Agricultural Meteorology
Mehdi Asadi
Abstract
Introduction
Human activities and the substantial increase in greenhouse gas concentrations—particularly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—have exacerbated global warming and triggered significant alterations in climatic patterns (Alston & Pardey, 2014; Pawlak ...
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Introduction
Human activities and the substantial increase in greenhouse gas concentrations—particularly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—have exacerbated global warming and triggered significant alterations in climatic patterns (Alston & Pardey, 2014; Pawlak & Kołodziejczak, 2020). Consequently, climate change has emerged as a critical challenge for natural resource management and agricultural systems in recent decades. These changes, especially temperature and precipitation fluctuations, directly impact plant phenological and vegetative cycles and may even shift the suitable geographical ranges for cultivating certain plant species. Among these species is Ziziphus jujuba Mill. (Family: Rhamnaceae), a medicinally valuable plant (Cheng et al., 2000; Shen et al., 2009; Sabzghabaee et al., 2013) that exhibits relative adaptability to arid and semi-arid climates, such as those in Iran. However, it remains vulnerable to climate change impacts (Zittis et al., 2022; Waqas et al., 2024; Ghasemnejad et al., 2024). Historically cultivated in South Khorasan Province, this region now accounts for over 72% of Iran’s jujube production (Rad et al., 2020). Investigating climatic trends and their effects on the reproductive and vegetative thresholds of Ziziphus jujuba is both scientifically and practically significant. Such analyses enhance our understanding of regional climate change dynamics and facilitate predictive assessments of its agricultural consequences. Consequently, the objective of the present study is to identify the reproductive and vegetative thresholds of jujube throughout the year in the counties of South Khorasan Province and to spatially analyze these thresholds in terms of temperature and precipitation, both under baseline conditions and future scenarios influenced by trends in temperature and precipitation changes.
Material and Methods
In this study, the modified Mann-Kendall test, Sen's slope estimator, and linear regression analysis were employed to analyze trends in data related to determining the cultivation range of the jujube plant. The data under investigation included monthly temperature and precipitation averages from seven synoptic stations within the study area, covering a statistical period of 25 years from 2000 to 2024. These data were extracted from the National Meteorological Organization and served as the foundation for the study. Station data were converted into z-scores using the modified Mann-Kendall test in Minitab software. Additionally, linear trends of variables such as minimum temperature, maximum temperature, mean temperature, precipitation, sunshine hours, and hot days, along with their corresponding slopes, were examined.
Results and Discussion
Jujube plants, like other plant species, require specific temperature ranges for optimal growth during different vegetative and reproductive stages. This study examined the thermal thresholds that impact the growth of jujube trees. It was found that 25°C is the threshold at which reproductive growth stops, while 40°C is the threshold for the cessation of vegetative growth. (Yang et al., 2021, 2024; Hao et al., 2021). Additionally, the biological zero for jujube growth has been established at 11°C, and this plant can tolerate low temperatures down to -33°C (Wang et al., 2022). Some studies have even reported the plant's ability to withstand temperatures as low as -40°C (Hao et al., 2021). In this research, each of the seven studied stations in the region was individually analyzed in terms of maximum temperatures and critical points leading to the cessation of vegetative and reproductive growth.
Conclusion
The findings reveal that the Zirkuh station, with an average annual precipitation of 182.8 mm, receives the highest rainfall among the studied stations. Nevertheless, even at this station, jujube plants require supplementary irrigation of 267.2 mm. Fortunately, the region's climatic conditions are characterized by rare and minimal summer rainfall—a phenomenon that could otherwise cause fruit cracking—making this area particularly suitable for jujube cultivation. Analysis of climatic data from 2000 to 2024 demonstrates significant spatial heterogeneity in temperature trends. Modified Mann-Kendall test results indicate a warming trend across all stations, with the most pronounced increase observed in Nehbandan station (3.43°C) and the least in Zirkuh station (0.94°C). These spatial variations can be attributed to altitudinal differences, geographical positioning, and localized microclimatic conditions. Sen's slope estimator corroborates these findings, showing the steepest positive slope in Ferdows station (0.24) and the gentlest in Khosf station (0.03). Linear regression analysis reveals a decadal temperature increase ranging from 0.07°C in Birjand and Zirkuh stations to 2.48°C in Nehbandan station. Statistical analysis of p-values demonstrates significant spatial patterns in temperature changes. While northern and central stations (e.g., Birjand, Boshruyeh, and Ghaen; p ≤ 0.05) show no significant trend, southern stations, particularly Nehbandan (p ≤ 0.02), exhibit statistically significant warming. Regarding precipitation, all stations show decreasing trends, with a maximum reduction in Nehbandan (-3.32 mm) and a minimum in Birjand (-0.63 mm). Sen's slope analysis indicates the steepest decline in Ferdows (-0.34) and the mildest in Zirkuh (-0.13). Regression analysis estimates an annual precipitation decrease ranging from 0.04 mm/decade in Zirkuh to 1.80 mm/decade in Ghaen. Statistically, northern and central stations (p ≤ 0.05) show significant drying trends, while southern stations like Nehbandan (p = 0.28) exhibit no statistically significant trend.
