جستجوی مقالات مرتبط با کلیدواژه « فائو-پنمن-مانتیث » در نشریات گروه « کشاورزی »

The purpose of this research was to investigate the sensitivity of reference evapotranspiration to meteorological variables and to introduce the most accurate database in providing these variables in the Urmia Lake basin. For this purpose, 24 synoptic stations were selected and their meteorological data was prepared on a daily basis between 2010 and 2019. Then, the reference evapotranspiration was calculated using the FAO-Penman-Monteith equation, and the effect of changes in meteorological variables on ET0 was investigated individually in the range of ±20%. In the next step, the accuracy of ERA5, CFSv2 and MERRA2 meteorological data was evaluated and the most accurate one was introduced. The ten-year average of reference evapotranspiration of the meteorological stations was obtained 3.1 mm d-1, and the results showed that the maximum temperature is the most influential meteorological variable on reference evapotranspiration changes. After that, wind speed and minimum temperature had the greatest effect, respectively. The value of sensitivity coefficient for maximum temperature, wind speed and minimum temperature was obtained 0.4, 0.2 and 0.1 respectively. In the review of meteorological data of ERA5, CFSv2 and MERRA2, statistical indicators showed that the ERA5 dataset has the most accuracy. Based on the results, the 10-year average of reference evapotranspiration of the ERA5 was obtained 2.86 mm d-1 and according to the CRM index, this value was 8% underestimated compared to the value obtained from meteorological stations. Finally, the values of EF indices equal to 0.92 and nRMSE equal to 0.17 put the reference evapotranspiration of the ERA5 in a suitable and reliable rank.

Water resources management, especially irrigation practices, is heavily reliant on reference evapotranspiration (ET0). ET0 is the rate of evaporation and transpiration from a standard reference surface with a presumed surface resistance of 70 s.m-1, the height of 0.12 m and an albedo of 0.23. Penman-Monteith FAO-56 (P-M FAO-56) approach is the most commonly used method for calculating ET0. In spite of the fact that FAO-PM is achievable, its implementation remains inconvenient because it requires a large amount of meteorological data, which is derived from standard meteorological observation stations. In the absence of complete climate data, it is highly desirable to have a model with fewer input climatic dates. Therefore, remote sensing methods have been used and improved over time to estimate ET0 at various spatial scales. Alternatively, it has been observed that the research community has become increasingly interested in obtaining data from metaheuristic algorithms that are based on artificial intelligence (AI).

In this research, it has been attempted to estimate the amount of daily reference evapotranspiration (ET0) using two data-driven models, using a combination of inputs from meteorological station data and satellite imagery data from MODIS sensor, by considering different inputs from these sources. The models include the random forest (RF) and hybridized RF with genetic algorithm optimization (GA-RF). Moreover, the correlation of input variables with ET0 is evaluated and the possibility of training a simple and accurate machine learning model in the conditions of lack or absence of meteorological data using satellite image data is investigated. So, this study aimed to determine ET0 in the time period of 2003-2021 using land surface temperature (LST) data and leaf area index (LAI) acquired from MODIS sensor and Tabriz meteorological station data including maximum and minimum air temperatures (Tmax, Tmin), average temperatures (T), wind speeds (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin), and sunny hours (n). For the study area, daily LST were extracted from the Terra (MOD11A1) and Aqua (MYD11A1) satellites. Moreover, the LST of Terra and Aqua satellites were combined, since the LST values had missing data due to the presence of clouds. Furthermore, MODIS MCD15A3H version 6.1 using four-day data from Terra and Aqua satellites was used to determine the leaf area index (LAI). The standard P-M FAO-56 method for calculating daily reference evapotranspiration was considered as the base method. The set of input parameters was considered based on the cross-correlation of the parameters with reference evapotranspiration obtained from the FAO-Penman-Monteith equation.

