ARIMA(2, 0, 3)×(1, 0, 1)12 1.3312 1.2881 1.2601 0.9665 1.9873
Kerman ARIMA(5, 1, 1)×(1, 0, 1)12 1.8097 1.7608 1.8097 0.8557 2.0042
Yazd ARIMA(2, 1, 3)×(1, 1, 1)12 1.7472 1.7032 1.6746 0.5264 1.9943
The seasonal ARIMA models presented in Table 2, were used at the 12 months (2004-2005) forecasting horizon. The results showed that the models produce good out-of-sample forecasts, which in all the stations the lowest correlation coefficient and the highest root mean square error were obtained 0.988 and 0.515 mm day−1, respectively.
Conclusion: In the presented paper, reference evapotranspiration in the five synoptic stations, including Esfahan, Semnan, Shiraz, Kerman, and Yazd, were calculated using the FAO Penman-Monteith method for the 41 years, and the time series were formed. The selected models gave good out-of-sample forecasts of the monthly evapotranspiration for all the stations. The models can be used in the short-term prediction of monthly reference evapotranspiration. Note that, the use of models in long-term forecasting was not recommended. The time series model can be used in lost data. Even though more methods are available for model building, the use of time series models in water resources are advocated in modeling and forecasting. Time series can be used as a tool to find lost data.
H. Zareabyaneh; M. GHobaeisoogh; Abolfazl Mosaedi
Abstract
Introduction: Drought is a natural and recurrent feature of climate. The characterizations of it may change under the effect of climate change in future periods. During the last few decades a number of different indices have been developed to quantify drought probabilities. Droughts are caused by disruptions ...
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Introduction: Drought is a natural and recurrent feature of climate. The characterizations of it may change under the effect of climate change in future periods. During the last few decades a number of different indices have been developed to quantify drought probabilities. Droughts are caused by disruptions to an expected precipitation pattern and can be intensified by unusually high temperature values. Precipitation-based drought indices, including the Standardized precipitation index (SPI), cannot identify the role of temperature increase in drought condition and in addressing the consequences of climate change. Recently, two new standardized drought indices have been proposed for drought variability analysis on multiple time scales, the Reconnaissance Drought Index (RDI, Tsakiris et al., 2007) and the Standardized Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al., 2010). The objective of this study is to evaluate the characterization of wet and dry periods under the effect of climate change according to SPEI index in synoptic station of Hamedan for the next thirty years (2011-2040).
Materials and Methods: In this study, the indices of SPEI, SPI and RDI were investigated and the SPEI index as a multiscalar and suitable index was used to detect, monitor, and explore the consequences of global warming on drought conditions in synoptic station of Hamedan (airport). For this purpose, the period of 1981-2010 was chosen as the base period and the simulation of the future climate variables were done based on A1B, A2 and B2 emissions scenarios and performance of multi model ensemble via LARS-WG5 model for the period of 2011-2040. The performance of the multi model ensemble was done by using five global climate models including IPCM4, MPEH5, HADCM3, GFCM21, and NCCCS in the IPCC Fourth Assessment Report (Semenov and Stratonovitch, 2010). By simulating the values of precipitation ,and the values of temperature and the values of estimated evapotranspiration , the values of SPEI, RDI and SPI indices were calculated annually and 1, 3 and 6 months (short- term period) and 12, 18 and 24 months (long- term period) time scales for the base period and the three next decades. Then, the relation among them was computed and investigated via correlation coefficient. Then, by monitoring the humidity condition via SPEI index, the characterization of wet and dry periods including period numbers, longest period, total deficit or surplus, and maximum deficit or surplus were derived based on Run theory and were comprised for the base period and three future decades.
Results and Discussion: Evaluation of LARS-WG5 model for base period showed that the model was able to simulate minimum and maximum temperatures and precipitation data with high accuracy based on statistic error and can be used to generate data for future years according to emission scenario. According to the simulated results of performance of multi model ensemble, the average values of mean temperature and precipitation will increase by 0.820C and 2.5 % for A2 scenario, respectively. In addition, the minimum and maximum temperatures have increased in all of the months according to the three scenarios in comparison with the base period. The correlation results between the investigated indices showed that the maximum and minimum of correlation can be observed between SPI & RDI and SPEI & SPI indices in the base period and future decade for each scenario, respectively. Drought assessment based on the SPEI index in the base period shows that the main drought episodes occurred in the 1999 to 2001 that were consistent with FAO report (2006). Comparison of wet and dry periods in relation to the base period showed that the number of dry periods will increase in time scales of 1 and 3 months and will decrease in other long-term time scales.