Irrigation
K. Raispour; B. Salahe; B. Abad
Abstract
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow ...
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Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow and climate change are on a regional and global scale. Despite the large number of studies conducted in this field, there is still a lot of research need in many parts of the world for reasons such as lack of weather stations to access ground observation data and the non-uniform spatial distribution of these stations. Nowadays, with significant technological advances, including the advent of various satellites, access to a variety of precipitation data has been greatly facilitated. Among the latest precipitation products of various satellites, we can refer to the Global Precipitation Measurement (GPM) satellite data. Related to the subject of the present study, it is stated that most of the studies on rainfall in the Jazmourian catchment area have been based on station data, which due to the poor distribution of meteorological stations; it is not possible to estimate the temporal-spatial distribution of precipitation in the study basin. In this study, the temporal-spatial analysis of precipitation using GPM satellite precipitation products as one of the most important climatic parameters in the basin Due to the undeniable importance of rainfall in this basin, it seems that the analysis of variable rainfall can provide valuable climatic information to researchers and planners. To pave the way for new study platforms.Materials and Methods In this study, satellite data (GPM) with a spatial resolution of 0.1 × 0.1 degrees from January 2001 to December 2019 have been used for spatiotemporal analysis of precipitation in the Jazmourian catchment. The GPM satellite provides more accurate and realistic estimates than other TRMM satellites. In this study, a calibrated precipitation product of level 3 of 6 GPM satellite versions was used. Relevant data are in NCDF format and have UTM image system with WGS84 datum, which after quality control and preprocessing, by specialized software (ENVI, ArcGIS and EXCELL) is converted into network data and data tables and the necessary outputs based on the geographical boundary of the catchment was extracted. The average monthly rainfall was extracted from the average daily rainfall belonging to each month and the seasonal average was extracted from the average of three months related to each season. Spatially, the values of each pixel are the conditions of the average amount of precipitation related to each time series (monthly, seasonal and annual) during the statistical period.Results and Discussion Based on the results, the average rainfall in the Jazmourian catchment was estimated as 144 mm, the spatial distribution of which ranged from 83 to 232 mm. The maximum rainfall occurred in the northern and western parts and the minimum occurred in the central and eastern parts of the basin. Furthermore; based on the annual distribution of rainfall during the statistical period under study, the highest rainfall was in 2019 with 239 mm and the lowest with 53 mm in 2001. In terms of seasonal distribution, winter and spring with values of 118 and 88 mm, respectively, showed the highest and autumn and summer with values of 22 and 45 mm, showed the lowest values of precipitation. Also, during the statistical period under study, winter 2005 with 193 mm had the highest and autumn 2003 with 1 mm had the lowest seasonal rainfall in the basin. In addition, an interesting point is the spatial displacement of high-pressure nuclei in different seasons of the year; so that these nuclei are observed in the cold seasons of the year in the northern and western parts and in the warm seasons of the year in the southwestern and southeastern parts of the basin. The spatial distribution of monthly precipitation indicates the occurrence of the highest monthly precipitation in February and March and the lowest in May and September. Also, the monthly rainfall time series indicates the maximum incidence of precipitation in February 2001 (94 mm) and it’s minimum in January 2001 (no precipitation).Conclusion Precipitation as a source of fresh water on Earth is one of the most important hydrological parameters, the importance of which is undeniable in the survival of human communities and natural ecosystems. Due to the large temporal-spatial variations of precipitation, its study seems necessary. But one of the main challenges for studying this phenomenon is the lack of ground stations as well as their improper distribution. Today, with advancement of technology and remote sensing, a diverse range of satellite data has become available to environmental scientists. In this regard, in the present study, using GPM satellite data and in the statistical period 2001-2019, the temporal-spatial distribution of precipitation in the Jazmourian catchment area in southeastern Iran has been investigated. In general, the high variability of rainfall in Jazmourian catchment in different months and seasons of the year, shows the dominance of arid and low climate in this basin. Therefore, due to the rainfall situation and its high fluctuations under climate change conditions, in the near future, this basin will face serious challenges and crises in water resources management and the sustainability of natural ecosystems. The GPM satellite data used in this study showed appropriate and expected results from the spatial-temporal distribution of precipitation in the Jazmourian catchment and showed a good correlation with meteorological stations. In general, the use of GPM satellite data in the present study is appropriate, which due to its appropriate spatio-temporal separation, gives reliable and satisfactory results. On the other hand, inadequate spatial coverage of meteorological stations and their large statistical vacuum in such a relatively large basin justify the use of this valuable and useful satellite data.