The results of two data-driven models including standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) to estimate ET0 values were compared with calculated ET0 by P-M FAO-56 equation. The results indicated that all of the studied input variables are highly correlated with the target variable. Based on the P-M FAO-56 method, the average air temperature with the highest value (R2=0.903) and the wind speed with the lowest value (R2=0.282) has a high and low correlation with reference evapotranspiration. Also, by comparing LAI and LST MODIS parameters, LST has the highest correlation coefficient with ET0 with R2=0.865. A total of twelve scenarios for estimating ET0 are evaluated, each with a different set of input parameters. Based on the correlation between the parameters and ET0, the first ten scenarios are categorized. Additionally, the eleventh scenario is based only on satellite images, and the twelfth scenario is based solely on weather station data. Based on the results, the GA-RF-10 (R2=0.976, RMSE=0.200, MAPE=11.373, and MBE=0.028), which includes all input parameters, outperforms the other models. There was a greater degree of accuracy with the RF-10 (R2=0.949, RMSE=0.293, MAPE=16.442, and MBE=0.017) when compared with the other random forest models. Based on the comparison of scenario 11 (satellite image data) and scenario 12 (meteorological station data), it appears that scenario 12 is more accurate for both RF (R2=0.922, RMSE=0.357, MAPE=20.712, and MBE=0.009) and GA-RF (R2=0.944, RMSE=0.306, MAPE=17.037, and MBE=0.013) models. Despite the fact that only satellite image parameters did not provide accurate estimation of ET0 compared to independent meteorological parameters, the inclusion of these parameters in the ET0 estimation resulted in more acceptable results, demonstrating the importance of satellite image parameters. Thus, satellite data may be useful and recommended for estimating ET0, particularly in areas without meteorological stations.

According to the effects of climate change on evapotranspiration and using of water resources, climate change prediction is vital due to water resources management improvement and decreasing damages of drought. The first rank of mango production in Iran belonged to Hormozgan province and the most amount of mango produced in Minab plain. In the present study, the amount of evapotranspiration of mango plants was calculated with FAO Penman-Monteith from 1985 to 2020 using meteorological data at Minab station. The evapotranspiration values of the plant were estimated from 2021 to 2100 with two optimistic and pessimistic scenarios using the last version of CMIP (CMIP6), atmospheric-ocean general circulation models, and performing statistical deviation corrections by the Python software. The results showed that the values of annual evapotranspiration will increase by 0.31 and 1.23 mm on average in the optimistic and pessimistic scenario, respectively in the future due to the increase in annual temperature.

Irrigation performance assessments under actual operation conditions are required as a first step toward improving agricultural water management. In this study, the seasonal irrigation performance of peaches and nectarines was evaluated under actual operation conditions by monitoring 24 orchards, in Ardabil Province (Parsabad and Meshginshahr counties), Iran, during the growing season 2018-2019. The total sum of seasonal applied water and effective precipitation (I + Pe) and the fruit yield ranged from 280-1675 mm and 1.00-32.43 ton ha-1 (with a weighted average of 582 mm and 14.61 ton ha-1), respectively. The mean Relative Irrigation Supply index (RIS) for the drip irrigation method (1.25) was significantly (P < 0.05) lower than the surface irrigation method (1.66). The water productivity indicators were significantly (P < 0.05) affected by the orchardist's skill level, interplantion of trees, tree spacing, irrigation method, and disease/frost (DF) damage. Post-harvest period accounted for a mean proportion of 75, 25, and 15% of the seasonal applied water in orchards with early-season, late-season, and mixed early- and late-season cultivars, respectively. DF damage accounted for an 87 and 85% reduction in fruit yield and water productivity (WPI+Pe), respectively, compared to the orchards without severe DF damage. Under the current technological and economic constraints, most of the study orchards experienced a rational (but yet with low productivity) irrigation water management. Improving irrigation efficiency and fruit yield, controlling DF damage, and implementing regulated deficit irrigation during pre- and post-harvest stages are the most effective approaches to improve water productivity indicators in the study area.

Reliable estimates of the seasonal applied water and irrigation performance assessment indicators under current irrigation and farm management are perquisites for improving water resources management. In this study, seasonal applied water and irrigation performance assessment indicators of winter barley were studied by monitoring 25 farms under actual conditions, in Ardabil Province (Ardabil, Namin, Nir and Kosar counties), Iran, during the growing season 2020-2021. The seasonal estimates of the net irrigation requirement during the growing season 2020-2021 and its 10-year average ranged from 476 to 652 mm and from 403 to 535 mm (with a mean of 530 and 449 mm), respectively, over the study farms. The total sum of seasonal applied water and effective precipitation (I + Pe) and the barley grain yield ranged from 266 to 716 mm and from 0.14 to 4.07 ton ha-1 (with a weighted average, WA, of 475 mm and 2.33 ton ha-1), respectively. As a result of limited irrigation water availability, 3-91% (with a WA of 52%) of the intended yield of irrigated barley in the study area (4.5 ton ha-1) was achieved. Physical and economic water productivity indicators were significantly (P < 0.05) affected by farmer's skill level, crop rotation, type of irrigation water source, and irrigation method. The results indicated that farms with surface water supply showed the highest vulnerability to drought periods. Improving the on-farm water management flexibility and mitigating the negative impacts of hot and windy days during the grain filling stage can improve water productivity indicators in the study area.