Conclusion: Climate change and its effects are among the main challenges of water resources management in the present century. In this study, the effects of this phenomenon on drought monitoring and change of characterizations were investigated. For this purposes, we used daily meteorological variables during thirty years (1981-2010) from Hamedan Synoptic station. The results of drought monitoring were based on SPEI index, and it revealed the high variability of humidity condition in the first decade of simulation in comparison with the second and third decades. This issue indicated that this decade requires more attention and management measurements. Also, according to the results of the derived characterization via Run theory, the number of dry periods will decrease and persistence of the longest dry period and consequently the volume of deficit will increase in the next three decades. In addition, the total volume surplus of wet periods will decrease in relation to the base period that can be interpreted as the increasing of moisture deficit in future decades The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. Thus, we recommend SPEI, as a suitable index for studying and identifying the effect of climate change on drought conditions.
M. Akbari; Z. Seif; H. Zare Abyane
Abstract
Abstract
Evapotranspiration (ET) is one of the most important components of the water balance, it is also one of the most difficult to measure. Despite the importance of ET, methods to obtain values of ET are still limited. Conventional methods are very local, ranging from point to field scale. Estimates ...
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Abstract
Evapotranspiration (ET) is one of the most important components of the water balance, it is also one of the most difficult to measure. Despite the importance of ET, methods to obtain values of ET are still limited. Conventional methods are very local, ranging from point to field scale. Estimates of the ET over the entire area, especially for irrigated areas, are essential, as these can differ substantially depending on the crop and the management applied. Today, actual and potential evapotranspiration under different conditions can be estimated by using satellites and remote sensing (RS) techniques. So that in this research, recent twenty years metrological data was assessed and based on precipitation, temperature and wind speed, three period include drought, normal and wet years (2000, 1995 and 2007 respectively) was chosen. The actual and potential evapotranspiration was estimated from a time series of NOAA-AVHRR satellite images using the SEBAL (Surface Energy Balance Algorithm for Land) algorithm in Abshar irrigation system in Esfahan during the three selected years. The results show that the maximum evapotranspiration (8.1 mm/day for ETa and 9.5mm/day for ETp) occurs in 2000 as a drought year. Comparing the potential evapotranspiration results of SEBAL method with Hargreaves and FAO-56 Penman-monteith methods show that, SEBAL method and conventional methods has the same results under current condition, so remote sensing techniques can estimated actual evapotranspiration and produce high spatial coverage of important terms in the water balance for large areas, but at the cost of a rather sparse temporal resolution. As water is highly manageable in irrigation systems, it is an application typically suitable to establish improvements in irrigation water management at large scale such as basin and irrigation systems.
Keywords: Evapotranspiration, Remote sensing, Water management, Abshar irrigation system
H. Zare Abyaneh; M. Bayat Varkeshi
Abstract
Abstract
From Longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was evaluated empirical, artificial neural network (ANN) and ca-active neuro-fuzzy inference ...
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Abstract
From Longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was evaluated empirical, artificial neural network (ANN) and ca-active neuro-fuzzy inference system (CANFIS) models in estimation of runoff. For this purpose, by using climatic and physiographic information in three stations of Pole Zamankhan, Ghale Shahrokh and Sade Zayandeh Rood, runoff values were estimated from empirical models and intelligent models were compared to annual runoff values. Input parameters include rain, mean temperature, mininmum temperature and maximum temperature. The results showed that the artificial intelligent models had good accuracy in estimating runoff. Among the empirical methods, method of Di Souza was appropriate. Comparison statistical parameters between methods was showed that mean percent error (MPE) in ANN, CANFIS and empirical method was 7, 12 and 43 percent respectively that confirmed differences of between the methods is significant. Also, CANFIS model did not artificial improve ANN results. The results showed, with reduction of input variables from 4 parameters to one parameter of precipitation, modeling error reaches its maximum value (from MPE=7% to MPE=16%). Versus, the optimal structure of ANN had less sensitivity to remove the mean air temperature parameter (from MPE=7% to MPE=10%(. Therefore, according to empirical models required information limitations and high accuracy of artificial intelligent models, intelligent models application is recommended.