N. Khalili Samani; A. Azizian
Abstract
Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables ...
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Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables that influence the ground water resources. The existence of models for predicting the annual precipitation and subsequent management of water resources in arid, semi-arid and also humid regions is useful . In this study, the simple regression models that relate the annual precipitation to the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) and mean annual precipitation (Pm), in Khuzestan (2), Kerman (3) and southern and western provinces of Iran (4) were evaluated using long-term daily precipitation data of Shahrekord and Yazd Weather stations and, if necessary, modified equations.
Materials and methods: In this study, long-term daily precipitation data of Shahrekord and Yazd Weather stations (1360-1392) from Meteorological Administration of Chaharmahal and Bakhtiari and Yazd were prepared, completed and used for analysis. At each station the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) for each year, annual precipitation and mean annual precipitation for subsequent calculations were extracted. Then, the homogeneity and adequacy of data were checked using RUN Test. Equations of 1 to 8 were used for predicting the annual precipitation using 70% of the data. The relationship between observed and predicted annual precipitation were evaluated. Then the coefficients of equations were corrected by 70% of the data set using SPSS Software in Shahrekord and Yazd Weather Stations. The remaining 30% of data were used to validate the modified models. Index of agreement (d) and normalized root mean square error (NRMSE), were used to evaluate the models. The NRMSE values close to zero and d values close to 1 indicate proper operation of the model.
Results and Discussion: Results showed that the models with straight and reverse relationships between t42.5 or t47.5 and Pm were not suitable to estimate the annual precipitation in Shahrekord. However, these models were relatively acceptable for Yazd. While the simple regression model using t42.5, t47.5 and the long-term Pm as independent inputs could be able to predict the annual precipitation of Shahrekord and Yazd stations with acceptable accuracy.
Conclusion : Using the relationship between t42.5, t47.5 and Pa (equations of 1, 3, 4 and 7) for estimating the annual precipitation in Shahrekord and Yazd stations, NRMSE values obtained greater than 0.3 and d index less than 0.7 (Fig. 3 and 4). Furthermore , the models included t42.5, t47.5 and Pm versus Pa (equations of 2, 5, 6 and 8), had not acceptable results (Fig. 5 and 6). By modifying the above mentioned equations (models of 10 to 14 for Shahrekord and 15 to 19 for Yazd) and comparison of measured and predicted annual precipitation by the modified models, the results showed that the linear and inverse relationship between t42.5, t47.5 and annual precipitation could not be an appropriate model for Shahrekord Station (Fig. 7-A and 7-B and 7-C) and results of the evaluation of these relationships for estimating of the average annual precipitation of Yazd were relatively acceptable (Fig. 8-A and 8-B and 8-C results in Yazd station). While the simple linear model including the relationship between those time periods (t42.5, t47.5 ) and the long-term average annual precipitation with corrected coefficients could accurately estimate the annual rainfall in the Shahrekord and Yazd stations (Fig. 7-d and 7-H for Shahrekord and 8-D, 8-H for Yazd station). In order to validate the above results, the models were evaluated with the remaining 30% of the data . Results showed in Figs. 9 and 10. The NRMSE values in Figs. 10-A, 10-B and 10-C, confirm the validity of the relationship between t42.5, t47.5 and annual precipitation.
vajiheh mohammadi sabet; Mohammad Mousavi Baygi; Hojat Rezaee Pazhand
Abstract
Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This ...