Climate change significantly affects net primary production (NPP) and evapotranspiration (ET). To estimate NPP and reference ET (ET0), long-term (1986-2017) meteorological data collected by 96 synoptic weather stations located in five of Iran's forest vegetation zones: Hyrcanian, Arasbaran, Zagros, Khalijo-Omanian, and Irano-Turanian were used. The synthetic model and FAO Penman-Monteith combination equation were employed for estimation. Mann-Kendall test detected annual trends of NPP and ET0. The Hyrcanian zone demonstrated the highest amount of NPP at 10.7 t ha-1 y-1, while Khalijo-Omanian displayed the highest annual ET0 at 2099 mm. The NPP of Khalijo-Omanian and Irano-Turanian zones were roughly similar, ranging between 2 and 3 t ha-1 y-1. Out of 96 observed annual trends of NPP and ET0 in diverse vegetation zones, statistically significant trends were found in 22 trends (23 percent) of NPP and 73 trends (76 percent) of ET0. Understanding the impact of climate change on ecosystem function requires trending of NPP and ET.

Evapotranspiration calculations by the FAO Penman-Monteith method require many parameters, the lack of some of which makes the calculations difficult. On the other hand, it is necessary to use methods that are easier to calculate and are easily understood by farmers. One of the indirect methods of estimating reference evapotranspiration is the pan evaporation method, which can be considered as a suitable index to estimate the evapotranspiration of the reference plant and finally the main plant. In this method, in order to calculate the potential evapotranspiration, the results of pan evaporation must be multiplied by the pan coefficient. Accurate calculations related to the estimation of the pan coefficient are very important because it can be used for irrigation planning when there is no lysimeter equipment. In this study, by using of daily, hourly meteorological data, and data from class pan A evaporation from meteorological stations of Kermanshah province in the long term, potential evapotranspiration values of the FAO Penman-Monteith and then pan coefficients were calculated. The index coefficients as a measure of estimation methods for Cuenca, Allan, Snyder, Modified Snyder, and Orang were used. The results showed that the, Orang and modified Snyder method had a higher level of accuracy compared to the other methods. The R2 with Orang method for different cities of Islamabad Gharb, Qasr-e Shirin, Kermanshah, Sararood, Sonqor, Gilan-e Gharb, Sarpol-e zahab, Kangavar were determined as: 0.84, 0.77, 0.72, 0.76, 0.85, 0.79, 0.77, and 0.73, respectively. Finally, the values of pan coefficients calculated by all methods in study stations in different months were calculated and presented. It is hoped that the results of this study will be used by managers and farmers in the region.

Due to limitation of available water resources, improving agricultural water productivity has become an inevitable necessity. Therefore, it is important to have reliable estimates of the seasonal applied water and water productivity under current irrigation and farm management. In this paper, the seasonal applied water and physical and economic water productivity of soybean were studied through monitoring 29 farms under actual conditions located at the tail end region of Moghan irrigation and drainage network, Ardabil Province, Iran, during the growing season 2020-2021. The net water requirement estimates of soybean during the growing season 2020-2021 and its 10-year average ranged from 417-719 mm and 457-797 mm with a mean of 539 and 581 mm, respectively, over the studied farms. The total applied water (irrigation + effective precipitation, I + Pe) and the grain yield ranged from 3859-7105 m3 ha-1 and 1.30-2.80 ton ha-1, with a mean of 5664 m3 ha-1 and 2.35 ton ha-1, respectively. The lack of flexibility in water allocations led irrigation schedule to be not adapted with the crop water requirement. The limiting factors of soybean production in the study area caused the observed maximum grain yield to be significantly lower than the potential level of soybean yield in Moghan plain (4.00 ton ha-1). The soybean grain yield exhibited a quadratic correlation with I + Pe. Total water productivity (WPI+Pe) and economic water productivity (WP$) ranged from 0.33 to 0.47 kg m-3 and 21.18 ´ 103 to 48.29 ´ 103 Rials m-3 with a mean of 0.42 kg m-3 and 39.89 ´ 103 Rials m-3, respectively. The mean Israelsen's application efficiency (AE) over initial, development, and mid-season plant growth stages in the study fields were obtained 19, 95, and 100%, respectively.