Keywords: Estimation of runoff, Empirical method, ANN, CANFIS, Zayandeh rood Basin
A.A. Sabziparvar; H. Zreabyaneh; M. Bayat
Abstract
Abstract
Soil temperature is one of the key parameters affecting most hydrologic and agricultural processes. Therefore, its measurement and prediction is very crucial. So far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with ...
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Abstract
Soil temperature is one of the key parameters affecting most hydrologic and agricultural processes. Therefore, its measurement and prediction is very crucial. So far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with lack or shortage of data. In this work, soil temperature data are estimated at six different depths for three typical climates (Zahedan, Tehran, Ramsar) by a new approach namely Adaptive Neuro-Fuzzy Inference System (ANFIS), and the results are compared with those of estimated by regression methods. In addition, the most important meteorological parameters (maximum temperature, minimum temperature, mean daily temperature, relative humidity, sunshine hour, and wind speed) which influence soil temperature at the study sites are used during the 15-years period (1992-2006) of study. The comparison of soil temperature data predicted by ANFIS and regression methods indicated that the performance of ANFIS model is 4% more accurate than regression methods. It was found that the accuracy of prediction using ANFIS model for arid climates of Zahedan and Tehran was 12% and 4.5% better than Ramsar (humid), respectively. The statistical comparison of the estimations derived by ANFIS model and the observed soil temperature data of drier climates showed that the coefficients of correlation (r) are reduced (up to 10%) for deeper layers. In contrast, for the humid climate of Ramsar, the model accuracy for near surface layers (5 and 10 cm) was up to 18% less than deeper layers (100 cm).
Keywords: Soil temperature, Regression models, ANFIS, Arid climate, Humid climate
H. Zreabyaneh; M. Bayat; S. Marofi; R. Amiri Chayjan
Abstract
Abstract
The present study is attempted to present the minimum required meteorological parameters for reference evapotranspiration estimation at Hamedan region of Iran from 1997 to 1998. Employing Pierson test, six meteorological parameters which are used by Penman-Montieth FAO-56 method including maximum ...
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Abstract
The present study is attempted to present the minimum required meteorological parameters for reference evapotranspiration estimation at Hamedan region of Iran from 1997 to 1998. Employing Pierson test, six meteorological parameters which are used by Penman-Montieth FAO-56 method including maximum and minimum air temperature, maximum and minimum relative humidity, wind speed and daily sunshine were composed and considered as 4 difference scenarios (called 1, 2, 3 and 4). These scenarios were applied to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for reference evapotranspiration estimation of the area using the Matlab software. The results of the scenarios were evaluated using the actual reference evapotranspiration (lysimeter data). The results showed that increasing of number of input layers data could not be based as obtaining the more exact results. Using the scenario 2, which was based on minimum and maximum temperature as well as daily sunshine, showed more reliable results using the ANN and ANFIS methods. The root mean square error (RMSE), mean absolute error (MAE) and R2 of examination step of this scenario were 0.09, 0.07 mm/day and 0.9, respectively. Overall, the statistic performances revealed that ANN and ANFIS had the same results and similar input layer sensitivity. The iteration times of the ANN and ANFIS methods to reach the best results were 26 and 40, respectively. Comparison between ANN (RMSE= 0.09 mm/day) and standard Penman-Montieth method (RMSE= 0.34 mm/day) confirmed that the intelligence approaches such as ANN are more accurate for reference evapotranspiration estimation.
Keywords: Reference evapotranspiration, Pierson test, Intelligence methods, Hamedan
H. Zareabyaneh; E. Farokhi; M. Vazifeh Doost; Kh. Azhdari
Abstract
Abstract
Assessing moisture in the soil under cultivation of crops to achieve high performance and reduced water are necessary. Knowledge of the moisture distribution in the root zone, time consuming and costly field tests that simulation models, a suitable alternative in answer to issues of movement ...