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Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This phenomenon is caused by changing the water slope in the Pacific Ocean between Peru (northwestern South America) and Northern Australia (about Indonesia and Malaysia). ENSO phenomenon is formed of Elnino (warm state) and La Niña (cold state). There is high pressure system in the East and low pressure system in the West Pacific Ocean in normal conditions (Walker cycle). The trade winds blow from East to West with high intensity. ENSO start when the trade winds and temperature and pressure balance on both sides of the PacificOcean change. High pressure will form in the west and low pressure will form in the East. As a result, west will have high and east will have low rainfall. Temperature will change at these two locations. Enso longs about 6 to 18 months. This research investigated the impact of ENSO on monthly precipitation and temperature of Mashhad.The results showed that temperature and rainfall have a good relation with ENSO.This relation occurs in 0-5 month lag.
Materials and Methods: The severity of ENSO phenomenon is known by an index which is called ENSO index. The index is the anomaly of sea surface temperature in the Pacific. The long-term temperature and precipitation data of Mashhad selected and analyzed. The Rainfall has no trend but temperature has trend. The trend of temperature modeled by MARS regression and trend was removed.The rainfall data changed to standard and temperature changed to anomaly for comparison with ENSO index. The 2016 annual and monthly temperature of Mashhad is not available. The 2016 Annual temperature was forecasted by ARMA (1,1) model. Then this forecast disaggregated to monthly temperature. For each period of occurring high ENSO, these three indexes (ENSO index, standardized rainfall and anomalies temperature) were compared. The co-variation of these indexes was compared. Also, the correlation and cross correlation for each period of occurring ENSO, with rain and temperature of Mashhad was calculated.
Results and Discussion: Mashhad monthly temperature and precipitation were compared with the extreme values of ENSO index in periods of the occurrence this phenomenon (1950-2016). In addition, the correlation and cross-correlation between ENSO-Rainfall index and ENSO-temperature index for this period were calculated.Forecasted temperature for 2016 by ARMA (1,1) was 13.2 Degrees Celsius, which has 0.2 degree increase in comparison to last year. Results showed thatthere is no an obvious relation between ENSO-Temperature and ENSO-Rainfall in interval (-1, +1). But there are good relation between ENSO-Temperature and ENSO-Rainfall beyond of (-1,+1). The results of Elnino showed that the monthly precipitation and temperature increase with a lag of 2 to 5 months and 0 to 4 months, respectively. The results of Lanina showed that the monthly precipitation and temperature decrease with a lag of 3 to 5 months and 1 to 4 months, respectively. Also when ENSO index is located in the interval (-1, +1), there is no certain harmony with temperature and precipitation of Mashhad.
Conclusions: The aim of this study was evaluating the effect of the ENSO phenomenon on monthly temperature and precipitation of Mashhad.Mashhad monthly temperature and precipitation, respectively, for 132 and 124 years were available.Precipitation was static and has no trend, but temperature was not static and has two changed (jumped) point in 1976 and 2000. MARS regression was used for patterning the process. Removing the trend was done by MARS model and the data was obtained without trend. Monthly ENSO index since 1950 from reliable websites worldwide (NOAA) was obtained. Mashhad monthly temperature data was animalized and precipitation data was standardized. This was performed for comparing Temperature and Rain with ENSO index. The effect of the ENSO phenomenon on Mashhad precipitation and temperature in both graphical and cross-correlation was performed.As a final result, there is a good relation with latency zero up to 5 months for temperature and precipitation of Mashhad beyond the interval (-1, + 1). It cannot be claimed that after the phase of La Nina, El Nino must be entered and vice versa. This note is important for forecasting the temperature and precipitation of 2016coming months. If ENSO index in the coming months, especially in autumn and winter, decrease and inter in La Nina phase, the winter will be cold with low rainfall.
