Agricultural Meteorology
S.F. Ziaei Asl; A.A. Sabziparvar
Abstract
Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. ...
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Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. This will prevent the waste of capital and agricultural inputs and ultimately prevent the reduction of the final crop due to the mismatch of genotype-crop with the current climate. In the present study, an attempt has been made to study and compare the trend in the start and end of the growing season, the growing season length (GSL), and growing degree-days(GDD) during 1959-2018 in the elevated and coastal areas of Iran.Materials and Methods: For this study, the daily temperature of 27 synoptic stations were used including 19 stations in elevated areas and 8 stations in coastal areas during 1959-2018. The first day with a minimum daily temperature equal to or greater than 0, 5, and 10 °C was considered as the start of the growing season (SGS). Moreover, the first day after the start of the growing season which has a minimum daily temperature of less than 0, 5, and 10 °C was considered as the end of the growing season (EGS). Trend analysis was performed in time series of GSL and GDD based on thresholds of 0, 5, and 10 °C using the Mann-Kendall test. To compare the results, the statistical period of 60 years was divided into two periods of 30 years (1959-1988 and 1989-2018). In both periods, the statistical characteristics of the GSL and GDD based on the three thresholds mentioned in coastal and elevated areas were surveyed and compared. In this study, deviation from the mean was used to complete the study of changes in the GSL. This index shows the scatter of data around the mean.Results and Discussion: The GSL extension came from both the advance in SGS and the delay in EGS. Comparison results of the two 30-year periods (1959-1988 and 1989-2018) showed that during 1989-2018, in most stations the GSL has increased. During this period, based on 0 °C, the earliest and latest SGS were on February 24 and April 30 in Yazd and Shahrekord, respectively. Accordingly, the earliest and latest EGS were on October 15 and December 11 in Shahrekord and Gorgan, respectively. Based on 5 °C, the earliest and latest SGS were on February 10 and June 2 in Abadan and Gorgan, respectively. Accordingly, the earliest and latest EGS on September 17 and December 6 were at Shahrekord, Bam, and Abadan, respectively. Based on 10 °C, the earliest and latest SGS was on February 11 and June 20 at stations, respectively. Accordingly, the earliest and latest EGS were on August 27 and December 8 in Shahrekord and Bushehr, respectively. The shortest and longest GSLs based on all three thresholds of 0, 5, and 10 °C were Shahrekord and Bandar Abbas, respectively. The highest and lowest coefficient of variation of GSL were 20.8% in Zanjan and 4.9% in Bandar Abbas, respectively. Based on 0, 5, and 10 °C, the lowest GDDs in GSL are 3233, 1767, and 880 °C.d, respectively, and all of them were obtained at Shahrekord. On the other hand, the highest GDD0, GDD5, and GDD10 in GSL were 6783, 7372, and 5761 °C.d, respectively, in Yazd, Abadan, and Bandar Abbas. The most significant trend in GSL was in Zanjan, Zahedan, and Khorramabad.Conclusion: Examination of changes in the GSL indicates the existence of a significant trend in a limited number of stations. Also, with increasing the threshold from 0 to 5 and from 5 to 10 °C, there is a significant decreasing trend in more stations. At the threshold of 10 °C a significant and decreasing trend of GSL was observed in Urmia, Sanandaj, Khorramabad, Birjand, and Bandar Abbas stations, In following, a significant increasing trend was observed in Tabriz, Tehran, Kermanshah, Isfahan, Yazd, and Bushehr stations. The results of the studies showed fewer changes in the time series of the GSL based on thresholds of 0 and 5 °C in the statistical period of 1989-2018. On the other hand, the results showed that the GSL trend is significant in more stations in the recent period based on the threshold of 10 °C. Deviation from the average GSL in coastal areas was greater than the elevated areas so that the GSL based on 10 °C in both areas increased with greater slope and continuity. This increasing trend of deviation from the average in the coastal areas from the early '70s and the elevated areas from the early '90s and continues until now. In this regard, Bandar Abbas station and then Bushehr station had the longest GSL, and Shahrekord station had the shortest GSL among other stations which has been studied. Comparison of GDDs of the GSL during 1989-2018 showed the decrease of GDDs from south to north and from west to east of the country. Accordingly, in the southern stations of the country, the conditions for tropical plants (threshold of 10 °C) have become more suitable than the cold stations of the west and northwest, Time series analysis of the average annual GDDs based on the three thresholds during 1989-2018 showed a significant increasing (positive) trend in 93% of the stations. During the second 30-years period, Shahrekord and Shiraz stations did not show a significant trend in all three mentioned thresholds. However, the analysis of the annual average of GDDs during 1959-1988 showed the trend in 41% of the stations. According to the results of this study, it can be concluded that in cold regions, due to the increase in GDDs, the supply of cooling units for plants with certain cooling needs is more difficult. In the south of the country, as the total required GDD is achieved earlier, the GSL gets shorter, and therefore less dry biomass will accumulate in the product. This likely leads to a reduction in crop yields in this part of the country.