Accurate estimation of water requirements of plants is a key factor in controlling several hydrological processes including: planning and management of water resources, especially in arid and semi-arid regions (Laaboudi et al., 2012; Wen et al., 2015) water pricing and water requirement for Irrigation (Yassin et al., 2016). In this study, multivariate regression methods and gene expression planning were evaluated to estimate reference evapotranspiration. For model input data, the Khorramabad Synoptic Station information including: maximum and minimum temperatures, maximum and minimum relative humidity, sunny hours and monthly wind speeds in the range of 1983-2017(420 months) were used. Based on the relationship between input and output parameters, six input patterns were determined for modeling.70% of the data were used for training and 30% were used for model validation.The results of multivariate regression showed that the proposed model had acceptable accuracy with R2 = 0.952.The analysis of model coefficients showed the greatest effect of maximum temperature with a coefficient of 0.604 on reference evapotranspiration. Gene expression planning results showed that the fifth pattern with four main operators was R2 = 0.958 and RMSE = 0.704 in the training phase and R2=0.977 and RMSE = 0.615 in the test phase had better performance.

The purpose of this study is to compare and evaluate five different methods of estimating the evapotranspiration of the reference plant on a nine-year daily data scale in Yazd province. Selected methods included Hargreaves-Samani (HS) , Priestley-Taylor (PT) , Turk (Turc) , Makkink (MK) and Dalton (D). For this purpose, data from ten synoptic meteorological stations were used covering a period of 9 years. The results of the mentioned methods were evaluated by FPM-56 method. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. RMSE, NS, SI, MAE, R^2 statistical criteria were used to evaluate the results. The results showed that before calibration the results of different methods are very different from FPM-56. The only acceptable model before calibration was the HS model. After calibration, the results of the models improved and model D, which was the worst model before calibration, was recognized as the best model for estimating evapotranspiration in Yazd province among the five selected methods. Mean values of Model D before calibration MAE = 3.83, R^2= 0.84, NS = -1.99, SI = 0.83, RMSE = 4.21 and after calibration MAE=0.83, R^2=0.86, NS=0.72, SI = 0.22, RMSE=1.02 was obtained. After calibration, Turc, PT and MK models were in the next categories of the best models.

Due to the low annual precipitation rate and, therefore, the existence of a weak and fragile ecosystem, arid regions are more subject to drought. Moreover, significant fluctuations in the temporal and spatial distribution of precipitation make drought forecasting in these areas a complicated task. Having a dry and fragile climate, Yazd Province has experienced numerous droughts within the past few decades. Therefore, it is highly important to monitor and forecast drought in this region. Population growth has increased water demand. Moreover, the increase in the concentration of greenhouse gases due to rising fossil fuels consumption has caused global warming and climate change, changing the hydrological cycle components including precipitation patterns (snow and rain). Therefore, climate change has changed the frequency, severity, and duration of droughts in many areas, especially in arid and semi-arid regions. So far, several indices have been developed to assess drought. This study used the RDI, which is based on precipitation (as system input) and potential evapotranspiration (as output), to estimate the drought. Furthermore, there are several methods for forecasting drought, among which projections of global climate models derived from greenhouse gas emission scenarios are quite common.