Nooshin Ahmadibaseri; A. Shirvani; mohammad jafar nazemosadat
Abstract
In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for ...
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In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.
mohammad jafar nazemosadat; K. Shahgholian
Abstract
The aim of this study is to assess some synoptic characteristics of heavy precipitations in southwestern parts of Iran and evaluate the relationship between them with the Madden-Julian Oscillation (MJO). Research is conducted with regard to distribution of precipitation per month and identifying their ...
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The aim of this study is to assess some synoptic characteristics of heavy precipitations in southwestern parts of Iran and evaluate the relationship between them with the Madden-Julian Oscillation (MJO). Research is conducted with regard to distribution of precipitation per month and identifying their steam sources. Daily records of the November-April precipitation data in Abadan, Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations for the 1975- 2011 period are collected as well as same panel data for Yasuj station from 1990 to 2011. Rainfall data are sorted in descending order and precipitation values that were fallen within the 5% and 10% of highest records are categorized as the heavy precipitation. The most frequent precipitations occurred in January, February and December. The most frequent heavy precipitations in Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations occurred in phase 8, while in Abadan station occurred in phases 7 and 8. Apparently, due to the short duration precipitations data at Yasuj station, the most frequent heavy precipitation observed in phase 2.Synoptic maps show that harmonized with eastward movement of convective precipitation in Indian or pacific oceans.Heavy precipitation forms in the west region of Iran and moves toward southwest and south Central of Iran and then appears to Afghanistan.Formation of a cyclonic circulation that encompasses the Mediterranean Sea, Red Sea and Persian Gulf plays an important role for moisture supplement of these storm activities. The synoptic maps have indicated that main sources of these heavy rainfalls are moisture produced at the Arab sea and western parts of the Indian Ocean.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
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Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
mohammad banayan
Abstract
Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation ...
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Detection of rainfall characteristics in arid and semi-arid regions such as northeast of Iran has a critical role in any adaptation and mitigation plan to drought. The main goal of this study was to determine the rainy season starting date and daily rainfall threshold by using Rainfall Uncertainty Evaluation Model. The Starting Analysis Date (SAD) characterize the beginning of a new rainy season in a given region, whereas, the Rainy Season Beginning Date and the Rainy Season Ending Date are used to determine the Rainy Season Length. In this study, seventeen locations in northeast of Iran with available long daily rainfall data (24 to 48 years) were selected. Our results indicated three annual courses of rainy season, 12 locations showed a uni-model annual course with shorter values in summer (A courses), 4 locations indicated uni-model with shorter rainy season length across April and March (B courses) and only 1 location showed bi-model annual course (C courses). These classifications were confirmed by multivariate statistical methods analysis. SAD was determined by differences between shortest median RSL and the shortest median RSL of the first day of each month. According to this approach SAD values for different classes determined as: July 1st in (A) region, March 1st in (B) region and February 1st in (C) region. Appropriate daily rainfall threshold was 1.0 mm for only 3 locations, and the highest value of this index obtained in Torbat-j location (2.2 mm), therefore 1.0 mm value which traditionally is in use as daily rainfall threshold need to be revised.
H. Asakereh; R. Khoshraftar; F. Sotudeh
Abstract
Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted ...
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Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted to detect cycles in rainfall and debit time series at Mashinkhaneh station in Talesh (Garakanrood) catchment’s during Mehr 1354 to Shahrivar 1386 in the three time scales (annual, seasonal and monthly). Accordingly, the discharge and precipitation data at Mashinkhaneh station in Talesh (Garakanrood) catchment from Mehr 1354 to Shahrivar 1386 have been used. The results of applying the spectral analysis procedures to discharge and rainfall time series in each three category of time scales, suggested the absence of significant non-sinusoidal (trend) in the 95% confidence level. However, significantly sinusoidal cycles various in the two time series were extracted. The 2-4 year cycle, and 4-5.3 years have the most occurrences in the both time series. In the annual scale, 6.4 years cycle, 2-5.3 years, 7.7 years seasonal and 2-4, 4- 5.3, 6.4, 8, 10.7 and 16 year in the monthly scale cycles has been extracted. Studies carried out by many researchers indicate that the mentioned cycles are in relation with oscillation periods of ENSO, NAO and QBO in other parts of the world.