Agricultural Meteorology
A,.A,. Sabziparvar; A.R. Seifzadeh Momensaraei
Abstract
Introduction: Although the contribution of Ultra-violet (UV) radiation is about 5-7% of the whole solar energy; nevertheless, it plays an important role in regulating the biological and photochemical processes within the Earth-atmospheric system. Ultra-violet radiation is well-known for its significant ...
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Introduction: Although the contribution of Ultra-violet (UV) radiation is about 5-7% of the whole solar energy; nevertheless, it plays an important role in regulating the biological and photochemical processes within the Earth-atmospheric system. Ultra-violet radiation is well-known for its significant influence on human health and the environment. High UV doses have negative effects on the skin (erythema (sunburn), skin cancer) and cause eye diseases and immune suppression. However, moderate UV doses have positive effects causing vitamin D production. Apart from the solar elevation, ozone and cloudiness are the main factors affecting UV level and providing significant year-to-year variability of UV radiation. The effect of clouds on UV radiation is as varied as the clouds change. Fully overcast skies lead to reductions in surface UV irradiance. On average, scattered or broken clouds also cause reductions, but short-term or localized UV levels can be larger than for cloud-free skies if direct sunlight is also present. It is noted that long-term cloud type and amount trends are largely unknown due to the relatively short data record of comprehensive cloud observations and the high variability of clouds on interannual and longer time scales. So far, most studies have focused on in-vitro impacts of UV radiation on human health and plant physiology. Unfortunately, not much research has addressed the effect of ozone and clouds distribution on total daily UVB irradiances in central arid deserts of Iran. Meanwhile, these limited investigations have used Tropospheric Ultraviolet-Visible (TUV5) radiation. The present work is aimed to evaluate the influence of clouds and ozone on daily UVB in different sky conditions.
Materials and Methods: To estimate the total daily UVB irradiances (280-315 nm), 13-year (2005-2017) historical data from 22 meteorological sites (9 provinces) were applied as the input of the TUV5 multilayer radiative transfer model. The Tropospheric Ultraviolet-Visible (TUV) model is used widely by the scientific community for applications including atmospheric photochemistry, solar radiometry, and environmental photobiology. The model calculates spectral radiance, irradiance, and actinic flux over 120-750 nm at an underlying resolution of 0.01 nm, as well as weighted spectral integrals including wavelength bands (visible, UVA, UVB, UVC), photolysis coefficients (112 reactions), and biologically active irradiances (UV index, DNA damage, vitamin D production, etc.). Atmospheric inputs include vertical profiles of N2, O2, O3, NO2, SO2, clouds, and aerosols. The propagation of radiation through multiple atmospheric layers (concentric spherical shells for direct solar beam, plane-parallel for diffuse radiation) is computed using a fast 2-stream approximation or a multi-stream discrete ordinates scheme. Version 5.3 provides updated spectroscopic data for a number of photolysis reactions (7). The aforesaid dataset includes Total column ozone (TCO), Cloud optical depth (COD), Aerosol optical depth (AOD), and Surface albedo (SALB), which were freely extracted from ://disc.gsfc.nasa.gov satellite-based images.
