Agricultural Meteorology
M. Abdollahi Fuzi; B. Bakhtiari; K. Qaderi
Abstract
IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, ...
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IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, North America, and Europe. Also, these phenomena have contributed to low crop yields in Iran. The latest statistics released by the Food and Agriculture Organization of the United Nations (FAO) show that Iran is one of the largest producers of agricultural products and the world’s second-biggest producer of pistachios. Kerman province is one of the significant areas of pistachio production. This province has a large share of the pistachio word area plantation. Spring frost damage to pistachio crops has led to low yields in recent years. A key aspect of studying frost is the ability to accurately estimate its occurrence. In this study, artificial neural network methods have been used to estimate late spring frost in the pistachio crop of Kerman city. Materials and MethodsIn this study, the efficiency of this method was investigated in the estimation of minimum temperature. For this purpose, the daily data of the synoptic station of Kerman city were obtained from Iran Meteorological Organization from 2000 to 2020. Meteorological data including mean, maximum, and minimum temperatures, relative humidity, wind speed, saturated vapor pressure, and sunshine hours were used. Five different combinations of these variables was considered as input variables in artificial neural network method for minimum temperatures modeling. After entering data into network and modeling with each combination, RMSE and R2 values were calculated. Finally, the combination of 8 variables including average and maximum temperature, the minimum temperature the previous day and two days prior, relative humidity, wind speed, saturated vapor pressure, and sunny hours were selected as the most suitable combination of variables. Subsequently, a simulation of minimum temperature values was conducted using 10% of the data. The performance of the methods was evaluated using statistical indices of coefficient of determination (R2), mean square of error (RMSE), Mean Bias Error (MBE), and Coefficient of Nash–Sutcliffe (NSE). Results and DiscussionThe accuracy of an analytical method is the degree of agreement between the test results generated by the method and the true value. Upon examining the models, the M1 model was identified as the best due to its lowest RMSE and higher R². ANN model results were evaluated using various performance measure indicators. The simulated outcome of the model indicated a strong association with actual data, where the correlation coefficient was above 0.95, and the MBE index was zero. Also, the RMSE value was positive and close to zero, and the NSE value was above 0.75. Therefore artificial neural network method had high accuracy. In this study, mean annual minimum temperature was estimated using artificial neural network models (from March 10 to May 20). Comparison between the observed and calculated data showed that these data were in good agreement. Also, the results showed that temperature fluctuations were high between March 10 and March 31. From 2011 to 2017, an almost uniform temperature trend has been observed between March 10 and March 31. However, the years 2000, 2006, and 2020 showed a noticeable decrease in temperature. From 2018 to 2020, this trend of temperature reduction continued. In April, the temperature values were between 7 and 10 degrees Celsius. The years 2001, 2005, 2006, 2009, 2016, and 2019 had a noticeable decrease in temperature. In May, the mean minimum temperature was between 10 and 14 degrees Celsius. Therefore, the probability of frost occurrence in early-flowering cultivars was higher in late March than in April and May. The years 2000, 2004, 2005, 2012, 2015, 2019 and 2020 had the highest number of frost days in the last two decades. ConclusionThe results showed that the artificial neural network method had a high performance in estimating the minimum temperature. The values of the statistical indicators were R2=0.963, RMSE=0.027oC, MBE= 0 and NSE=0.966 respectively. In addition, the ANN method performed well in estimating the number of critical frost days for pistachio crops. The results showed that, although reducing the amount of input data in models decreases their output precision, data-driven methods can still be useful tools for minimum temperature estimation.
S.M. Farmanara; B. Bakhtiari; N. Sayari
Abstract
Introduction: Drought is an extreme climate effect and a creeping phenomenon which directly affects the human life. A drought analysis usually requires characterizing drought severity, duration and frequency (SDF). These characteristic variables are commonly not independent, so this phenomenon is a complex ...