This study sought to analyze the efficiency of global climate models in predicting drought in arid regions (Yazd Synoptic Station, Yazd, Iran). To this end, 1961 to 2005 was selected as the base period, RDI values of which were imported to downscale the model (SDSM). Moreover, 2006 to 2018 was selected as the forecast period whose data were not imported to the model. Finally, the predictions of the model for the period 2006 to 2018 were compared with the observed RDI values of the same period for time scales of one, three, and six months. Accordingly, first, the climatic data collected from Yazd Synoptic Station (minimum and maximum temperature, relative humidity, sunshine hours, and wind speed) were prepared, and then the potential evapotranspiration (PET) was calculated for the period 1961 to 2018 via FAO-Penman-Monteith method. RDI value for the period 1961 to 2018 was then estimated on a monthly basis.Moreover, to predict drought through global climate models, CanESM2 global model forecasts were obtained based on the RCP8.5 greenhouse emission scenario from 2006 to 2018 for monthly precipitation, minimum temperature, maximum temperature, average temperature, sunshine hours, wind speed, and relative humidity. Using SDSM statistical downscaling model, the observed data for the period 1961 to 2005 and the projections of the CanESM2 model for the period 2006 to 2018 were downscaled. On the other hand, the mean surface temperature predictor was used to a downscale minimum and maximum temperatures and sunshine hours. Wind speed and surface-specific humidity predictors were also used to downscale wind speed and relative humidity outputs, respectively. Furthermore, to increase the accuracy, three predictors of precipitation, surface specific humidity, and mean surface temperature were used to downscale precipitation outputs. Finally, as for the high spatial and temporal variability of precipitation in arid regions, possible biases in precipitation data were corrected using the Linear Scaling bias correction method, which is based on the average difference between monthly observed time series and GCM historical simulations time series over the same period of the observed series. These differences were then applied to the future GCM simulated climate data to get bias-corrected climate variables.

The results obtained from the application of the FAO-Penman-Monteith method indicated that the variable trend presented by PET values during the study period was mostly induced by changes in wind speed fluctuations. Moreover, downscaled precipitation values showed a more significant error rate than those of the downscaled temperature outputs, probably due to the fact that precipitation values had more variability than the temperature ones, especially in arid climates. On the other hand, the application of the Linear scanning bias correction model showed that the precipitation values downscaled by SDSM for the base period (1961-2005) were overestimated compared to the real precipitation data over the same period. Therefore, the overestimation was corrected using the Linear scanning bias correction method. After correcting the probable precipitation biases and calculating the RDI index, the results suggested that the use of the Linear scanning bias correction model remarkably increased the accuracy of CanESM2 precipitation outputs.Then, the RDI was calculated by replacing the predicted precipitation and potential evapotranspiration values with the real data in the period 2006 to 2018 by measuring the potential evapotranspiration based on the data mentioned. Accordingly, R2 between real RDI values and the CanESM2-extracted forecasted RDI values for the 2006-2018 period was 0.472 for the 1-month time scale, while R2 was 0.738 and 0.762for for 3 and 6-month time scales, respectively.

The results indicated that the model presented a good performance at 3 and 6-month timescales. Moreover, considerable fluctuations in one-month precipitation values resulted in high variability of the one-month RDI time series, decreasing the model's performance at this timescale. Therefore, for projecting precipitation in arid and hyper-arid zones, a bias correction method should be applied to minimize probable biases. Furthermore, the comparison of actual droughts that occurred from 2006 to 2018 with the values ​​predicted by the model at 1, 3, and 6-month time scales showed that the CanESM2 model predicted the drought patterns with relatively good accuracy at 3 and 6-month timescales. However, as for the one-month timescale, the model presented lower efficiency than the other two time scales due to more fluctuations. Therefore, it can be concluded that GCM forecasts have a relatively acceptable efficiency for long-term drought prediction in arid climates.

In this work, the seasonal applied water and physical water productivity of rapeseed (Brassica napus L.) were evaluated through monitoring 26 farmer fields (including 18, five, and three farmer fields with furrow, center-pivot, solid-set irrigation systems, respectively) over Moghan Plain, Ardabil Province, Iran, during the growing season 2019-2020. The rapeseed seasonal water use (irrigation + effective precipitation) and the grain yield value ranged from 2921-6762 m3 ha-1 and 1.00-3.70 ton ha-1, respectively (with a mean of 4798 m3 ha-1 and 2.64 ton ha-1, respectively). The net rapeseed water requirement during the growing season 2019-2020 and its 10-year mean value ranged from 285-399 mm and 282-354 mm, respectively (with a mean of 325 and 304 mm, respectively). The mean seasonal applied water at farmer fields with furrow irrigation (4328 m3 ha-1) was significantly (p < 0.01) higher compared to the farmer fields with center pivot and solid set sprinkler irrigation (2154 and 2633 m3 ha-1, respectively). Total water productivity (WPI+Pe) and irrigation water productivity (WPI) ranged from 0.28 to 0.95 kg m-3 and 0.41 to 1.45 kg m-3, respectively (with a mean of 0.58 and 0.77 kg m-3, respectively). Based on the results of multivariate linear regression analysis, the factors, including irrigation water salinity, sowing date, seeding rate, date of the first irrigation, growth period length, strong winds near harvest time, the latitude of farmer fields, flowrate of delivered water, mean irrigation interval, and rapeseed grain moisture content and impurity rate were recognized as the most important factors affecting the grain yield and the physical water productivity indices.

Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a deficiency of meteorological stations. Therefore, today numerical models such as WRF are a powerful tool for generating and predicting meteorological quantities (wind speed, humidity, etc.) that are needed to estimate evapotranspiration. So far, no research has been conducted to investigate the effect of different schemes of the WRF model on the estimate of rice evapotranspiration. The purpose of this study is to evaluate the efficiency of the WRF model and obtain the result for estimating evaporation for rice plant in the central plain of Guilan.

Evapotranspiration rates vary from 2.7 to 8.5 mm per day. The average ET during three different periods of plant growth, including the initial, middle, and final periods, is estimated to be 4.63, 5.97, and 5.98 mm per day, respectively. The three configurations 1, 2, and 4 are mainly overestimated in predicting evapotranspiration of rice plants, and the computational values are estimated to be higher than the values measured by the lysimeter. The results show that the highest amount of RMSE occurred in configuration No. 4 at 8.47 and the lowest rate occurred in configuration No. 3 at 1.26. Summary of results shows that configuration No. 3 in all four criteria mentioned has performed better than other configurations to predict daily evapotranspiration of rice. The results showed that the non-local schema used in the model, simulates better than the local schemas for the daily evapotranspiration of the rice plant. Findings show that in the local YSU schema, the accuracy of predictions is significantly increased and is only 0.64 mm on average less than the estimated lysimetric data.

The results showed that using appropriate schemas in the surface layer and boundary layer of the WRF model, affects on accuracy of evapotranspiration predictions. The results of this study showed that, this model by using the YSU non-local boundary layer scheme can accurately predict the evapotranspiration rates of the rice plant for the next day and this is due to the higher ability of this schema in predicting the parameters affecting evapotranspiration (including temperature and wind). Therefore, the WRF model can be implemented by using GFS forecast data for the next few days and by applying the FAO-Penman-Monteith equations to the model outputs, the values of potential evapotranspiration for different regions of the country can be calculated. Since evapotranspiration is directly related to atmospheric thermodynamic processes, so using other different atmospheric physics schemas (not considered in this study) can produce different results.

The Crop coefficient (Kc) is one of the most important parameters in irrigation scheduling. The purpose of this study is to determine silage maize Kc under different irrigation levels using the soil water balance method and presenting the equations for estimating silage maize crop coefficients in terms of the days after sowing during the summer growing season in the Varamin region. The Kc was calculated from the division of actual evapotranspiration (ETa) to reference evapotranspiration (ETo). Soil water balance equation was used to determine silage maize ETa during the growing season. An experiment in the form of split-strip plots based on a randomized complete block design with three replications was conducted in 2019. The main factor included three levels of irrigation, supplying 100, 80, and 60% of maize water requirement (I1, I2, and I3, respectively) and the sub-main factor included two types of irrigation management: pulsed (P) and continuous (C). The value of ETa calculated from the soil water balance method for different treatments ranged from 319.6 to 242.5 mm. Also, the value of irrigation depth during the growing season for full and deficit irrigation treatments ranged from 325 to 195 mm. The mean values of silage maize crop coefficient for initial, middle and final growing stages of full irrigation treatments were 0.27, 1.04, and 0.89, respectively. The water stress coefficients were calculated for deficit irrigation treatments during the growing season. Statistical analysis between ETc calculated from Kc values obtained from the proposed equation in this study and Ks values and Eta obtained from the water balance method indicated that the proposed Kc equation and Ks values in this study have good accuracy in the research area. Therefore, the extracted Ks and proposed Kc equations can be used to estimate silage maize evapotranspiration in the studied region with good accuracy.