Results and Discussion: TUV5 Model estimated total daily UVB radiation for three different sky conditions (Clear-sky, whole sky cover, real sky) and the results compare to each other. The maximum daily UVB for clear-sky and overcast conditions (whole cloud cover) was found in summer and for the south and south-east of the region (Kerman, Fars, and Yazd provinces). The observed decline in daily UVB due to the clouds varied from 33% in summer to 67% in autumn, which highlights the importance of total cloud cover (overcast) in reducing the UVB radiation in the study sites. For the real sky condition (all-sky), the maximum daily UVB irradiances were found in southern parts of the region for most of the seasons. However, as the Indian summer Monsoon result, the maximum UVB has slightly moved toward the northwest of the region. Meanwhile, the inter-comparison of daily UVB maps with total column ozone (TCO), cloud optical depth (COD), aerosol optical depth (AOD), and surface albedo (SALB) maps show that the geographical position of maximum UVB radiation in southern provinces is in good agreement with the total column ozone and cloud optical depth. In this regard, variations of monthly SALB and AOD have less influence on the determination of displacing the maximum UVB.
Conclusion: Results of the present work highlight the high biological risk of solar UVB irradiances during clear-sky days over the study region. For full cloud cover (overcast condition), the maximum and minimum UVB are observed in the south and northeast of the region, respectively. A relative comparison of total daily UVB in clear-sky conditions versus the UVB of overcast conditions highlights the fact that clouds can significantly reduce the biological risk from 33% in summer to 67% in autumn. The UVB reduction by clouds is more pronounced during cold seasons due to the combined interaction of larger solar zenith angle (lower sun angle) with clouds and ozone.
Nooshin Ahmadibaseri; A.A. Sabziparvar; M. Khodamoradpour; L. Alados Arboledas
Abstract
Introduction: Surface Solar Radiation (SSR) as the largest source of land-surface energy is an important parameter in meteorological and climatological studies. Limitations in ground-based measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, ...
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Introduction: Surface Solar Radiation (SSR) as the largest source of land-surface energy is an important parameter in meteorological and climatological studies. Limitations in ground-based measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, for the regions where the ground-based radiation data are sparse. Different methods have been developed for estimating SSR including empirical models, radiative transfer models, semi-empirical models, and models based on satellite and reanalysis products. In most studies in Iran, empirical methods have been investigated. Despite the simplicity of these models, they do not accurately represent SSR variations because of not considering all the parameters affecting radiation variations, at large spatial scales with different climates. The Global Land Data Assimilation System (GLDAS) is a combination of measured and satellite data that uses advanced land surface modeling and data assimilation methods. One of the strengths of this model that makes GLDAS unique is that it has global coverage, high spatial-temporal resolution and is available for free. GLDAS is a terrestrial modeling system uncoupled to the atmosphere. This work was aimed to evaluate SSR derived from GLDAS using ground measurements over Iran from 2012 to 2015 on a daily basis.
Materials and Methods: In this study, measured SSR in 24 radiometer stations of Iran from 2012 to 2015 was extracted. Since the measured data are associated with some errors, the quality of the data must be checked and screened before use. In this study, Moradi's proposed method was used to control data quality. The studied areas were classified into three zones of coastal, arid and semi-arid climates based on Digital Elevation Model (DEM) and UNESCO climate classification approach. The GLDAS SSR outputs were extracted with a spatial and temporal resolution of 0.25° grid cell and 3-hourly from 2012 to 2015. The GLDAS is one of the LDAS projects and has been extended jointly by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP). The purpose of GLDAS is to produce high quality temporal and spatial land surface data. GLDAS drives three land surface models: Mosaic, Noah, and CLM. GLDAS assessments SSR at the land surface using a method and cloud and snow products from the Air Force Weather Agency's (AFWA) Agricultural Meteorology modeling system (AGRMET). Since the GLDAS data are created using the gridded Binary format, the nearest neighborhood interpolation method was used to match these data with ground-based data and GLDAS datasets were generated for station points using CDO software. In this study, GLDAS datasets were compared against measured SSR datasets by four validation metrics. The metrics used are determination coefficient (R2), the mean bias error (MBD), the mean absolute error (MABD), relative mean absolute error (RMABD) and root mean squared error (RMSE).