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Introduction: Drought is an extreme climate effect and a creeping phenomenon which directly affects the human life. A drought analysis usually requires characterizing drought severity, duration and frequency (SDF). These characteristic variables are commonly not independent, so this phenomenon is a complex natural disaster and climate change makes it likely to become more frequent and immense in many areas across the world. Therefore, in drought analysis, it is needed to investigate its multivariate nature and spatial variability clearly. Copula, as a model of multivariate distribution, has been used widely in hydrological studies. As the standardized precipitation index (SPI) is more accessible than other indices, it is the most commonly used indicators for analyzing the SDF of meteorological drought. Here, the study has two major focuses: 1) Fitting drought characteristics from SPI to appropriate copulas, then using fitted copulas to estimate conditional drought severity distribution and joint return periods for both historical and future time periods in Fars province. 2) Inquiring the effects of climate change on the frequency and severity of meteorological drought. Materials and Methods: Among the weather stations of Fars province, six synoptic stations were selected, which had longer historical data than others. The data used included 24-hour precipitation during 15 (2004-2018) to 33 (1986-2018) years. Three steps were carried out. Stage one: downscaling of outputs of the large scaling (CanESM2) based on two intermediate (RCP4.5) and pessimistic (RCP8.5) scenarios using SDSM, ver. 4.2.9 during the period of 2020 to 2050. Stage two: calculation of SPIand drought characteristics in the base and future periods (2050-2020). Stage three: extracting SDF curves for the base and future periods under RCP4.5 and RCP8.5 scenarios using copula. The SPIwas used to extract the drought duration and drought severity in the Fars province using GCM models under two selected scenarios (RCP4.5 and RCP8.5) from the IPCC Fifth Assessment Report (AR5) scenarios. The gamble copula function was used to construct the joint distribution function for evaluating the drought return periods in the study area. Because short-term drought prediction is more practical than long-term prediction, we used the 1-month SPI for the copulas-based analysis. Drought severity and duration were calculated based on computed SPIvalues by using the past available data. Drought duration is defined as successive months with SPIvalue less than -1 and drought severity as the accumulative SPIvalue during the period with successive SPIvalue less than -1. The normal and log-normal functions were selected as the candidate distribution function for drought duration and drought severity. Results and Discussion: The results showed that the frequency of drought occurrence in the Fars province will increase during the period of 2020-2050 under the both two scenarios. In the RCP8.5 scenario, the duration of the drought will also increase. The increase and decrease of monthly rainfall in RCP 4.5 and RCP 8.5 were 2.8 and 6.5%, respectively.The duration of the drought were obtained to be 5.25, 5.5 and 6 days at Shiraz station, with a 2 and 5 years return period, in the baseline and future periods under RCP4.5 and RCP8.5 scenarios, respectively. These values were estimated to be 4, 3.5 and 5 days at Bavanat Station.It is expected that the precipitation will decrease at Shiraz station under the two scenarios.Similarly, this amount is expected to increase and decrease at Bavanet station in the RCP4.5 and RCP8.5 scenarios, respectively. Conclusion: Changing droughts based on climate change is important in many aspects. In this study, the performance of two-variable statistical distribution of severity and duration of drought was investigated based on the copula function. The comparison of the drought period calculated using the SPIshowed that due to the climate change, the frequency of drought periods is expected to increase in the base and future periods. The results showed that the value of the precipitation changes in the RCP8.5 scenario is higher than the RCP4.5 scenario. Generally, the performance criteria showed that the SDSM had a good performance for the past and the future periods in Fars province for precipitation data. It is expected that with consideration of the amendments in the sixth report of the IPCC, more precision can be obtained in precipitation modeling. Therefore, reviewing the output of the SDF curves with the availability of the results of this report is suggested.
F. Khadempour; B. Bakhtiari; S. Golestani
Abstract
Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to ...
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Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to Penman-Monteith method. This method requires radiation, temperature, humidity and wind speed data with high reliability rate in vast ranges of climates and areas represent precise outcome from reference plant Evapotranspiration.
Materials and Methods: Study stations in De Martonne classification system are divided into 6 climates such as Hyper-arid, Arid, Semi-arid, Mediterranean, Humid and Very humid (a) climates. Study stations statistical span during 19 years (1996-2015) were selected and temperature, relative humidity, sunshine hours, and wind speed in 2 meter height daily data were used. Figure 1 showed studied stations position all over the country. In this study, in order to obtain daily ETo, Penman-Monteith standard method represented by FAO-56 was used. In local sensitivity analysis, factors local influences on model output were shown. Such an analysis usually carried out through output functions minor deviants computation due to input variables. In this analysis, usually it was used one-factor- at-a- time method (OAT), so that, one variable factor and other input factors kept constant.