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Abstract
Assessing moisture in the soil under cultivation of crops to achieve high performance and reduced water are necessary. Knowledge of the moisture distribution in the root zone, time consuming and costly field tests that simulation models, a suitable alternative in answer to issues of movement and are water distribution. In this study assessment of soil moisture, to aid SWAP simulation model was and above model Empowerment compared with field results was assessed. SWAP model based on the information in a field irrigated onion, equipped with drip irrigation systems and soil hydraulic parameters obtained from model RETC, were performed. Moisture Information with harvest soil from emitter place and 10 cm of the layers 15-0, 30-15, 45-30 and 60-45 were obtained. Comparison of simulated moisture with observations moisture to a depth of 60 cm in the emitter place and 10 cm it, in the form of graphs and calculation criteria of Root Mean Squared Error (RMSE), Root Mean Squared error of normal (NRMSE) and mean absolute error (MAE) was performed. Values of RMS, RRMSE and MAE in the normal place dropper 0.001, 0.03 and 0.07 cm3cm-3 and in 10 cm were 0.08, 0.02 and 0.07 cm3cm-3, respectively. Low errors calculated from the model SWAP, shows good accuracy of the model simulated moisture distribution in the root zone. Operations irrigation through a drip irrigation system, with irrigation 48 hours was equivalent to performance kg/ha 14780 against 14134 kg/ha estimated by the model will follow. In total, the results indicate that the SWAP model is able to respond with a valid enough accuracy and precision in a relatively short time to provide. This model can be as effective and useful tool for evaluating and optimizing the distribution of moisture in the root-crop area, used.
Keywords: Moisture simulation, SWAP model, Drip irrigation, Onion farm
H. Zreabyaneh; A. Ghasemi; M. Bayat; S. Marofi
Abstract
Abstract
Evapotranspiration as one of the important elements in agriculture has a considerable role in water resource management. Therefore, using a more exact estimation method is an essential step of agricultural development, especially in arid semi-arid area. In this research, in order to exact estimate ...
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Abstract
Evapotranspiration as one of the important elements in agriculture has a considerable role in water resource management. Therefore, using a more exact estimation method is an essential step of agricultural development, especially in arid semi-arid area. In this research, in order to exact estimate of garlic evapotranspiration using lysimeteric data, an artificial neural network (ANN) model was developed. Maximum and minimum air temperatures, maximum and minimum relative humidity values, wind speed and sunshine hours were used as the input layer data. The crop evapotranspiration was measured using 4 lysimetres of 2×2×2m of the Bu-Ali Sina agriculture collage’s meteorology station during 2006-2008. Statistic indicators RMSE, MAE, STDMAE R2 were used for performance evaluation of the models. The results showed the more exact method concerned to the multilayer perceptron (MLP) model with the back propagation algorithm. The 6-6-1 layout with Levenberg-Marquat rule and sigmoid function had the best topology of the model. The evaluation criteria were 0.088, 0.07 and 0.061 mm/day as well as 0.88, respectively. The results also showed that the average daily garlic evapotranspiration were 8.3 and 6.5 mm based on the lysimeter ANN methods, respectively. Overall, evaluation of ANN results showed that the errors of ANN were negligible. The ANN showed high and low sensitivity to maximum air temperature and minimum relative humidity, respectively.
Key words: Artificial Neural Networks, Evapotranspiration, Lysimeter, Garlic, Hamedan
A.A. Sabziparvar; F. Tafazoli; H. Aareabyaneh; M. Mousavi baygi; M. Ghafoori; S.A. Mohseni Movahed; Z. Merianji
Abstract
Abstract
The estimation of reference evapotranspiration (ETo) is of great importance due to its applications in water resource management as well as irrigation scheduling. Difficulties associated with using lysimeters have encouraged researchers to use various ETo models, while the shortage of actual ...
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Abstract
The estimation of reference evapotranspiration (ETo) is of great importance due to its applications in water resource management as well as irrigation scheduling. Difficulties associated with using lysimeters have encouraged researchers to use various ETo models, while the shortage of actual radiation data seems the main obstacle for users of radiation-based models. In this research the output of four radiation-based evapotranspiration models including: Penman-Montieth-FAO56 (PMF56), Penman-Montieth FAO-Irmak (PMFI), modified Jensen-Haise (JH1), and Jensen-Haise (JH2) are evaluated for a cold semi-arid climate. The daily ETo values were generated for 16 different scenarios and the results were compared against a two-year lysimeter data during the growing season (May to November). Deviations of model results were investigated using mean of R2, RMSE, MBE and t-test criteria. The results indicated that the JH2 model which uses radiation model of Daneshyar, can generate the most accurate ETo values (R2>0.85, P