Results and Discussion: Statistical analysis showed that the performance of GLDAS in SSR evaluation is reasonable in Iran with a high-efficiency coefficient of 0.88. Also, it was shown that the GLDAS has a higher ability to estimate SSR under clear sky (warm seasons) conditions than cloudy conditions (cold seasons). Similar to the obtained results, Träger-Chatterjee et al. (2010); Jia et al. (2013); Boilley and Wild (2015) and Heidary Beni and Yazdanpanah (2017) also showed that the ERA- Interim, NCEP-DOE, RegCM4 and angstrom model are also more capable of estimating SSR in warm seasons. Seasonal bias variations at three studied areas showed that the most changes occurred in summer and least changes in winter. The highest overestimation was also observed in the coastal areas in summer and the lowest overestimation in the semi-arid regions in spring. The evaluation of the GLDAS performance against the site measured SSR data suggests that the GLDAS tends to underestimate in 71% of the studied stations. Moreover, the stations located in the arid region provided a better estimation of SSR as compared with semi-arid and coastal locations. These results were compared with those of Boilley and Wald (2015) that showed ERA-Interim and MERRA reanalysis models have high uncertainty in areas with tropical humid climates, and in regions with arid climates, models perform better in SSR estimation. Our findings were also in good agreement with their results.
Conclusion: GLDAS SSR outputs can be used for agricultural studies. This is due to the facts that arid and semi-arid climates are dominant in Iran and the growing season is mostly in the warm season.
Aliakbar Sabziparvar; ALI KARIMI
Abstract
Introduction: Exposure of human, animal and plants to sunlight has a major role for their growth. One of the most important applications of solar radiation is the agricultural sector. Photosynthesis is a photobiological phenomenon that depicts the ability of plants to convert light energy into chemical ...
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Introduction: Exposure of human, animal and plants to sunlight has a major role for their growth. One of the most important applications of solar radiation is the agricultural sector. Photosynthesis is a photobiological phenomenon that depicts the ability of plants to convert light energy into chemical energy. In fact, if we provide suitable water and temperature, plant growth and consequently crop yields are directly dependent on photosynthetic active radiation (PAR). Regarding the importance of monitoring of PAR flux in agriculture, unfortunately, in most meteorological stations, this parameter is not routinely measured, as its determination is acostly process.
Materials and Methods: In this study, the radiation parameters were measured in a meteorological station located at the faculty of Agriculture, Bu-Ali Sina University in Hamedan. The station has a geographical position of 34 degrees and 47.91 minutes North latitude, 48 degrees and 28.98 minutes Eastern longitude and 1851 meters above sea level in an open space land inside the university campus (Hamedan, Iran). The climate of Hamedan is cold and semi-arid.Geonica Data Logger (GDL) and the PAR detector asradiation devices wereused in this study. The scientific name Pyronometer light sensor which is connected to GDL is LPO2 (Huksellux). The sensitivity of the sensor is between zero to 2000 watts per square meter, and its spectroral response rangesfrom 305 to 2800 nm. The intensity of the irradiance measured by the PAR device is from zero to 2000 µmols/(m2.sec) and its spectroral response covers 380 nm to 750 nm. The method used inthis workwas to measure daily PAR data from a PAR device (ELE) at least four times a day at a local time from April 2016 to February 2017. At the same time, the TSR data was also recorded bythe Geonica Loggerin nearby meteorological site. In this study, simple linear regression and exponential regression wereemployedto investigate the relationship between the TSR data(independent variable, predectors) and the PAR variable (a dependent variable). Using SPSS software, 70%of the data was used to construct the regression relationships and the remainder for evaluating the accuracy of the obtained relationships. Due to the different weather conditions, the measured data are divided into four groups: Clear Sky, Partly Cloudy Sky, Overcast, and All Condition (All sky). To report the cloudiness, Okta unit is used parts (e.g. each Okta corresponds to about 12.5% cloud coverage).
Results and Discussion: The analysis of regression relationshipbetween TSR and PAR in the clear sky, partly cloudy sky, overcast and all sky wasperformed for monthly, seasonal and annual scales. There wasa linear relationship between TSR and PAR fluxes. This linear relationship decreasedwith increasing cloudiness for both monthly and seasonal scales. These results were compared with those ofEscobedo et al. (2009) whomodelled hourly and daily fractions of UV, PAR and NIR to global solar radiation under various sky conditions at Botucatu, Brazil. Our findings were also in good agreement with their results, as they also observed a linear correlation between PAR and TSR fluxes at Botucatu. Moreover, the ratio between PAR and TSR was determined for all time scales. Our results showed that the highest and lowest ratio of PAR /TSR occurs in July (0.448) and February (0.407), respectively. Onseasonal and annual scales, the ratio PAR /TSR increasedas the sky conditions changed from the clear sky to the cloudy sky, mainly because of the effect of cloudiness. Cloudy sky absorbs longer wavelength radiation of solar spectrum (such as infrared radiation) ascompared withshort wavelengths (such as PAR and UV). This increases the radiation proportions from the clear sky to the cloudy Sky. Our results are in good agreement with the results of Alados et al. (southeast Spain), Papaioannou et al. (Athens), Jacovides and et al. (Eastern Mediterranean basin) and Udo and Aro in central Nigeriawhoexamined the PAR/TSR ratio.