Figure 1. The geographical location of weather stations
The FAO-56 PM model for estimating ETo is as follows (3).
(1)
where ETo is reference crop evapotranspiration (mm day−1), Δ is the slope of vapor pressure versus temperature curve at temperature Tmean (kPa°C−1), γ is the psychometric constant (kPa °C−1), u2 is the wind speed at a 2 m height (m s−1), Rn is the net radiation at crop surface (MJ m−2 d−1), G is the soil heat flux density (MJ m−2 d−1), T is the mean daily air temperature at 2 m height (°C), and (es-ea) is the saturation vapor pressure deficit (kPa).
Results and Discussion: Weather parameters in stations showed that mean temperature sensitivity coefficient ( ) in all study stations varied between 0.21 to 0.78 so that the maximum temperature sensitivity coefficient related to Bushehr station in arid climate (in April, May, June, July, October and November) and minimum temperature sensitivity coefficient related to Shahrekordstation in semi-arid climate (in January, March, April and November). Maximum and minimum net radiation sensitivity coefficient value ( ) related to Rasht and Zahedanstations respectively. Also, maximum and minimum wind speed sensitivity coefficient value ( ) related to Zahedan and Ardebilstations are 0.54 and 0.07 respectively. Yazd station in Hyper-arid climate showed minimum relative humidity sensitivity coefficient value ( ) about 0.20 and Rasht station in very-humid (a) showed the maximum values 0.45. So the northern coastal areas are more sensitive to and SRH. The highest value is in northern coastal areas and lowest in southern coastal and southwest areas of the country. Some other studies showed that in many climates evapotranspiration was more sensitive to Rn (6, 14 and 17).In current study, also, showed the highest sensitivity in Very-humid climate (a) includes Rasht station in February, March, April, October and November. For example, = 0.82 means that 100% increase in Rn parameter result in 82% increase in ETo.
Conclusion: Sensitivity analysis experiment on FAO Penman-Monteith standard method is one of the most efficient methods to understand various climate parameters influence on reference evapotranspiration (ETo). In this study, results showed that computed ETo in all climates showed highest sensitivity to Rn and temperature respectively. Temperature sensitivity coefficient showed the highest value at April. May, June, July, October and November and Rn showed its highest value at March, April, October and November. While, minimum in all of months but May and July and maximum value showed in January, July, August and September by 0.07 and 0.54 respectively. So, in most months of the spring and the fall was larger and smaller during the winter months. Sensitivity coefficient related to mean temperature is higher during summer season and lower during winter season. Results of this study may be useful for assessing the response of the standardized FAO Penman-Monteith model in different climatic conditions. The results can also be used to predict changes in ETo values with respect to climatic variable changes obtained from climate change models.
B. Bakhtiari; A. Khalili; A. Liaghat; M.J. Khangani
Abstract
Abstract
In recent years, automatic weather stations have been widely used for recording meteorological data in different time scales. Therefore the accurate estimation of ETo by combination equations can be evaluated using these set of short time scales data. Daily ETo can also be calculated by summation ...
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Abstract
In recent years, automatic weather stations have been widely used for recording meteorological data in different time scales. Therefore the accurate estimation of ETo by combination equations can be evaluated using these set of short time scales data. Daily ETo can also be calculated by summation of hourly ETo values. The purpose of this study is to compare the ETo values estimated by hourly and daily data. Totally, 7270 hourly meteorological data obtained from the automated weather reference station where placed in Shahid Bahonar university of Kerman, Iran during April to December 2005 and January to March 2006. The Penman-Monteith equations proposed by the Food and Agriculture Organization (FAO-56) and American Society of Civil Engineers (ASCE) were used for hourly and daily (24 hours) ETo estimation. The paired t- student test was used for comparison of estimated ETo values by two methods (daily and hourly summation) in each month. The results of this study showed significant differences between ETo values estimated by daily and hourly summation data in both equations at 5 percent level. The hourly summation method overestimated ETo values from 5.8 to 44.6 percent in different months using FAO-56 Penman-Monteith equation and from 7.4 to 47.6 percent using ASCE Penman-Monteith equation. The regression coefficients of correlation equations between the daily and hourly summation method in both combination models were strongly significant.
Keywords: Reference evapotranspiration, Hourly time scale, FAO-56 Penman-Monteith, ASCE Penman-Monteith