Drought forecasting is of particular importance in water resources management. Drought forecasting allows planners to schedule for reducing the negative impacts of drought as well as to adapt to it. Drought prediction is more important in arid regions. Because these areas are inherently water scares and the consequences of drought in these areas are more severe. Due to the high variabilities of the temporal and spatial distribution of precipitation in these areas, the frequency of drought is higher and results in more difficulty to model and predict drought. In this study, since drought time series is nonlinear and cyclic, nonlinear autoregressive neural networks (NARs) were used to predict short-term and long-term drought in Yazd synoptic station from 2006 to 2018. Reconnaissance Drought Index (RDI) which in addition to precipitation, considers potential evapotranspiration to monitor droughts, for one, three, and six months timescales was calculated. Potential evapotranspiration was calculated using the FAO-Penman-Monteith method. The results of short-term (one month) drought prediction presented that the model provides high performance in predicting three and six months RDI values. The results of long-term (13-years) drought forecasting (without access to real drought data from 2006 to 2018) indicated that RDI values in dry months show best fit to real values ​​ in ​​ three months’ timescale. To improve the efficiency of the model in the long-term drought forecasting, long-term precipitation and potential evapotranspiration (without model access to real data from 2006 to 2018) were predicted. RDI values ​​were then calculated based on the predicted precipitation and potential evapotranspiration data. The results showed that the prediction accuracy increased in one and three months scales. Also, on six months’ timescale, RDI data were more accurately predicted in dry months.

Due to the scarcity of water around the world especially in arid and semi-arid regions, accurate determination of crop water requirement is essential for proper irrigation planning and management. One of the common methods for estimating crop evapotranspiration is the use of reference evapotranspiration and crop coefficient (Kc) (or the FAO-56 Kc-ETo approach). Different climatic conditions, plant variety, and differences in crops, soils and irrigation management practices result in variations in crop coefficient for the same crop between the locations, therefore locally developed Kc values are necessary for more accurate estimation of crop evapotranspiration. The aims of this research were to estimate silage maize crop coefficient using water balance method under pulsed drip irrigation system during two growing seasons (spring and summer) and to develop an equation to calculate silage maize crop coefficient based on growing-degree-days in Varamin. Silage maize actual evapotranspiration based on water balance method was 465 and 373 mm for spring and summer growing seasons respectively. Silage maize crop coefficient for initial, mid and late growth stages of spring and summer growing seasons were calculated 0.24-0.27, 1.28-1.3 and 0.8-0.88 respectively. The results showed that using silage maize crop coefficient proposed by FAO-56 caused 8% underestimation in crop evapotranspiration. Significant difference ( ) was found between  and , while using the equation presented in this study estimates silage maize evapotranspiration reasonably well, with the mean absolute error (MAE) of 0.53 mm/day, the root mean square error (RMSE) of 0.7 mm/day and the agreement index (d) of 0.98. Therefore, using developed regionally based and growth-stage-specific Kc helps in irrigation management and provides precise water applications for this region.

Reference evapotranspiration (ETo) is one of the most critical parameters in proper design of irrigation systems. Accurate estimation of ETo leads to reduction of water losses. Due to the ability of Artificial Neural Networks (ANNs) in computational analysis of complex processes, the main objective of this study was to investigate the sensitivity of the ETo trends to key climatic factors in Tehran province using the artificial neural networks, and compare it with the ETo-calculator software results. The ETo was calculated using meteorological data (10-year data of 12 meteorological stations in Tehran province) using the ETo-calculator software. In order to model ETo, a set of inputs to artificial neural networks including the minimum and maximum air temperature (Tmax and Tmin), the minimum and maximum relative humidity (RHmin and RHmax), sunshine hours (n), and wind speed (U2) were considered. After data tagging, by optimizing the number of hidden layers and network algorithms, output values were estimated. The results indicated that artificial neural network is a suitable technique for ETo analysis(R^2≅98% ). The best model for estimation of ETo is feed-forward Multi-Layer Perceptron (MLP) with two hidden layers in its structure (6-11-14-1), Levenberg–Marquardt training algorithm for both hidden and output layers and Linear Tanh and Tanh transfer functions for hidden and output layers, respectively. The sensitivity analysis of the model for input parameters showed that the optimal artificial neural network model and ETo calculator software have the same trend and the Tmax and n are the most effective and least effective parameters in ETo estimation, respectively. Also, based on PCA analysis results the scenario of using of four parameters (Tmax, Tmin, RHmax and U2) as the only inputs to the selected artificial neural network, can estimate ETo with an acceptable accuracy〖(R〗^2≅94% ).