Conclusion: In the present study, the following results were achieved:
In monthly, seasonal and annual time scales, there wasa linear regression relationship between PAR and TSR varying with the change in clouds cover. The best correlations were observed in June and July, but the correlation coefficients decreased from October to February (autumn and winter) due to the increased cloudiness.
The PAR/TSRratio in the seasonal time scale showed an increment as the cloud cover increased. On annual scale, the ratio of photosynthetic active radiation (PAR) to total global irradiance (TSR) increased from 0.430 in clear sky to 0.489 in overcast condition.
aliakbar sabziparvar; B. Khatar
Abstract
Introduction: Solar Net Radiation (Rn) is one of the most important component which influences soil heat flux, evapotranspiration rate and hydrological cycle. This parameter (Rn) is measured based on the difference between downward and upward shortwave (SW) and longwave (LW) irradiances reaching the ...
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Introduction: Solar Net Radiation (Rn) is one of the most important component which influences soil heat flux, evapotranspiration rate and hydrological cycle. This parameter (Rn) is measured based on the difference between downward and upward shortwave (SW) and longwave (LW) irradiances reaching the Earth’s surface. Field measurements of Rn are scarce, expensive and difficult due to the instrumental maintenance. As a result, in most research cases, Rn is estimated by the empirical, semi-empirical and physical radiation models. Almorox et al. (2008) suggested a net radiation model based on a linear regression model by using global solar radiation (Rs) and sunshine hours. Alados et al. (2003) evaluated the relation between Rn and Rs for Spain. They showed that the models based on shortwave radiation works perfect in estimating solar net radiation. In another work, Irmak et al. (2003) presented two empirical Rn models, which worked with the minimum numbers of weather parameters. They evaluated their models for humid, dry, inland and coastal regions of the United States. They concluded that both Rn models work better than FAO-56 Penman-Monteith model. Sabziparvar et al. (2016) estimated the daily Rn for four climate types in Iran. They examined various net radiation models namely: Wright, Basic Regression Model (BRM), Linacre, Berliand, Irmak, and Monteith. Their results highlighted that on regional averages, the linear BRM model has the superior performance in generating the most accurate daily ET0. They also showed that for 70% of the study sites, the linear Rn models can be reliable candidates instead of sophisticated nonlinear Rn models. Having considered the importance of Rn in determining crop water requirement, the aim of this study is to obtain the best performance Rn model for cold semi-arid climate of Hamedan.
Materials and Methods: We employed hourly and daily weather data and Rn data, which were measured during December 2011 to June 2013 in climatology station of Bu-Ali Sina University. This experiment was performed for the cold semi-arid site of Hamedan (Iran). The study site (Hamedan) is a mountainous research station (1860 meters above sea-level) which is located at the eastern side of central Zagros Mountain Range. The net radiation fluxes were measured by four SW (300-2800 nm) and LW (4500-42000 nm).Hukseflux Thermal Sensors mounted on an automatic logger system. The logger reported four upward and downward solar components in every 8-minute intervals. In this study, total daily net radiation was estimated by 12 empirical and semi-empirical Rn models including: Basic Regression Models (BRM), Extended Regression Models (ERM), Linacre, Berliand, Wright and FAO-56 Penman-Monteith. The model performances were evaluated by R2, RMSE, MBE and MPE criteria and the best model was selected accordingly.