One of the most fundamental processes, which influence climate and weather, both global and local scales that a fact which gives it the status of agriculture, is Evapotranspiration (ET). In irrigation and water resources management, ecosystem modelers, environmental assessment and solar energy system, accurate assessment of evapotranspiration is essential. Potential evapotranspiration (ET) has commonly applied to calculate the actual evapotranspiration, which was difficult to estimate by lysimeter measurement and water balance approach under field conditions. Until now, many methods have reported to estimating ET, however, due to the availability of the observed data, it is difficult to choose the optimal method. In this study, to determination of the best potential evapotranspiration method for the Khorasan Razavi Province, three temperature-based methods, Hargreaves–Samani (HS), Hamon (HAM) and Linacre (LIN) and five radiation-based methods, Jensen-Haise (JH), Makkink (MAK), McGuinness and Bordne (MB), Abtew (ABT), and Priestley–Taylor (PT), were compared with PM at yearly scale, using long-term (11-67 years) data from 13 meteorology stations. Indicators, viz. The correlation coefficient (CC), Relative bias (BIAS), normalized root mean squared error (NRMSE) and Relative error(Re) were used to evaluate the performance of ET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in ET estimation varied among regions; ETLIN overestimated ET, while others underestimated. In Dargaz and Golmakan, ETHS yielded similar estimations to that of ETPM, while, in Torbate-jam and Khaf, ETLIN showed better performances. Also in Mashhad and Gonabad, ETHAM, in Neyshabour, ETJH and in Torbat-e Heydarieh, ETABT showed better performances. But in Fariman, Kashmar, Sabzevar, Sarakhs and Quchan, all methods showed poor performance. It indicated that ETPM is acceptable for ET simulation for the yearly timescale in these areas.    ​

Evapotranspiration is one of the most important components of the hydrology cycle for planning irrigation systems and assessing the impacts of climate change hydrology and correct determination is important for many studies such as hydrological balance of water, design of irrigation irrigation networks, simulation of crop yields, design, optimization of water resources, nonlinearity, inherent uncertainty, and the need for diverse climatic information in estimating evapotranspiration have been the reasons why researchers have used artificial intelligence-based approaches. In this study, to estimate accurately the daily reference evapotranspiration between 2009-2018 in Zabol city, north of Sistan and Baluchestan province,  first was used a standard FAO-Penman-Montith method and Zabol synoptic station meteorological data- the ETo reference transpiration is calculated and then presented by various scenarios of meteorological parameters including: maximum, minimum and mean temperature, maximum, minimum and mean humidity, precipitation, sunshine, wind speed and evaporation as inputs for deep learning models, Random forest and generalized linear model were attempted on a daily time scale More accurately. In estimating daily evapotranspiration in these models, 25 scenarios were selected from meteorological data combination and FAO-Penman-Monteith method was used to evaluate the models. Among the investigated scenarios, the M5 scenario (maximum, minimum and mean temperature, maximum, minimum and mean humidity, wind speed, pan evaporation) for deep learning model with minimum error (0.517) and highest correlation coefficient (0.517). 0.996 had the best performance among the above models. The deep learning model showed more accuracy and stability than other models. Hence, this study is recommended a deep learning model for estimating reference plant evapotranspiration in Sistan plain.

Plant water requirement or evapotranspiration (ET) is one of the main components of water balance and a key factor in irrigation planning to improve water use efficiency of agricultural lands. Different methods have been proposed to determine evapotranspiration directly using lysimeter and indirectly using computational methods. Evaporation pan is one of the indirect, simple and suitable methods for estimating the evapotranspiration of the reference plant and the main plant, which shows the combined effects of atmospheric parameters such as air temperature, air humidity, radiation and wind. In this study, using 20-year meteorological data (1999-2018) of the all synoptic stations in Kurdistan province, the value of pan coefficient was estimated using the methods Cuenca (1989), Raghuwanshi & wallender (1998), Modified Snyder, Mohamed et al (2008), Allen and Pruitt (1991), Snyder (1992), Orang (1998), Pereira (1995), Christiansen (1990) and FAO-24 (1997). In order to evaluate the accuracy of estimating evapotranspiration obtained from pan evaporation mehod, the FAO Penman-Mantith 56 method was used. To evaluate the accuracy of the models and to select the best one, four indicators; (RMSE), (MAE), (MBE) and t-test were used. The final results showed that on a daily, monthly and seasonal basis, the methods of FAO-24 (1997) and Christiansen (1990) had the best performance and the methods of Raghuwanshi & Wallender (1998) and Allen and Perot (1991) had the worst performance.