Results and Discussion: In this research, the model calculations were done for seasonal and annual time scales. The results indicate that Basic Regression Model Rn(BRM-4) performs the best estimates in spring time. Further, for summer and autumn seasons, Rn (BRM-3) was superior for the cold semi-arid climate of Hamedan. Therefore, with the exception of winter, the BRM models performed the best estimates. Unlike the other seasons, for winter, Irmak presented the most accurate results. This is due to the fact that net radiation as estimates by Irmak Model is mainly dependent on daily maximum (Tmax) and minimum temperatures. For Irmak model, as the Tmax is increased, Rn will be reduced proportionally. For this reason, Irmak does not perform good estimates for warm months. In annual time scale, the Basic Regression Model of Rn (BRM-3) presented the most accurate estimates of net radiation. The study of Monteith and Szeicz (1961)and MirgaloyBayat (2011) also showed that Rn (BRM-3) model can generated the best Rn estimate in annual scale for mountain regions.
Conclusion: Unlike the recommendation of FAO for using Penman-Monteith and Wright approaches in evapotranspiration models, it was found that the aforesaid Rn models are not suitable for cold semi-arid regions such as Hamedan. This result is in good agreement with the findings of Izoimon et al. (2000) and MirgaloyBayat (2011). In general, for cold climate condition of Hamedan, the Basic Regression Models are more reliable than the other Rn models. This study was performed based on 18-month field data and 12-Rn models. To achieve more accurate results, using a longer term experimental data and examining more Rn models are suggested as the future works. To achieve a regional Rn zoning, inclusion of satellite-based dataset is also recommended.
A.A. Sabziparvar; S. Tanian
Abstract
The main aim of this research is investigating the effect of ENSO phenomenon on reference evapotranspiration (ET0) on monthly, seasonal and annual time scales, using Southern Oscillation Index (SOI). For this purpose, 13 sites located in cold climate regions with 50 years (1957-2006) meteorological data ...
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The main aim of this research is investigating the effect of ENSO phenomenon on reference evapotranspiration (ET0) on monthly, seasonal and annual time scales, using Southern Oscillation Index (SOI). For this purpose, 13 sites located in cold climate regions with 50 years (1957-2006) meteorological data were selected. In the first step, the reference evapotranspiration rates were determined for the selected sites by using FAO recommended approach. In the second step, different phases (El Nino, La Nina and normal) were separated in terms of SOI and the mean deviation of ET0 values at each phase were compared by Mann-Whitney test. At statistical significant levels (p< 0.1), good correlation were found between the ET0 values and SOI. About 72% of correlations were positive and the rest (28%) were negative. In positive SOI-ET0 correlations, the monthly averages of ET0 values during El Nino phases were 14.8% and 10.8% lower than ET0 of La Nina and Normal phases, respectively. On the contrary, the average ET0 rates in La Nina phases were 13.1% higher than the corresponding values of normal pahses. The mean time lag to observe the highest impact of ENSO on ET0 was 3.2 months. The highest effective months in the study sites was found to be November, October and December, respectively. In seasonal time scale, 68% of the statistical significant affecting cases were occurred in autumn. It was found that the cold climates were more sensitive to the ENSO signals than warm climates. The results can be useful for policy makers in water resources management and agricultural sectors.
A.A. Sabziparvar; M. Shadmani
Abstract
Abstract
In this research, temporal trends of reference evapotranspiration (ET0) values were investigated in arid regions of Iran. For this purpose, the meteorological observations collected from 11 high quality meteorological sites for a 41-year period (1965-2005) were used and statistically significant ...
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Abstract
In this research, temporal trends of reference evapotranspiration (ET0) values were investigated in arid regions of Iran. For this purpose, the meteorological observations collected from 11 high quality meteorological sites for a 41-year period (1965-2005) were used and statistically significant ET0 trends in the monthly, seasonal and annual time basis were detected by the non-parametric Mann-Kendall and Spearman tests at confidence level of 95 %. For eliminating the effect of serial correlation on test results, the Trend Free Pre-Whitening (TFPW) approach was applied. The results indicated that the ET0 trends for some sites were increasing (positive) but for some sites showed decreasing (negative) trends. The most significant ET0 trends on the monthly time scale occurred at Birjand but no significant trend was observed for Bandarabbas, Sabzevar and Semnan sites. On the annual time scale, Mashhad revealed the highest positive ET0 trend (7.5 mm per year). On the contrary, Esfahan showed the highest decreasing (negative) ET0 trend of about -6.38 mm per year. In general, the performances and capabilities of Mann-Kendall and Spearman tests were consistence at the verified confidence level.
Keywords: Trend, Reference evapotranspiration, Mann-Kendall test, Spearman test
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
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
